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 

  • 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.
  • 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.
  • Optimal weighting models based on linear uncertain constraints in intuitionistic fuzzy preference relations
    Optimal weighting models based on linear uncertain constraints in intuitionistic fuzzy preference relations Gong, Zaiwu; Tan, Xiao; Yang, Yingjie Although the classic exponential-smoothing models and grey prediction models have been widely used in time series forecasting, this paper shows that they are susceptible to fluctu- ations in samples. A new fractional bidirectional weakening buffer operator for time series prediction is proposed in this paper. This new operator can effectively reduce the negative impact of unavoidable sample fluctuations. It overcomes limitations of existing weakening buffer operators, and permits better control of fluctuations from the entire sample period. Due to its good performance in improving stability of the series smoothness, the new op- erator can better capture the real developing trend in raw data and improve forecast accu- racy. The paper then proposes a novel methodology that combines the new bidirectional weakening buffer operator and the classic grey prediction model. Through a number of case studies, this method is compared with several classic models, such as the exponential smoothing model and the autoregressive integrated moving average model, etc. Values of three error measures show that the new method outperforms other methods, especially when there are data fluctuations near the forecasting horizon. The relative advantages of the new method on small sample predictions are further investigated. Results demonstrate that model based on the proposed fractional bidirectional weakening buffer operator has higher forecasting accuracy. 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 new method to mitigate data fluctuations for time series prediction
    A new method to mitigate data fluctuations for time series prediction Li, Chong; Yang, Yingjie; Liu, Sifeng Although the classic exponential-smoothing models and grey prediction models have been widely used in time series forecasting, this paper shows that they are susceptible to fluctuations in samples. A new fractional bidirectional weakening buffer operator for time series prediction is proposed in this paper. This new operator can effectively reduce the negative impact of unavoidable sample fluctuations. It overcomes limitations of existing weakening buffer operators, and permits better control of fluctuations from the entire sample period. Due to its good performance in improving stability of the series smoothness, the new operator can better capture the real developing trend in raw data and improve forecast accuracy. The paper then proposes a novel methodology that combines the new bidirectional weakening buffer operator and the classic grey prediction model. Through a number of case studies, this method is compared with several classic models, such as the exponential smoothing model and the autoregressive integrated moving average model, etc. Values of three error measures show that the new method outperforms other methods, especially when there are data fluctuations near the forecasting horizon. The relative advantages of the new method on small sample predictions are further investigated. Results demonstrate that model based on the proposed fractional bidirectional weakening buffer operator has higher forecasting accuracy. 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.
  • Comparative analysis of properties of weakening buffer operators in time series prediction models
    Comparative analysis of properties of weakening buffer operators in time series prediction models Li, Chong; Yang, Yingjie; Liu, Sifeng Reducing the negative influence of stochastic disturbances in sample data has always been a difficult problem in time series analysis. In this paper, three new fractional weakening buffer operators are proposed, and then some desirable properties of these proposed se- quence operators are investigated. Their potential effect in smoothing unexpected distur- bances while maintaining the normal trend in sample series is analyzed and compared with other widely used sequence operators in time series modeling. Results of theoretical and empirical research show that the proposed novel fractional weakening buffer oper- ators are effective in improving the development pattern analysis of time series in dis- turbance scenarios, while also avoid too subjectively weighting experimental data from collected samples. The robust of the proposed operator-based prediction algorithm against noise effect is tested in five different types of noise scenarios. Result of empirical study demonstrates that the proposed method improves the series prediction performance and it also improves the robustness of corresponding forecasting algorithms. These unique prop- erties of the proposed weakening buffer operators make them more attractive in time se- ries analysis. 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.
  • Prediction of air quality indicators for the Beijing-Tianjin-Hebei region
    Prediction of air quality indicators for the Beijing-Tianjin-Hebei region Wu, L.; Li, N.; Yang, Yingjie The Beijing-Tianjin-Hebei region is facing a very serious air pollution problem. To obtain the future trend of air quality, the GM(1,1) model with the fractional order accumulation (FGM(1,1)) is used to predict the average annual concentrations of PM2.5, PM10, SO2, NO2, 8-h O3, and 24-h O3 in the Beijing-Tianjin-Hebei region from 2017 to 2020. The concentrations of PM2.5 and SO2 will decrease and the 8-h O3 and 24-h O3 will increase in this region. The concentrations of PM10 and NO2 will decrease in the Taihang-Mountain-adjacent region (Baoding, Shijiazhuang, Xingtai, Handan and Hengshui) and increase in the Northern region (Zhangjiakou, Chengde and Qinhuangdao). The concentration of PM10 will decrease and NO2 will increase in the Bohai Sea region (Tangshan, Tianjin, Cangzhou, Beijing and Langfang). Our results can be directly exploited in the decision-making processes for air quality management. 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.
  • Grey Models and Their Roles in Data Analytics
    Grey Models and Their Roles in Data Analytics Yang, Yingjie; Liu, Sifeng This paper gives an introduction to the basic concepts of grey systems, grey numbers and grey models, and discusses their roles in data analysis of data science. The necessity of small data models is demonstrated, and their complementary functions to Big Data models are investigated. Based on these investigations, a novel framework for combining Big Data models with grey models is proposed. 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.
  • Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China
    Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China Wu, L.; Gao, X; Xiao, Y.; Yang, Yingjie; Cheng, X. The electricity consumption forecasting problem is especially important for policy making in developing region. To properly formulate policies, it is necessary to have reliable forecasts. Electricity consumption forecasting is influenced by some factors, such as economic, population and so on. Considering all factors is a difficult task since it requires much detailed study in which many factors significantly influence on electricity forecasting whereas too many data are unavailable. Grey convex relational analysis is used to describe the relationship between the electricity consumption and its related factors. A novel multi-variable grey forecasting model which considered the total population is developed to forecast the electricity consumption in Shandong Province. The GMC(1,N) model with fractional order accumulation is optimized by changing the order number and the effectiveness of the first pair of original data by the model is proven. The results of practical numerical examples demonstrate that the model provides remarkable prediction performances compared with the traditional grey forecasting model. The forecasted results showed that the increase of electricity consumption will speed up in Shandong Province. 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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