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Deep Reinforcement Learning Based Robust Communication for Internet of Vehicles

AUTOMATIC CONTROL AND COMPUTER SCIENCES 2023年第4期57卷 364-370页

作者： Gasmi, Rim Illizi Univ Ctr Illizi 33000 Algeria

The high number of connected nodes in Internet of Vehicles (IoVs) drives to high data exchange between nodes, which increases the network overhead. Moreover, the recurrent change in vehicle mobility in Internet of Vehicles (IoVs) drives to frequent changes in network topology which in turn causes frequent link disconnections. Therefore, the most addressed issues in IoVs are to manage the high quantity of packets sent by the huge number of vehicles connected with IoT devices, to reduce communication delays and guarantee the longest communication stability. clustering techniques have been utilized to reduce network overhead in IoVs networks. Classical clustering algorithms have been proposed to enhance network performances. However, IoVs environment is characterized by the high dynamicity of nodes, therefore, the optimization methods already proposed cannot perfectly deal with the characteristics of IoVs. Reinforcement learning (RL) is a machine learning algorithm, where the agent learns from its environment and tries to enhance its policies to obtain the best reward. In this paper, we propose to use deep reinforcement learning (DRL) to select the best cluster heads based on node's degree, node's buffer size, and signal strength. In the proposed work, the vehicle can perfectly select the cluster heads by choosing the best state-action values taking in consideration the high dynamicity of the network.

关键词： reinforcement learning clustering algorithm IoVs reliable communication

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Digital Divide in the European Union: A Typology of EU Citizens

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SOCIAL INDICATORS RESEARCH 2025年第1期176卷 149-172页

作者： Gomes, Ana Dias, Jose G. Inst Univ Lisboa ISCTE IUL Business Res Unit BRU IUL Lisbon Portugal Inst Univ Mil Portuguese Air Force Acad Ctr Invest Acad Forca Aerea Sintra Portugal

This paper addresses the heterogeneity of the digital divide and internet use among citizens in the 28 European Union (EU) countries (at the time of the survey). Drawing from the Eurobarometer Surveys, three indicators of the digital divide are used to define the groups: frequency of internet access, means of internet access, and online activities. The categorical clustering algorithm identifies six groups of internet users: Non-Users, Basic Users, Information Exchangers, Instrumental Users, Socializers, and Advanced Users, each with distinct socio-demographic profiles. The study reveals significant socio-economic and demographic profiling variables characterizing these patterns, including age, education, gender, occupation, type of community and geographic location. A major digital divide is detected in many countries;Notably, Romania, Greece, and Bulgaria have the largest proportion of Non-Users, emphasizing the need for targeted policy interventions. These results provide crucial insights for the European Commission's digitization strategy, suggesting that more nuanced and targeted measures are needed to ensure equitable digital access across the EU.

关键词： Internet Digital divide European Union clustering algorithm Unobserved heterogeneity Social networks

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YOLO-Banana:An Effective Grading Method for Banana Appearance Quality

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Journal of Beijing Institute of Technology 2023年第3期32卷 363-373页

作者： Dianhui Mao Xuesen Wang Yiming Liu Denghui Zhang Jianwei Wu Junhua Chen School of Computer Beijing Technology and Business UniversityBeijing 100048China Research Center of Information Technology Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing PAIDE Science and Technology Development Co.Ltd. Beijing 100097China Institute of Standardization Theory and Strategy China National Institute of StandardizationBeijing 100088China

The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed *** this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect *** to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing *** address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition ***,the optimized progressive aggregated network(PANet)enables better multi-level feature ***,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.

关键词： YOLOv5 banana appearance grading clustering algorithm weighted non-maximum suppression(weighted NMS) progressive aggregated network(PANet)

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Uncovering Patterns in Electric Vehicle Charging: A Data-Driven Approach

Uncovering Patterns in Electric Vehicle Charging: A Data-Dri...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Nazari, Zahra Musilek, Petr Univ Alberta Elect & Comp Engn Edmonton AB Canada

ISBN: (纸本)9798350381849;9798350381832

According to the International Energy Agency, the worldwide count of electric vehicles is projected to surpass 130 million by 2030. Over the past 10 years, this growth has led to much research on electric vehicles, ranging from preliminary studies and experimental testbeds to data analysis. This study examines the charging behavior of electric vehicle users using the Caltech JPL site data set. Using several methods, such as the intersection-based clustering algorithm and more well-known ones like k-means and ward linkage, user behavior patterns are examined focusing on connection time, session duration, and energy delivered. Four distinct user groups with unique charging patterns and energy demands are identified. Notably, afternoon users exhibit a 23-77 % split in high energy demand, with early afternoons being the peak period. Encouraging high-demand users to charge in the morning and on weekends would optimize the operation of the charging networks. These findings have important implications for shaping electric vehicle charging infrastructure, grid management, and energy distribution.

关键词： clustering algorithm electric vehicle user behavior

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Optimized YOLOv7 for Small Target Detection in Aerial Images Captured by Drone

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第9期14卷 70-79页

作者： Liu, Yanxin Chen, Shuai Luo, Lin Liaoning Petrochem Univ Sch Informat & Control Engn Fushun Peoples R China

It is challenging to detect small targets in aerial images captured by drones due to variations in target sizes and occlusions arising from the surrounding environment. This study proposes an optimized object detection algorithm based on YOLOv7 to address the above-mentioned challenges. The proposed method comprises the design of a Genetic Kmeans (1IoU) clustering algorithm to obtain customized anchor boxes that more significantly apply to the dataset. Moreover, the SPPFCSPC_group structure is optimized using group convolutions to reduce model parameters. The fusion of Spatial Pyramid Pooling-Fast (SPPF) and Cross Stage Partial (CSP) structures leads to increased detection accuracy and enhanced multi-scale feature fusion network. Furthermore, a Detect Head is incorporated into the classification phase for more accurate position and class predictions. According to experimental findings, the optimized YOLOv7 algorithm performs quite well on the VisDrone2019 dataset in terms of detection accuracy. Compared with the original YOLOv7 algorithm, the optimized version shows a 0.18% increase in the Average Precision (AP), a reduction of 5.7 M model parameters, and a 1.12 Frames Per Second (FPS) improvement in the frame rate. With the above described enhancements in AP and parameter reduction, the precision of small target detection and the real-time detection speed are increased notably. In general, the optimized YOLOv7 algorithm offers superior accuracy and real-time capability, thus making it well-suited for small target detection tasks in real-time drone aerial photography.

关键词： Small target detection drone aerial photography YOLOv7 clustering algorithm spatial pyramid pooling

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Medical Image clustering algorithm Based on Graph Entropy

Medical Image Clustering Algorithm Based on Graph Entropy

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International Conference on Fuzzy Systems and Knowledge Discovery

作者： Yu Zhan Haiwei Pan Qilong Han Xiaoqin Xie Zhiqiang Zhang Xiao Zhai Coll. of Comput. Sci. & Technol. Harbin Eng. Univ. Harbin China

ISBN: (纸本)9781467376839

Recently, a variety of medical imaging technologies have been used widely in clinical diagnosis. As a large number of medical images are produced everyday, it becomes a hot issue of data mining on medical image in current that how to make full use of these medical images and cluster efficiently to help doctors to diagnose. In this paper, we propose a medical image clustering method. Firstly, medical image dataset is represented as a weighted, undirected and completed graph. Secondly, the graph is sparsified and pruned. This model can describe the similarity between medical images very well. Last, weighted and undirected graph clustering method based on graph entropy is proposed to cluster these medical images. The experimental results show that this method can cluster medical images efficiently and run well in time complexity and clustering results.

关键词： component medical image entropy-based sparsification clustering algorithm

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An Improved Fast Search clustering algorithm Based on Kernel Density

An Improved Fast Search Clustering Algorithm Based on Kernel...

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IEEE International Conference on Smart City

作者： Ruisheng Zhang Huiyi Ma Qidong Liu Zhili Zhao School of Information Science and Engineering Lanzhou University

ISBN: (纸本)9781509018949

clustering is an important algorithm for data mining. FSC is a kind of clustering algorithm based on density, which has been proposed in the journal Science in 2014. FSC only requires one input parameter and has a higher practicability. RFSC, which is an improved algorithm of FSC algorithm, is less sensitive to the input parameters and faster. However, neither RFSC nor FSC can deal with uneven density data sets. In order to solve that problem, we propose an improved algorithm KFSC in this paper by dynamically controlling of the width of the kernel function. KFSC uses the idea of attractor of the DENCLUE and can customize their own personalized attraction for each point. The experimental results on synthetic data sets show that KFSC has a better performance on uneven density data sets than FSC and RFSC.

关键词： clustering algorithm Density-based clustering Kernel function Uneven density

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A clustering-based Location Allocation Method for Delivery Sites under Epidemic Situations

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系统仿真学报 2024年第12期36卷 2782-2796页

作者： Zhou Yaqiong Chen Junqi Li Weishi Qiu Sihang Ju Rusheng College of System Engineering National University of Defense TechnologyChangsha 410073China

To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic *** establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal *** on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun *** results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.

关键词： location problem clustering algorithm intelligent optimization algorithm Pareto front

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Automatic Detection Method of Website Vulnerabilities Based on an Associated Data Drive

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JOURNAL OF WEB ENGINEERING 2025年第2期24卷 217-242页

作者： Li, Xiaoli Zhao, Ling Shen, Haobin Du, Hanlin Guo, Zhida Guangdong Power Grid Co Ltd Huizhou Power Supply Bur Huizhou 516000 Peoples R China

In order to reduce the probability of website users being attacked and maintain the safety of website operation, this study proposes an automatic vulnerability detection method of websites based on associated data. We use plug-ins to scan the website in all directions, establish a scanning database, and classify and store the scanned web data. By applying optimized an a priori association rule algorithm, key features are extracted from web scan data, which are then transformed into input samples for a K-means clustering algorithm. The aim is to efficiently extract feature attributes of website vulnerability data and ultimately construct a text vectorized representation of vulnerability data. Convolutional neural networks can automatically detect website vulnerabilities by using the constructed text vector as input. Experimental verification shows that this method demonstrates comprehensive data coverage, efficient processing speed, and high-precision recognition perforensures the accuracy and timeliness of vulnerability detection.

关键词： A priori algorithm website website vulnerability automated detection clustering algorithm convolutional neural network

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A novel method of quantitative evaluation and comprehensive classification of low permeability-tight oil reservoirs: A case study of Jidong Oilfield, China

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Petroleum Science 2022年第4期19卷 1527-1541页

作者： Dong-Liang Jiang Hao Chen Jian-Peng Xing Lin Shang Qun-Hui Wang Yan-Chun Sun Yao Zhao Jian Cui Ian Duncan State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum(Beijing)Beijing102249China Key Lab of Petroleum Eng.of MOE China University of Petroleum(Beijing)Beijing102249China Postdoctoral Station of PetroChina Jidong Oilfield Company TangshanHebei 063000China Postdoctoral Program of PetroChina Research Institute of Petroleum Exploration and Development Beijing100083China Exploration and Development Research Institute PetroChina Jidong Oilfield CompanyTangshanHebei063000China Bureau of Economic Geology The University of Texas at Austin78705United States

The classification of low permeability-tight reservoirs is the premise of development. The deep reservoir of Shahejie 3 member contains rich low permeability-tight reserves, but the strong heterogeneity and complex micro pore structure make the main controlling factors subjective and the classification boundaries unclear. Therefore, a new indicator considering the interaction between fluid and rock named Threshold Flow Zone Indicator(TFZI) is proposed, it can be used as the main sequence of correlation analysis to screen the main controlling factors, and the clustering algorithm is optimized combined with probability distribution to determine the classification boundaries. The sorting coefficient, main throat radius, movable fluid saturation and displacement pressure are screened as the representative parameters for the following four key aspects: rock composition, microstructure, flow capacity and the interaction between rock and fluid. Compared with the traditional probability distribution and clustering algorithm, the boundary of the optimized clustering algorithm proposed in this paper is more *** classification results are consistent with sedimentary facies, oil levels and oil production *** method provides an important basis for the development of low permeability-tight reservoirs.

关键词： Low permeability-tight reservoir Classification boundary Correlation analysis Probability distribution clustering algorithm

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