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Research On Fatigue Driving Detection Based On Deep Learning

engineering Letters 2025年第2期33卷 348-356页

作者： Zhou, Huinan Dai, Hong University of Science and Technology Liaoning Liaoning AnshanCO 114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning AnshanCO 114051 China

Fatigue driving is one of the main causes of traffic accidents. Effective fatigue driving detection technology can reduce traffic accidents caused by fatigue driving. Traditional fatigue driving detection methods usually use a two-stage detection method, which has the problems of low efficiency and poor accuracy. To solve these problems, this paper designs a single-stage fatigue driving detection model based on separable convolution Road Safety Net (RSNet), which extracts the features in the driver's image or video and classifies these features to determine whether the driver is in an abnormal driving state. RSNet borrows the structure of Swin Transformer and adopts a hierarchical design, which makes the model better able to extract features and classify. Each level is composed of a GDS Layer and multiple RS Blocks. To solve the problem of low efficiency of traditional methods, we use separable convolution in RS Block to replace the traditional convolutional neural network, which reduces the complexity of the model and effectively improves the computational efficiency of the model. Experimental results show that the Top-1 and Top-5 Accuracy of RSNet on the HMDB51 dataset reaches 85 % and 97.35 %, respectively, and the delay is 5.97 ms. Compared with ResNet, EfficientNet, and Swin Transformer, RSNet has higher accuracy and efficiency. In addition, this paper takes the YOLACT algorithm as an example, selects different backbone networks for object detection, and compares the indicators of mean Average Precision (mAP) and Frames Per Second (FPS). Experiments show that RSNet performs better when used as the backbone network for object detection, with mAP reaching 32.4 % and FPS of 39.6 ms. It indicates that RSNet performs well in action recognition tasks. © 2025, International Association of Engineers. All rights reserved.

关键词： Convolutional neural networks

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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science China(Information sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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A comparative assessment of OMP and MATLAB for parallel computation

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International Journal of Hybrid Intelligent Systems 2024年第1期20卷 57-66页

作者： Dash, Yajnaseni Abraham, Ajith School of Computer Science Engineering and Technology (SCSET) Bennett University India

The prime goal of parallel computing is the simultaneous parallel execution of several program instructions. Consequently, to accomplish this, the program should be divided into independent sets so that each processor can execute its program part concurrently with the other processors. This study compares OMP and MATLAB, two important parallel computing simulation tools, through the use of a dense matrix multiplication technique. The results showed that OMP outperformed the MATLAB parallel environment by over 8 times in sequential execution and 6 times in parallel execution. From this proposed method, it was also observed that OMP with an even slower processor performs much better than MATLAB with a higher processor. Thus, the present analysis indicates that OMP is a superior environment for parallel computing and should be preferred over parallel MATLAB. © 2024 - IOS Press. All rights reserved.

关键词： MATLAB matrix multiplication OMP Parallel computing

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DCL-depth: monocular depth estimation network based on iam and depth consistency loss

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Multimedia Tools and Applications 2025年第8期84卷 4773-4787页

作者： Han, Chenggong Lv, Chen Kou, Qiqi Jiang, He Cheng, Deqiang The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China

The self-supervised monocular depth estimation algorithm obtains excellent results in outdoor environments. However, traditional self-supervised depth estimation methods often suffer from edge blurring in complex textured regions and the loss of depth information in pixels within weakly-textured areas. To enhance the perception ability of the deep network for complex textured areas and the accuracy of depth estimation in weakly-textured regions, the following methods are proposed in this paper. First of all, the image activity measure (IAM) is used to segment the image features. Based on the multi-directional distribution of image contours, the network's perception ability has been improved, resulting in effective enhancement of depth estimation in complex regions. Furthermore, a new loss function called depth consistency loss (DCL) is proposed, which is based on recursive recurrent networks. The DCL aims to measure the similarity between the output images of the first-order network and the second-order network, and the network's constraint on weak-texture regions has been strengthened. By employing this approach, the accuracy of estimating depth information in weakly-textured regions can be enhanced. Through adequate experimentation on the public indoor datasets, the results show that our network outperforms the compared algorithms in terms of accuracy and visualization of predicted depth. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Complex networks

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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Core-Competitiveness in Partially Observable Networked Market 24

Core-Competitiveness in Partially Observable Networked Marke...

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33rd ACM Web Conference, WWW 2024

作者： Li, Bin Hao, Dong School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798400701719

In auction theory, a core is a stable outcome where no subgroup of participants can achieve better results for themselves. Core-competitive auctions aim to generate revenue that is achievable in a core. They are particularly important because they not only generate optimized revenue for the seller, but also provide an efficient and stable environment for participants. We generalize the design of core-competitive auctions to encompass partially observable networked markets (PONM). Unlike traditional auctions, which often deal with scenarios of limited trading activity, our approach to core-competitive auctions for PONM captures the nature of real-world transaction markets, which is a large linking world for the economic entities and commodities circulate among the entities in the market. Our generalizing the auction market to PONM can much improve the liquidity of the auction, and is especially meaningful for the web economics. Specifically, we quantify the upper and lower bounds of the minimum core revenue in PONM, and further prove that there does not exist any truthful auction for PONM which is efficient and core-competitive. Governed by this impossible result, we identify the criteria that the allocation rule for PONM should meet. Based on these criteria, we propose a new class of auction mechanisms for PONM that is individually rational, incentive-compatible, and core-competitive. © 2024 ACM.

关键词： Competition

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Cooperative Action Decision based on State Perception Similarity for Deep Reinforcement Learning

Cooperative Action Decision based on State Perception Simila...

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2024 International Conference on Cloud and Network Computing, ICCNC 2024

作者： Chen, Yitian Liu, Bo Zhang, Shigeng Liu, Xuan Central South University School of Computer Science and Engineering Changsha China Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (纸本)9798350367805

In practical applications, implementing reinforcement learning in multi-Agent environments is very important. Most of the existing multi-Agent reinforcement learning algorithms lack communication capabilities, forcing agents to learn solely from their local observations. Some reinforcement learning algorithms that include communication also limit the number of communicating individuals. We propose a multi-Agent reinforcement learning algorithm that incorporates a local communication mechanism. This algorithm does not need to limit the number of individuals with whom the agent communicates. The agent can communicate with any number of agents in each time step, and the agent can choose whether to communicate and with whom to communicate. Besides, this algorithm requires no additional communication content and only uses the information obtained by the agent from the environment. Compared with recent methods, this method makes better use of the environmental information that the agent can obtain and plays a greater role in the team. This makes the actions of multi-Agents more in line with human group actions. To verify the effect of our algorithm, we conducted experiments on several mature open-source multi-Agent reinforcement learning environments. The experimental results show that our algorithm is better than MADDPG and M3DDPG. © 2024 IEEE.

关键词： Contrastive Learning

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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

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DHAA: Distributed heuristic action aware multi-agent path finding in high density scene

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Multimedia Tools and Applications 2024年 1-19页

作者： Zhou, Dongming Pang, Zhengbin Li, Wei School of Computer Science National University of Defense Technology Deya Road Hunan Changsha413000 China School of Computer and Electronic Information Guangxi University University Road Guangxi Nanning530004 China

Multi-agent path finding (MAPF) in highly structured environments is an exciting and complex problem. Compared with lower-density environments, the problems of agent credit allocation and differentiated communication need to be effectively solved in a grid world with complex structures. Through assistance and decentralized training between agents, which local agent information is shared through communication. Therefore, this paper proposes a multi-agent path finding model for distributed heuristic behavior awareness in high-density scenes(DHAA). First, this paper uses a query mechanism to assign different weights to the agents in the neighborhood based on priority order to distinguish the importance of different information to the agents. Then, according to the differentiated communication and collision detection strategies, selective communication is carried out with agents that can change their strategies to achieve conflict avoidance. Finally, we encoder the values of the observation and state networks and input them into the deep Q network to get the following action. By embedding collision-free heuristic search paths into graph networks, which the model ability to handle long tasks is enhanced. Experiments show that the performance of the DHAA model proposed in this paper is close to the performance of the coupled centralized algorithm. We demonstrate the effectiveness of the DHAA model in high-density scenarios by exploring different benchmarks and ablation experiments. Experiments show that deep Q networks can reduce the average step size and differentiated communication can reduce bandwidth. The DHAA model also shows robustness in untrained maps, improving 23 % over the baseline model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Nanopore design of sulfur doped hollow carbon nanospheres for superior potassium-ion battery anodes

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Rare Metals 2024年第5期43卷 2103-2114页

作者： Zhen-Dong Liu Hui-Yan Feng Yu-Chen Wang Fei Wang Yue Liu Jian-Xiao Yang Yue Gu Jun Tan Chong Ye Cheng-Zhi Zhang Ji Hua Laboratory Foshan 528000China School of Chemical Engineering and Light Industry Guangdong University of TechnologyGuangzhou 510006China Hunan Province Key Laboratory for Advanced Carbon Materials and Applied Technology College of Materials Science and EngineeringHunan UniversityChangsha 410082China

Sulfur doped carbonaceous materials are promising anodes for potassium-ion batteries because of their ability to bridge active sites and induce C/S electron coupling,resulting in increased ion storage capacitance However,the large potassium ions could cause significant volume expansion and structure collapse during operation in sulfur doped carbonaceous anodes,which lead to rapidly capacity sacrifice during long-term *** design for anchoring sulfur atom in carbon skeleton is a novel way to alleviate the structure collapse and maintain the cycling ***,this study developed a controlled nanopore and sulfur doped carbon sphere structure(S-NPHCSs).In potassium-ion batteries S-NPHCSs anode demonstrated exceptional performance with a high reversible capacity of 247 mAh·g^(–1)after 50 cycles at 0.2 A·g^(–1)and delivered a long cycle stability of 600 cycles at a high current density of 1.0 A·g^(–1).Interconnected nanopores and doped sulfur structure not only expand the accumulation space and offer ample active sites for diffusion and adsorption of potassium ions,but also build stable channels through nanopore structure to ensure the cyclic *** finding provides a fundamental theory for designing nanopore structures and introducing sulfur doped carbonaceous materials to enhance capacitive potassium storage and long cycle stability.

关键词： Nanopore Carbon materials Sulfur doping Anode Potassium-ionbatteries

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