Table of Content

10 December 2024, Volume 36 Issue 6

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Estimating the Thermoelectric Performance Parameters of High Entropy Materials by the Improved Residual Error Non-homogeneous Grey Model(1, 1)

Cholho Pang , Sonil Jo , Yonsun O , Songhun Kwak

2024, 36(6):  1-12. 

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In this paper, an improved residual error non-homogeneous grey model(1,1) was newly developed and estimated the thermoelectric performance parameters (ZT figure of merit) of the high entropy materials(HEMs) using this model. Firstly, by combining the nonhomogeneous grey model, residual error processing method and Markov model, the forecasting accuracy of the model was improved. Secondly, the forecasting accuracy of the proposed model was compared with other grey models in simulating ZT values of AgSnSbSe1.5Te1.5. The comparative results showed that the forecasting accuracy of the proposed model was the highest (MAPE value < 0.02). Thirdly, this model was used to predict ZT values of high entropy materials at the high temperatures. The simulation result showed that ZT value of increased rapidly over 750K and were higher than 1.0 above 950K, continued to increase over the whole temperature range in Bi0.9Li0.1Cu0.9Mn0.1SeO. Meanwhile, ZT reached 1.81 at 1000K in Sn0.555Ge0.15Pb0.07Mn0.275Te, and ZT value of the Sn0.5Pb0.5Ge0.5Te(MnTe)0.15 had a maximum value of 1.72 at 900K and after that, it decreased. The results indicate that the proposed model is effective in predicting ZT values of high entropy materials.  

Single-period Static Seru Scheduling Problem with Grey Processing Time

Rui Tao, Liangyan Tao, Naiming Xie

2024, 36(6):  13-26. 

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With the increase in demand for personalized customization and small-batch production in the manufacturing industry, the seru production system has been widely applied as a flexible and efficient production model. This paper primarily investigates the singleperiod static seru scheduling problem. The uniqueness of this study lies in considering the uncertainty of product processing time within the seru production system. It introduces interval grey numbers to represent the processing time of individual products and establishes a mathematical model. Additionally, this paper summarizes the methods for comparing the magnitude of interval grey numbers from previous research and proposes a new method for interval grey number comparison. To solve the model, this paper presents an improved genetic algorithm (GA-NS) that incorporates a neighbourhood search strategy. In the numerical experiment section, we compare the results obtained using the traditional genetic algorithm (GA) and the GA-NS algorithm. The results indicate that the GA-NS algorithm outperforms the traditional genetic algorithm in terms of optimization effectiveness and can effectively address seru scheduling problems that consider the uncertainty of processing times. This study not only enriches the theoretical research of interval grey number comparison methods but also provides a new optimization algorithm for solving seru production scheduling problems with uncertain processing times, offering significant theoretical and practical application value. 

A Grey Multi-objective Model for Slot Secondary Allocation of Arrival and Departure Flights under Autonomous Cancellation 

Kejia Chen, Zhenkun Chen

2024, 36(6):  27-41. 

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The Greyness and Applications of Grey Set

Xican Li, Li Li

2024, 36(6):  42-53. 

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In order to quantitatively describe the grey properties of grey set, based on the possibility function of grey set, this paper discusses the expression method of the greyness of grey set and its applications. Firstly, the axiomatic definition and a method of calculating the greyness of grey set are given, and its rationality is analyzed through examples. Then, according to the principle of unity of opposites, the concept of whiteness of grey set is given, and the applications of greyness of grey set are analyzed. Finally, some examples are given to verify the validity of greyness of grey set and its application model. The results show that the axiomatic definition of greyness of grey set not only conforms to the grey immortal axiom, but also can quantitatively describes the dynamic evolution state of grey hazy set (grey set). The application examples show that the grey relational degree model and the grey decision model based on the greyness of grey set are feasible and effective. The research results not only enrich the theory of grey mathematics and grey system, but also provide a new method for grey relational analysis and grey decision.  

Research on the Optimization of Flatness Grey Prediction Control Based on CNN-BiLSTM

Haixia Wang , Kunjie Li , Linsen Wang , Xiao Cheng

2024, 36(6):  54-68. 

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In the production of non-ferrous metal plates, strips, and foils, the shape control system has nonlinear, multivariable, and time-delay characteristics, making it difficult to achieve satisfactory control results through conventional control methods. Based on the grey residual error and CNN-BiLSTM(Convolutional Neural Networks- Bidirectional Long Short-Term Memory) model, this paper proposes a predictive rolling optimization control algorithm for accurately predicting the tensile stress value of plate shapes between roll gaps. With the parallel computing capability and error compensation technology of CNN-BiLSTM network, the proposed algorithm overcomes the shortcomings of no feedback mechanism and inaccurate dynamic process prediction value in residual prediction. As a result, it obtains more accurate full-process prediction data in rolling and compensates for the inertia and lag characteristics of the plate shape control actuator through rolling optimization control. The prediction accuracy of the grey neural network combination algorithm is compared with that of various models such as grey prediction, BP, CNN, and LSTM. The prediction accuracy is increased by 7.46%, 6.28%, 17.85%, and 17.2%, respectively, and the dynamic control accuracy is improved by 30%. This proves the effectiveness of the proposed algorithm and provides a new avenue for shape control and real-time data prediction.

Multivariate Forecasting of Seasonal Carbon Dioxide Emissions via a Discrete Grey Multivariate Forecasting Model with a New Information Priority Accumulation Operator

Jianming Jiang, Yandong Ban, Ming Zhang, Chiwen Qu

2024, 36(6):  69-78. 

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In this study, a more efficient Discrete Grey Multivariate Forecasting Model with A New Information Priority Accumulation Operation is proposed to depict the development trend of energy-related seasonal carbon dioxide emissions. The new information priority accumulation operation and an adaptive grey action quantity in the new model ensure excellent nonlinear fitting capabilities. The presence of the virtual variable allows the model to directly simulate seasonal fluctuations in seasonal carbon dioxide emissions without removing seasonal effects, showcasing the model's superiority. Therefore, the model can fit the nonlinear seasonal time series better. Experiments based on quarterly carbon dioxide emissions from energy consumption in the United States demonstrate the new method's optimal forecasting performance. Additionally, the optimization capability of each component in the new model is further validated by a more in-depth experiment. The effectiveness of this method in fitting seasonal carbon dioxide emissions is confirmed.  

Remaining Useful Life Prediction of Lithium Battery Based on LSTM and Improved GM (1, N) Model

Qinqin Shen, Yang Cao, Shuang Liang, Quan Shi

2024, 36(6):  79-93. 

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In response to the difficulties of online obtaining enough direct performance parameters for lithium battery remaining useful life (RUL) prediction, a novel prediction method that combines the long short-term memory (LSTM) neural network and an improved GM(1, N) model is proposed in this paper. Firstly, two correlation analysis methods are used to obtain the health indicators that characterize the health status of the lithium battery. Then, the existing lithium battery dataset is used to train the LSTM model and predict the initial capacity of the same type lithium battery in operation. Finally, the initial capacity and corresponding indirect health indicators are substituted into an optimized damping accumulation discrete multi-variable convolution model, and accurate prediction of the entire life cycle of lithium battery is achieved through rolling technique. Experiments are conducted by using for lithium battery datasets from the Center for Advanced Life Cycle Engineering at the University of Maryland. The results show that the proposed model performs much better than the LSTM model and the conventional GM(1, N) model. In particular, the mean absolute percentage errors of the proposed method for predicting the RUL of four lithium batteries are less than 2.5%, the root mean square errors are about 0.02, and the mean absolute errors are only about 0.01. 

Research on Supply-Need Precise Matching of Aid to Disadvantaged University Students Based on Grey Correlation Analysis 

Hongqi Liu, Zhiwen Tang, Jie Xiao

2024, 36(6):  94-103. 

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The purpose of this paper is to explore a new approach to solve the problem of optimizing the allocation of university aid resources, then propose a grey relational model of aid supply-need precise matching. The conceptual connotations of precise aid and the process of need-oriented aid supply-need precise matching have been introduced at first. Then using the basic idea of grey relational analysis, the definition of supply-need matching grey nearness relational grade is proposed, and the steps of aid supply-need precise matching grey relational model are given. Finally, the constructed model is used to explore the problem of matching decision-making between aid recipients and aid resources, and differentiated aid strategy that effectively meets the needs of students and efficiently utilizes aid resources is given. The validity and rationality of the model are verified. The research shows that the grey relational model of aid supplyneed precise matching can be used to describe the precise matching problem between aid recipients and aid programs. It enables the optimal allocation of aid resources and the maximum satisfaction of aid needs to promote the organic integration of poverty alleviation and talent development. It can provide the decision-making basis and methodological support for the university aid administration, and have certain practical value. The model adheres to the student need-oriented configuration of aid programs, and it can form a differentiated aid strategy that effectively meets the needs of students and efficiently utilizes aid resources. This study provides a new perspective and a new way to solve the optimal allocation problem of aid resources for university students. It can provide the decisionmaking basis and methodological support for the university aid administration.