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Battery Swapping Dispatch for Self-Sustained Highway Energy System Based on Spatiotemporal Deep-Learning Traffic Flow Prediction

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第1期60卷 1058-1070页

作者： Sun, Yuxin Li, Yiqun Borozan, Stefan Wang, Guibin Qiu, Jing Strbac, Goran Univ Liverpool Dept Elect & Elect Engn Liverpool L69 7ZX England Shenzhen Urban Transport Planning Ctr Shenzhen 518060 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Due to the complexity of traffic flow and the stochastic swapping behavior of electric vehicles (EVs), efficient battery dispatch is challenging. Therefore, the battery swapping dispatch framework based on traffic flow prediction is proposed to overcome this inconvenience. The framework is solved by minimizing the total transportation cost and satisfying the EV battery swapping requirement. Naturally, precise traffic flow prediction plays a vital role in efficient battery dispatch. Therefore, this article designs a deep learning prediction framework by leveraging the graph convolutional network (GCN) and the temporal convolutional network (TCN), named Spatiotemporal traffic flow network (STFNet). GCN is applied to learn the topology characteristic of the daily spatiotemporal traffic, which enables STFNet to capture the spatial feature. TCN is developed to acquire the daily traffic flow temporal dependence. Additionally, a pre-partition method based on k-means clustering is applied to improve the effectiveness of the battery dispatch framework. The experimental results indicate that the proposed battery dispatch framework is skillful. Due to the precise prediction of STFNet, the battery swapping dispatch based on STFNet prediction is the most economical, achieving a minimum of 25.82% reduction in the total transportation cost compared to benchmark models. Furthermore, the impact of the pre-partition method has been proven in the case of studies with a huge routing distance declining, dramatically reducing the total transportation cost and making the dispatch more reasonable.

关键词： Batteries Costs Road transportation Transportation Spatiotemporal phenomena Predictive models Biological system modeling Battery swapping daily traffic flow prediction deep learning k-means clustering algorithm vehicle routing problem

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The comprehensive evaluation of innovative education quality from the perspective of balanced and stable development

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INTERNATIONAL JOURNAL OF INNOVATION AND SUSTAINABLE DEVELOPMENT 2025年第1期19卷 43-57页

作者： Feng, Xiaoping Hunan City Univ Coll Humanities Yiyang 413000 Hunan Peoples R China

In order to overcome the problems of long data collection time, high error rate of index weight calculation and low accuracy of traditional evaluation methods, a comprehensive evaluation method of innovative education quality from the perspective of balanced and stable development is proposed. Firstly, the comprehensive evaluation index of innovative education quality under the background of balanced and stable development is preliminarily determined. The k-means clustering algorithm is used to collect education data, and the data are fused and reduced. Then, the comprehensive evaluation system of innovative education quality is established. Finally, the weight of evaluation index is calculated by AHP, and the comprehensive evaluation of education quality is realised by three-level fuzzy comprehensive evaluation model. The experimental results show that the average data acquisition time of this method is 0.63 s, the maximum error rate of evaluation index weight calculation is 2%, and the evaluation accuracy is above 94%.

关键词： balanced and stable development innovative education comprehensive quality evaluation k-means clustering algorithm reduction processing three-level fuzzy comprehensive evaluation

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Personalised recommendation of mobile learning materials based on hierarchical hidden Markov models

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2025年第3-4期35卷 297-311页

作者： Li, Zhaofeng Hu, Ping Hu, Yuchuan Zhang, Yan Henan Inst Sci & Technol Sch Software Xinxiang 453003 Henan Peoples R China

The research on personalised recommendation of mobile learning materials can meet learners' learning needs, thus further improving their learning efficiency and effectiveness. In order to solve the problems of low accuracy, low recall and low user satisfaction in traditional personalised recommendation methods for mobile learning materials, a personalised recommendation method of mobile learning materials based on hierarchical hidden Markov model is proposed. k-means clustering algorithm is used to mine the data of mobile learning platform, and user portraits of mobile learning platform are extracted according to the data mining results and user interest feature vectors. According to the user portrait and hierarchical hidden Markov model, the top-N recommendation list is generated to realise personalised recommendation of mobile learning materials. The experimental results show that the maximum accuracy of personalised recommendation of mobile learning materials is 97.3%, the maximum recall rate is 97.9%, and the average satisfaction rate is 98.1.

关键词： hierarchical hidden Markov model mobile learning materials personalised recommendation k-means clustering algorithm user portrait

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Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第2期11卷 1-12页

作者： Pan, Lijun Zhengzhou Normal Univ Coll Elementary Educ Zhengzhou 450044 Henan Peoples R China

INTRODUCTION: As the essential part of intelligent learning, innovative learning model construction is conducive to improving the quality of intelligent new teaching models, thus leading the deep integration of teaching and artificial intelligence and accelerating the change and development of teaching supported by artificial ***: Aiming at the current intelligent teaching evaluation design method, there are problems such as more objectivity, poor precision, and a single method of evaluation ***: his paper proposes an intelligent learning construction method based on cluster analysis and deep learning algorithms. First of all, the intelligent learning model construction process is sorted out by clarifying the idea of clever learning model construction and extracting model elements;then, the intelligent learning model is constructed through a k-means clustering algorithm and deep compression sparse self-encoder;finally, the effectiveness and high efficiency of the proposed method is verified through simulation experiment ***: Solved the problem that the intelligent learning model construction method is not objective enough, has poor accuracy and is not efficient ***: The results show that the proposed method improves the model's accuracy.

关键词： Intelligent learning model Model element extraction k-means clustering algorithm Deep compression sparse self-encoder

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Research on Urban Storm Flood Simulation by Coupling k-means Machine Learning algorithm and GIS Spatial Analysis Technology into SWMM Model

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WATER RESOURCES MANAGEMENT 2024年第6期38卷 2059-2078页

作者： Liu, Chengshuai Hu, Caihong Zhao, Chenchen Sun, Yue Xie, Tianning Wang, Huiliang Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China

Accurate flood simulation has significant practical implications for urban flood management. The focus of this study is to develop a new flood model (k-SWMMG) based on the Storm Water Management Model (SWMM), which innovatively couples the k-means clustering machine learning algorithm and GIS spatial analysis techniques. The k-means clustering machine learning algorithm is used to determine the uncertain parameters of the SWMM model, while GIS spatial analysis techniques enhance the two-dimensional realism of flood simulation. We applied the k-SWMMG model to six historical observed flood events in a specific catchment area in Zhengzhou City, using rainfall and flow data. The study shows that: 1) k-SWMMG optimizes the sub-basin division method of urban stormwater models, avoiding the tedious and complex parameter calibration process, and improving modeling efficiency to some extent. 2) The two-dimensional visualization of inundation provided by GIS spatial analysis techniques better meets the production requirements of current urban flood simulation. 3) k-SWMMG outperforms SWMM in terms of simulation performance, with improvements in absolute error (AE), relative error (RE), Nash-Sutcliffe efficiency coefficient (NSE), and coefficient of determination (R2) by 0.019m, 5.36%, 0.068, and 0.042, respectively. The findings can provide scientific decision-making references for urban flood forecasting and early warning.

关键词： Urban flood forecast k-SWMMG Uncertainty parameters City functional area k-means clustering algorithm GIS

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Predicting stability of integrated circuit test equipment using upper side boundary values of normal distribution

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The Journal of China Universities of Posts and Telecommunications 2024年第2期31卷 85-93页

作者： Zhan Wenfa Hu Xinyi Zheng Jiangyun Yu Chuxian Cai Xueyuan Zhang Lihua School of Electronic Engineering and Intelligent Manufacturing Anqing Normal UniversityAnqing 246133China School of Computer and Information Technology Anqing Normal UniversityAnqing 246133China

In response to the growing complexity and performance of integrated circuit(IC),there is an urgent need to enhance the testing and stability of IC test equipment.A method was proposed to predict equipment stability using the upper side boundary value of normal ***,the k-means clustering algorithm classifies and analyzes sample *** accuracy of this boundary value is compared under two common confidence levels to select the optimal threshold.A range is then defined to categorize unqualified test *** experimental verification,the method achieves the purpose of measuring the stability of qualitative IC equipment through a deterministic threshold value and judging the stability of the equipment by comparing the number of unqualified data with the threshold value,which realizes the goal of long-term operation monitoring and stability analysis of IC test equipment.

关键词： k-means clustering algorithm the upper side boundary of normal distribution threshold integrated circuit(IC)test equipment stability analysis

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A task unloading strategy of IoT devices using deep reinforcement learning based on mobile cloud computing environment

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WIRELESS NETWORkS 2024年第5期30卷 3587-3597页

作者： Qi, Hui Mu, Xiaofang Shi, Ying Taiyuan Normal Univ Dept Comp Jingzhong 030619 Shanxi Peoples R China

Aiming at the task unloading mode in cloud computing environment, the task unloading problem for IoT devices is studied. Through theoretical analysis, we can know that in the task unloading problem, it is usually contradictory to improve the utilization of cloud resources and reduce the task delay. In order to solve this problem, a task unloading scheme for Internet of things devices using deep reinforcement learning algorithm is proposed. The deep reinforcement learning algorithm is used to model the task unloading problem. The return value with weight is introduced into the algorithm, and the utilization rate of cloud resources and the delay of unloading task are weighed by adjusting the return value of the weight. First of all, the improved k-means clustering algorithm with weighted density is used to cluster the physical machines. The physical machines of each cluster have similar bandwidth and task waiting time. Then, deep reinforcement learning is used to select the best physical machine cluster from the current unloading tasks. Finally, the improved PSO algorithm is used to select the optimal physical machine from the optimal cluster, and Pareto is used to improve the convergence speed. Experimental results show that compared with the traditional method, the proposed algorithm has a good performance, and can achieve the goal of increasing the utilization of physical machine resources and reducing task delay.

关键词： Task unloading Deep reinforcement learning Mobile cloud computing k-means clustering algorithm Physical machine Convergence speed

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Residential Building Duration Prediction Based on Mean clustering and Neural Network

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ADVANCES IN CIVIL ENGINEERING 2024年第1期2024卷

作者： Ji, Fanrong Nan, Yunquan Wei, Aifang Fan, Peiyan Luo, Zhaoyuan Song, Xiaoqing Shandong Jianzhu Univ Sch Management Engn Jinan 250101 Peoples R China Shandong Jianzhu Univ Coll Surveying & Geoinformat Jinan 250101 Peoples R China Univ Canberra Sch Design & Built Environm Canberra 2601 Australia

The duration of a residential building project will directly influence its successful implementation;hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA-BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and average error rate, demonstrating that the GA-BP neural network model is effective in predicting the duration of residential buildings. To enhance the predictive accuracy of the GA-BP neural network model, this research utilized an artificial bee colony (ABC)-improved k-means clustering algorithm to categorize 111 experimental datasets and 33 new datasets. The results indicated that the ABC-k-means-GA-BP model exhibited robust generalization capabilities and high predictive accuracy, with the fitness function showing optimal performance after 10, 15, and 35 generations, and the best validation performances recorded as 0.0019156, 0.00035905, and 0.0036914. This validates that the proposed ABC-k-means-GA-BP neural network model significantly aids in forecasting the construction period of residential buildings, which holds substantial practical value for enhancing construction efficiency.

关键词： artificial bee colony algorithm BP neural network duration prediction genetic algorithm k-means clustering algorithm

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Temporal forecasting by converting stochastic behaviour into a stable pattern in electric grid

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第9期15卷 4426-4442页

作者： Qashou, Akram Yousef, Sufian Hazzaa, Firas Aziz, kahtan Anglia Ruskin Univ Cambridge England

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, k-means clustering is employed, followed by long-short-term memory and gated recurrent unit algorithms are used to perform the short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures.

关键词： Smart home Short-term prediction Stochastic behavior k-means clustering algorithm LSTM GRU

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A method for recognising wrong actions of martial arts athletes based on keyframe extraction

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INTERNATIONAL JOURNAL OF BIOMETRICS 2024年第3-4期16卷 256-271页

作者： Li, Zhiqiang Jilin Police Coll Dept Police Sports Teaching & Res Changchun 130000 Peoples R China

In order to improve the accuracy of incorrect action recognition and shorten the time required for action recognition, the paper proposes a method for recognising incorrect actions of martial arts athletes based on keyframe extraction. Firstly, the optical flow method is used to filter the key frames of actions, and the shot adaptive k-means clustering algorithm is used to extract the texture features of image frames. Secondly, Euclidean distance is used to calculate the distance between cluster centres and complete the initial selection of keyframes. Finally, the sequence position and video frame rate of the initially selected keyframes is optimised to obtain the final keyframe sequence number and output the error action recognition result. The experimental results show that the error action recognition accuracy of this method is 96.58%, the recognition error is 1.9%, and the recognition time is 11 seconds.

关键词： keyframe extraction optical flow method Lens adaptation k-means clustering algorithm Euclidean distance

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