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Professor Yingjie Yang

Job: Professor of Computational Intelligence

Faculty: Computing, Engineering and Media

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: https://www.dmu.ac.uk/cci

 

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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.

Research group affiliations

Centre for Computational Intelligence (CCI) 

De Montfort University Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Publications and outputs

  • Designing optimal proactive replacement strategies for degraded systems subject to two types of external shocks
    Designing optimal proactive replacement strategies for degraded systems subject to two types of external shocks Dong, Wenjie; Yang, Yingjie; Cao, Yingsai; Zhang, Jingru; Liu, Sifeng This paper mainly investigates a proactive replacement policy for a stochastically deteriorating system concurrently subject to two types of shocks. Firstly, the closed form representation of system reliability function suffering from both a degradation process and environmental shocks is derived based on the degradation-threshold failure (DTS) modelling framework. An age- and state-dependent competing risks model with mutual dependence between the two failure processes is embedded into system reliability modelling, where two types of shocks are taken into consideration upon arrival of an external shock including a minor one and a major one. Based on which, a bivariate maintenance policy is put forward for the deteriorating system, where the system is proactively replaced before failure at a planned time, or at an appropriate number of minimal repairs, whichever takes place first. The expected long-run cost rate (ELRCR) is formulated, and optimal solutions are evaluated analytically for two special cases. Finally, an illustrative example is redesigned to validate the theoretical results, exploring the significance of two types of shocks and mutual dependence in system reliability modelling, and illustrating the potential applications in maintenance decisions in various manufacturing systems. 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. Dong, W., Yang, Y., Cao, Y., Zhang, J. and Liu, S. (2023) Designing optimal proactive replacement strategies for degraded systems subject to two types of external shocks. Communications in Statistics - Theory and Methods,
  • Application of the novel-structured multivariable grey model with various orders to forecast the bending strength of concrete
    Application of the novel-structured multivariable grey model with various orders to forecast the bending strength of concrete Zeng, Bo; Yin, Fengfeng; Yang, Yingjie; Wu, You; Mao, Cuiwei Bending strength of concrete is one of the significant indexes to measure the mechanical properties of concrete. A reliable prediction about the bending strength of concrete is of great importance to maintain the health state and service life of concrete. However, it is difficult to obtain reliable data of large samples due to the high cost, serious destructiveness and complex influencing factors of concrete bending strength test data collection. In view of this, based on the multivariable grey prediction model whose modeling object is small data, we construct a new novel-structured multivariable grey prediction model with various orders for predicting the bending strength of concrete. It defines and optimizes the accumulative orders differentially and introduces a nonlinear correction term to expand the model structure. Then, the bending strength of concrete is modeled using the new model, and its comprehensive error is only 0.035 %, which is much smaller than the conventional NSGM(1,N) and FMGM(1,N) models (5.232 % and 2.624 %, respectively). The findings provide a new modeling method for the prediction of concrete bending strength in areas with large temperature difference, and have significance for enriching and improving the methodologies of grey prediction 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. Zeng, B., Yin, F., Yang, Y., Wu, Y. and Mao, C. (2023) Application of the novel-structured multivariable grey model with various orders to forecast the bending strength of concrete. Chaos, Solitons and Fractals, 168, 113200
  • Emerging studies and applications of grey systems
    Emerging studies and applications of grey systems Yang, Yingjie; Liu, Sifeng At a time when most researchers and businesses are talking about Big Data, small and incomplete data are never far away from us. In fact, most of our decisions are made under incomplete and small data. There are many areas where small and incomplete data are inevitable, such as health care, traffic management, economic analysis, etc. The theory of grey systems emerged as a novel solution to deal with small and incomplete data, and it is attracting more and more attention from researchers and professionals in different disciplines. It has the ability to establish models from extremely limited data under uncertainty, which is usually difficult for other models. Although grey systems provide powerful capability in data analysis, its impacts in applications have mainly materialized in China. I have frequently received questions regarding the differences between Grey systems and other related models, and to provide examples of their real world use. To promote grey systems and its applications, an international research network was formed under the support of the Leverhulme Trust in 2015. The leading experts from UK, China, Canada, Romania and Spain formed the core of the network. A number of activities were conducted to foster the collaboration between members of the network. As a result, the international association of grey systems and uncertainty analysis was established in 2016. Through our discussion in the network and the association, it was deemed necessary to assemble the latest advance in this field to clarify confusions between grey systems and grey sets with other models, and provide a collection of successful application cases as guidance for people interested in applying grey systems to their problems. Therefore, a collection of contributions from our network partners and leading experts in the field is put together to form this book. The contributors include world leading experts as well as a new generation of research leaders from both Europe and China. The application covers many different disciplines, ranging from social economics to engineering, energy and management. The book focuses mainly on two themes: 1. The connection between grey systems and other related models, such as fuzzy sets and rough sets (chapter 3-5). 2. Successful real world applications in different disciplines (chapter 6-12). To introduce to the readers who have no background in grey systems, a brief introduction is also included (chapter 1-2). Chapter 1 is a general overview of the whole field of grey systems and its recent development. It helps the readers to see the development and the state of the art of this field. Chapter 2 then gives a brief introduction to the fundamental concepts in grey systems, especially grey models so as to enable the readers to understand other chapters. These two chapters lay the essential backgrounds for the contents in other chapters. Chapter 3 then moves into the connection between grey systems, especially grey sets with various fuzzy sets and rough sets. It helps to clarify the confusions between grey systems and fuzzy sets and rough sets. Chapter 4 focuses on the hybridization of grey systems with neural-fuzzy systems and illustrates how they can be integrated together. Chapter 5 puts grey systems into a more general level and puts forward the novel concept of grey knowledge. These three chapters demonstrate the distinctive and collaborative features of grey systems in uncertainty modelling. Starting from Chapter 6, the remaining chapters cover various real world application studies. Chapter 6 reveals how to apply grey systems in economic studies using agent-based systems. Chapter 7 demonstrates how grey systems help with the short term forecasting of traffic flow. Chapter 8 illustrates how grey models are applied to predict and manage Yellow River ice disasters in China. Chapter 9 showcases the application of grey systems to social network data analysis. Chapter 10 gives some case studies in applying grey systems into the energy-economic system. Chapter 11 explains how to select business strategy using grey stratified decisions model. Finally, Chapter 12 illustrates a cost level analysis for the components of the smartphones using greyness based quality function deployment. The cases studies in Chapter 6-12 covers a wider range of disciplines, geographical areas and different culture backgrounds, and it can serve as a useful reference for practitioners challenged by small and incomplete data. Combined with the connections covered in Chapter 3-5 and the basics in Chapter 1-2, this book will provide a convenient handbook for the practical application of grey systems. Yang, Y. and Liu, S. (2023) Emerging studies and applications of grey systems, editors, Springer, pp.330. .
  • Agent-based modelling in grey economic systems
    Agent-based modelling in grey economic systems Delcea, Camelia; Yang, Yingjie; Liu, Sifeng; Cotfas, Liviu-Adrian The economic systems are basically grey systems due to their components and to their interactions which enable the occurrence of uncertainty. First, the human component plays an important role as a consequence of its usually unpredictable and sometimes irrational behavior, a situation strictly related to the way the humans are thinking and acting. From here, it can easily be demonstrated that when analyzing a system, we are facing grey knowledge. This kind of knowledge exists and it represents that small piece of puzzle needed to successfully fill the gap separating the explicit knowledge form the tacit one, also conducting to uncertainty. Delcea, C., Yang, Y., Liu, S. and Cotfas, L. A. (2023) Agent-based modelling in grey economic systems. In: Yang, Y. and Liu, S. (Eds.) Emerging studies and applications of grey systems, Singapore: Springer, pp. 105-139
  • Grey systems and uncertainty modelling
    Grey systems and uncertainty modelling Yang, Yingjie; Khuman, Archie; Liu, Sifeng Information can, and often is, rather uncertain; with only partial information initially being made available, from which one would be able to hopefully provide for a solution. The information itself may contain conflicts that have arisen from the possible different sources used to acquire it. In addition, the information may be viewed and interpreted differently by different cohorts, this in itself can be the cause of extenuating circumstances. These are just some of the issues that one can face with uncertain information. These issues can understandably create problems when considering the deployment of applications. Being able to cater for the volatility that is inherently present in uncertainty, becomes an objective with high importance and precedence. Yang, Y., Khuman, A. and Liu, S. (2023) Grey systems and uncertainty modelling. In: Yang, Y. and Liu, S. (Eds.) Emerging studies and applications of grey systems, Singapore: Springer, pp. 59-80
  • Development of grey system research
    Development of grey system research Yang, Yingjie; Liu, Sifeng The first chapter presents an introduction to grey system theory. We begin with some background information on the appearance and growth of grey system theory and founder “Professor Julong Deng”, who established the new branch of science. Then we examine the development history and current state, followed by a comparison of grey system theory with other uncertain systems theories such as probability and statistics, fuzzy mathematics, and rough set theory. Finally, the elementary concepts, the fundamental principles and the framework of grey system theory are presented. This introductory chapter vividly presents the reader with an overall picture of grey system theory and its frontier research. Liu, S. and Yang, Y. (2023) Development of grey system research. In: Yang, Y. and Liu, S. (Eds.) Emerging studies and applications of grey systems, Singapore: Springer, pp. 1-17
  • Grey Systems Analysis: Methods, Models and Applications
    Grey Systems Analysis: Methods, Models and Applications Liu, Sifeng; Yang, Yingjie; Forrest, Jeffrey Yi-Lin The book covers up-to-date theoretical and applied advances in grey systems theory from across the world and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models and methods in the book are original by the authors, including simplified form of grey number, general grey number and the operations of grey numbers; the axiomatic system of buffer operators and a series of weakening and strengthening operators; a series of grey relational analysis models, including grey absolute, relative, synthetic, similarity, closeness, negative and three dimension degree, etc.; grey fixed weight clustering model, grey evaluation models based on center-point and end-point mixed possibility functions; original difference grey model (ODGM), even difference grey model (EDGM), discrete grey model (DGM), fractional grey models, self-memory grey models; multi-attribute intelligent grey target decision models, weight vector group with kernel and the weighted comprehensive clustering coefficient vector, and spectrum analysis of sequence operators, etc. This book will be appropriate as a reference and/or professional book for courses of grey system theory for graduate students or high-level undergraduate students, majoring in areas of science, technology, agriculture, medicine, astronomy, earth science, economics, and management. It can also be utilized by researchers and practitioners in research institutions, business entities, and government agencies. Liu, S., Yang, Y. and Forrest, J. (2022) Grey Systems Analysis Methods, Models and Applications, Springer
  • Does financial leverage volatility induce systemic financial risk? Empirical insight based on the Chinese fintech sector
    Does financial leverage volatility induce systemic financial risk? Empirical insight based on the Chinese fintech sector Zheng, Zhiyong; He, Jian; Yang, Yingjie; Zhang, Mengting; Wu, Desheng; Bian, Yang Financial leverage volatility is a significant factor contributing to the formation of systemic financial risk, which is more apparent in China's fast-growing fintech (financial technology) field. Using the Conditional Value-at-risk approach (ΔCoVaR) risk metric, the generalized autoregressive conditional heteroskedasticity (Structural Vector Autoregression with Stochastic Volatility model [SV-TVP-SAVR]) model, and the generalized forecast error variance decomposition (Tvpdy) model, this paper discusses how financial leverage volatility shocks fintech sectoral risks and the evolution of the risk within fintech under the shocks on the basis of the classification criteria of the Chinese fintech enterprise database and daily trading data of A-share listed companies. The statistical results show that financial leverage volatility causes risk changes across fintech sectors, which is especially significant during economic downturns or government interventions. Also, under the shock of financial leverage volatility, the fintech sectors will absorb or diffuse risks outward through spillover channels, with significant differences in the risk spillover conditions of different types of sectors. Finally, the fintech sector can produce a contagion system with the Internet consumer finance, payment, and Internet microcommercial credit sectors as the core of risks, resulting in a systemic risk crisis. Our findings have major implications for Chinese regulators to balance financial leverage and prevent systemic risks in the fintech sector. 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. Zheng, Z., He, J., Yang, Y., Zhang, M., Wu, D., Bian, Y. and Cao, J. (2023) Does financial leverage volatility induce systemic financial risk? Empirical insight based on the Chinese fintech sector. Managerial and Decision Economics, 44 (2), pp. 1142-1161
  • An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles
    An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles Zhou, Huimin; Dang, Yaoguo; Yang, Yingjie; Wang, Junjie; Yang, Shaowen An accurate prediction of electric vehicles stock and sales is a prerequisite for planning industrial policies for renewable sources to be used by a transportation system. We propose a novel time-varying grey Bernoulli model to investigate the nonlinear, complexity, and time-varying characteristics associated with electric vehicles stock and sales. We first design the time-varying parameters and a power exponent to explore the nonlinear developing trends of sequences. Subsequently, the cuckoo search algorithm determines optimum solutions because of its competence in dealing with complex optimization problems. Furthermore, its relationship with existing grey prediction models is presented, which demonstrates the flexibility and practicality of the newly-designed model. In order to validate this new model, the global electric vehicles stock and electric vehicles sales in France are predicted in comparison with six benchmark models. As demonstrated by the empirical findings, the proposed model is superior in terms of its capacity for forecasting, confirming its significant potential as a promising tool for electric vehicles stock and sales prediction. 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 Zhou, H., Dang, Y., Yang, Y., Wang, J. and Yang, S. (2023) An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles. Energy, 263, Part C, 125871
  • An improved grey multivariable time-delay prediction model with application to the value of high-tech industry. Expert Systems with Applications
    An improved grey multivariable time-delay prediction model with application to the value of high-tech industry. Expert Systems with Applications Zhou, Huimin; Dang, Yaoguo; Yang, Deling; Wang, Junjie; Yang, Yingjie To analyse the time lag effects between independent variables and dependent variables, we propose a discrete time-delay grey multivariable model . There are three improvements in this new model compared to the existing models. First, the time lag parameters are assigned different values for each independent variable. A linear correction term expands the new model. Second, with the given time lag, the least square method can be used to calculate the parameter vector. The time response function of is generated, which has the advantage of eliminating the jumping errors between discrete and continuous functions over the existing grey forecasting models. Third, all of the feasible combinations of the time lag parameters are compared by using a traversal algorithm to identify the best values with the minimized mean absolute percentage error (MAPE). In three different case studies, the performance of the new model is evaluated and compared to that of a number of mainstream grey models as well as non-grey models. According to the findings, the newly designed model performs significantly better than the compared 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 Zhou, H., Dang, Y., Yang, D., Wang, J. and Yang, Y. (2023) An improved grey multivariable time-delay prediction model with application to the value of high-tech industry. Expert Systems with Applications, 213, Part B, 119061

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