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Table of Content

    01 March 2022, Volume 34 Issue 1
    Grey Relational Frame Prediction Method for Anomaly Detection
    Chaobo Li, Hongjun Li, Xiaohu Sun, Guoan Zhang
    2022, 34(1):  1-16. 
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    Anomaly detection is a common problem in security and protection systems. Unlike conventional methods, considering the feature correlation and the uncertain predicted frames, a grey relational frame prediction method is proposed for the anomaly detection task. The future frame prediction network is designed by adversarial learning, consisting of generative and discriminant modules. In order to solve the lack of feature correlation, we integrate Deng’s grey relation into the generative module to calculate the correlation between the predicted features and previous features during training. Furthermore, the grey absolute relation is introduced to deal with the uncertainty of predicted future frames. This network is optimized with different loss functions that combine the adversary, grey relation, pixel, gradient, and optical flow. These losses can well measure the difference between the predicted future frames and real future frames in temporal, spatial, and feature aspects. Experiential results show the proposed method obtains the averaged AUC of 84.1%, 95.7%, 85.6% on UCSD Ped1, Ped2, and CUHK Avenue datasets, which are 1%, 0.3%, 0.5% higher than the network without grey relation analysis. Extensive experiments demonstrate the superiority of our model in anomaly detection.
    Multi-variable GMU(1,N) Grey Prediction Model Considering Unknown Factors
    Ye Li, Yuanping Ding, Jianping Wang
    2022, 34(1):  17-33. 
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    The multi-variable grey prediction model represented by the GM(1,N) is an important casual relationship forecasting model. However, the traditional GM(1,N) model shows some defects which affect the modeling accuracy and applicability. In this paper, the modeling process of the traditional GM(1,N) model is studied, and three defects are observed in terms of “modeling mechanism,” “modeling structure,” and “parameter estimation.” To address these defects, a novel multi-variable GMU(1,N) grey prediction model considering unknown factors is proposed by introducing an exponential function \beta e^(\alpha(k-1)) in this paper, the modeling assumption in the traditional GM(1,N) model that \sum b_i X_i (k) can be treated as a grey constant with a small variation range of X_i (1) (i=2,3,……,N) is dropped, and the derivation form of the GMU(1,N) model is defined, which solve these three defects in the traditional GM(1,N) model effectively. Meanwhile, the genetic algorithm toolbox and recursive method are used to solve the parameter \alpha and time response function, respectively. Additionally, it is theoretically proved that the GMU(1,N) model can be completely compatible with the GM(1,1) model, GM(1,1,e^at) model, GM(1,N) model, and GMC(1,n) model by adjusting the parameters’ values. The GMU(1,N) model is used to simulate and predict grain production in Henan province to verify the effectiveness of these improvements. The mean average simulated and predicted relative errors of the GMU(1,N) model are 0.000% and 0.811%, in comparison with the traditional GM(1,1) model and the GM(1,N) model, which are 1.234%, 1.487% and 8.105%, 8.874% respectively. Results show that the GMU(1,N) model has superior performance, which confirms the effectiveness of the model improvement.
    Analyzing the Aging Population and Density Estimation of Nanjing by Using a Novel Grey Self-Memory Prediction Model Under Fractional-Order Accumulation
    Xiaojun Guo, Jiaxin Li, Sifeng Liu, Naiming Xie, Yingjie Yang and Hui Zhang
    2022, 34(1):  34-52. 
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    At present, China's aging population is becoming increasingly prominent. Accurately predicting the number and density distribution of the elderly population in the future is conducive to accelerating the development of aged care services and has important reference value for formulating relevant policies and social development. In this paper, a novel SM-FGM model is constructed to predict the quantity and density distribution of the elderly population in Nanjing from 2021 to 2030. The combined model combines the advantages of fractional-order accumulation and self-memory algorithm and has good prediction accuracy and generalization ability. Fractional-order accumulation can effectively weaken the randomness of the original data sequence, and the memory function in the self-memory algorithm breaks through the limitation that the traditional grey model is sensitive to the initial value. The results show that the number of elderly people in Nanjing's administrative districts will show a high base and high growth trend in the next 10 years. There are significant differences in the density of elderly people among the administrative districts. The density of elderly people in the central city is higher and becomes a dense area for the elderly, and the population density gradually decreases with the direction of suburban areas.
    Grey Clustering of the Variations in Reverse Pyramid Boarding Method Considering Pandemic Restrictions
    Camelia Delcea, Liviu-Adrian Cotfas, Rafał Mierzwiak, Corina Ioanas
    2022, 34(1):  53-69. 
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    The COVID-19 pandemic has significantly hit the airline industry mostly due to the reduced number of flights between regions, the implementation of different protocols, restrictions, and the reluctance of the passengers to travel by airplane. In this context, the airlines have tried to offer an appropriate environment for their customers by ensuring a safe boarding process while considering the imposed restrictions related to social distancing. According to the literature, the Reverse Pyramid boarding method offers superior results in terms of boarding time and health risks in times of pandemics when compared to other classical airplane boarding methods. As the variations in Reverse Pyramid implementation are numerous, the present paper aims to determine which of these variations can be used when the airplane boarding process is made through the front door of the airplane. For this purpose, an agent-based model is created and used for simulating the variations in the Reverse Pyramid boarding method, while grey clustering is applied for dividing the variations into categories based on their performance. Three performance indicators, as reported in the scientific literature related to airplane boarding in times of COVID-19, are used, namely the boarding time, aisle seat risk, and window seat risk. Different scenarios are presented and analyzed in depth.
    Prediction of Mine Gas Concentration Based on Multi-variable Time-delayed DOGM(1, N) Model
    Zhiming Wang, Yanzi Miao, Shoujun Li, Wei Dai, Shan Li, Yue Wang
    2022, 34(1):  70-83. 
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    Accurate underground gas concentration change prediction is essential for achieving safe and efficient production. Actual downhole systems often have time lags in the causal effects between variables, which may lead to poor performance of the traditional grey prediction model and affect the subsequent production and optimization operations. Under the time lag characteristics of the traditional grey prediction model, to solve the problems of the unclear mechanism of the driving term, as well as the deficiency of introduction rule. A new multivariate grey prediction DOGM(1,N) model with time lag characteristics is proposed in this paper. Based on the traditional OGM(1,N) model, the time-delay parameter is introduced into the driving term sequence. In order to solve the lack of analysis of the complete process of identifying the driving term sequence in the existing multi-variable grey model with time delay, this paper proposes a method for identifying the time-delay parameters and related factors sequence of driving term based on grey correlation analysis. Finally, the effectiveness of the method proposed in this paper has been verified by the simulation study of downhole gas concentration prediction. The results show that the DOGM(1,N) model has high prediction accuracy for the prediction problem of a small sample multivariable system with time-delay characteristics.
    A Conformable Fractional Grey Model CFGM(\alpha,\gamma) and Its Applications in Forecasting Regional Electricity Consumption of China
    Wenqing Wu, Xin Ma, Bo Zeng, Hui Zhang, Gaoxun Zhang
    2022, 34(1):  84-104. 
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    This paper proposes a conformable fractional-order grey system model abbreviated as CFGM(\alpha,\gamma), which is an extension of the integer-order GM(1,1) model. The explicit expressions of the time response function and the restored values are derived by defining the conformable fractional-order derivative and accumulation. By using the least square estimation method, linear system parameters are derived, and then the ant lion optimizer algorithm is applied to search nonlinear system parameters. The effectiveness and feasibility of the CFGM(\alpha,\gamma) model are verified with seven-time series of the M3-Competition. Finally, the new model is applied to the electricity consumption of China. With data from 2009 to 2018, we establish 21 subcases for each selected province and calculate its overall performance, which shows that the new model is more stable than the GM(1,1) and FGM(q,1) models and can obtain competitive results.
    The Fractional Accumulative Time-Delay GM(1,N) Model and Its Application
    Jiakang Song, Mingli Hu
    2022, 34(1):  105-113. 
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    Aiming at the problem of modeling small sample systems with time-delay cumulative effects, this paper introduces a time-delay coefficient control term and a fractional cumulative generation operator. The paper also proposes a fractional accumulative time-delay GM (1,N) grey prediction model, using particle swarm optimization algorithm to determine the optimal time-lag effect control coefficient 𝜆 and the optimal fractional order r. Finally, combining the GDP, Fixed Asset Investment and General Public Budget Expenditure of Jiangsu province from 2013 to 2020 to establish a forecast model, and comparing the prediction results of the GM (1,1) and GM(1,N) model shows that The Fractional Accumulative Time-delay GM(1,N) model (FATGM(1,N)) can better solve the small sample multivariate with cumulative timedelay characteristics system prediction problem.
    A Large-scale Group Grey-DEMATEL Decision Framework for Analyzing Factors Affecting Pandemic Control: A Case in Ghana during COVID-19
    Jinmuzi Zhang, Bismark Appiah Addae, Ginger Y. Ke, Lingyao Liu, Haiyan Xu
    2022, 34(1):  114-138. 
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    Aiming at the pandemic control as a complex interactive relationship problem with high uncertainty and usually involving a large number of decision makers, this study integrates grey theory, large-scale group decision-making and DEMATEL method to innovatively propose a new large-scale group Grey-DEMATEL decision framework, which can examine the interdependence of relationships and system components. The framework mainly uses the grey relational clustering method to cluster large-scale group decision makers, so as to gather the decision makers’ evaluation information on factors and construct the relevant DEMATEL matrix to extract the key factors. In addition, under the proposed framework, a detailed inspection of the actual situation in Ghana was carried out. Through a comprehensive review of relevant literature and the actual situation in the country, a series of factors that affect the detection and control of COVID-19 have been identified and explained. Then use the proposed decision-making framework to extract the key factors, which are formally listed as priorities, so that policymakers can invest scarce resources. The results of detailed case studies and policy recommendations on the situation in Ghana prove the effectiveness of this novel approach.
    Commonality Refinement and Code Reuse of Grey Prediction Model Based on MATLAB
    Shuangyi Yang, Bo Zeng, Shuliang Li, Sifeng Liu, Hanif Heidari
    2022, 34(1):  139-153. 
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    This paper realizes the rapid development of the MATLAB of grey prediction models through public module call. Firstly, the multiple modeling steps of grey prediction models are divided into three types: Model, View, and Controller. Then, it is analyzed which steps are completely common to all models. Finally, these steps are encapsulated into general modules similar to JavaBean. These modules can be called program building blocks for compiling grey prediction modeling software, which greatly improves the development efficiency, reduces code redundancy, and improves the stability of the software. This is of great value to the popularization of grey prediction models.
    Service Quality Evaluation of Medical Caring and Nursing Combined Institutions for the Aged Based on IVPFS-DEMATEL and Two-Stage Decision Model with Grey Synthetic Measures
    Lan Xu, Long Yang
    2022, 34(1):  154-172. 
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    Aiming at the ambiguity, uncertainty, and grey characteristics in the service quality evaluation process of medical caring and nursing combined institutions for the aged, a service quality evaluation method is proposed through the interval-valued Pythagorean fuzzy set (IVPFS)-decision-making trial and evaluation laboratory (DEMATEL) and a two-stage decision model with grey synthetic measures. Based on the SPO model(structure-process-outcome), a service quality evaluation index system for medical caring and nursing combined institutions for the aged is established. Further, a new method is proposed to determine the index weight through combination of IVPFS and DEMATEL, followed by the two-stage decision model with grey synthetic measures is used to assess the service quality of medical caring and nursing combined institutions for the aged. Taking medical caring and nursing combined institutions for the aged in four typical cities of Jiangsu Province as target examples, the effectiveness of the proposed model is verified by comparing with other method. The results show that the proposed methodology can effectively evaluate the service quality of medical caring and nursing combined institutions for the aged.