• Sep. 2018
    Jie Li (Principal supervisor) Successfully defended his thesis. Many congratulations, Dr. Li! He published 13 papers, won two best paper awards, and two IEEE CIS Outstanding Student Paper Travel Grants.
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    Northumbria University, UK, 29 Sep. 2018
  • Sep. 2018
    Best Paper Award at the 18th UKCI
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    Nottingham Trent University, 5-7 September 2018
  • Sep. 2018
    Successfully organised CloudComp 2018 and BMVC 2018
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    Exeter, 4 September 2018 and Newcastle, 3-6 September 2018
  • July. 2018
    Successfully organised the Cyber Security special session (with 9 papers published) at WCCI 2018, and visited Campinas University, Sao Paulo, Brizal
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    7-19 July, 2018 image
  • May 2018
    The first CIS Workshop on Digital Forensics and Cyber Security
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    Two guests: Prof. Prashant Pillai, Professor in Cyber Security, Wolverampton University
    Dr. Nitin Naik, Reader in Cyber Security and Big Data, DSCIS, Ministry of Defence
  • May 2018
    The kick-off meeting in Thailand for the RAE Cyber Security project (UNN, MFL, MoD UK, MoD Thai, Thai Royal Air Force, T-Net)
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    This project is formed by 80% of research and 20% fo teaching. In addition to vairous meetings, experiments and discussions regarding research, a workshop on Padegogic research and teaching collaboration was also held. Chiang Rai, Thailand, 21-25 May, 2018 image
  • May 2018
    Nominated Student-Led Teaching Award
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    Student Union, Northumbria University, 18 May, 2018 image
  • Apr. 2018
    Invited talk at Xiamen University, China
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    School of Management, Xiamen University, China; 9 April, 2018.
  • Apr. 2018
    Invited talk at Wuhan University of Science and Technology, China
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    Wuhan University of Science and Technology, China; 2 April, 2018.
  • Mar. 2018
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    Wuhan University of Technology, China; 28-31 March, 2018.
  • Mar. 2018
    RAE Cyber Security project is awarded (PI, £50,000), titled "Anomaly traffic identification through artificial intelligence, cyber security and big data analytics technologies", with partners including Northumbira Universtiy (UK PI), MFL University (Thailand PI), Ministry of Defence UK, Ministry of Defence Thailand, Thai Royal Air Force, and T-Net Co. Ltd Thailand
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    March, 2018
  • Mar. 2018
    General Chair of The 8th EAI International Conference on Cloud Computing, which will be hosted at Northumbria University
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    Exeter, UK, 4 September, 2018
  • Jan. 2018
    Guest Editor of Multimedia Tools and Applicatiosn for special issue on “Soft computing Techniques and Applications for Intelligent Multimedia Systems”
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    AIMS & SCOPE: In the recent times, the rapid development of social networks and multimedia technologies has delivered high Quality of Experience to the users globally. However, with the massive increase in the number of social network users, the demands for the sharing and exchange of multimedia content between users are ever-increasing. Therefore, the huge need for the research on multimedia analytics and development of efficient approaches to handle online multimedia content is high. Multimedia is increasingly becoming the most important and valuable source of insights and information from social networks, and it covers from everyone’s experiences to everything happening in the world. The huge volume of multimedia big data from social networks makes the traditional multimedia handling systems inefficient and spurs towards the development of new technologies for handling multimedia big data.

    Soft computing techniques are delivering promising solutions to the complex research problems and make innovations at a rapid pace. As soft computing differs from traditional computing approaches, it is tolerant towards uncertainty, imprecision, and approximation. Inspired by the human mind model, soft computing is applied to the various research problems and has been proven to be proficient with the experimental performances. This special Issue will report the recent increasing interests in the design and development of Soft computing techniques for various applications of intelligent multimedia systems. Moreover, the authors are expected to investigate state-of-art research issues, architectures, applications and achievements in the field of multimedia big data. Unpublished innovative papers which are not currently under review to another journal or conference are solicited in the following relevant areas.
  • Jan. 2018
    Invited talk at the Department of Computer Science and Technology, University of Bedfordshire
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    Title: Fuzzy Rule Interpolation Systems
    Time: 13:00-14:00, Wednesday, 17 January, 2018
    Venue: Department of Computer Science and Technology, University of Bedfordshire, UK
  • Dec. 2017
    Principle academic investigator of the research innovation project collaborated with Shoes2Run Limited, funded by Creative Fuse North East.
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    Being the prinical academic investigator, our collaborator Shoes2Rune Limited has been awarded an Innovation Development Award by Creative Fuse North East twoards the development of wearable technology, Artificial Intelligence System and Big Data capability. This is a joint project between Shoes2Run, Northumbria University and Sunderland University.
  • Dec. 2017
    Invited talk at the School of Computing, the University of Portsmouth
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    Title: Fuzzy Rule Interpolation Systems
    Time: 16:00-17:00, Wednesday, 13 Dec, 2017
    Venue: School of Computing, University of Portsmouth, UK
  • Oct. 2017
    Leading Chair of Special Session "Fuzzy Logic Systems for Cyber Security and Forensics" in the 2018 World Congress on Computational Intelligence. Please click here for more information. For a quick short version of the special session, please click here
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    Computational Intelligence has taken the centre of the research in cyber security and digital forensics. As one of the three most important parts in Computational Intelligence, fuzzy logic has been successfully applied in many applications in this field. Thanks to its ability to provide human comprehensible solutions to cyber security and digital forensics problems under uncertainty environment and also to the fuzziness of security and forensics problems themselves, fuzzy logic is expected to have a more significant impact in such field.
    The aim of this special session is to provide a forum: (1) to disseminate and discuss the advances and significant research efforts in the field of fuzzy logic systems, security and forensics, (2) to promote both theoretical development and practical applications of fuzzy logic systems in the field of security, privacy and forensics, (3) to foster the integration of communities from academic, industry, and other organisations who have been working in the field of fuzzy logic, security and forensics.
  • Sep. 2017
    Guest Editor for special issue of Healthcare System Innovation in Applied System Innovation. Please click here for more information.
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    Healthcare is undergoing a sector-wide transformation thanks to technological innovations. We anticipate significant improvements in areas ranging from decision support for healthcare professionals through big data analytics to supporting behavior changes through technology-enabled self-management, as well as social and motivational support. Furthermore, with cutting edge sensors and computer technologies, healthcare delivery could also yield better efficiency, higher quality and lower cost. In this Special Issue, we welcome original research, as well as review articles, in all areas of healthcare system innovation.

    Potential topics include, but are not limited to: 1. Smart healthcare system analysis and design; 2. Computer-aided methods for design procedure and manufacture of healthcare system; 3. Computer and human-machine interaction of healthcare system; 4. Internet technology on healthcare system innovation; 5. Application of IoT (Internet of Things) on healthcare system; 6. Big data and artificial intelligence enabled healthcare systems.
  • Sep. 2017
    Co-Chair of UKCI 2019
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    I made a presentation in UKCI 2017 in Cardiff and also helped to successfully bid UKCI 2019 which will be chaired by Prof. Honghai Liu and co-chaired by myself.
  • Aug. 2017
    Teaching Excellence and Innovation Award
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    I have been awareded the Teaching Excellence and Innovation fund (£500).
  • Aug. 2017
    Poster Chair of BMVC 2018. Please click here for more information.
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    I will be the Poster Chair of BMVC2018, which will be held in Newcastle 2018.
  • Jun. 2017
    IEEE Computational Intelligence Society (CIS) Outstanding Student Paper Travel Grants Award
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    A paper titled "Dynamic Qos Solution for Enterprise Networks Using TSK Fuzzy Interpolation" has been awarded IEEE CIS Outstanding Student Paper Travel Grant. This travel grant will support Jie Li to attend FUZZ-IEEE 2017 in Italy in July.
  • Mar. 2017
    Northumbria (joint) CAPEX Bids: £70,000
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    Northumbria CAPEX Bids 17-18, £70,000, 2017-2018.
  • Apr. 2017
    Founding chair of IEEE Special Interest Group of Big Data for Cyber Security and Privacy. Please click here for more information.
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    I have founded the IEEE Special Interest Group of Big Data for Cyber Security and Privacy. The five vice-chairs are from the best university from five contenients.
  • Sep. 2016
    Best UKCI2016 Paper Award issued by Springer
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    A paper titled "TSK Inference for Sparse Fuzzy Rule Bases" has been awarded the best paper award by the committee of UKCI 2016. Please click here to download the paper.
  • Jun. 2016
    A paper accpeted for publication at Fuzzy Sets and Systems (IF=2.098)
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    A paper "Closed Form Fuzzy Interpolation: An Improvement to Compositional Rule of Inference" has been accepted for publication at Fuzzy Sets and Systems with minor correction.
  • May 2016
    IEEE CIS Best Student Paper Nomination
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    A paper of year 2 students Mr Yao Tan and Mr Jie Li has been accepted by World Congress on Computational Intelligence (WCCI) 2016. This paper has been formally notimated for the Best Student Paper Award, and the final winner will be announced in Vancouver, Canada in July during the conference banquet.
  • May 2016
    A paper accepted for publication at IEEE Transactions on Fuzzy Systems (IF=6.701)
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    A paper "Generalised Adaptive Fuzzy Rule Interpolation" has been accepted for publication at IEEE Transactions on Fuzzy Systems, which has the second highest impact factor within all the journals in the field of Computer Science and Informatics, i.e., REF UoA11.
    Please click here to download the Open Access paper.
  • Apr. 2016
    Head of Department of Computer Science from Xiamen University visited Northumbria
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    The Head of Department of Computer Science Prof. Lei and his colleagues, Dr. Guo and Dr. Wang have visited us in April 2016. Potential collaboration in student exchange, research projects, and grant application have been discussed.
  • Apr. 2016
    TWO IEEE Computational Intelligence Society (CIS) Outstanding Student Paper Travel Grants Award for my year 2 PhD students
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    Two papers of year 2 students first-authored by Mr Yao Tan and Mr Jie Li have been accepted by the World Congress on Computational Intelligence (WCCI) 2016. Both students have won the IEEE CIS Outstanding Student Paper Travel Grant to support them to present their outstanding work in WCCI in Vancouver, Canda in July.
  • 2015-2016
    Research and Enterprise Reward Scheme: £2,983
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    Research and Enterprise Reward Scheme: £2,983, 2015-2016.
  • 2015-2018
    Jointly funded PhD studentship: £84,000
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    Jointly funded PhD studentship by Chengdu Yubo, China and Northumbria University, £84,000, 2015-2018.
  • 2015-2016
    Northumbria CAPEX Bids: £15,000
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    Northumbria CAPEX Bids 15-16, £15,000, 2015-2016.
  • Jan. 2015
    UK Higher Education Innovation Fund (HEIF): £3000
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    UK Higher Education Innovation Fund (HEIF) Impact Fund, PI, £3,000, 2015.
  • Apr. 2015
    The 12th Haiyue Lecture in 2015, Xiamen University
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    I visited Xiamen University and delivered the 12th open lecture for the Haiyue Lecture Series in April 2015, at Xaimen University, China. image
  • 2014-2017
    University Funded PhD Studentship: £62,000
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    A unversity funded PhD student, which covers the tuition fee and bursary for three years from 2014 to 2107.
  • 2014-2015
    Simpson Group Job Scheduling Project: £20,670
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    Simpson Group Job Scheduling Project, PI, £20,670, 2014-2015.
  • Aug. 2014
    UK Higher Education Innovation Fund (HEIF): £5,000
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    UK Higher Education Innovation Fund (HEIF), PI, £5,000, 2014.
  • 2014-2015
    Northumbria CAPEX Bids: £6,170
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    Northumbria CAPEX Bids 14-15,£6,170 , 2014-2015.
  • Apl. 2014
    Nominated Best Paper Award
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    Nominated Best Paper Award for the 22th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'14), 2014.
  • Apl. 2011
    Nominated Best Student Paper Award
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    Nominated Best Student Paper Award for the 20th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2011.
  • Apl. 2011
    IEEE Computational Intelligence Society (CIS) Outstanding Student-Paper Travel Grant
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    IEEE Computational Intelligence Society (CIS) Outstanding Student-Paper Travel Grant for the 20th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2011.
  • Feb. 2011
    Nominated Best Journal Paper
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    Nominated Best Journal Paper for the International Journal of Advanced Computational Intelligence and Intelligent Informatics.
  • Apr. 2010
    Best Student Paper Award
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    Best Student Paper Award of the 19th IEEE International Conference on Fuzzy Systems (FUZZIEEE), selected out of 617 submissions worldwide, and awarded at the conference banquet of the 2010 IEEE World Congress on Computational Intelligence (WCCI).
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  • Apr. 2010
    IEEE Computational Intelligence Society (CIS) Outstanding Student-Paper Travel Grant
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    IEEE Computational Intelligence Society (CIS) Outstanding Student-Paper Travel Grant for the 20th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2010.
  • Apr. 2009
    IEEE Computational Intelligence Society (CIS) Outstanding Student-Paper Travel Grant
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    IEEE Computational Intelligence Society (CIS) Outstanding Student-Paper Travel Grant for the 20th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2009.
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    Curvature-based Rule Base Generation for Fuzzy Modelling

    PhD project, co-funded by Yobo Technology Ltd. China and the University

    This project aims to develop a new fuzzy modelling approach using the curvature values. This PhD project is co-funded by Yobo Technology Ltd., China and the university. Prinicple supervisor: Dr. Longzhi Yang

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    Intelligent Home Energy Management

    PhD project, funded by the University.

    Smart meters have been partially deployed to UK homes to support consumers in managing their energy and expenditure intelligently, to help implement the CO2 emission reduction goal. However, the information from smart meters is still not enough for efficient intelligent home appliance control, as the residents’ living behaviour patterns, as well as the surrounding environments also have great influences on the control decisions for home appliances. Despite of the advance in machine learning and expert system technologies, the extraction of decision patterns based on residents’ behaviours and the surrounding environments is still of great challenge as every resident has their own living style and each dwelling has its own unique characteristics.

    Take intelligent home heating systems as an example. In order to accurately and economically control the home heating systems such that a property can always be properly pre-heated when the residents getting home whilst no energy is unnecessarily wasted on heating empty dwellings, the control system should be developed based on a well understating of the information from smart meters (such as electricity supply/demand pattern) and the residents’ living styles, and be timely updated when the supply/demand pattern or the living styles change. However, it is unrealistic for system manufacturer to collect highly personalised data and to consequently produce customised heating controllers for each household, such that the heating controller can make accurate decisions based on the residents’ unique living style in addition to the current electricity supply/demand pattern and surrounding environment.

    This research proposes an algorithm which would enable intelligent home heating system to efficiently learn the residents’ behaviour patterns associated with the information from smart maters in a dynamic and adaptive manner. Consequently, mass-production of intelligent heating controllers is allowed. In particular, these devices are initialised by the most general and common rules which are suitable for the majority of people. Then the intelligent controller will learn more customised details in real time after it is deployed. Also, once the electricity supply/demand pattern and the living style or the users of the controller have changed, the controller will be able to catch the changes up in real time. It has been reported that around 40% residential energy use is consumed to deliver ‘unused’ energy services. The proposed approach will potentially provide significant help in reducing energy waste and CO2 emission but in the same time improving the quality of life.

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    Manufacturing Production/Process Control, Scheduling and Optimisation

    Industrial project funded by Simpson Group Print Ltd.

    PI: Dr. Longzhi Yang

    Simpson Group is one of the UK’s leading manufactures of posters and 3D displays for promotions. The company has a number of customers we are all very familiar with, such as Next, Morrison's, and Danbenhams. A promotion usually involves multiple display objects, and a display object is typically produced in a sequence of operations. In addition, an operation can be alternatively conducted by different capable machines with different costs. This project developed a software prototype which intelligently schedules the manufacturing production line use CI techniques and thus improves production efficiency.

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    Robotic Writing System

    Collaborative project, the collaborator was funded by National Natural Science Foundation of China (No. 61203336)

    This project aims to teach robots to write in an analogy to how humban being writes. PI: Dr. Fei Chao at Xiamen University, China

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    Interaction-based Human Motion Analysis and Retrieval

    Collaborative research, the collaborator is funded by the Engineering and Physical Sciences Research Council (EPSRC) (EP/M002632/1)

    Interaction based on motion analysis. PI: Dr. Hubert P.H. Shum at Nothumbria University

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    Multi‐layer Lattice Model for Real‐Time Dynamic Character Deformation

    Collaborative research, the collaborator was funded by Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT)

    MEXT Top Global University Project Scholarship: £5,500, Contributing Researcher (PI: Prof. Shigeo Morishima). Received from the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT)To support 1 Japanese PhD (Waseda University) working in the UK for 6 months .

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Fuzzy Interpolation Systems and Applications

Longzhi Yang, Z Zuo, Fei Chao, Y Qu
BooksMathematics, Modern Fuzzy Control Systems and Its Applications, book edited by S. Ramakrishnan, ISBN 978-953-51-3390-2, Print ISBN 978-953-51-3389-6

Abstract

Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy interpolation addresses this. It enables fuzzy inferences with sparse rule bases when the sparse rule base does not cover a given input, and it simplifies very dense rule bases by approximating certain rules with their neighbouring ones. This chapter systematically reviews different types of fuzzy interpolation approaches and their variations, in terms of both the interpolation mechanism (inference engine) and sparse rule base generation.

Adoption of Cloud Computing in Hotel Industry as Emerging Services

Elaine Vella, Longzhi Yang, Naveed Anwar, Nanlin Jin
Conference13th International Conference, iConference 2018, Sheffield, UK, March 25-28, 2018.

Abstract

The hotel industry is experiencing forces of change as a result of data explosion, social media, increased individualized expectations by customers. It is thus appealing to study the cloud computing adoption in the hotel industry to respond such changes. This paper reported an investigation on such topic by identifying the cloud computing services and summarising their benefits and challenges in organization, management and operation. The research findings were comparatively studied in reference to the results appeared in the literature. In addition, recommendations were made for both cloud service providers and hotels in strategic planning, investment, and management of cloud-oriented services.

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

D Zhou, M Shi, F Chao, CM Lin, L Yang, C Shang
JournalNeurocomputing (Impact factor=3.317, 5-year impact factor=3.211)

Abstract

Mobile robots with manipulators have been more and more commonly applied in extreme and hostile environments to assist or even replace human operators for complex tasks. In addition to autonomous abilities, mobile robots need to facilitate the human¨Crobot interaction control mode that enables human users to easily control or collaborate with robots. This paper proposes a system which uses human gestures to control an autonomous mobile robot integrating a manipulator and a video surveillance platform. A human user can control the mobile robot just as one drives an actual vehicle in the vehicle¡¯s driving cab. The proposed system obtains human¡¯s skeleton joints information using a motion sensing input device, which is then recognized and interpreted into a set of control commands. This is implemented, based on the availability of training data set and requirement of in-time performance, by an adaptive cerebellar model articulation controller neural network, a finite state machine, a fuzzy controller and purposely designed gesture recognition and control command generation systems. These algorithms work together implement the steering and velocity control of the mobile robot in real-time. The experimental results demonstrate that the proposed approach is able to conveniently control a mobile robot using virtual driving method, with smooth manoeuvring trajectories in various speeds.

Gaze-Informed Egocentric Action Recognition for Memory Aid Systems

Z Zuo, L Yang, Y Peng, F Chao, Y Qu
JournalIEEE Access (Impact factor=3.244)

Abstract

Egocentric action recognition has been intensively studied in the fields of computer vision and clinical science with applications in pervasive health-care. The majority of the existing egocentric action recognition techniques utilize the features extracted from either the entire contents or the regions of interest (ROI) in video frames as the inputs of action classifiers. The former might suffer from moving backgrounds or irrelevant foregrounds usually associated with egocentric action videos, while the latter may be impaired by the mismatch between the calculated and the ground truth ROI. This paper proposes a new gaze-informed feature extraction approach, by which the features are extracted from the regions around the gaze points and thus representing the genuine ROI from a first person of view. The activity of daily life can then be classified based only on the identified regions using the extracted gaze-informed features. The proposed approach has been further applied to a memory support system for people with poor memory, such as those with amnesia or dementia, and their carers. The experimental results demonstrate the efficacy of the proposed approach in egocentric action recognition, and thus the potential of the memory support tool in health care.

An extended Takagi¨CSugeno¨CKang inference system (TSK+) with fuzzy interpolation and its rule base generation

Jie Li, Longzhi Yang, Yanpeng Qu, Graham Sexton
JournalSoft Computing (Impact factor=2.472)

Abstract

A rule base covering the entire input domain is required for the conventional Mamdani inference and Takagi¨CSugeno¨CKang (TSK) inference. Fuzzy interpolation enhances conventional fuzzy rule inference systems by allowing the use of sparse rule bases by which certain inputs are not covered. Given that almost all of the existing fuzzy interpolation approaches were developed to support the Mamdani inference, this paper presents a novel fuzzy interpolation approach that extends the TSK inference. This paper also proposes a data-driven rule base generation method to support the extended TSK inference system. The proposed system enhances the conventional TSK inference in two ways: (1) workable with incomplete or unevenly distributed data sets or incomplete expert knowledge that entails only a sparse rule base and (2) simplifying complex fuzzy inference systems by using more compact rule bases for complex systems without the sacrificing of system performance. The experimentation shows that the proposed system overall outperforms the existing approaches with the utilisation of smaller rule bases.

a Developmental Learning approach of Mobile manipulator via playing

R Wu, C Zhou, F Chao, Z Zhu, CM Lin, L Yang
JournalFrontiers in Neurorobotics (Impact factor=2.486)

Abstract

Inspired by infant development theories, a robotic developmental model combined with game elements is proposed in this paper. This model does not require the definition of specific developmental goals for the robot, but the developmental goals are implied in the goals of a series of game tasks. The games are characterized into a sequence of game modes based on the complexity of the game tasks from simple to complex, and the task complexity is determined by the applications of developmental constraints. Given a current mode, the robot switches to play in a more complicated game mode when it cannot find any new salient stimuli in the current mode. By doing so, the robot gradually achieves it developmental goals by playing different modes of games. In the experiment, the game was instantiated into a mobile robot with the playing task of picking up toys, and the game is designed with a simple game mode and a complex game mode. A developmental algorithm, ¡°Lift-Constraint, Act and Saturate,¡± is employed to drive the mobile robot move from the simple mode to the complex one. The experimental results show that the mobile manipulator is able to successfully learn the mobile grasping ability after playing simple and complex games, which is promising in developing robotic abilities to solve complex tasks using games.

Integration of fuzzy CMAC and BELC networks for uncertain nonlinear system control

Dajun Zhou, Fei Chao, Chih-Min Lin,Longzhi Yang, Minghui Shi, Changle Zhou
Conference2017 IEEE International Conference on Fuzzy Systems.

Abstract

This paper develops a fuzzy adaptive control system consisting of a new type of fuzzy neural network and a robust controller for uncertain nonlinear systems. The new designed neural network contains the key mechanisms of a typical fuzzy CMAC network and a brain emotional learning controller network. First, the input values of the new network are delivered to a receptive field structure that is inspired from the fuzzy CMAC. Then, the values are divided into a sensory and an emotional channels; and the two channels interact with each other to generate the final outputs of the proposed network. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. In the experimentation, a ¡°Duffing-Holmes¡± chaotic system and a simulated mobile robot are applied to verify the effectiveness and feasibility of the proposed control system. By comparing with the performances of other neural network based control systems, we believe our proposed network is capable of producing better control performances of complex uncertain nonlinear systems control.

Adaptive fuzzy interpolation with uncertain observations and rule base

Longzhi Yang, Qiang Shen
Conference2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

Abstract

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It views interpolation procedures as artificially created system components, and identifies all possible sets of faulty components that may each have led to all detected contradictory results. From this, a modification procedure takes place, which tries to modify each of such components, termed candidates, in an effort to remove all the contradictions and thus restore consistency. This approach assumes that the employed interpolation mechanism is the only cause of contradictions, that is all given observations and rules are believed to be true and fixed. However, this may not be the case in certain real situations. It is common in fuzzy systems that each observation or rule is associated with a certainty degree. This paper extends the adaptive approach by taking into consideration both observations and rules also, treating them as diagnosable and modifiable components in addition to interpolation procedures. Accordingly, the modification procedure is extended to cover the cases of modifying observations or rules in a given rule base along with the modification of fuzzy reasoning components. This extension significantly improves the robustness of the existing adaptive approach.

A robot calligraphy system: From simple to complex writing by human gestures

Fei Chao, Yuxuan Huang, Xin Zhang. Changjing Shang, Longzhi Yang, Changle Zhou, Huosheng, Hu, Chih-Min Lin
JournalEngineering Applications of Artificial Intelligenc. (Impact factor: 2.894, 5-year impact factor: 3.177)

Abstract

Robotic writing is a very challenging task and involves complicated kinematic control algorithms and image processing work. This paper, alternatively, proposes a robot calligraphy system that firstly applies human arm gestures to establish a font database of Chinese character elementary strokes and English letters, then uses the created database and human gestures to write Chinese characters and English words. A three-dimensional motion sensing input device is deployed to capture the human arm trajectories, which are used to build the font database and to train a classifier ensemble. 26 types of human gesture are used for writing English letters, and 5 types of gesture are used to generate 5 elementary strokes for writing Chinese characters. By using the font database, the robot calligraphy system acquires a basic writing ability to write simple strokes and letters. Then, the robot can develop to write complex Chinese characters and English words by following human body movements. The classifier ensemble, which is used to identify each gesture, is implemented through using feature selection techniques and the harmony search algorithm, thereby achieving better classification performance. The experimental evaluations are carried out to demonstrate the feasibility and performance of the proposed method. By following the motion trajectories of the human right arm, the end-effector of the robot can successfully write the English words or Chinese characters that correspond to the arm trajectories.

Dynamic QoS solution for enterprise networks using TSK fuzzy interpolation

Jie Li, Longzhi Yang, Xin Fu, Fei Chao, Yanpeng Qu
Conference2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017).

Abstract

The Quality of Services (QoS) is the measure of data transmission quality and service availability of a network, aiming to maintain the data, especially delay-sensitive data such as VoIP, to be transmitted over the network with the required quality. Major network device manufacturers have each developed their own smart dynamic QoS solutions, such as AutoQoS supported by Cisco, CoS (Class of Service) by Netgear devices, and QoS Maps on SROS (Secure Router Operating System) provided by HP, to maintain the service level of network traffic. Such smart QoS solutions usually only work for manufacture qualified devices and otherwise only a pre-defined static policy mapping can be applied. This paper presents a dynamic QoS solution based on the differentiated services (DiffServ) approach for enterprise networks, which is able to modify the priority level of a packet in real time by adjusting the value of Differentiated Services Code Point (DSCP) in Internet Protocol (IP) header of network packets. This is implemented by a 0-order TSK fuzzy model with a sparse rule base which is developed by considering the current network delay, application desired priority level and user current priority group. DSCP values are dynamically generated by the TSK fuzzy model and updated in real time. The proposed system has been evaluated in a real network environment with promising results generated.

Intrusion detection system by fuzzy interpolation

Longzhi Yang,Jie Li,Gerhard Fehringer,Phoebe Barraclough,Graham Sexton, Yi Cao
Conference2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017).

Abstract

Network intrusion detection systems identify malicious connections and thus help protect networks from attacks. Various data-driven approaches have been used in the development of network intrusion detection systems, which usually lead to either very complex systems or poor generalization ability due to the complexity of this challenge. This paper proposes a data-driven network intrusion detection system using fuzzy interpolation in an effort to address the aforementioned limitations. In particular, the developed system equipped with a sparse rule base not only guarantees the online performance of intrusion detection, but also allows the generation of security alerts from situations which are not directly covered by the existing knowledge base. The proposed system has been applied to a well-known data set for system validation and evaluation with competitive results generated.

A new fuzzy-rough feature selection algorithm for mammographic risk analysis

Qian Guo, Yanpeng Qu, Ansheng Deng, Longzhi Yang
Conference2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017).

Abstract

Mammographie risk analysis is a useful means for the early diagnosis of breast cancer. There are many efforts have been devoted to improving the performance of the relevant assessment technologies. This paper presents an invasive weed optimization (IWO) based fuzzy-rough feature selection method for mammographic risk assessment. The advantage of IWO is that the offspring individuals are randomly spread around their parents according to a Gaussian distribution during the evolution process. Such Gaussian distribution is designated with a dynamical standard deviation. Therefore, the optimization algorithm can explore a new solution space aggressively. The diversity of the species can be maintained in the early and middle iterations, and the optimal individuals will be found in the final iteration of feature selection. The mechanism of IWO ensures a global optimal solution for the heuristic search. The performance of IWO is compared against the feature selection methods with ant colony optimization (ACO) and particle swarm optimization (PSO). In the last chapter, the experimental results indicate that the use of IWO entails better performance for the problem of mammographic risk analysis according to both dimensionality reduction and classification accuracy.

Automatic Estimation of the Number of Segmentation Groups Based on MI

Ziming Zeng, Wenhui Wang, Longzhi Yang, Reyer Zwiggelaar
JournalPattern Recognition and Image Analysis.

Abstract

Robotic writing is a very challenging task and involves complicated kinematic control algorithms and image processing work. This paper, alternatively, proposes a robot calligraphy system that firstly applies human arm gestures to establish a font database of Chinese character elementary strokes and English letters, then uses the created database and human gestures to write Chinese characters and English words. A three-dimensional motion sensing input device is deployed to capture the human arm trajectories, which are used to build the font database and to train a classifier ensemble. 26 types of human gesture are used for writing English letters, and 5 types of gesture are used to generate 5 elementary strokes for writing Chinese characters. By using the font database, the robot calligraphy system acquires a basic writing ability to write simple strokes and letters. Then, the robot can develop to write complex Chinese characters and English words by following human body movements. The classifier ensemble, which is used to identify each gesture, is implemented through using feature selection techniques and the harmony search algorithm, thereby achieving better classification performance. The experimental evaluations are carried out to demonstrate the feasibility and performance of the proposed method. By following the motion trajectories of the human right arm, the end-effector of the robot can successfully write the English words or Chinese characters that correspond to the arm trajectories.

Posture-based and action-based graphs for boxing skill visualization

Yijun Shen, He Wang, Edmond S.L. Ho, Longzhi Yang, Hubert P.H. Shum
JournalComputers & Graphics. (Impact factor: 1.176, 5-year impact factor: 1.355)

Abstract

Automatic evaluation of sports skills has been an active research area. However, most of the existing research focuses on low-level features such as movement speed and strength. In this work, we propose a framework for automatic motion analysis and visualization, which allows us to evaluate high-level skills such as the richness of actions, the flexibility of transitions and the unpredictability of action patterns. The core of our framework is the construction and visualization of the posture-based graph that focuses on the standard postures for launching and ending actions, as well as the action-based graph that focuses on the preference of actions and their transition probability. We further propose two numerical indices, the Connectivity Index and the Action Strategy Index, to assess skill level according to the graph. We demonstrate our framework with motions captured from different boxers. Experimental results demonstrate that our system can effectively visualize the strengths and weaknesses of the boxers.

A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches

Ryan Cameron, Zheming Zuo, Graham Sexton, Longzhi Yang
ConferenceProceedings of the 17th UK Wrokshop on Computational Intelligence (UKCI-2017), Cardiff, UK, 2017.

Abstract

Physically falling down amongst the elder helpless party is one of the most intractable issues in the era of ageing society, which has attracted intensive attentions in academia ranging from clinical research to computer vision studies. This paper proposes a fall detection/recognition system within the realm of computer vision. The proposed system integrates a group of gradient-based local visual feature extraction approaches, including histogram of oriented gradients (HOG), histogram of motion gradients (HMG), histogram of optical flow (HOF), and motion boundary histograms (MBH). A comparative study of the descriptors with the support of an artificial neural network was conducted based on an in-house captured dataset. The experimental results demonstrated the effectiveness of the proposed system and the power of these descriptors in real-world applications.

Engaging students for the learning and assessment of the advanced computer graphics module using the latest technologies

Liu Y, Yang L, Han J, Lu B, Yuen P, Zhao Y, Song R
ConferenceInternational Conference on Education and New Development, Lisbon, 2017.

Abstract

The advanced computer graphics has been one of the most basic and landmark modules in the field of computer science. It usually covers such topics as core mathematics, lighting and shading, texture mapping, colour and depth, and advanced modeling. All such topics involve mathematics for object modeling and transformation, and programming for object visualization and interaction. While some students are not as good in either mathematics or programming, it is usually a challenge to teach computer graphics to these students effectively. This is because it is difficult for students to link mathematics and programming with what they used to see in video games and the TV advertisements for example and thus they can easily be put off. In this paper, we investigate how the latest technologies can help alleviate the teaching and learning tasks. Instead of selecting the low level programming languages for demonstration and assignment such as Java, Java 3D, C++, or OpenGL, we selected Three.js, which is one of the latest and freely accessible 3D graphics libraries. It has a unique advantage that it provides a seamless interface between the main stream web browsers and 2D/3D graphics. The developed code can be run on a web browser such as Firefox, Chrome, or Safari for testing, debugging and visualization without code changing. The unique design patterns and objectives of Three.js can be very attractive to third party software houses to develop auxiliary functions, methods and tutorials and to make them freely available for the public. Such a unique property of Three.js and its widely available supporting resources are especially helpful to engage students, inspire their learning and facilitate teaching. To evaluate the effectiveness for using Three.js in teaching computer graphics we have set up an assignment for scene modeling in the last 4 years with focuses on the quality of the simulated scene (50%) and the quality of the assignment report (50%). We have evaluated different assessment forms of the module that we taught in the last four years: in 2013-2014 the module consisted of 20% assignment and 80% exam based on Java 3D; in 2014-2015 the same proportion of assignment/exam but based on WebGL, in 2015-2016 the module was 50-50% of assignment and exam but based on Three.js; and in this year the module is 100% assignment based on Three.js. The effectiveness of the module delivery has been evaluated both qualitatively and quantitatively from five aspects: a) average marks of students, b) moderator report, c) module evaluation questionnaire, d) external examiner’s comments and e) examination board recommendations. The results have shown that Three.js is indeed more successful in engaging students for learning and the 100% assignment assessment enables students to focus more on the design and development. This four year result is really encouraging to us as an educational institute to embrace the latest technologies for the delivery of such challenging modules as computer graphics and machine learning.

Depth Sensor-Based Facial and Body Animation Control

Yijun Shen, Jingtian Zhang, Longzhi Yang, Hubert P. H. Shum
BooksHandbook of Human Motion.

Abstract

Depth sensors have become one of the most popular means of generating human facial and posture information in the past decade. By coupling a depth camera and computer vision based recognition algorithms, these sensors can detect human facial and body features in real time. Such a breakthrough has fused many new research directions in animation creation and control, which also has opened up new challenges. In this chapter, we explain how depth sensors obtain human facial and body information. We then discuss on the main challenge on depth sensor-based systems, which is the inaccuracy of the obtained data, and explain how the problem is tackled. Finally, we point out the emerging applications in the field, in which human facial and body feature modeling and understanding is a key research problem.

Adaptive fuzzy interpolation with uncertain observations and rule base

Longzhi Yang, Qiang Shen
Conference2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

Abstract

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It views interpolation procedures as artificially created system components, and identifies all possible sets of faulty components that may each have led to all detected contradictory results. From this, a modification procedure takes place, which tries to modify each of such components, termed candidates, in an effort to remove all the contradictions and thus restore consistency. This approach assumes that the employed interpolation mechanism is the only cause of contradictions, that is all given observations and rules are believed to be true and fixed. However, this may not be the case in certain real situations. It is common in fuzzy systems that each observation or rule is associated with a certainty degree. This paper extends the adaptive approach by taking into consideration both observations and rules also, treating them as diagnosable and modifiable components in addition to interpolation procedures. Accordingly, the modification procedure is extended to cover the cases of modifying observations or rules in a given rule base along with the modification of fuzzy reasoning components. This extension significantly improves the robustness of the existing adaptive approach.

Adaptive fuzzy interpolation with prioritized component candidates

Longzhi Yang, Qiang Shen
Conference2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

Abstract

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It first identifies all possible sets of faulty fuzzy reasoning components, termed the candidates, each of which may have led to all the contradictory interpolations. It then tries to modify one selected candidate in an effort to remove all the contradictions and thus restore interpolative consistency. This approach assumes that all the candidates are equally likely to be the real culprit. However, this may not be the case in real situations as certain identified reasoning components may be more liable to resulting in inconsistencies than others. This paper extends the adaptive approach by prioritizing all the generated candidates. This is achieved by exploiting the certainty degrees of fuzzy reasoning components and hence of derived propositions. From this, the candidate with the highest priority is modified first. This extension helps to quickly spot the real culprit and thus considerably improves the approach in terms of efficiency.

Adaptive fuzzy interpolation and extrapolation with multiple-antecedent rules

Longzhi Yang, Qiang Shen
Conference2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010).

Abstract

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning owning to its efficient identification and correction of defective interpolated rules during the interpolation process [11]. This approach assumes that: i) two closest adjacent rules which flank the observation or a previously inferred result are always available; ii) only single-antecedent rules are involved. In practice, however, variable values of these rules may lie just on one side of the observation or inferred result. Also, there may be certain rules with multiple antecedents in the rule base. This paper extends the adaptive approach, in order to cover fuzzy extrapolation and to support rule base with multiple-antecedent rules. Adaptive fuzzy interpolation and extrapolation complement each other, which jointly improve the applicability of fuzzy interpolative reasoning, as it significantly reduces the restriction over the given rule base.

Manual Task Completion Time Estimation for Job Shop Scheduling Using a Fuzzy Inference System

Longzhi Yang, Jie Li, Phil Hackney, Fei Chao, Mark Flanagan
Conference2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

Abstract

Manual collating and packing is still the most cost-effective way of dispatching goods in many applications, despite of the rapid development of assembly robots. One such application, is the manufacturers of Point of Sale (POS) and Point of Purchase (POP) in the design and print industry, they produce and dispatch display objects in various quantities, shapes and sizes. The display objects, typically posters and 3D displays, are designed for different commercial promotion events in supermarkets, shopping malls and other high street shops. It is difficult to assemble and pack the objects using assembly robots due to the potential complexity and infinite variety of the tasks. The collate and pack department must manually pick, collate, assemble and pack items, often carried out in multiple lines based on the nature of the jobs, as the last stage of the manufacturing process. The jobs themselves are often unique bespoke arrangements defying a generic solution, flat-packed to minimise portage costs. The design of the lines and the schedule of the lines are determined by the area manager based on their expertise and historic knowledge, which seriously limits the effectiveness of the widely available automatic global scheduling system for these POP and POS print manufacturers. This paper proposes a job completion time estimation system which estimates the completion times for different tasks under different conditions such that the intelligent scheduling system can make a schedule globally by artificially treating the assembly lines as virtual machines. The system is implemented using a particular fuzzy inference system, fuzzy interpolation, and an illustrative example demonstrates the working and potential of the proposed solution.

Manual Collate and Pack Area Completion Time Estimation for POS and POP Manufacturing Scheduling

Longzhi Yang, Jie Li, Phil Hackney, Fei Chao, Mark Flanagan
ConferenceThe International Workshop on Engineering Data- & Model-driven Applications.

Abstract

Manual collating and packing is still the most cost-effective way of dispatching goods in many applications, despite of the rapid development of assembly robots. One such application, is the manufacturers of Point of Sale (POS) and Point of Purchase (POP) in the design and print industry, they produce and dispatch display objects in various quantities, shapes and sizes. The display objects, typically posters and 3D displays, are designed for different commercial promotion events in supermarkets, shopping malls and other high street shops. It is difficult to assemble and pack the objects using assembly robots due to the potential complexity and infinite variety of the tasks. The collate and pack department must manually pick, collate, assemble and pack items, often carried out in multiple lines based on the nature of the jobs, as the last stage of the manufacturing process. The jobs themselves are often unique bespoke arrangements defying a generic solution, flat-packed to minimise portage costs. The design of the lines and the schedule of the lines are determined by the area manager based on their expertise and historic knowledge, which seriously limits the effectiveness of the widely available automatic global scheduling system for these POP and POS print manufacturers. This paper proposes a job completion time estimation system which estimates the completion times for different tasks under different conditions such that the intelligent scheduling system can make a schedule globally by artificially treating the assembly lines as virtual machines. The system is implemented using a particular fuzzy inference system, fuzzy interpolation, and an illustrative example demonstrates the working and potential of the proposed solution.

Associated multi-label fuzzy-rough feature selection

Yanpeng Qu, Yu Rong, Ansheng Deng, Longzhi Yang
Conference2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS)

Abstract

Ahead of the process of selecting a subset of relevant features, the labels commonly need to be combined into a single one for multi-label feature selection. However the existing label combination methods assume that all labels are independent of each other and consequently suffer from high computation complexity. In this paper, association rules implied in the labels are explored to implement a fuzzy-rough feature selection method for multi-label datasets. Specifically, in order to reduce the scale of label and avoid the label overlapping phenomenon, the association rules between labels make the combination of labels collapse to a set of sub-labels. Then each set of sub-labels is regarded as a unique class during the following course of fuzzy-rough feature selection. Empirical results suggest that the quality of the selected features can be improved by the proposed approach compared to the alternative multi-label feature selection algorithms.

Closed Form Fuzzy Interpolation: An Improvement to Computational Rule of Inference

Longzhi Yang, Qiang Shen
JournalFuzzy Sets and Systems. To Appear. (Impact factor: 2.098, 5-year impact factor: 2.376)

Abstract

Fuzzy rule interpolation (FRI) was originally proposed as a supplement to ordinary fuzzy reasoning systems such that inference can be made with given (partially) sparse rule bases. Therefore, the results derived by either of the two approaches are expected to be compatible with one another. That is, similar results should be derivable from the same observation using FRI for cases where the ordinary compositional rule of inference may be directly applied. Unfortunately, this is untrue for most of the existing FRI approaches, except for the recently proposed initial work on closed form fuzzy interpolation~\cite{Yang20131}. As a follow on development of~\cite{Yang20131}, this paper systematically investigate the compatibility between closed form fuzzy interpolation and compositional rule of inference for situations where ordinary fuzzy reasoning can be performed while still entailing interpolative inference for situations where an observation matches no fuzzy rules. This indirectly proves the efficacy of closed form fuzzy interpolation, as compositional rule of inference has been evaluated by many real world applications. The paper has concluded that closed form fuzzy interpolation offers a better alternative to compositional rule of inference, supported by a number of typical illustrative FRI examples.

Generalised Adaptive Fuzzy Rule Interpolation

Longzhi Yang, Fei Chao, Qiang Shen
JournalIEEE Transactions on Fuzzy Systems. (Impact factor=6.701, 5-year impact factor=7.198)

Abstract

As a substantial extension to fuzzy rule interpolation that works based on two neighbouring rules flanking an observation, adaptive fuzzy rule interpolation is able to restore system consistency when contradictory results are reached during interpolation. The approach first identifies the exhaustive sets of candidates, with each candidate consisting of a set of interpolation procedures which may jointly be responsible for the system inconsistency. Then, individual candidates are modified such that all contradictions are removed and thus interpolation consistency is restored. It has been developed on the assumption that contradictions may only be resulted from the underlying interpolation mechanism, and that all the identified candidates are not distinguishable in terms of their likelihood to be the real culprit. However, this assumption may not hold for real world situations. This paper therefore further develops the adaptive method by taking into account observations, rules and interpolation procedures, all as diagnosable and modifiable system components. Also, given the common practice in fuzzy systems that observations and rules are often associated with certainty degrees, the identified candidates are ranked by examining the certainty degrees of its components and their derivatives. From this, the candidate modification is carried out based on such ranking. This work significantly improves the efficacy of the existing adaptive system by exploiting more information during both the diagnosis and modification processes.

Closed Form Fuzzy Interpolation

Longzhi Yang , Qiang Shen
JournalFuzzy Sets and Systems. vol. 225, pp.1-22, 2013. (Impact factor: 2.098, 5-year impact factor: 2.376)

Abstract

Fuzzy interpolation enhances the robustness of fuzzy systems and helps to reduce systems complexity. Although a number of important fuzzy rule interpolation approaches have been proposed in the literature, most of these approaches cannot be expressed in a closed form. This is usually caused by the effort to avoid possible invalid interpolated results. This paper proposes a different fuzzy rule interpolation approach. It not only can be represented in a closed form but also guarantees that the interpolated results are valid fuzzy sets. This approach is based on a direct use of the extension principle which has been widely utilised for the development of a variety of fuzzy systems. The mathematical properties of the proposed approach are analysed by taking the advantage of the closed form representation. This approach has been applied to a practical problem of predicting diarrhoeal disease rates in remote villages. The results demonstrate the potential of the proposed work in enhancing the robustness of fuzzy interpolation.

Adaptive Fuzzy Interpolation

Longzhi Yang , Qiang Shen
JournalIEEE Transactions on Fuzzy Systems, vol. 19, no. 6, pp. 1107-1126, 2011. (Impact factor=8.746, 5-year impact factor=7.881)

Abstract

Fuzzy interpolative reasoning strengthens the power of fuzzy inference by the enhancement of the robustness of fuzzy systems and the reduction of the systems' complexity. However, after a series of interpolations, it is possible that multiple object values for a common variable are inferred, leading to inconsistency in interpolated results. Such inconsistencies may result from defective interpolated rules or incorrect interpolative transformations. This paper presents a novel approach for identification and correction of defective rules in interpolative transformations, thereby removing the inconsistencies. In particular, an assumption-based truth-maintenance system (ATMS) is used to record dependences between interpolations, and the underlying technique that the classical general diagnostic engine (GDE) employs for fault localization is adapted to isolate possible faulty interpolated rules and their associated interpolative transformations. From this, an algorithm is introduced to allow for the modification of the original linear interpolation to become first-order piecewise linear. The approach is applied to a realistic problem, which predicates the diarrheal disease rates in remote villages, to demonstrate the potential of this study.

Fuzzy System Approaches to Negotiation Pricing Decision Support

Xin Fu, XiaoJun Zeng, Di Wang, Di Xu, Longzhi Yang
JournalJournal of Intelligent and Fuzzy Systems, vol. 29, no. 2, pp. 685-699, 2015. (Impact factor=1.812, 5-year impact factor=1.607) 

Abstract

With the emergence of customisation services, business-to-business price negotiation plays an increasingly important role in economic and management science. Negotiation pricing aims to provide different customers with products/services that perfectly meet their requirements, with the "right" price. In general, pricing managers are responsible for identifying the "right" negotiation price with the goal of maintaining good customer relationship, while maximising profits for companies. However, efficiently and effectively determining the "right" negotiation price boundary is not a simple task; it is often complicated, time-consuming and costly to reach a consensus as the task needs to take a wide variety of pricing factors into consideration, ranging from operation costs, customers' needs to negotiation behaviours. This paper proposes a systematic fuzzy system (FS) approach, for the first time, to provide negotiation price boundary by learning from available historical records, with a goal to release the burden of pricing managers. In addition, when the number of involved influencing factors increases, conventional FS approach easily suffers from the curse of dimensionality. To combat this problem, a novel method, simplified FS with single input and single output modules (SFS-SISOM), is also introduced in this paper to handle high-dimensional negotiation pricing problems. The utility and applicability of this research is illustrated by three experimental datasets that vary from both data dimensionality and the number of training records. The experimental results obtained from two approaches have been compared and analysed based on different aspects, including interpretability, accuracy, generality and applicability.

Multi-layer Lattice Model for Real-Time Dynamic Character Deformation

Naoya Iwamoto, Hubert Shum, Longzhi Yang, Shigeo Morishima
JournalComputer Graphics Forum, vol. 34, no. 7, pp. 99-109, 2015. (Impact factor=1.642, 5-year impact factor=1.902)

Abstract

Due to the recent advancement of computer graphics hardware and software algorithms, deformable characters have become more and more popular in real-time applications such as computer games. While there are mature techniques to generate primary deformation from skeletal movement, simulating realistic and stable secondary deformation such as jiggling of fats remains challenging. On one hand, traditional volumetric approaches such as the finite element method require higher computational cost and are infeasible for limited hardware such as game consoles. On the other hand, while shape matching based simulations can produce plausible deformation in real-time, they suffer from a stiffness problem in which particles either show unrealistic deformation due to high gains, or cannot catch up with the body movement. In this paper, we propose a unified multi-layer lattice model to simulate the primary and secondary deformation of skeleton-driven characters. The core idea is to voxelize the input character mesh into multiple anatomical layers including the bone, muscle, fat and skin. Primary deformation is applied on the bone voxels with lattice-based skinning. The movement of these voxels is propagated to other voxel layers using lattice shape matching simulation, creating a natural secondary deformation. Our multi-layer lattice framework can produce simulation quality comparable to those from other volumetric approaches with a significantly smaller computational cost. It is best to be applied in real-time applications such as console games or interactive animation creation.

Towards a Fuzzy Expert System on Toxicological Data Quality Assessment

L. Yang, D. Neagu, M. Cronin, M. Hewitt, S. Enoch, J. Madden, K. Przybylak
JournalMolecular Informatics, Vol. 32, no. 1, pp. 65-78, 2013. (Impact factor=1.647, 5-year impact factor=1.886)

Abstract

Quality assessment (QA) requires high levels of domain‐specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order to tag data instances with respect to their qualities; ToxRTool2 is an extension of the Klimisch approach aiming to increase the transparency and harmonisation of the approach. Note that the processes of QA in many other areas have been automatised by employing expert systems. Briefly, an expert system is a computer program that uses a knowledge base built upon human expertise, and an inference engine that mimics the reasoning processes of human experts to infer new statements from incoming data. In particular, expert systems have been extended to deal with the uncertainty of information by representing uncertain information (such as linguistic terms) as fuzzy sets under the framework of fuzzy set theory and performing inferences upon fuzzy sets according to fuzzy arithmetic. This paper presents an experimental fuzzy expert system for toxicological data QA which is developed on the basis of the Klimisch approach and the ToxRTool in an effort to illustrate the power of expert systems to toxicologists, and to examine if fuzzy expert systems are a viable solution for QA of toxicological data. Such direction still faces great difficulties due to the well‐known common challenge of toxicological data QA that “five toxicologists may have six opinions”. In the meantime, this challenge may offer an opportunity for expert systems because the construction and refinement of the knowledge base could be a converging process of different opinions which is of significant importance for regulatory policy making under the regulation of REACH, though a consensus may never be reached. Also, in order to facilitate the implementation of Weight of Evidence approaches and in silico modelling proposed by REACH, there is a higher appeal of numerical quality values than nominal (categorical) ones, where the proposed fuzzy expert system could help. Most importantly, the deriving processes of quality values generated in this way are fully transparent, and thus comprehensible, for final users, which is another vital point for policy making specified in REACH. Case studies have been conducted and this report not only shows the promise of the approach, but also demonstrates the difficulties of the approach and thus indicates areas for future development.

Towards Model Governance in Predictive Toxicology

A. Palczewska, X. Fu, P. Trundle, L. Yang, D. Neagu, M. Ridley, K. Travis
JournalInternational Journal of Information Managment, vol. 33, no. 3, pp. 567-582, 2013. (Impact factor=1.550, 5-year impact factor=2.432)

Abstract

Efficient management of toxicity information as an enterprise asset is increasingly important for the chemical, pharmaceutical, cosmetics and food industries. Many organisations focus on better information organisation and reuse, in an attempt to reduce the costs of testing and manufacturing in the product development phase. Toxicity information is extracted not only from toxicity data but also from predictive models. Accurate and appropriately shared models can bring a number of benefits if we are able to make effective use of existing expertise. Although usage of existing models may provide high-impact insights into the relationships between chemical attributes and specific toxicological effects, they can also be a source of risk for incorrect decisions. Thus, there is a need to provide a framework for efficient model management. To address this gap, this paper introduces a concept of model governance, that is based upon data governance principles. We extend the data governance processes by adding procedures that allow the evaluation of model use and governance for enterprise purposes. The core aspect of model governance is model representation. We propose six rules that form the basis of a model representation schema, called Minimum Information About a QSAR Model Representation (MIAQMR). As a proof-of-concept of our model governance framework we develop a web application called Model and Data Farm (MADFARM), in which models are described by the MIAQMR-ML markup language.

Generalisation of Scale and Move Transformation-Based Fuzzy Interpolation

Qiang Shen , Longzhi Yang
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics, vol. 15, No. 3, pp. 288-298, 2011. (Invited paper, Best Paper Nomination)

Abstract

Fuzzy interpolative reasoning has been extensively studied due to its ability to enhance the robustness of fuzzy systems and reduce system complexity. In particular, the scale and move transformation-based approach is able to handle interpolation with multiple antecedent rules involving triangular, complex polygon, Gaussian and bell-shaped fuzzy membership functions [1]. Also, this approach has been extended to deal with interpolation and extrapolation with multiple multi-antecedent rules [2]. However, the generalised extrapolation approach based on multiple rules may not degenerate back to the basic crisp extrapolation based on two rules. Besides, the approximate function of the extended approach may not be continuous. This paper therefore proposes a new approach to generalising the basic fuzzy interpolation technique of [1] in an effort to eliminate these limitations. Examples are given throughout the paper for illustration and comparative purposes. The result shows that the proposed approach avoids the identified problems, providing more reasonable interpolation and extrapolation.

TSK Inference with Sparse Rule Bases

Longzhi Yang, Yanpeng Qu, Hubert Shum, Longzhi Yang
ConferenceProceedings of the 16th UK Wrokshop on Computational Intelligence (UKCI-2016), Lancaster, UK, 2016.

Abstract

The Mamdani and TSK fuzzy models are fuzzy inference engines which have been most widely applied in real-world problems. Compared to the Mamdani approach, the TSK approach is more convenient when the crisp outputs are required. Common to both approaches, when a given observation does not overlap with any rule antecedent in the rule base (which usually termed as a sparse rule base), no rule can be fired, and thus no result can be generated. Fuzzy rule interpolation was proposed to address such issue. Although a number of important fuzzy rule interpolation approaches have been proposed in the literature, all of them were developed for Mamdani inference approach, which leads to the fuzzy outputs. This paper extends the traditional TSK fuzzy inference approach to allow inferences on sparse TSK fuzzy rule bases with crisp outputs directly generated. This extension firstly calculates the similarity degrees between a given observation and every individual rule in the rule base, such that the similarity degrees between the observation and all rule antecedents are greater than 0 even when they do not overlap. Then the TSK fuzzy model is extended using the generated matching degrees to derive crisp inference results. The experimentation shows the promising of the approach in enhancing the TSK inference engine when the knowledge represented in the rule base is not complete.

Invasive Weed Optimization Based Fuzzy-rough Feature Selection for Mammographic Risk Analysis

Qian Guo, Yanpeng Qu, Ansheng Deng, Longzhi Yang
ConferenceProceedings of the 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2016), China, 2016.

Abstract

Abstract—Mammographic risk analysis is an important task for assessing the likelihood of a woman developing breast cancer. There are many efforts have been devoted to improving the performance of the relevant assessment technologies. This paper presents an invasive weed optimization (IWO) based fuzzy-rough feature selection method for mammographic risk assessment. The advantage of IWO is that the offspring individuals are randomly spread around their parents according to a Gaussian distribution during the evolution process. Such Gaussian distribution is designated with a dynamical standard deviation. Therefore, the optimization algorithm can explore a new solution space aggressively to maintain the diversity of the species in the early and middle iterations, and the optimal individuals in final iteration of feature selection. The mechanism of IWO ensures a global optimal solution for the heuristic search. The performance of IWO is compared with particle swarm optimization (PSO) and ant colony optimization (ACO). The experimental results indicate that the use of IWO entails better performance for the problem of mammographic risk analysis. At the same time, in this case the level of dimensionality reduction and the increase of in classification accuracy are confirmed.

Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation

Yao Tan, Jie Li, Martin Wonders, Fei Chao, Hubert Shum, Longzhi Yang
ConferenceProceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), Canada, 2016. (IEEE Computational Intelligence Society Outstanding Student Paper Travel Grant Award, Bset Student Paper Award Nomination)

Abstract

Experience-Based Rule Base Generation and Adaptation for Fuzzy Interpolation

Jie Li, Hubert Shum, Xin Fu, Garham Sexton, Longzhi Yang
ConferenceProceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), Canada, 2016. (IEEE Computational Intelligence Society Outstanding Student Paper Travel Grant Award)

Abstract

Intelligent Home Heating Controller Using Fuzzy Rule Interpolation

Jie Li, Longzhi Yang, Hubert Shum, Graham Sexton, Yao Tan
ConferenceProceedings of the 2015 UK Workshop on Computational Intelligence, UK, 2015.

Abstract

The reduction of domestic energy waste helps in achieving the legal binding target in the UK that CO2 emissions needs to be reduced by at least 34% below base year (1990) levels by 2020. Space heating consumes about 60% of the household energy consumption, and it has been reported by the Household Electricity Survey from GOV.UK, that 23% of residents leave the heating on while going out. To minimise the waste of heating unoccupied homes, a number of sensor-based and programmable controllers for central heating system have been developed, which can successfully switch off the home heating systems when a property is unoccupied. However, these systems cannot successfully effciently preheat the homes before occupants return without manual inputs or leaving the heating on unnecessarily longer than needed, which has limited the wide application of such devices. In order to address this limitation, this paper proposes a smart home heating controller, which enables a home heating system to effciently preheat the home by successfully predicting the users' home time. In particular, residents' home time is calculated by employing fuzzy rule interpolation, supported by users' historic and current location data from portable devices (commonly smart mobile phones). The proposed system has been applied to a real-world case and promising result has been generated.

A Smart Calendar System Using Multiple Search Techniques

Jake Cowton, Longzhi Yang
ConferenceProceedings of the 2015 UK Workshop on Computational Intelligence, UK, 2015.

Abstract

Calendars are essential for professionals working in industry, government, education and many other fields, which play a key role in the planning and scheduling of people’s day-today events. The majority of existing calendars only provide insight and reminders into what is happening during a certain period of time, but do not offer any actual scheduling functionality that can assist users in creating events to be optimal to their preferences. The burden is on the users to work out when their events should happen, and thus it would be very beneficial to develop a tool to organise personal time to be most efficient based on given tasks, preferences, and constraints, particularly for those people who have generally very busy calendars. This paper proposes a smart calendar system capable of optimising the timing of events to address the limitations of the existing calendar systems. It operates in a tiered format using three search algorithms, namely branch and bound, Hungarian and genetic algorithms, to solve different sized problems with different complexity and features, in an effort to generate a balanced solution between time consumption and optimisation satisfaction. Promising results have shown in the experimentation in personal event planning and scheduling.

Integration Strategies for Toxicity Data from an Empirical Perspective

Longzhi Yang, Daniel Neagu
ConferenceProceedings of the 14th Annual UK Workshop on Computational Intelligence (UKCI’14), pp. 1-8, UK, 2014.

Abstract

The recent development of information techniques, especially the state-of-the-art “big data” solutions, enables the extracting, gathering, and processing large amount of toxicity information from multiple sources. Facilitated by this technology advance, a framework named integrated testing strategies (ITS) has been proposed in the predictive toxicology domain, in an effort to intelligently jointly use multiple heterogeneous toxicity data records (through data fusion, grouping, interpolation/extrapolation etc.) for toxicity assessment. This will ultimately contribute to accelerating the development cycle of chemical products, reducing animal use, and decreasing development costs. Most of the current study in ITS is based on a group of consensus processes, termed weight of evidence (WoE), which quantitatively integrate all the relevant data instances towards the same endpoint into an integrated decision supported by data quality. Several WoE implementations for the particular case of toxicity data fusion have been presented in the literature, which are collectively studied in this paper. Noting that these uncertainty handling methodologies are usually not simply developed from conventional probability theory due to the unavailability of big datasets, this paper first investigates the mathematical foundations of these approaches. Then, the investigated data integration models are applied to a representative case in the predictive toxicology domain, with the experimental results compared and analysed.

Closed Form Fuzzy Interpolation with Interval Type-2 Fuzzy Sets

Longzhi Yang, Chengyuan Chen, Nanlin Jin, Xin Fu and Qiang Shen
ConferenceProceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’14), pp. 2184-2191, China, 2014. (Nominated best paper by two reviewers)

Abstract

Fuzzy rule interpolation enables fuzzy inference with sparse rule bases by interpolating inference results, and may help to reduce system complexity by removing similar (often redundant) neighbouring rules. In particular, the recently proposed closed form fuzzy interpolation offers a unique approach which guarantees convex interpolated results in a closed form. However, the difficulty in defining the required precise-valued membership functions still poses significant restrictions over the applicability of this approach. Such limitations can be alleviated by employing type-2 fuzzy sets as their membership functions are themselves fuzzy. This paper extends the closed form fuzzy rule interpolation using interval type-2 fuzzy sets. In this way, as illustrated in the experiments, closed form fuzzy interpolation is able to deal with uncertainty in data and knowledge with more flexibility.

Toxicity Risk Assessment from Heterogeneous Uncertain Data with Possibility Probability Distribution

Longzhi Yang, Daniel Neagu
ConferenceProceedings of the 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’13), pp. 1-8, India, 2013.

Abstract

Due to the advance of modern computing technology, decisions can be made based on all the existing related data instances scattered across multiple data storages, such that available information has been entirely taken into consideration. Particularly in the predictive toxicology domain, because of the heterogeneity of data sources, multiple data instances with respect to the same endpoint are usually inconsistent, and the quality (or reliability) of the data instances is typically different. Also, the quantity of data instances is often not sufficient to conduct a study using conventional statistics-based methods. This paper presents a novel risk analysis approach for chemical toxicity assessment which considers all the available heterogeneous data instances in the same time, assisted by their quality (or reliability) values. The system is developed on the basis of possibilityprobability distribution, where the uncertainty of the approximated probability values based on traditional statistics methods is represented by possibility. The uncertainty considered herein is led not only by the statistics on limited small number of data instances, but also by the poor quality (or reliability) of data instances. The possibility-probability distribution is automatically computed from available data instances by employing a modified diffused-interior-outer-set model (where the reliability of data is considered) based on nformation diffusion theory. Toxicity value for a given chemical compound is then estimated as the fuzzy expected value based on the resulted possibility-probability distribution. Toxicity risk with respect to regulatory threshold is also introduced, in order to evaluate the probability of which the toxicity may be classified into a certain regulatory range. The proposed approach is applied to a real-world dataset to illustrate the utility and the potential of the approach in risk assessment of chemical toxicity.

Optimisation of Classifier Ensemble for Predictive Toxicology Applications

Mokhairi Makhtar, Longzhi Yang, Daniel Neagu, Mick Ridley
ConferenceProceedings of the 14th International Conference on Computer Modelling and Simulation (UKSim 2012), pp. 236-241, UK, 2012.

Abstract

Ensembles of classifiers proved potential in getting higher accuracy compared to a single classifier. High diversity in an ensemble may improve the performance results significantly. We propose an ensemble approach which has diversity calculated using disagreement measure of classification output. A CRS (Classifier Ranking System) is introduced for the selection of relevant classifiers. We also propose the Optimisation of Classifiers Ensemble Method (OCEM) technique which applies to the ensemble selection. In this paper, we focus on classification models for predictive toxicology applications, for which computational models are required to replace in vivo experiments. The results show that our method performs well in selecting the relevant ensemble model to improve the prediction from a collection of classifiers.

Towards the integration of heterogeneous uncertain data

Longzhi Yang , Daniel Neagu
ConferenceProceedings of the 13th IEEE International Conference on Information Reuse and Integration (IRI-2012), pp. 296-302, USA, 2012.

Abstract

Along with the rapid development of data storing and sharing techniques in terms of both hardware and software, multiple data instances scattered across multiple databases may be available to support one single task, and then making choices of data are necessary from time to time. Research has been conducted on quality or reliability evaluation for individual piece of data assisted by domain knowledge to guide the data selecting processes. However, the choice still can be very difficult if the supporting data instances are contradictory or inconsistent. This paper presents a novel data integration approach based on Credibility Measure, which was developed on the basis of Possibility Measure and Necessity Measure under the framework of fuzzy set theory and fuzzy logic. In particular, the approach is able to combine any new piece of data into the existing decision by an effective credibility revision algorithm such that the revised results have taken all the currently available information into consideration. The proposed approach is applied to a decision problem in the predictive toxicology domain to illustrate the potential in improving the effectiveness of data sharing and the robustness of decisions made from the related data sources.

Adaptive Fuzzy Interpolation with Prioritized Component Candidates

Longzhi Yang, Qiang Shen
ConferenceProceedings of the 2011 IEEE International Conference on Fuzzy Systemses (FUZZ-IEEE'11), pp. 428-435, Taiwan, 2011. (IEEE Computational Intelligence Society Outstanding Student Paper Travel Grant Award)

Abstract

Adaptive fuzzy interpolation strengthens the poten- tial of fuzzy interpolative reasoning. It first identifies all possible sets of faulty fuzzy reasoning components, termed the candidates, each of which may have led to all the contradictory interpolations. It then tries to modify one selected candidate in an effort to remove all the contradictions and thus restore interpolative consistency. This approach assumes that all the candidates are equally likely to be the real culprit. However, this may not be the case in real situations as certain identified reasoning components may be more liable to resulting in inconsistencies than others. This paper extends the adaptive approach by prioritizing all the generated candidates. This is achieved by exploiting the certainty degrees of fuzzy reasoning components and hence of derived propositions. From this, the candidate with the highest priority is modified first. This extension helps to quickly spot the real culprit and thus considerably improves the approach in terms of efficiency.

Automatic Estimation of the Number of Segmentation Groups Based on MI

Ziming Zeng , Wenhui Wang, Longzhi Yang, Reyer Zwiggelaar
ConferenceProceeedings of the 5th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2011), pp. 532-539, Spain, 2011.

Abstract

Clustering is important in medical imaging segmentation. The number of segmentation groups is often needed as an initial condition,but is often unknown. We propose a method to estimate the number of segmentation groups based on mutual information, anisotropicdiffusion model and class-adaptive Gauss-Markov random fields. Initially, anisotropic diffusion is used to decrease the imagenoise. Subsequently, the class-adaptive Gauss-Markov modeling and mutual information are used to determine the number of segmentationgroups. This general formulation enables the method to easily adapt to various kinds of medical images and the associated acquisition artifacts. Experiments on simulated, and multi-model data demonstrate the advantages of the method over the current state-of-the-art approaches.

Adaptive Fuzzy Interpolation with Uncertain Observation and Rule Base

Longzhi Yang, Qiang Shen
ConferenceProdeedings of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’11), pp. 471-478, Taiwan, 2011. (Nominated Best Student Paper, IEEE Computational Intelligence Society Outstanding Student Paper Travel Grant Award)

Abstract

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It views interpolation procedures as artificially created system components, and identifies all possible sets of faulty components that may each have led to all detected contradictory results. From this, a modification procedure takes place, which tries to modify each of such components, termed candidates, in an effort to remove all the contradictions and thus restore consistency. This approach assumes that the employed interpolation mechanism is the only cause of contradictions, that is all given observations and rules are believed to be true and fixed. However, this may not be the case in certain real situations. It is common in fuzzy systems that each observation or rule is associated with a certainty degree. This paper extends the adaptive approach by taking into consideration both observations and rules also, treating them as diagnosable and modifiable components in addition to interpolation procedures. Accordingly, the modification procedure is extended to cover the cases of modifying observations or rules in a given rule base along with the modification of fuzzy reasoning components. This extension significantly improves the robustness of the existing adaptive approach.

Adaptive Fuzzy Interpolation and Extrapolation with Multiple-Antecedent Rules

Longzhi Yang , Qiang Shen
ConferenceProceedings of the 2010 IEEE International Conference of Fuzzy Systems (FUZZ-IEEE'11), pp. 1-8, Spain, 2010. (IEEE Computational Intelligence Society Outstanding Student Paper Travel Grant Award, Best Student Paper Award)

Abstract

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning owning to its efficient identification and correction of defective interpolated rules during the interpolation process [11]. This approach assumes that: i) two closest adjacent rules which flank the observation or a previously inferred result are always available; ii) only single-antecedent rules are involved. In practice, however, variable values of these rules may lie just on one side of the observation or inferred result. Also, there may be certain rules with multiple antecedents in the rule base. This paper extends the adaptive approach, in order to cover fuzzy extrapolation and to support rule base with multiple-antecedent rules. Adaptive fuzzy interpolation and extrapolation complement each other, which jointly improve the applicability of fuzzy interpolative reasoning, as it significantly reduces the restriction over the given rule base.

Extending Adaptive Interpolation: From Triangular to Trapezoidal

Longzhi Yang, Qiang Shen
ConferenceProceedings of the 9th UK Workshop on Computational Intelligence, pp. 25-30, UK, 2009.

Abstract

Fuzzy interpolative reasoning strengthens the power of fuzzy inference by enhancing the robustness of fuzzy systems and reducing systems complexity. However, during the interpolation process, it is possible that multiple object values for a common variable are inferred which may lead to inconsistency in interpolated results. A novel approach [10] was recently proposed for identification and correction of defective rules in the transformations computed for interpolation, thereby removing the inconsistencies. However, the implementation of this work is limited to rule models involving triangular fuzzy variables. This paper extends the adaptive approach as presented in [10], by introducing trapezoidal variables in the representation and manipulation of fuzzy rule models. This significantly improves the applicability of adaptive fuzzy interpolation reasoning, as many fuzzy systems are modelled with trapezoidal (as well as triangular) variables.

Towards Adaptive Interpolative Reasoning

Longzhi Yang, Qiang Shen
ConferenceProceedings of the 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’09), pp. 542-549, South Korea, 2009. (IEEE Computational Intelligence Society Outstanding Student Paper Travel Grant Award)

Abstract

Fuzzy interpolative reasoning has been extensively studied due to its ability to enhance the robustness of fuzzy systems and to reduce system complexity. However, during the interpolation process, it is possible that multiple object values for a common variable are inferred which may lead to inconsistency in interpolated results. Such inconsistencies may result from defective interpolated rules or incorrect interpolative transformations. This paper presents a novel approach for identification and correction of defective rules in transformations, thereby removing the inconsistencies. In particular, an assumption-based truth maintenance system (ATMS) is used to record dependencies between reasoning results and interpolated rules, while the underlying technique that the general diagnostic engine (GDE) employs for fault localization is adapted to isolate possible faulty interpolated rules and their associated interpolative transformations. From this, an algorithm is introduced to allow for the modification of the original linear interpolation to become first-order piecewise linear. The approach is applied to a carefully chosen practical problem to illustrate the potential in strengthening the power of interpolative reasoning.

Closed form fuzzy interpolation: an improvement to compositional rule of inference

Longzhi Yang , Qiang Shen
ManuscriptFuzzy Sets and Systems, under review.

Abstract

Fuzzy Sets and Systems, under review

Interactive-based Human Motion Analysis and Retrieval

Y. Shen , L. Yang, E. Ho, and H. Shum
ManuscriptInteractive-based Human Motion Analysis and Retrieval, submitted to IEEE Transactions on Visualization and Computer Graphics in May 2016

Abstract

IEEE Transactions on Visualization and Computer Graphics, under review.

Enhanced Robotic Hand-eye Coordination Inspired from Human-like Behavioral Patterns

F. Chao , Z. Zhu, L. Yang, H. Hu, and C. Zhou
ManuscriptIEEE Transactions on Human-Machine Systems, under review.

Abstract

Submitted to IEEE Transactions on Cybernetics

Use of Human Gesture Recognition and Reduced Classifier Ensemble for a Robotic Writing System

F. Chao, Y. Huang, X. Zhang, C. Shang, L. Yang, C. Zhou, H. Hu, and C.-M. Lin
ManuscriptEngineering Applications of Artificial Intelligence, under review.

Abstract

Submitted to IEEE Transactions on Cybernetics

Principally Supervised PhD Students

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    Dr. Jie Li (Graduated, now work in Therapyaudit Limited

    Principal supervisor: Dr. Longzhi Yang
    Second Supervisor: Dr. Graham Sexton
    External Suerpvisor: Prof. Xin Fu
    External Examiner: Prof. Daniel Neagu
    Internal Examiner: Dr. Huseyin Seker

    2014-2018

    Please click here for more details.

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    Mr. Yao Tan

    2015-2019

    Please click here for more details.

  • More details are on its way......

  • More details are on its way......

  • Highly motivated PhD candidates and visiting scholars are welcomed to join my research team, and self-funded students/scholars can be accepted all year round.

Secondly Supervised PhD Students

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    Dr. Yijun Shen (Graduated)

    Principal supervisor: Dr. Hubert Shum
    Second Supervisor: Dr. Longzhi Yang
    Second Supervisor: Dr Edmond Ho
    External Examiner: Prof. Adrian Hilton
    Internal Examiner: Prof. Shaun Lawson

    2014-2018

    Please click here for more details.

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    Mr. Zequn Li

    2015-2019

    More details are on its way......

  • More details are on its way......

  • In addition to these formal official supervisions, I have informally supervised a number of MSc (research) and PhD students internally and externally, nationaly and internationally at Aberystwyth Univeristy when I did my PhD, at Bradford University when I did my Post-doc, and here are Northumbria university.
    Please feel free to contact me if any supervisors or PhD/MSc students (with their supervisors' approval) would like to collaborate with me in co-supervising students.

Research Collaborators

Mr. Martin Wonders

Senior Lecturer at Northumbria University

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Prof. Yunqi Lei

Professor at Xiamen University, Head of Deartment

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Dr. Graham Sexton

Previous Head of Department at Northumbria University

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Prof. Daniel Neagu

Professor at Bradford University

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Dr. Yanpeng Qu

Associate Professor at Dalian Maritime University

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Mr. Yijun Shen

PhD student at Norghumbria University

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Mr. Naoya Iwamoto

PhD student at Waseda University

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Dr. Fei Chao

Associate Professor at Xiamen University

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Dr. Xin Fu

Associate Professor at Xiamen University

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Dr. Hubert Shum

Senior Lecturer at Northumbria University

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Dr. Mark Flanagan

Information Services Manager at Simpson Group

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Ms Elaine Taylor-Whilde

CEO of Nine Health CIC

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Lead and Managements

Director of Teaching and Learning

Chair of IEEE SIG of Big Data for Cyber Security and Privacy

Taught-Programme Projects

1. EAE0004: Placement year tutor - Accenture and HMRC

2. CM0645: Individual Project (7 students, 2015-2016)

3. IS0749: MSc Individual Project (2 students, 2015-2016)

Teaching modules

1. CM0671: Artificial Intelligence and Affective Computing

2. CM0672: Professionalism and Artificial Intelligence Case Project

3. EN0578: Advanced Operating Systems

4. EN0402: Programming Fundamentals with Robots

Conference and Special Session Organiser

General Co-chair, UKCI 2019

Poster Chair, BMVC 2018

Special Session Chair of FUZZ-IEEE-04 "Fuzzy Logic Systems for Security and Forensics" at WCCI 2018

Journal Editor

Healthcare System Innovation - Applied System Innovation - Please click here for more information.

Guest Editor of Multimedia Tools and Applicatiosn for special issue on “Soft computing Techniques and Applications for Intelligent Multimedia Systems”

Journal Review

IEEE Transaction on Fuzzy Systems

IEEE Transaction on Cybernetics

IEEE Transaction on Big Data

IEEE Transaction on Circuits and Systems for Video Technology

IEEE MultiMedia

IEEE Access

IEEE Jounal of Biomedical and Health Informatics

The IEEE/CAA Journal of Automatica Sinica

Fuzzy Sets and Systems (Elsevier)

Expert Systems (Wiley)

Soft Computing

Information Science

Neural Computing and Applications (Elsevier)

Computers and Electrical Engineering (Elsevier)

Neurocomputing

Knowledge Based Systems

International Journal of Machine Learning and Cybernetics

Invited Talks/Seminars

2018: Fuzzy Rule Interpolation Systems, Deaprtment of Comptuer Science, Aberystwyth University

2018: Fuzzy Rule Interpolation Systems, Department of Computer Science and Technology, University of Bedfordshire

2017: Fuzzy Rule Interpolation Systems, School of Computing, the University of Portsmouth

2015: Fuzzy Interpolation and Its Application in Smart Home, the 12th Haiyun Lecturer of 2015 at Xiamen University

2014: AI Techniques and Their Potential Application in Print Industry, Simpson Group, Newcastle upon Tyne, UK

2012: Data Quality Assessment: from Computing Point of View - CSOMOS Symposium: A Roadmap to Navigate from Databases to Adverse Outcome Pathways, Bradford, UK

2012: Data Quality Assessment and Control - SEURAT-1 Summer School, Oeiras, Portugal

2011: Fuzzy Interpolation and Its Adaptation - Research Seminar of SCIM, University of Bradford, UK

Conference Technical Committee Memeber

The IEEE International Conference on Fuzzy Systems

The IEEE International Conference on Tools with Artificial Intelligence

The International Conference on Fuzzy Systems and Knowledge Discovery

The UK and Ireland Workshop on Computational Intelligence

The IEEE International Symposium on Multimedia

The International Conference on on Information Science and Security

The International Conference on IT Convergence and Security

The International Conference on Information Science and Applications

The International Conference on Advanced Computational Intelligence

... ...

PhD Student Position

Highly motivated PhD candidates are welcomed to join, and self-funded student can be accepted all year around.

Undergraduate and MSc Internship Position

Internship students and vistings students are welcome to join us for summer projects.

Visiting Scholar

Motivated visiting scholars are particularly welcome, and we will provide support for candidate to apply the fund for visiting.

Post-doc Research Fellow Position

Motivated post-docs candidates are also welcome, self-funded ones will be particularly encouraged to apply, support will be provided to apply for other funding opportunities, such as Newton International fellowships.

At My Office

ELB206, Ellison Building B Block
Department of Computer and Information Science,
Faculty of Engineering and Environment,
Northumbria University,
Newcastle upon Tyne,
NE1 8ST, UK.