Browsing by Author "Yang, Yingjie"

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    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.
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    A novel fractional order variable structure multivariable grey prediction model with optimal differential background-value coefficients and its performance comparison analysis
    (Emerald, 2024-02-09) Xia, Chao; Zeng, Bo; Yang, Yingjie
    Purpose – Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance. Design/methodology/approach – A novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance. Findings – The new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models. Originality/value – This study has positive implications for enriching the method system of multivariable grey prediction model.
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    A Novel Framework for Reservoir Permeability Prediction using GPR with Grey Relational Grades and Uncertainty Quantification
    (2024) Lawal, Ahmad; Yang, Yingjie; Baisa, Nathanael L.; He, Hongmei
    Reservoir permeability prediction is crucial for hydrocarbon exploration and production. Traditional methods have limitations, and Gaussian Process Regression (GPR) offers a powerful alternative. However, GPR can be sensitive to kernel parameters. This paper proposes a novel framework, GPR with Grey Relational Lengthscale Adaptation (GRLA-GPR), that incorporates Grey Relational Grades (GRG) from NMR log data into GPR lengthscale updates to improve permeability prediction with a focus on uncertainty quantification. The framework utilizes a Radial Basis Function (RBF) and Matern kernels' GPR model and calculates GRG to capture relationships between NMR data sequences. The calculated GRG values are then used to update the GPR lengthscale during training. A validation strategy is employed to evaluate the performance. The effectiveness of the framework is assessed using accuracy metrics (mean absolute error, mean squared error and R2) and uncertainty quantification metrics (variance and prediction interval normalized average width). The results are compared to a baseline GPR model without GRG-based updates. The proposed framework achieved a better performance in terms of accuracy and uncertainty quantification, providing more reliable permeability estimates for informed decision-making in reservoir characterization.
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    A novel grey prediction model with four-parameter and its application to forecast natural gas production in China
    (Elsevier, 2024-04-25) Song, Nannan; Li, Shuliang; Zeng, Bo; Duan, Rui; Yang, Yingjie
    Due to the non-homology problem and the simple structural characteristics, a grey prediction model will have defects in modeling. In this paper, the structure of the model is deformed, and additional parameters are added. A novel four-parameter grey prediction model NFGM(1,1) is established to avoid the non-homology problem. The accumulation order of the NFGM(1,1) model is optimized to enhance its performance. This paper first introduces a nonlinear term and a linear term into the to compensate for its structural defects, which can enhance the accuracy of the model in modeling complex modeling sequences. Secondly, a simplified basic formula of the model is proposed to estimate its parameters and iteratively establish the model, which can avoid the problem of non-homologous errors during modeling. Then a novel four-parameter grey prediction model NFGM(1,1) is constructed. Thirdly, the unbiasedness of NFGM(1,1) is proved and verified by matrix theory. Fourthly, by optimizing the order of the NFGM(1,1) model, the model is more flexible and adjustable, and a novel fractional-order four-parameter grey prediction model FNFGM(1,1) can be obtained. Finally, the FNFGM(1,1) model is applied to the prediction of natural gas production in China. The model results show that the FNFGM(1,1) model exhibits superior performance compared to the NFGM(1,1), TWGM(1,1), TDGM(1,1), DGM(1,1), and GM(1,1) models, with the mean relative simulation/prediction/comprehensive percentage errors of 0.92%/1.42%/1.07%, respectively. According to the predicted results, China's natural gas production will reach 3542.9 × 108 m3 in 2027 and some relevant policy recommendations are put forwarded.
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    A Novel Time Series Forecasting Model for Capacity Degradation Path Prediction of Lithium-ion Battery Pack
    (Springer, 2024-01-10) Chen, Xiang; Yang, Yingjie; Sun, Jie; Deng, Yelin; Yuan, Yinnan
    Monitoring battery health is critical for electric vehicle maintenance and safety. However, existing research has limited focus on predicting capacity degradation paths for entire battery packs, representing a gap between literature and application. This paper proposes a multi-horizon time series forecasting model (MMRNet, which consists of MOSUM, flash-MUSE attention, and RNN core modules) to predict the capacity degradation paths of battery packs. First, domain knowledge (DK) extracts the features from extensive battery aging datasets. The moving sum (MOSUM) and improved flash multi-scale attention (MUSE) methods are proposed to capture capacity curve mutations and multi-scale trends. Dynamic dropout training, transposition linear architecture, residual connections, and module stacking improve model generalization and accuracy. Experiments on battery pack and cell datasets demonstrate the superior performance of MMRNet over six baseline time series models. The proposed data-driven approach effectively predicts battery degradation trajectories, with implications for condition monitoring and the safety of electric vehicles.
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    A recursive polynomial grey prediction model with adaptive structure and its application
    (Elsevier, 2024-03-26) Liu, Lianyi; Liu, Sifeng; Yang, Yingjie; Fang, Zhigeng; Shuqi Xu
    As a sparse data analysis algorithm, ensuring a reasonable model structure is an important challenge for grey models to identify the control mechanism of the uncertain system from observational data. To improve the intelligence and adaptability of the model, this study presents a synchronized optimization strategy for data prioritization and model structure for discrete polynomial grey prediction model. The proposed polynomial grey model contains two hyper-parameters: memory factor parameter and structural parameter. The memory factor is introduced into the discrete model to reconstruct the objective function of structural parameter optimization, thereby avoiding the problem of information superposition. The structural parameter is used to enhance the adaptability of grey prediction model in uncertain data analysis tasks. By employing a recursive estimation approach, an adaptive strategy for estimating model hyper-parameters is proposed, which focuses on minimizing prediction errors within the in-sample data. Additionally, a comparison is made between the proposed improved polynomial grey model and existing polynomial grey models in terms of data information mining, estimation stability, and robustness against measurement noise. The proposed model is applied to the practical engineering application of wear prediction, further validating the effectiveness of the proposed approach in non-equidistant time series prediction tasks.
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    Advances in grey system research
    (Nanjing University of Aeronautics & Astronautics, 2015) Liu, S.; Yang, Yingjie
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    Advances in grey systems research
    (Research Information Ltd, 2013) Liu, S.; Forrest, J.; Yang, Yingjie
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    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.
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    Airport noise simulation using neural networks
    (2008) Yang, Yingjie; Hinde, Chris J.; Gillingwater, David
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    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.
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    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.
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    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.
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    Applying neural networks and geographical information systems to airport noise evaluation.
    (Springer Verlag, 2005-01-01) Yang, Yingjie; Gillingwater, David; Hinde, Chris J.
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    Artificial neural networks linked to GIS
    (Elsevier Science, 2002) Yang, Yingjie; Rosenbaum, Michael
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    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.
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    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.
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    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.