Browsing by Author "Yang, Yingjie"

Now showing 1 - 20 of 177
  • No Thumbnail Available
    ItemEmbargo
    A generalized grey model with symbolic regression algorithm and its application in predicting aircraft remaining useful life
    (Elsevier, 2024-07-18) Liu, Lianyi; Liu, Sifeng; Yang, Yingjie; Guo, Xiaojun; Sun, Jinghe
    As a sparse data analysis method, a grey model faces challenges in interpretability for its effective application in uncertain systems. This study proposes a generalized grey model (GGM) based on symbolic regression, designed to improve the intelligence and adaptability of grey models. The GGM serves as a unified framework, integrating various grey model families and addresses regression challenges to determine the model structure. Symbolic regression in the GGM identifies symbolic input-output relationships, offering an interpretable approach for structure determination. By leveraging the non-uniqueness principle in grey system theory and employing structural penalty parameters, the model balances complexity and interpretability. A comparative analysis between GGM and conventional grey function models is conducted focusing on the differences in modeling, structure identification, and parameter optimization. Validation on the M3 competition dataset demonstrated the GGM's superior performance, achieving a significant reduction in prediction error compared to other grey forecasting models. Additionally, a rigorous analysis of aircraft lifespan data underscored the robustness and accuracy of GGM in practical engineering applications.
  • No Thumbnail Available
    ItemMetadata only
    Advances in grey system research
    (Nanjing University of Aeronautics & Astronautics, 2015) Liu, S.; Yang, Yingjie
  • No Thumbnail Available
    ItemMetadata only
    Advances in grey systems research
    (Research Information Ltd, 2013) Liu, S.; Forrest, J.; Yang, Yingjie
  • No Thumbnail Available
    ItemMetadata only
    Agent-based modelling in grey economic systems
    (Springer, 2023-02-05) 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.
  • No Thumbnail Available
    ItemMetadata only
    Airport noise simulation using neural networks
    (2008) Yang, Yingjie; Hinde, Chris J.; Gillingwater, David
  • No Thumbnail Available
    ItemMetadata only
    An analysis on investment policy effect of China's photovoltaic industry based on feedback model
    (Elsevier, 2014-12) Yuan, C.; Liu, S.; Yang, Yingjie; Chen, D.; Fang, Z.; Shui, L.
  • No Thumbnail Available
    ItemOpen Access
    Analysis on Scientific and Technological Innovation of Grain Production in Henan Province Based on SD-GM Approach
    (Hindawi, 2022-06-16) Li, Bingjun; Yang, Yingjie; Zhang, Yifan; Zhang, Shuhua
    Relying on scientific and technological progress to improve the yield per unit area is the main way to achieve sustained growth of grain output. From the perspective of scientific and technological innovation, taking the grain production process as the research object, and based on the relevant data of Henan Province from 2010 to 2019, a system model of scientific and technological innovation in grain production is constructed. Firstly, the internal mechanism of grain production scientific and technological innovation is explored, and the feedback loop of grain production scientific and technological innovation is then constructed. Secondly, the combination of system dynamics and grey system theory is implemented to construct the table function and logic function, and the model of grain production scientific and technological innovation system is constructed. To prove the stability and feasibility of the model through tests, the medium and long-term simulation and prediction of grain production scientific and technological innovation system in Henan Province are carried out. Thirdly, in order to explore the impact of feasible policy schemes on grain production, seven policy plans are designed to simulate grain production policy scenarios from the perspective of scientific and technological innovation. Finally, from the perspective of the composition of scientific and technological innovation system in Henan Province, this study puts forward countermeasures and suggestions for the implementation of the strategy of “storing grain in technology” in Henan Province.
  • No Thumbnail Available
    ItemOpen Access
    Application of the novel-structured multivariable grey model with various orders to forecast the bending strength of concrete
    (Elsevier, 2023-02-10) 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.
  • No Thumbnail Available
    ItemMetadata only
    Applying neural networks and geographical information systems to airport noise evaluation.
    (Springer Verlag, 2005-01-01) Yang, Yingjie; Gillingwater, David; Hinde, Chris J.
  • No Thumbnail Available
    ItemMetadata only
  • No Thumbnail Available
    ItemMetadata only
    Artificial neural networks linked to GIS
    (Elsevier Science, 2002) Yang, Yingjie; Rosenbaum, Michael
  • No Thumbnail Available
    ItemMetadata only
  • Thumbnail Image
    ItemEmbargo
    A Business Process Oriented Dynamic Cyber Threat Intelligence Model
    (IEEE, 2020-04-09) Xu, Yuanchen; Yang, Yingjie; He, Ying
    Cyber threat intelligence (CTI) is a method for strengthening information security. CTI provides information on threats and the countermeasures. Businesses can benefit from the defensive knowledge if the relevant CTI is found. However, business environments involve miscellaneous dynamics of the business processes that can dynamically change the contexts. Correspondingly, threats associated with the contextual risk factors can change dynamically at the same time. Every time the contextual changes take place, CTI-based defensive strategies for businesses may not be useful and effective any more. However, the existing connection strategies between CTI and business risk contexts are still somewhat static. This paper proposes a business process oriented dynamic CTI model. The model can observe and capture the dynamics from the business environments. Every time the dynamics are captured, the model will then trigger adjustments of the connection strategies within the model. We use a case study to illustrate the use of the model and present how the model adjusts the connection strategies according to the dynamics. We then conclude the paper with future directions of the research.
  • No Thumbnail Available
    ItemOpen Access
    Canonical variate residuals-based contribution map for slowly evolving faults
    (Elsevier, 2019-02-23) 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.
  • Thumbnail Image
    ItemOpen Access
    A commentary on some of the intrinsic differences between grey systems and fuzzy systems
    (IEEE, 2014-10-05) Khuman, A. S.; Yang, Yingjie; John, Robert, 1955-
    The aim of this paper is to distinguish between some of the more intrinsic differences that exist between grey system theory (GST) and fuzzy system theory (FST). There are several aspects of both paradigms that are closely related, it is precisely these close relations that will often result in a misunderstanding or misinterpretation. The subtly of the differences in some cases are difficult to perceive, hence why a definitive explanation is needed. This paper discusses the divergences and similarities between the interval-valued fuzzy set and grey set, interval and grey number; for both the standard and the generalised interpretation. A preference based analysis example is also put forward to demonstrate the alternative in perspectives that each approach adopts. It is believed that a better understanding of the differences will ultimately allow for a greater understanding of the ideology and mantras that the concepts themselves are built upon. By proxy, describing the divergences will also put forward the similarities. We believe that by providing an overview of the facets that each approach employs where confusion may arise, a thorough and more detailed explanation is the result. This paper places particular emphasis on grey system theory, describing the more intrinsic differences that sets it apart from the more established paradigm of fuzzy system theory.
  • No Thumbnail Available
    ItemOpen Access
    Comparative analysis of properties of weakening buffer operators in time series prediction models
    (Elsevier, 2018-08-23) 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.
  • No Thumbnail Available
    ItemMetadata only
    Computational Intelligence and Its role in enhancing sustainable transport systems
    (2011) Goodyer, E. N.; Ahmadi, Samad; Chiclana, Francisco; Elizondo, David; Gongora, Mario Augusto; Passow, Benjamin N.; Yang, Yingjie
  • No Thumbnail Available
    ItemMetadata only
    Computational intelligence and its role in enhancing sustainable transport systems transportation infrastructure & surface analysis ECU design & development, including exploitation of GNSS & telematics The Cyber Security Centre (CSC)
    (2012) Goodyer, E. N.; Ahmadi, Samad; Chiclana, Francisco; Collop, Andy; Elizondo, David; Gongora, Mario Augusto; Passow, Benjamin N.; Watson, Tim; Yang, Yingjie
  • No Thumbnail Available
    ItemMetadata only
    A conceptual framework for society-oriented decision support
    (2005-05-14) Yang, Yingjie; Gillingwater, David; Hinde, Chris J.
  • No Thumbnail Available
    ItemOpen Access
    Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis
    (Elsevier, 2019-06-06) 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.