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A novel cooperative collision avoidance system for vehicular communication based on deep learning

International Journal of Information Technology (Singapore) 2024年第3期16卷 1661-1675页

作者： Farhat, Wajdi Ben Rhaiem, Olfa Faiedh, Hassene Souani, Chokri Laboratory of Microelectronics and Instrumentation University of Monastir Monastir Tunisia Higher Institute of Applied Sciences and Technology Sousse University Sousse Tunisia Computer Science Department College of Science Northern Border University Arar Saudi Arabia Digital Research Center of SFax (CRNS) SM@RTS (Laboratory of Signals systeMs aRtificial Intelligence and neTworkS) Sfax Tunisia

Global road traffic injuries represent a significant safety challenge, with the highest fatality rates worldwide stemming from a combination of drivers' reckless behavior and the ever-expanding number of vehicles on the roads. In light of these factors, Autonomous Vehicles (AVs) have received a lot of attention as a technology that has the potential to revolutionize various industries and solve a lot of problems. The rise of the Internet of Vehicles (IoV) and Connected Vehicles (CVs) enables AVs to perceive and react swiftly in complex road scenarios, prioritizing safety. These vehicles prioritize individual safety in all situations, significantly enhancing road safety. Mobile Edge Computing (MEC) plays a crucial role thanks to its capacity for minimal latency and high bandwidth capabilities, enabling critical vehicular applications. In this paper, we introduce a cooperative collision avoidance system based on MEC, named 2CAS-MEC, that proactively identifies and locates road hazards. Our system is based on MEC servers positioned alongside roads, it consistently analyzes traffic and hazard data, issuing targeted alerts to nearby vehicles regarding potential risks. Particularly, our work introduces an advanced deep learning system that utilizes 5G, mobile edge computing, and cloud intelligence to prevent vehicle collisions. The experimental results show that our system outperforms the existing related works and exhibits the anticipated performance, effectively helping drivers in accident avoidance. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cloud Connected and autonomous vehicles Deep learning IOV MEC Vehicle to-everything (V2X)

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A Discrete artificial Bee Colony Algorithm for Stochastic Vehicle Scheduling

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Complex System Modeling and Simulation 2022年第3期2卷 238-252页

作者： Yuanyuan Li Yindong Shen Jingpeng Li Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computer Science and Mathematics University of StirlingStirlingFK94LAUK

Vehicle scheduling plays a profound role in public ***,stochastic vehicle scheduling may lead to more robust *** solve the stochastic vehicle scheduling problem(SVSP),a discrete artificial bee colony algorithm(DABC)is *** to the discreteness of SVSP,in DABC,a new encoding and decoding scheme with small dimensions is designed,whilst an initialization rule and three neighborhood search schemes(i.e.,discrete scheme,heuristic scheme,and learnable scheme)are devised individually.A series of experiments demonstrate that the proposed DABC with any neighborhood search scheme is able to produce better schedules than the benchmark results and DABC with the heuristic scheme performs the best among the three proposed search schemes.

关键词： public transportation stochastic vehicle scheduling discrete artificial bee colony search scheme

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Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

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Complex System Modeling and Simulation 2022年第1期2卷 18-34页

作者： Yindong Shen Leqin Yu Jingpeng Li Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computer Science and Mathematics University of StirlingStirlingFK94LAUK

Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution *** electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the *** some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles,the literature on the integration of uncertain demand and energy consumption of electric vehicles is still ***,practically,it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles(EVs)in actual ***,we propose the robust optimization model based on a route-related uncertain set to tackle this ***,adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the *** effectiveness of the method is verified by experiments,and the influence of uncertain demand and uncertain parameters on the solution is further explored.

关键词： electric vehicle routing problem time windows uncertain demand energy consumption model robust optimization adaptive large neighbourhood search

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Surrogate-Assisted Neighborhood Search with only a Few Weight Vectors for Expensive Large-Scale Multiobjective Binary Optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Gu, Haoran Wang, Handing Mei, Yi Zhang, Mengjie Jin, Yaochu Xidian University School of Artificial Intelligence Xi'an710071 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Westlake University Trustworthy and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Large-scale multiobjective binary optimization problems often occur in real-world applications, where the function evaluation can only be performed through computationally expensive simulations, which renders standard exact and heuristic methods ineffective. Aggregation-based surrogate-assisted multiobjective evolutionary algorithms have been developed and shown to be promising for solving such problems. They define a set of uniformly distributed weight vectors as search directions, on which all search resources are placed to perform the evolution. However, the Pareto fronts of large-scale multiobjective binary optimization problems are discrete and non-uniform. As a result, many weight vectors are useless, thus a lot of search resources are wasted. To address this challenge, we propose a surrogate-assisted neighborhood search (SANS) for expensive large-scale multiobjective binary optimization. SANS uses only a few weight vectors to save search resources while maintaining an adequate diversity. To further utilize the limited search resources, a Q-learning-based method is designed to dynamically allocate search resources to weight vectors. Furthermore, a surrogate-assisted variable neighborhood search is developed to speed up the search without getting trapped in a local optimum prematurely. To robustly and reliably predict the quality of the found solutions, global and local surrogate models are trained by different training samples and then work collaboratively. The experimental results have demonstrated the superiority of SANS over seven state-of-the-art algorithms on the multiobjective binary optimization problems with up to 1000 decision variables using only 500 real solution evaluations. © 1997-2012 IEEE.

关键词： Vectors

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An artificial Immune System Algorithm for the Permutation Flow Shop Scheduling Problem with Sequence Dependent Setup Times 10

An Artificial Immune System Algorithm for the Permutation Fl...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Mesmar, Khadija Lebbar, Maria Aqil, Said Asebriy, Zahra Allali, Karam National School of Mines of Rabat Rosdm Research Team Lmaid Laboratory of Applied Mathematics and Business Intelligence Rabat Morocco Ensam Casablanca Hassan Ii University Laboratory Artificial Intelligence and Complex Systems Engineering Casablanca Morocco Est Essaouira Cadi Ayyad University Laboratory mathematics Computer sciences and modeling of complex systems Essaouira Morocco Fst Mohammadia Hassan Ii University Laboratory of Mathematics Computer Science and Applications Mohammadia Morocco

ISBN: (纸本)9798350387353

This paper is concerned with a set of independent jobs in a permutation flow shop that have setup times on a set of machines. The M-IAIS algorithm, which is based on immunoglobulins and has been modified, is designed to reduce the makespan. A unique procedure known as B cell repertoire updating, which comprises size C of jobs that are taken out of the sequence and reinserted into all possible positions of the remaining sequences of jobs, has an impact on the M-IAIS algorithm's performance. To increase the convergence velocity, three iterations of the algorithm are suggested. Thorough testing was done, and the three versions' performances on a set (benchmark) are compared. Problem instances are described in Sistemas de Optimizacion Aplicada (http://***/problem-instances) In the paper, percentage deviation is considered to evaluate the performance of the proposed algorithms. Furthermore, the ensuing outcomes demonstrate its suitability for addressing for the Permutation Flow Shop Scheduling Problem with Sequence Dependent Setup Times. © 2024 IEEE.

关键词： Scheduling algorithms

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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GestureTeach: A gesture guided online teaching interactive model

GestureTeach: A gesture guided online teaching interactive m...

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作者： Liu, Hongjun Yao, Chao Zhang, Yalan Ban, Xiaojuan Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial Intelligence School of Computer and Communication Engineering School of Intelligence Science and Technology Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Institute for Materials Intelligent Technology Liaoning Academy of Materials Shenyang China

Online education has become more popular and effective due to the availability of high-speed internet and technological innovations, which allow people from different locations to access educational resources and opportunities. However, online classes often face challenges such as limited interactivity and display options, which can affect the quality and effectiveness of the online learning experience. In this article, we propose GestureTeach, a new pedagogical paradigm that enables free handwriting interaction and animation generation for online teaching. GestureTeach uses gestures as a natural and intuitive way of interaction, which enhances the teacher's intention expression and the student's engagement. GestureTeach also generates animations from handwritten sketches, which improves the display effects of the interaction and the student's knowledge comprehension. We conducted a two-stage study with 15 teachers and 90 students to evaluate the effectiveness of GestureTeach in facilitating classroom interaction. The results show that GestureTeach is preferred by both teachers and students over traditional online teaching methods and has the potential to transform the online teaching landscape by providing a seamless and interactive experience. © 2023 John Wiley & Sons Ltd.

关键词： Students

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Cost-Effective Data Caching Optimization with Distance-aware in Edge Computing 2

Cost-Effective Data Caching Optimization with Distance-aware...

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2022 2nd Conference on High Performance Computing and Communication Engineering, HPCCE 2022

作者： Wang, Futian Han, Yinchao Wu, Zhimin Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

ISBN: (纸本)9781510663374

In recent years, edge data caching has been concerned by more and more research scholars. Caching data, especially popular data, on edge servers can significantly reduce users’ latency, while also greatly reducing direct data transmission between users and remote clouds, and slowing down the congestion of the backbone network. Most of the existing research on edge data caching is mainly aim at minimizing users’ latency, saving devices’ energy, and improving the hit rate of data caching, etc. From the perspective of an app vendor, how to formulate an edge data caching solution that can bring him the most profit while complying with a given budget constraint and considering the experience requirements of different users is a very realistic and challenging problem. In this paper, we formulate the Profit-Maximizing Edge Data Caching(PMEDC) as a constrained optimization problem. For PMEDC in small scale scenarios, we propose an optimization model named PMEDC-IP that can derive the optimal caching strategy. For PMEDC in large scale scenarios, we propose an approximation algorithm named MPF(Maximum Profit First) that can find an approximate optimal caching scheme in a reasonable time. We conduct extensive experiments on a real-world dataset, and based on the final experimental results, PMEDC-IP and MPF proposed by us significantly outperform the other three representative methods. © 2023 SPIE.

关键词： Profitability

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Thermal Infrared Image Semantic Segmentation with Lightweight Edge Assisted Context Guided Network 3

Thermal Infrared Image Semantic Segmentation with Lightweigh...

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3rd International Conference on Machine Learning and computer Application, ICMLCA 2022

作者： Wang, Futian Zheng, Yu Lv, Weijie Tang, Jin Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

ISBN: (纸本)9781510664814

Thermal power image segmentation is the key step of power equipment infrared diagnosis. Power equipment infrared diagnosis can timely and accurately diagnose the potential accidents and fault precursors of operating power equipment, which is an important part of power inspection. With the increasing popularity of mobile intelligent terminals, modern power inspection requires deploying semantic segmentation models on mobile devices. Considering the small memory capacity of mobile devices, we propose a new lightweight network architecture, called Edge-Assisted Context Guided Network (ECGNet), for semantic segmentation of thermal infrared electrical equipment images. ECGNet has been carefully designed to learn the context information of thermal infrared images and improve the problem of edge blur on the premise of small parameters and small memory consumption. Under the same number of parameters, a large number of experiments on LS-ETS dataset show that ECGNet can obtain better results than the most advanced method. © 2023 SPIE.

关键词： Semantic Segmentation

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Fuzzy intelligence Learning Based on Bounded Rationality in IoMT Systems: A Case Study in Parkinson's Disease

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IEEE Transactions on Computational Social Systems 2023年第4期10卷 1607-1621页

作者： Zhang, Chao Ding, Juanjuan Zhan, Jianming Sangaiah, Arun Kumar Li, Deyu Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University School of Computer and Information Technology Taiyuan030006 China Hubei Minzu University School of Mathematics and Statistics Enshi445000 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As a cause of interfering with routine activities, freezing of gait (FOG) is a severe syndrome of Parkinson's disease (PD) and usually performs as an abrupt and momentary inability to effective stepping forward. Advanced wearable acceleration sensors based on socially implemented Internet of medical things (IoMT) devices can remotely provide a platform for recognizing FOG. However, due to the diverse data acquisition modes that appear in classic IoMT devices, the obtained data may contain imprecise, hesitant, and incomplete ones. Meanwhile, the bounded rationality owned by neurologists usually has a big impact on using wearable acceleration sensors to predict illnesses. Therefore, the objective of this article lies in exploring a fuzzy intelligence learning approach based on bounded rationality in IoMT systems and providing a valid scheme for biomedical data analysis. Specifically, a brand-new three-way group decision-making approach by means of TODIM (an acronym in Portuguese for interactive multicriteria decision-making) with incomplete dual hesitant fuzzy (DHF) information and its applications in detecting FOG in PD using IoMT devices are systematically explored. First, taking advantage of DHF sets (DHFSs) when depicting realistic group decision information, the concept of multigranulation (MG) incomplete DHF information systems is built. Second, adjustable MG DHF probabilistic rough sets (PRSs) are further put forward via DHF similarity relations. Third, a three-way group decision-making approach is constructed by virtue of adjustable MG DHF PRSs and TODIM. Finally, the validity, effectiveness, and practicality of the constructed three-way group decision-making approach are investigated by a University of California, Irvine (UCI) dataset with several experimental analyses in the background of FOG detection in PD using IoMT devices. The experimental result indicates that the developed fuzzy intelligence learning approach achieves reasonable diagnostic conclusions

关键词： Diagnosis

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