Computers & Electrical Engineering最新文献_第7页

Open-set security authentication: A novel CAN-bus recognition algorithm based on metric learning

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-10 DOI: 10.1016/j.compeleceng.2025.110209

Caidan Zhao, Wenxin Hu, Yilin Wang, Mingxian Shi

{"title":"Open-set security authentication: A novel CAN-bus recognition algorithm based on metric learning","authors":"Caidan Zhao, Wenxin Hu, Yilin Wang, Mingxian Shi","doi":"10.1016/j.compeleceng.2025.110209","DOIUrl":"10.1016/j.compeleceng.2025.110209","url":null,"abstract":"<div><div>The CAN bus is a communication protocol that is commonly used in vehicular systems. Due to the lack of security mechanisms in controller area networks, they have become increasingly susceptible to attacks. While the physical-layer authentication can detect masquerade attacks by exploiting the uniqueness of the ECU features, such as signal voltage, to formulate the fingerprints, it suffers from low accuracy in large-scale CANs. In this paper, the study proposes an open-set physical-layer authentication scheme, which applies deep metric learning to improve the authentication accuracy for CANs with a large number of ECUs against both masquerade attacks and ranged attacks. Our scheme improves the triplet loss function to learn the latent feature representation of the known ECU signals. Experimental results verify the efficacy of our proposed scheme. The SigTLNet algorithm exhibits optimal recognition performance for both masquerade and remote attacks. It achieves average recognition rates of 99.59% for known nodes and 100% for unknown nodes during masquerade attacks, while for remote attacks, the rates are 99.30% and 100%, respectively. Additionally, experimental results reveal that SigTLNet provides shorter and more stable recognition times compared to existing algorithms.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110209"},"PeriodicalIF":4.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diverse multi-scale features absorption for lightweight object detection models in inclement weather conditions

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-10 DOI: 10.1016/j.compeleceng.2025.110221

Trung-Hieu Le , Quoc-Viet Hoang , Van-Hau Nguyen , Shih-Chia Huang

{"title":"Diverse multi-scale features absorption for lightweight object detection models in inclement weather conditions","authors":"Trung-Hieu Le , Quoc-Viet Hoang , Van-Hau Nguyen , Shih-Chia Huang","doi":"10.1016/j.compeleceng.2025.110221","DOIUrl":"10.1016/j.compeleceng.2025.110221","url":null,"abstract":"<div><div>In recent years, numerous lightweight object detection models have been introduced and successfully deployed on low-computation devices. However, these models mainly focus on detecting objects in favorable weather conditions and do not adequately account for inclement conditions, particularly in the presence of fog. This significantly leads to the drastic performance degradation of object detectors, primarily attributable to the decreased visibility. To tackle the aforementioned deficiency, we introduce a novel diverse multi-scale feature absorption network (DMFA-Net) to guide lightweight detectors work efficiently in foggy weather conditions. Our approach achieves its objective through the close collaboration of three subnetworks: a detection enhancement subnetwork, a depth mining subnetwork, and a lightweight detection subnetwork. The lightweight detection subnetwork achieves a significant accuracy improvement by absorbing and learning a range of useful features from both the detection enhancement and depth mining subnetworks through diverse multi-scale feature absorption loss. Extensive experiments demonstrate that our DMFA-Net effectively boosts baseline lightweight detectors in accurately localizing and classifying objects, without adding any computational cost. Additionally, it outperforms representative competing approaches on both synthesized and real-world foggy image datasets.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110221"},"PeriodicalIF":4.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CM-FusionNet: A cross-modal fusion fatigue detection method based on electroencephalogram and electrooculogram

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-09 DOI: 10.1016/j.compeleceng.2025.110204

Fuzhong Huang , Chunfeng Yang , Wei Weng , Zelong Chen , Zhenchang Zhang

{"title":"CM-FusionNet: A cross-modal fusion fatigue detection method based on electroencephalogram and electrooculogram","authors":"Fuzhong Huang , Chunfeng Yang , Wei Weng , Zelong Chen , Zhenchang Zhang","doi":"10.1016/j.compeleceng.2025.110204","DOIUrl":"10.1016/j.compeleceng.2025.110204","url":null,"abstract":"<div><div>Mental fatigue detection plays an important role in preventing fatigue-related diseases and reducing traffic accidents caused by mental exhaustion. In this effort, existing studies have presented interesting results by using physiological signals. However, most works focus primarily on single physiological signal like electroencephalography (EEG). To address this gap, we propose an innovative cross-modal fusion method (CM-FusionNet) and conduct a multi-modal study using EEG and electrooculogram (EOG) for mental fatigue detection. Specifically, a variance channel attention (VCA) module is introduced to adaptively learn the optimal weights for each channel. Then, a Transformer fusion module is applied to extract and integrate the global features of EEG and EOG. Finally, we classify mental fatigue using the fused features. With this method, we conduct independent and cross-subject experiments on the public SEED-VIG dataset. The results of multi-modal experiment show an average accuracy of 84.62% and F1-score of 85.25%, an increase by 1.48% in accuracy 2.46% and in F1-score compared to the EOG-only experiment, and increase by 2.88% and 3.92% compared to the EEG-only experiment, respectively. This demonstrates the benefits of incorporating multi-modalities in fatigue detection and highlights the increased accuracy achieved with our CM-FusionNet approach. It also indicates that this method has potential for further exploration in the field of biomedical signal processing.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-focus image fusion algorithm based on adaptive connection and hybrid convolution attention

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-09 DOI: 10.1016/j.compeleceng.2025.110236

Yanjie Qi, Huibin Liu, Xiaomin Ji

{"title":"Multi-focus image fusion algorithm based on adaptive connection and hybrid convolution attention","authors":"Yanjie Qi, Huibin Liu, Xiaomin Ji","doi":"10.1016/j.compeleceng.2025.110236","DOIUrl":"10.1016/j.compeleceng.2025.110236","url":null,"abstract":"<div><div>In the field of multi-focus image fusion, in order to integrate the previous and subsequent information, most existing models simply overlay shallow and deep features directly at the encoding stage, but do not fully consider the importance of each feature layer, thus limiting the performance of image fusion. A multi-focus image fusion algorithm based on adaptive connection and hybrid convolution attention is proposed. The model uses a codec structure. Firstly, in the coding stage, the hybrid convolution attention module is used to enhance the feature extraction capability of the model. The design of adaptive connection helps the model to better capture the context information of the source image, and can adaptively calculate the weight of the feature map, and superimpose the feature map according to the weight. Secondly, deformable convolution module is used in decoding phase to enhance the modeling ability of complex edge details in the focused region and improve the feature reconstruction ability of the network. The experimental results on Lytro, MFI-WHU and HBU-CVMDSP datasets show that the proposed fusion algorithm achieves better fusion effect in both subjective and objective evaluation compared with other 10 fusion algorithms.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110236"},"PeriodicalIF":4.0,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A swarm intelligence approach to minimum weight independent dominating set problem

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-09 DOI: 10.1016/j.compeleceng.2025.110222

Mohd Danish Rasheed , Alok Singh , Rammohan Mallipeddi

引用次数: 0

Optimal day-ahead scheduling and reconfiguration of active distribution systems considering energy hubs, residential demand response aggregators, and electric vehicle parking lot aggregators

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-09 DOI: 10.1016/j.compeleceng.2025.110227

Hashmatollah Nourizadeh, Mehrdad Setayesh Nazar

{"title":"Optimal day-ahead scheduling and reconfiguration of active distribution systems considering energy hubs, residential demand response aggregators, and electric vehicle parking lot aggregators","authors":"Hashmatollah Nourizadeh, Mehrdad Setayesh Nazar","doi":"10.1016/j.compeleceng.2025.110227","DOIUrl":"10.1016/j.compeleceng.2025.110227","url":null,"abstract":"<div><div>This paper presents the optimal day-ahead scheduling and reconfiguration of an active distribution system considering energy hubs, residential demand response aggregators, and electric vehicle parking lot aggregators. The main contribution of this paper is that it introduces a three-level framework for scheduling and reconfiguring active distribution systems and modeling the competition among residential demand response aggregators in the day-ahead horizon.This framework examines the flexible integrated demand response program for smart residential consumers and multi-carrier energy hubs. The study evaluates the impacts of power exchange by consumers on the profits and costs of stakeholders while minimizing operational costs, maximizing power exchange profits, and ensuring residential comfort levels. Additionally, optimal scheduling and reconfiguration of the active distribution system have been proposed. In the first level, the optimization process focuses on the residential demand response aggregators and electric vehicle parking lots, incorporating Cournot-based competition among residential aggregators and prioritizing carbon emissions reduction and fuel savings. In the second level, self-scheduling of multi-carrier energy hubs is performed to determine profits and costs. In the third level, the proposed procedure considers the contribution of lower-level agents that can be utilized for scheduling and reconfiguring the active distribution system. The effectiveness of the model was assessed using a modified IEEE 123 bus system. The simultaneous implementation of the proposed demand response program and optimal reconfiguration reduced operational costs by 5.28%. Furthermore, competition among residential demand response aggregators and the implementation of the proposed demand response program increased profits by $75.49 and $61.03, respectively.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110227"},"PeriodicalIF":4.0,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An adaptive feature fusion framework of CNN and GNN for histopathology images classification

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-08 DOI: 10.1016/j.compeleceng.2025.110186

Linhao Li , Min Xu , Shuai Chen , Baoyan Mu

{"title":"An adaptive feature fusion framework of CNN and GNN for histopathology images classification","authors":"Linhao Li , Min Xu , Shuai Chen , Baoyan Mu","doi":"10.1016/j.compeleceng.2025.110186","DOIUrl":"10.1016/j.compeleceng.2025.110186","url":null,"abstract":"<div><div>This study introduces an Adaptive Feature Fusion Classification Network (AFFC<img>Net) designed for cancer detection in histopathology images. AFFC<img>Net leverages Convolutional Neural Network (CNN) and Graph Neural Network (GNN) as parallel feature extractors, significantly improving the ability to capture complex histopathological features. The network includes an adaptive feature fusion module that weights and fuses features from the two branches using adaptive scaling factor and attention mechanisms. The fused features are subsequently utilized to construct a graph structure. The global feature aggregation unit then performs sampling and aggregation on this graph to extract high-level semantic features. Experimental results demonstrate the effectiveness of AFFC<img>Net. On the BRACS dataset, a breast cancer subtype pathology image dataset containing 4391 images, the model achieved an F1-score of 67.23 %, representing a 2.83 % improvement over previous methods. On the LC25000 dataset, a pathology image dataset of lung and colon tissues containing 25,000 images, it achieved Precision, Recall, Specificity, Accuracy, and F1-score of 99.84 %, 99.84 %, 99.96 %, 99.84 %, and 99.84 %, respectively, showing improvements of 1.36 %, 0.37 %, 0.34 %, 0.45 %, and 0.52 % compared to existing approaches. These results highlight AFFC<img>Net's capability to leverage advanced semantic features and achieve competitive performance compared to state-of-the-art methods.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110186"},"PeriodicalIF":4.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven dynamic trust management and blockchain-based security in industrial IoT

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-07 DOI: 10.1016/j.compeleceng.2025.110213

Rajesh Kumar, Rewa Sharma

{"title":"AI-driven dynamic trust management and blockchain-based security in industrial IoT","authors":"Rajesh Kumar, Rewa Sharma","doi":"10.1016/j.compeleceng.2025.110213","DOIUrl":"10.1016/j.compeleceng.2025.110213","url":null,"abstract":"<div><div>The Industrial Internet of Things (IIoT) revolutionizes industrial operations through real-time data exchange and analytics but introduces significant security and trust challenges particularly in dynamic and distributed IIoT environments. We hypothesize that integrating an AI-driven Trust Management System (TMS) with blockchain technology can address these issues effectively. This paper proposes a framework combining an AI-driven Dynamic TMS (AI-DTMS) with a private blockchain. AI-DTMS evaluates the reliability of the device and data using machine learning, achieving 96.31% accuracy with minimal false positives. The blockchain module ensures secure authentication, achieving nearly 100% success. It mitigates critical threats, including spoofing, Sybil, node-capturing, replay, and DDoS attacks, ensuring robust security in IIoT environments. Performance evaluations demonstrate 35% improvement in response time and up to 97.8% reduction in latency, underscoring scalability and efficiency. By integrating AI-DTMS with blockchain, the framework enhances trust, security, and performance in dynamic IIoT environments, offering a scalable and robust solution.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110213"},"PeriodicalIF":4.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Q-learning-based hierarchical routing protocol in underwater acoustic sensor networks

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-07 DOI: 10.1016/j.compeleceng.2025.110211

Amir Masoud Rahmani , Jawad Tanveer , Abdulmohsen Mutairi , May Altulyan , Entesar Gemeay , Mahfooz Alam , Mohammad Sadegh Yousefpoor , Efat Yousefpoor , Mehdi Hosseinzadeh

{"title":"A Q-learning-based hierarchical routing protocol in underwater acoustic sensor networks","authors":"Amir Masoud Rahmani , Jawad Tanveer , Abdulmohsen Mutairi , May Altulyan , Entesar Gemeay , Mahfooz Alam , Mohammad Sadegh Yousefpoor , Efat Yousefpoor , Mehdi Hosseinzadeh","doi":"10.1016/j.compeleceng.2025.110211","DOIUrl":"10.1016/j.compeleceng.2025.110211","url":null,"abstract":"<div><div>In order to guarantee a reliable data forwarding process, underwater acoustic sensor networks (UASNs), which are widely used in water environments like oceans and seas, need efficient routing protocols. Because sensor nodes are expensive to deploy in underwater environments and have limited energy capacities, energy optimization is a significant and practical issue, particularly for extending network lifetime. Today, various energy-efficient routing strategies have been suggested by combining opportunistic routing (OR) and reinforcement learning (RL). However, this subject deals still with different challenges. This paper introduces a Q-learning-based hierarchical routing protocol (QHRP) in UASNs. This approach builds a Q-learning-based routing tree, which contains a state set filtered by a two-step filtering process. It effectively increases the convergence speed of the Q-learning algorithm and lowers delay due to the tree construction process. In QHRP, the reward function considers network conditions and is obtained based on four metrics, namely remaining energy, strategic depth, the size of the state set, and successful transmission probability. Moreover, QHRP solves the void area problem in the routing tree by redefining the set of states and reward function. To evaluate QHRP compared to the three routing methods, namely RLOR, EE-DBR, and MURAO, various experiments are performed in terms of packet delivery rate (PDR), end-to-end delay (EED), data integrity, consumed energy, and the number of hops in the forwarding routes. These results show that QHRP improves PDR, delay, data integrity, energy consumption, and the number of hops by 9.068%, 9.03%, 9.84%, 15.61%, and 10.31%, respectively.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110211"},"PeriodicalIF":4.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-load parity-time symmetry wireless power transmission system with active directional energy transmission

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-03-07 DOI: 10.1016/j.compeleceng.2024.109980

Biao-Biao Xu, You-Long Wen, Xing-Yu Zhang, Xiao-San Ma, Mu-Tian Cheng

{"title":"Multi-load parity-time symmetry wireless power transmission system with active directional energy transmission","authors":"Biao-Biao Xu, You-Long Wen, Xing-Yu Zhang, Xiao-San Ma, Mu-Tian Cheng","doi":"10.1016/j.compeleceng.2024.109980","DOIUrl":"10.1016/j.compeleceng.2024.109980","url":null,"abstract":"<div><div>A novel multi-load Parity-Time (PT) wireless power transmission (WPT) system with active directional energy transmission is designed. Firstly, we utilize a variable capacitor array to achieve selective mutual coupling between a single transmitting coil and multiple receiving coils. Then, under the action of the control circuit, the system can prioritize charging low-capacity batteries and automatically switch charging modes after the load is charged. Furthermore, different from the existing multi-load PT-WPT system, the DC-DC converter is added to the receiving device in this design. The DC-DC converter ensures that the system can provide constant current (CC) or constant voltage (CV) charging modes depending on the load requirements. Through the above design, we have constructed a new multi-stage charging mode in the multi-load PT-WPT system. The experimental results show that the system can achieve active directional energy transfer and provide different charging modes according to the needs of the loads. Moreover, the system also exhibits features such as a wide coupling and actively switching charging modes. The use of a multi-load PT-WPT system with active directional energy transmission can provide efficient charging strategies for wireless sensor networks with multiple loads.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 109980"},"PeriodicalIF":4.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0