Professor Yingjie Yang

Job: Professor of Computational Intelligence

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI) and De Montfort University Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 257 7939

E: yyang@dmu.ac.uk

W: www.dmu.ac.uk/cci

 

Personal profile

Dr. Yingjie Yang was awarded his first PhD in Engineering from Northeastern University in 1994, and his second PhD in Computer Science in 2008. He has published more than 100 papers in international journals and conferences. He has been involved in more than 90 international conferences as a member of program committees and organised a number of international conferences and special sessions such as 2015 IEEE International Conference on Grey Systems and Intelligent Service, IEEE SMC 2014 and IEEE WCCI2008. As a senior member of IEEE, Dr. Yang serves as a co-chair of the Technical Committee on Grey Systems, IEEE Systems, Man and Cybernetics Society and the vice chair for the task force for competition in IEEE Fuzzy Systems Technical Committee. He is serving also as an associate editor for 5 international academic journals, including IEEE Transactions on Cybernetics. He had been invited to give plenary speech at a number of international confertences, such as the 2013, 2011 and 2009 IEEE Conferences on Grey Systems and Intelligent Services and the 2001 international conference on Airport Management.

Publications and outputs 

  • Data-based structure selection for unified discrete grey prediction model
    Data-based structure selection for unified discrete grey prediction model Wei, Bao-lei; Xie, Naiming; Yang, Yingjie Grey models have been reported to be promising for time series prediction with small samples, but the diversity kinds of model structures and modelling assumptions restrains their further applications and developments. In this paper, a novel grey prediction model, named discrete grey polynomial model, is proposed to unify a family of univariate discrete grey models. The proposed model has the capacity to represent most popular homogeneous and non-homogeneous discrete grey models and furthermore, it can induce some other novel models, thereby highlighting the relationship between the models and their structures and assumptions. Based on the proposed model, a data-based algorithm is put forward to select the model structure adaptively. It reduces the requirement for modeler’s knowledge from an expert system perspective. Two numerical experiments with large-scale simulations are conducted and the results show its effectiveness. In the end, two real case tests show that the proposed model benefits from its adaptive structure and produces reliable multi-step ahead predictions. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • The Quantification of Subjectivity: The R-fuzzy Grey Analysis Framework
    The Quantification of Subjectivity: The R-fuzzy Grey Analysis Framework Khuman, A. S.; Yang, Yingjie; John, Robert This paper puts forward a newly derived framework for capturing and inferring from subjective based uncertainty for any given observation. The framework is referred to as the R-fuzzy grey analysis framework (RfGAf), which itself is comprised of 3 distinct components: 1. R-fuzzy sets - to capture the uncertainty, which utilises crisp rough set bounding of uncertain possible fuzzy membership values. 2. A significance measure - to provide a means to allow for conditional probability to be undertaken, and also to allow for the translation of the data to that of a time series, allowing for the linking to that of the third component. 3. Grey analysis, more specifically, the absolute degree of grey incidence, where post-analysis can be undertaken and additional metrics obtained. The hybridisation of all three has allowed for the creation of a framework ideally suited for the quantification of perception based uncertainty, which by proxy will be inherently associated to subjectivity. It will be shown and demonstrated how such a framework can be made use of, showcasing the advantages of such an approach. By making use of R-fuzzy sets and the significance measure, an intermediary approach to that of a generalised type-2 fuzzy set can be obtained. As it is widely agreed upon that a generalised type-2 fuzzy approach is ideal for capturing higher degrees of resolution with regards to uncertainty, the associated computational burden of its complexity makes it unfavourable, hence why the interval-valued type-2 approach is favoured. The findings indicate the RfGAf can allow for the high capacity and detail one would expect when considering a type-2 fuzzy set representation, with that of the simplistic objectiveness one would associate to a typical type-1 fuzzy set. The novelty of the framework allows for one to fully capture all the nuances and individualities of a population without a single loss of information. That snapshot in time can tell an awful lot with regards to perceived perception. The framework can be deployed on varying sizes of populations, from the intrinsically small to the overtly large. Potentially, one can use that snapshot to predict how an observation could be perceived in future events. If one can forecast the perception ahead of time, one can improve drastically on efficacy and efficiency. Such a framework has a strong applicability with regards to expert and intelligent systems in allowing for more detailed inference to be utilised and acted upon. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A Novel Diagnostic and Prognostic Framework for Incipient Fault Detection and Remaining Service Life Prediction with Application to Industrial Rotating Machines
    A Novel Diagnostic and Prognostic Framework for Incipient Fault Detection and Remaining Service Life Prediction with Application to Industrial Rotating Machines Li, Xiaochuan; Yang, Xiaoyu; Yang, Yingjie; Ian, Bennett; Mba, David Data-driven machine health monitoring systems (MHMS) have been widely investigated and applied in the field of machine diagnostics and prognostics with the aim of realizing predictive maintenance. It involves using data to identify early warnings that indicate potential system malfunctioning, predict when system failure might occur, and pre-emptively service equipment to avoid unscheduled downtime. One of the most critical aspects of data-driven MHMS is the provision of incipient fault diagnosis and prognosis regarding the system’s future working conditions. In this work, a novel diagnostic and prognostic framework is proposed to detect incipient faults and estimate remaining service life (RSL) of rotating machinery. In the proposed framework, a novel canonical variate analysis (CVA)-based monitoring index, which takes into account the distinctions between past and future canonical variables, is employed for carrying out incipient fault diagnosis. By incorporating the exponentially weighted moving average (EWMA) technique, a novel fault identification approach based on Pearson correlation analysis is presented and utilized to identify the influential variables that are most likely associated with the fault. Moreover, an enhanced metabolism grey forecasting model (MGFM) approach is developed for RSL prediction. Particle filter (PF) is employed to modify the traditional grey forecasting model for improving its prediction performance. The enhanced MGFM approach is designed to address two generic issues namely dealing with scarce data and quantifying the uncertainty of RSL in a probabilistic form, which are often encountered in the prognostics of safety-critical and complex assets. The proposed CVA-based index is validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system, and the effectiveness of the proposed integrated diagnostic and prognostic method for the monitoring of rotating machinery is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump and one case study of an operational centrifugal compressor. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis
    Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis Li, Xiaochuan; Yang, Yingjie; Bennett, Ian; Mba, David Condition monitoring signals obtained from rotating machines often demonstrate a highly non-stationary and transient nature due to internal natural deterioration characteristics of their constituent components and external time-varying operational conditions. Traditional multivariate statistical monitoring approaches are based on the assumption that the underlying processes are linear and static and are apt to interpret the normal changes in operating conditions as faults, which would result in high false positive rates. On the other hand, the development of robust diagnostic techniques for the detection of incipient faults remains a challenge for researchers, given the difficulty of finding an appropriate trade-off between a low false positive ratio and early detection of emerging faults. To address these issues, this paper proposes a novel adaptive fault detection approach based on the canonical residuals (CR) induced by the combination of canonical variate analysis (CVA) and matrix perturbation theory for the monitoring of dynamic processes where variations in operating conditions are incurred. The canonical residuals are calculated based upon the distinctions between past and future measurements and are able to effectively detect emerging faults while still maintaining a low false positive rate. The effectiveness of the developed diagnostic model for the detection of abnormalities in industrial processes was demonstrated for slow involving faults in case studies of two operational industrial high-pressure pumps. In comparison with the variable-based and canonical correlation-based statistical monitoring approaches, the proposed canonical residuals-adaptive canonical variate analysis (CR-ACVA) fault detection method has demonstrated its superiorities by the detailed performance comparisons. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A novel energy consumption forecasting model combining an optimized DGM (1, 1) model with interval grey numbers
    A novel energy consumption forecasting model combining an optimized DGM (1, 1) model with interval grey numbers Ye, Jing; Dang, Yaoguo; Song, Ding; Yang, Yingjie Since energy consumption (EC) is becoming an important issue for sustainable development in the world, it has a practical significance to predict EC effectively. However, there are two main uncertainty factors affecting the accuracy of a region's EC prediction. Firstly, with the ongoing rapid changes in society, the consumption amounts can be non-smooth or even fluctuating during a long time period, which makes it difficult to investigate the sequence's trend in order to forecast. Secondly, in a given region, it is difficult to express the consumption amount as a real number, as there are different development levels in the region, which would be more suitably described as interval numbers. Most traditional prediction models for energy consumption forecasting deal with long-term real numbers. It is seldom found to discover research that focuses specifically on uncertain EC data. To this end, a novel energy consumption forecasting model has been established by expressing ECs in a region as interval grey numbers combining with the optimized discrete grey model (DGM(1,1)) in Grey System Theory (GST). To prove the effectiveness of the method, per capita annual electricity consumption in southern Jiangsu of China is selected as an example. The results show that the proposed model reveals the best accuracy for the short data sequences (the average fitting error is only 2.19% and the average three-step forecasting error is less than 4%) compared with three GM models and four classical statistical models. By extension, any fields of EC, such as petroleum consumption, natural gas consumption, can also be predicted using this novel model. As the sustained growth in EC of China's, it is of great significance to predict EC accurately to manage serious energy security and environmental pollution problems, as well as formulating relevant energy policies by the government. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines
    A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines Yang, Xiaoyu; Fang, Zhigeng; Yang, Yingjie; Mba, David; Li, Xiaochuan The small and fluctuating samples of lubricating oil data render the wear trend prediction a challenging task in operation and maintenance management of wind turbine gearboxes. To deal with this problem, this paper puts forward a method to enhance the prediction accuracy and robustness of the grey prediction model by introducing multi-source information into traditional grey models. Multi-source information is applied by creating a mapping sequence according to the sequence to be predicted. The significance of the key parameters in the proposed model was investigated by numerical experiments. Based on the results from the numerical experiments, the effectiveness of the proposed method was demonstrated using lubricating oil data captured from industrial wind turbine gearboxes. A comparative analysis was also conducted with a number of selected other models to illustrate the superiority of the proposed model in dealing with small and fluctuating data. Prediction results show that the proposed model is able to relax the quasi-smooth requirement of data sequence and is much more robust in comparison to exponential regression, linear regression and non-equidistance GM(1,1) models. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Canonical variate residuals-based contribution map for slowly evolving faults
    Canonical variate residuals-based contribution map for slowly evolving faults Li, Xiaochuan; Yang, Xiaoyu; Yang, Yingjie; Bennett, Ian; Collop, Andy; Mba, David The superior performance of canonical variate analysis (CVA) for fault detection has been demonstrated by a number of researchers using simulated and real industrial data. However, applications of CVA to fault identification of industrial processes, especially for faults that evolve slowly, are not widely reported. In order to improve the performance of traditional CVA-based methods to slowly developing faults, a novel diagnostic approach is put forward to implement incipient fault diagnosis for dynamic process monitoring. Traditional CVA fault detection approach is extended to form a new monitoring index based on indices, Hotelling’s T2, Q and a canonical variate residuals (CVR)-based monitoring index Td. As an alternative to the traditional CVA-based contributions, a CVR-based contribution plot method is proposed based on Q and Td statistics. The proposed method is shown to facilitate fault detection by increasing the sensitivity to incipient faults, and aid fault identification by enhancing the contributions from fault- related variables and suppressing the contributions from fault-free variables. The CVR-based method has been demonstrated to outperform traditional CVA-based diagnostic methods for fault detection and identification when validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system and an industrial centrifugal pump. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Do not try to evaluate research results in a hurry
    Do not try to evaluate research results in a hurry Liu, Sifeng; Yang, Yingjie We analysed the problems of the current research evaluation, and concluded that research results should be evaluated after their impacts (academic or non-academic) are fully released, and not immediately after publication. Many of the problems associated with mismanagement in research could be eradicated if people did not try to evaluate research results immediately after publication.
  • Multi-attribute Grey Target Decision-making Based on" Kernel" and Double Degree of Greyness
    Multi-attribute Grey Target Decision-making Based on" Kernel" and Double Degree of Greyness Guo, Sandang; Li, Ye; Dong, Fenyi; Li, Bingjun; Yang, Yingjie According to the characteristics of three-parameter interval grey number and the advantages of grey target, a multi-attribute grey target decision-making method is built. First, the "kernel" of the three-parameter interval grey number based on the most probability' is defined, and the upper bound degree of greyness and the lower bound degree of greyness are separately defined for the asymmetry on the two sides, then the distance measure formula affected by the risk attitude of the decision maker is given. Considering the proximity of schemes to the optimal vector and the worst vector, the comprehensive off-target distances and their space projection on the line connecting the point of the positive bull's eye and the negative bull's eye are obtained, and the ranking of the schemes is ultimately determined. Finally, an example validates the rationality and effectiveness of the method, which may provide a new way of thinking in terms of research on grey decision-making theory and application. The file attached to this record is the Publisher's final version.
  • A prediction method for plasma concentration by using a nonlinear grey Bernoulli combined model based on a self-memory algorithm
    A prediction method for plasma concentration by using a nonlinear grey Bernoulli combined model based on a self-memory algorithm Yang, Yingjie; Guo, X.; Liu, Sifeng The goal of this work is to present and explore the application of a novel nonlinear grey Bernoulli combined model based on a self-memory algorithm, abbreviated as SA-NGBM, for modeling single-peaked sequences of time samples of acetylsalicylate plasma concentration following oral dosing. The self-memorization SA-NGBM routine reduces the dependence on a solitary initial value, as the initial state of the model utilizes multiple time samples. To test its forecasting performance, the SA-NGBM was used to extrapolate the plasma concentration predicted data, in comparison with the later time samples. The results were contrasted with those of the traditional optimized NGBM (ONGBM), exponential smoothing (ES) and simple moving average (SMA) using four popular accuracy and significance tests. That comparison showed that the SA-NGBM was much more accurate and efficient for matching the individual, nonlinear-system stochastic fluctuations than the existing ONGBM, ES and SMA models. The findings have potential applications for signal matching to similar small sample size, single-peaked, plasma concentration series. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Click here to view a full listing of Yingjie Yang's publications and outputs.

Key research outputs

  • R-Fuzzy sets: a novel combination of fuzzy sets with rough sets with capability to represent some situations difficult with other extensions;
  • Grey sets: a formal formulation of the concept of grey sets and its operations;
  • Relative Strength of Effect: a factor analysis method based on trained neural networks;
  • Application of neural networks in overlay operation of GIS
  • Airport noise simulation using neural networks

Research interests/expertise

Dr. Yang’s research interests are mainly with uncertainty models and their applications. His theoretical work involves fuzzy sets, rough sets, grey systems and neural networks. In applications, his interests are transportation planning, environment evaluation and civil engineering simulation and analysis.

Areas of teaching

  • Databases
  • Data Warehousing
  • AI programming

Qualifications

  • PhD in Engineering (1994 from Northeastern University, China)
  • PhD in Computer Science (2008 from Loughborough University, UK)

Courses taught

  • IMAT5167
  • IMAT5118
  • IMAT5103
  • IMAT2427
  • PHAR5350

Honours and awards

Best Paper Award, the 2013 IEEE Conference on Computational Intelligenceand Computing Research.

Membership of external committees

  • Co-chair of the Technical Committee on Grey Systems of IEEE Systems, Man,and Cybernetics Society, 2012 -- present
  • Vice-chair of the Task Force on Competitions for Fuzzy Systems Technical Committeeof IEEE Computational Intelligence Society, 2011 -- present
  • PC members for over 90 international academic conferences

Membership of professional associations and societies

  • Senior Member of IEEE, 2013 -- present
  • Member of IEEE, Mar 2007 -- 2013
  • Member of the Rail Research UK Association, May 2013 -- present

Current research students

First supervisor for:

  • Manal Alghieth
  • Mohammad Al Azawi
  • Arjab Khuman
  • Nguyen Thi Mai Phuong
  • Tarjana Yagnik

Externally funded research grants information

    • "International Network on Grey Systems and its Applications", Leverhulme Trust, PI, £124997, 2015--2018.

    • "Grey Systems and Its Application to Data Mining and Decision Support", EU FP7 Marie Curie International IncomingFellowship, PI, €309235, 2015--2016.

    • "Modeling Conditions, Mechanism and Characters of Grey Prediction Model GM(1,1)", Leverhulme Trust InternationalVisiting Fellowship, PI, £25500, 2013--2014.

    • "Grey Systems and Computational Intelligence", Royal Society, PI, £12000, 2011-- 2013.

    • "ITRAQ: Integrated Traffic Management and Air Quality Control Using Space Services", Europe Space Agency, CI, €97834, 2011--2012.

    • "Conference grant", Royal Academy of Engineering, PI, £500, Oct 2007.

Internally funded research project information

  • "Project application on Grey Systems and Uncertainty", DMU Research Leave scheme, PI, £7104, 2012--2013.

  • "Initial preparation for EU research network on grey systems", DMU RIF Fund, PI, £7000, 2011--2012.

  • "Emerging uncertainty models and their applications", DMU PhD scholarship, PI, £55080, 2012--2016.

  • "Conference grant", DMU RITI Fund, PI, £1500, Jun 2009.

  • "Conference grant", DMU RITI Fund, PI, £1500, Jun 2008.

Professional esteem indicators

Editorial board:

  • Associate Editor of IEEE Transaction on Cybernetics (Institute of Electrical and Electronics Engineers) ISSN: 1083-4419
  • Associate Editor of Scientific World Journal (Hindawi Publishing Corporation) ISSN: 2356-6140
  • Associate Editor of Journal of Intelligent and Fuzzy Systems (IOS Press) ISSN: 1064-1246
  • Assocaite Editor of Journal of Grey Systems (Research Information Ltd) ISSN: 0957-3720
  • Associated Editor of Grey Systems: Theory and Applications (Emerald) ISSN: 2043-9377

Plenary talks and academic seminars

  • Keynote speaker at the 2013 IEEE International Conference on Grey Systems and Intelligent Services, Macau, 2013
  • Seminar on grey numbers at Nanjing University of Aeronautics and Astronautics, Nanjing, 2012
  • Keynote speaker at the 2011 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing,2011
  • Seminar on grey numbers at Nanjing University of Aeronautics and Astronautics, Nanjing, 2011
  • Seminar series on computational intelligence at Nanjing University of Aeronautics and Astronautics, full financialsupport from Nanjing University of Aeronautics and Astronautics, Nanjing, 2010
  • Keynote speaker at the 2009 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing,2009
  • Seminar on grey systems at University of Hull, 2008
  • Keynote speaker at the Airport Environmental Management Workshop in Singapore, full financial support fromSingapore Aviation Academy (organisor), Singapore, 2001

Conference management

  • Chair of the Program Committee for the 2015 IEEE International Conference on Grey Systems and Intelligent Services,Leicester, 2015
  • Chair of the Program Committee for the 2015 International Conference on Advanced Computational Intelligence,Wuyi, 2015
  • Chair of the Program Committee for the 2013 IEEE International Conference on Grey Systems and Intelligent Services,Macau, 2013
  • Co-chair of the special session on grey systems at the 2014 IEEE International Conference on Systems, Man and Cybernetics, San Diego, 2014
  • Co-chair of the special session on grey systems at the 2012 IEEE International Conference on Systems, Man and Cybernetics, Seoul, 2012
  • Co-chair of the special session on grey systems at the 2011 IEEE International Conference on Systems, Man and Cybernetics, Anchorage, 2011
  • Co-chair of the Program Committee for the 2011 IEEE International Conference on Grey Systems and IntelligentServices, Nanjing, 2011
  • Co-chair of the Program Committee for the 2009 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, 2009
  • Session chair for 3 regular sessions at the 2008 IEEE World Congress of Computational Intelligence, Hong Kong,2008
  • Co-chair of the special session on grey systems at the 2008 IEEE World Congress of Computational Intelligence,Hong Kong, 2008
  • Member of the organising committee of the 2007 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, 2007

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