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ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green Computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Du, Bochun Qu, Long Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350351637

In the current landscape of intelligent transportation, vehicle platooning has become a key strategy for improving traffic efficiency and safety. However, as multiple platoons move at high speeds, the platoon encounters an unpredictable communication environment, which poses a major challenge for coordinated control of multi-platoon vehicles. Meanwhile, the freshness of information plays a pivotal role in ensuring road traffic safety, quantified by the Age of Information (AoI). This study focuses on a UAV-assisted platooning system in Manhattan scenarios, aiming to minimize vehicle AoI while improving the communication success rate between vehicles and UAV nodes. To tackle this challenge, we model this problem as a Partially Observable Markov Decision Process (POMDP). We propose a trajectory and scheduling strategy based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach. Additionally, to better adapt to the dynamic nature of multi-platoon vehicular networks, we integrate the Prioritized Experience Replay (PER) technique to prioritize learning and processing of critical experience samples. Through simulation experiments and comparisons with baseline methods, the superiority of the proposed algorithm is verified. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Research on the Application of AI Intelligent Model of computer Deep Learning in Natural Language Processing 4

Research on the Application of AI Intelligent Model of Compu...

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4th ieee international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Shen, Miaofang College of Information Engineering Guangzhou Vocational and Technical University of Science and Technology Guangzhou510080 China

ISBN: (纸本)9798350359589

The relationships between entities in a document are extracted according to natural language processing methods. Deep neural network is used to recognize the required multi-label text. According to the general specification, the system is optimized, and the design and implementation of the system are obtained. This project explores four major NLP modes such as ALBERT, RNN Search, BERT-CRF, Text ING based on the high-performance hardware of the Centeno platform. According to the element relation, tree structure and network structure, a general MNet construction method is proposed. The extracted correlation information is used to determine whether the matching conditions of each security requirement template are established, and then the final set of security requirement templates is screened. The extracted security requirements are modeled and instantiated in this way. Simulation results show that the model can deal with semantic dependency and human-computer interaction in complex systems. By analyzing the semantics of the operation interface in SCADA system, it is transformed into a general MNet construction, which lays a foundation for realizing the semantic analysis of users. © 2024 ieee.

关键词： data acquisition deep learning Intelligent model natural language processing system control

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An improved leader follower formation control algorithm 10

An improved leader follower formation control algorithm

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10th international conference on Mechanical engineering, Materials, and automation Technology, MMEAT 2024

作者： Wang, Gang Ding, Simin School of Mechanical Engineering Jilin Communications Polytechnic Changchun130015 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China

ISBN: (纸本)9781510682634

In response to the problem that the leader's dominant role in the leader follower formation control algorithm can easily lead to the failure of formation system control, in this paper, the artificial potential field method is used to improve the virtual leader formation algorithm to solve the formation problem. The concept of virtual navigators is used for formation control, while the artificial potential field method is used for obstacle avoidance tasks. The combination of the two effectively solves the problem of traditional formation control being prone to failure. Through comparative simulation, it is found that under the use of the improved algorithm in this paper, the formation motion trajectory is more stable, and the time for the relative position error of formation members to stabilize is shortened by 15 seconds, indicating that the formation control under the algorithm in this paper is more stable and efficient. © 2024 SPIE.

关键词： Control systems Evolutionary algorithms computer simulations Design Telecommunications Detection and tracking algorithms Error analysis Navigation systems Windows Reflection

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Decoding Visual Perception from EEG Using Explainable Graph Neural Network 46

Decoding Visual Perception from EEG Using Explainable Graph ...

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46th Annual international conference of the ieee engineering in Medicine and Biology Society, EMBC 2024

作者： Huang, Chin-Wei Su, Chien-Hui Kuo, Po-Chih National Tsing Hua University Department of Computer Science Hsinchu City Taiwan

ISBN: (纸本)9798350371499

Brain decoding is an emerging area in the fields of neuroscience and machine learning. The goal of decoding is to utilize measured brain activity to understand the thoughts or sensations of individuals. In the fields of computer vision and machine learning, Graph Neural Networks (GNNs) have demonstrated considerable success. Furthermore, the integration of attention mechanisms in these networks provides a pathway for improved model explainability. This study employs GNNs in the analysis of electroencephalography (EEG), aiming to explore how our brain handles visual information and uncover functional brain networks. We utilize GNNExplainer, a tool designed for GNN interpretation, to pinpoint critical EEG channels and their interconnections relevant to visual EEG tasks. Our findings, which align with existing neuroscience literature, underscore the significance of specific channels. This implies that GNNs have the capability to provide valuable insights within the field of neuroscience research. © 2024 ieee.

关键词： Graph neural networks

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Comparative Study of Techniques for Congestion Avoidance & Control in Wireless Body Area Networks

Comparative Study of Techniques for Congestion Avoidance & C...

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2024 international conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Jakhar, Isha Vijraniya, Meenu Singh, Hitesh Kumar, Vivek K R Mangalam University Computer Science Department Gurugram India Noida Institute Of Engineering & Technology Computer Science Department Noida India

ISBN: (纸本)9798350366846

In past few years, considerable amount of research has been done in wireless sensor networks which have helped to discover the potential of wireless sensor networks (WSN) in many areas such as military, environment, communication, healthcare etc. Major improvements have been noted in healthcare having prominent discoveries in wireless body area networks (WBAN). WBANs help in monitoring the health of the patients remotely and prove to be a very economical solution for healthcare providers as well as patients by gathering biological data and transmitting it to the base station or the healthcare providers. Along with benefits, come various limitations of WBANs like coverage area, data security, congestion, scalability etc. Congestion has become one of the major issues in healthcare and if not addressed in time, can lead to critical packets not being sent and delay in receiving critical information which can prove harmful for the patient. In this paper, an extensive study has been done to study about various protocols developed to detect, avoid and mitigate congestion in order to provide seamless and timely delivery of services. © 2024 ieee.

关键词： Wireless sensor networks

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Lightweight Semantic Conflict Detection with Static Analysis 24

Lightweight Semantic Conflict Detection with Static Analysis

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46th international conference on Software engineering: Companion, ICSE-Companion 2024

作者： De Jesus, Galileu Santos Borba, Paulo Bonifácio, Rodrigo De Oliveira, Matheus Barbosa Informatics Center Federal University of Pernambuco Recife Brazil Computer Science Department University of Brasilia Brasilia Brazil

ISBN: (纸本)9798400705021

Version control system tools empower developers to independently work on their development tasks. These tools also facilitate the integration of changes through merging operations, and report textual conflicts. However, during the integration of changes, developers might encounter other types of conflicts that are not detected by current merge tools. In this paper, we focus on dynamic semantic conflicts, which occur when merging reports no textual conflicts but results in undesired interference-causing unexpected program behavior at runtime. To address this issue, we propose a technique that explores the use of static analysis to detect interference when merging contributions from two developers. We evaluate our technique using a dataset of 99 experimental units extracted from merge scenarios. The results provide evidence that our technique presents significant interference detection capability (F1 Score of 0.50 and Accuracy of 0.60). © 2024 ieee computer Society. All rights reserved.

关键词： Static analysis

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Infrared Image Captioning Based on Unsupervised Learning and Reinforcement Learning

Infrared Image Captioning Based on Unsupervised Learning and...

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2022 international conference on automation, Robotics and computer engineering, ICARCE 2022

作者： Gao, Chenjun Bian, Ganghui Dong, Yanzhi Yuan, Xiaohu Liu, Huaping Yantai University School of Physics and Electronic Information Yantai China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9781665475488

When sufficient prior knowledge is lacking or manual annotation is difficult, solving the problem directly based on training samples of unknown category can greatly reduce the time cost. Therefore, we add unsupervised learning to the preliminary groundwork of image captioning for efficient image domain conversion to achieve batch generation of the required images. At the same time, more and more infrared images are being applied to assist decision making and environment perception. Generating more diverse and discriminative image captions in similar scenes will be effective in enhancing decision making and perception capabilities. Our infrared image caption model trained with reinforcement learning has satisfactory results both in terms of quantitative scores and in real scene tests. © 2022 ieee.

关键词： Reinforcement learning

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Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

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ieee Transactions on automation science and engineering 2024年第3期21卷 4829-4850页

作者： Liu, Anbang Luh, Peter B. Sun, Kailai Bragin, Mikhail A. Yan, Bing Department of Automation and BNRist Beijing100084 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tsinghua University Chaired Professors Group Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States

Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view of the increasing demand for customized products, problem sizes are growing. A promising direction is to take advantage of Machine Learning (ML). Direct learning to predict solutions for job-shop scheduling, however, suffers from major difficulties when problem scales are large. In this paper, a Deep Neural Network (DNN) is synergistically integrated within the decomposition and coordination framework of Surrogate Lagrangian Relaxation (SLR) to predict good-enough solutions for subproblems. Since a subproblem is associated with a single part, learning difficulties caused by large scales are overcome. Nevertheless, the learning still presents challenges. Because of the high-variety nature of parts, the DNN is desired to be able to generalize to solve all possible parts. To this end, our idea is to establish 'surrogate' part subproblems that are easier to learn, develop a DNN based on Pointer Network to learn to predict their solutions, and calculate the solutions of the original part subproblems based on the predictions. Moreover, a masking mechanism is developed such that all the predictions are feasible. Numerical results demonstrate that good-enough subproblem solutions are predicted in many iterations, and high-quality solutions of the overall problem are obtained in a computationally efficient manner. The performance of the method is further improved through continuous learning. Note to Practitioners - Scheduling is important for the planning and operation of job shops, and high-quality schedules need to be obtained quickly at the beginning of each shift. To take advantage of ML, in this paper, a DNN is integrated within our recent decomposition and coordination approach to learn to predict 'good-enough' solutions to part subproblems. To be able t

关键词： Deep neural networks

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GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network

GHVC-Net: Hypervolume Contribution Approximation Based on Gr...

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2024 ieee international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wu, Guotong Nan, Yang Shang, Ke Ishibuchi, Hisao Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665410205

This paper proposes a framework called GHVC-Net that uses the graph neural network (GNN) model to approximate each solution's hypervolume contribution (HVC). GHVC-Net is permutation invariant and can handle solution sets of arbitrary size, similar to the properties of GNN. Compared to HVC-Net (i.e., a machine learning model for HVC approximation), GHVC-Net achieves better accuracy with less training time. GHVC-Net is also compared with traditional approximation methods, such as line-based and point-based methods, to demonstrate its ability to identify the solution with the smallest (largest) HVC. © 2024 ieee.

关键词： Neural network models

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Study on the Polarized Light Transmission Characteristics Under Complex Sea Fog Environment 4

Study on the Polarized Light Transmission Characteristics Un...

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4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Liu, Peng Qin, Yuze Duan, Jin Electronics and Information Engineering Institute Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

Aiming at the polarized light transmission characteristics under complex sea fog environment, the sea fog is divided into two layers of salt fog layer and water fog layer. Firstly, the simulation model of the corresponding layer is established based on Monte Carlo and T-matrix method, and the scattering characteristics of the particles between the two layers are linked by the multiple addition cumulative method;secondly, an indoor semi-physical simulation experiment platform was built to explore the effects of different optical thickness, incident wavelengths and polarization states on the transmission characteristics of polarized light;finally, the simulation and experimental results are compared and analyzed. The results show that when the incident light has a longer wavelength, it decreases more slowly, i.e. polarized light with a longer wavelength retains more polarization information after transmission;linearly polarized light decreases faster compared to circularly polarized light, i.e., circularly polarized light has better polarization preservation. This study provides a theoretical basis for the study of polarized light transmission characteristics under complex sea fog systems. © 2024 ieee.

关键词： Light transmission

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