Computers & Electrical Engineering最新文献

{"title":"Study on the Spacing between Movable Bus Stops and Signalized Intersections under Cooperative Vehicle Infrastructure Environment","authors":"Rui Li ,&nbsp;Tianjing Qi ,&nbsp;Xin Xue ,&nbsp;Le Gu ,&nbsp;Tao Chen","doi":"10.1016/j.compeleceng.2025.110278","DOIUrl":"10.1016/j.compeleceng.2025.110278","url":null,"abstract":"<div><div>Bus stops and signalized intersections are bottlenecks in urban road traffic, and their combined effect can exacerbate congestion in these areas. This study aims to address this bottleneck by proposing the use of a roadside movable bus stop within a cooperative vehicle infrastructure environment. Therefore, this paper studies the spacing between bus stops upstream and signalized intersections in a cooperative vehicle infrastructure environment. The study first analyzes the interaction between signal intersection and bus stop, then enhances the variable time-interval safety distance strategy through integration with the cooperative vehicle infrastructure environment. It then establishes vehicle operation rules for regular road sections, bus stop sections, and intersection areas and develops a simulation environment. Finally, the traffic flow characteristics under the combined configuration are analyzed, and reasonable recommendations are provided for setting the movable stop spacing upstream of intersections within the cooperative vehicle infrastructure environment. The results show that the connected vehicles can improve the traffic efficiency of the signalized intersection-stop section, and the intersection-stop spacing needs to be flexibly adjusted according to the traffic level, the penetration rate of connected vehicles, and bus dwell time. This paper provides a theoretical basis for urban bus stop planning in the cooperative vehicle infrastructure environment.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110278"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760344","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}

{"title":"Adaptive composing augmentations on multi-modal graph convolutional network for disease prediction","authors":"Longfei Han ,&nbsp;Mengzhen Wang ,&nbsp;Xiangsen Zhang ,&nbsp;Wenxin Li ,&nbsp;Haisheng Li ,&nbsp;Xiankai Huang","doi":"10.1016/j.compeleceng.2025.110277","DOIUrl":"10.1016/j.compeleceng.2025.110277","url":null,"abstract":"<div><div>Graph Convolutional Networks (GCNs) have demonstrated significant success in population-based disease prediction. With the rise of multimodal technologies, multimodal GCNs integrate information from diverse data types, enhancing prediction accuracy, particularly in the fusion of imaging and non-imaging data. However, constructing a reliable population graph from limited multimodal data may result in poor generalization performance. To address this issue, we introduce graph contrastive learning as a multimodal data augmentation strategy, which reinforces the graph structure’s robustness to disturbances. We propose an Adaptive Composing Augmentation framework that first employs a learnable similarity network to iteratively compute node confidence. Subsequently, the framework selectively perturbs edges of lesser importance within the graph through methods such as edge removal and edge weight permutation. Extensive experiments on three challenging medical datasets demonstrate that our method achieves state-of-the-art performance, including an accuracy (ACC) of 87.95% and area under the curve (AUC) of 90.05% on the ABIDE dataset. These results significantly outperform the baseline models, with improvements of 7.12% and 5.07%, and surpass existing methods by 6.2% and 4.83%, respectively. This confirms that contrastive learning with structured augmentations effectively enhances the generalization ability of multimodal GCNs. The code is avaliable at <span><span>https://github.com/drafly/ACA-GCN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110277"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767177","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}

{"title":"Intelligent management of waste bins in indoor public places based on waste detection and recognition","authors":"Yangke Li,&nbsp;Xinman Zhang","doi":"10.1016/j.compeleceng.2025.110298","DOIUrl":"10.1016/j.compeleceng.2025.110298","url":null,"abstract":"<div><div>Due to the lack of convenient waste bins nearby, many people choose to litter indiscriminately in indoor public places. Dirty floors not only diminish the shopping experience of customers, but also increase potential risks to pedestrian safety. For the intelligent management of waste bins in indoor public places, this paper proposes a novel solution based on automatic waste detection and recognition, which helps to minimize littering and improve the recycling efficiency of indoor waste. This solution mainly uses a deep learning model to detect and recognize waste items, which can effectively record the distribution of waste and provide a basis for the reasonable placement of waste bins. Specifically, we construct a high-quality indoor waste image dataset for waste detection, which can provide an effective data source for model optimization of this task. This dataset is collected from three common public places, including hospitals, supermarkets, and subway stations. At the same time, it contains four waste categories, 4000 color images, and 6968 box-level annotations. In addition, we propose a novel feature decoupling network for indoor waste detection and recognition, which disentangles specific features for different vision tasks. On the one hand, it uses a recognition enhancement module to generate discriminative feature maps with more semantic information. On the other hand, it introduces a detection enhancement module to output rich feature maps with more detail information. As plug-and-play modules, these two modules are suitable for different networks. Relevant experimental results show that our method is competitive with other representative object detection models and can achieve consistent performance improvements on distinct models. In general, our solution provides new insights into indoor waste bin management.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110298"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738043","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}

{"title":"Evaluating sustainable wind energy sources with multiple criteria decision-making (MCDM) techniques","authors":"Satyabrata Dash ,&nbsp;Sujata Chakravarty ,&nbsp;Nimay Chandra Giri ,&nbsp;Rohit Khargotra","doi":"10.1016/j.compeleceng.2025.110285","DOIUrl":"10.1016/j.compeleceng.2025.110285","url":null,"abstract":"<div><div>Rural regions with complex topographical constraints face significant challenges in implementing sustainable wind energy solutions due to variations in wind resource availability, infrastructure limitations, and policy gaps. To address this issue, this study integrates Multiple Criteria Decision Making (MCDM) techniques to systematically evaluate and prioritize various wind energy alternatives, considering technical, economic, environmental, and social factors. The PAPRIKA (Potentially All Pairwise RanKings of all possible Alternatives) method is employed to rank wind energy systems based on key criteria such as Capacity Factor, Environmental Impact, and Policy Framework. The findings indicate that Onshore Wind Turbines emerge as the most optimal solution (score: 69.9) due to superior energy production and cost-effectiveness (LCOE). Vertical Axis Wind Turbines (66.5) and Hybrid Wind Systems (60.8) follow, demonstrating balanced performance. Offshore Wind Turbines and Wind Farms with storage show promise but face grid integration and policy challenges, while Floating and Micro Wind Turbines rank lowest due to resource constraints. This research underscores the role of MCDM in integrating quantitative and qualitative assessments, providing a structured framework for energy planners and policymakers to make informed decisions. By optimizing wind energy deployment in rural settings, the study contributes to achieving Sustainable Development Goals (SDGs) 7, 9, and 13, fostering a resilient, low-carbon, and inclusive energy transition.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110285"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737772","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}

{"title":"Intelligent jamming decision-making system based on reinforcement learning","authors":"Xia Xiong ,&nbsp;Shengbo Hu ,&nbsp;Tingting Yan ,&nbsp;Zehua Xing ,&nbsp;Tianle Ma ,&nbsp;Kangjun Yin ,&nbsp;Jianbo Wang ,&nbsp;Xu Wei","doi":"10.1016/j.compeleceng.2025.110288","DOIUrl":"10.1016/j.compeleceng.2025.110288","url":null,"abstract":"<div><div>Cognitive communication countermeasures have increasingly been emphasized as an important research interests in cognitive electronic warfare. However, the low signal-to-noise ratio (SNR) and frequency hopping (FH) in communication countermeasures create significant difficulties for spectrum sensing and jamming decision-making. In this paper, an intelligent jamming decision-making system for FH communication is designed based on an improved deep Q- network (DQN). First, a spectrum sensing method utilizing a bidirectional long short-term memory (Bi-LSTM) network is introduced, which establishes the received signals as a binary hypothesis testing model and employs the Bi-LSTM network for signal classification. Second, the jamming channel selection problem is modeled as a Markov decision process (MDP), and an improved DQN algorithm is applied to facilitate intelligent decision-making for jamming channels. Finally, simulation experiments are conducted to evaluate the performance of the algorithms. The results show that the proposed Bi-LSTM network achieves a detection probability of over 88% even in low-SNR communication countermeasure environments at <span><math><mrow><mo>−</mo><mn>12</mn></mrow></math></span> dB. Furthermore, the improved DQN algorithm achieves a 100% channel jamming rate and the fastest convergence speed among the five compared algorithms, effectively learning the FH sequences and implementing jamming.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110288"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760342","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}

{"title":"An effective IDS using CondenseNet and CoAtNet based approach for SDN-IoT environment","authors":"Dimmiti Srinivasa Rao,&nbsp;Ajith Jubilson Emerson","doi":"10.1016/j.compeleceng.2025.110305","DOIUrl":"10.1016/j.compeleceng.2025.110305","url":null,"abstract":"<div><div>A developing technology called the Internet of Things (IoT) allows smart objects to interact over various heterogeneous channels, whether wired or wireless. However, for traditional networks, effectively controlling and managing the data flows of many devices has become difficult. Software-defined networking (SDN) provides a solution to this problem. It has attempted to address several IoT issues, such as flexibility, diversity, and intricacy, because it is programmable, adaptable, fast, and provides a broad overview of the network. As a result, the system for attack detection and mitigation presented in this research leverages deep learning techniques to analyze SDN logs. Once the attack detection process has begun, the input data can be preprocessed using various techniques to replace missing values and prepare the data for additional processing. Subsequently, CondenseNet and Osprey Optimization Algorithm (OOA) are utilized for feature extraction and selection to identify more noteworthy characteristics. Lastly, the very accurate attack prediction is provided by the CoAtNet-based classifier, which is in charge of identifying intrusions. An efficient mitigation procedure was carried out to shield the network from attack after intrusion detection. Furthermore, a conditional tabular generative adversarial network is used to augment the data and correct class imbalance. To validate our proposed model, we conducted research and testing on InSDN, Bot-IoT, and IoT-23 datasets and achieved 99.74 %, 99.61 %, and 99.64 % accuracy, respectively. These experimental findings demonstrate that the suggested framework performs better than current state-of-the-art systems, achieving higher accuracy and a lower false alarm rate.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776691","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}

{"title":"Internet of Things-enabled unmanned aerial vehicles for real-time traffic mobility analysis in smart cities","authors":"Murat Bakirci","doi":"10.1016/j.compeleceng.2025.110313","DOIUrl":"10.1016/j.compeleceng.2025.110313","url":null,"abstract":"<div><div>In modern traffic monitoring and mobility analysis, unmanned aerial vehicles (UAVs) have proven to be invaluable, overcoming the limitations of stationary surveillance cameras by offering dynamic, adaptable coverage. However, the full computational and communication potential of UAVs remains largely untapped in existing studies. This research presents an advanced UAV-based traffic monitoring system, integrating real-time image processing and Internet-of-Things (IoT)-enabled data transmission for enhanced mobility assessment. The UAV platform incorporates a high-performance neural accelerator for onboard image processing and IoT-compatible communication modules, transforming it into an autonomous, intelligent, and highly efficient traffic analysis tool. By leveraging the YOLOv8n object detection algorithm, the UAV achieves an 88% average success rate in real-time vehicle detection, enabling precise spatial mobility mapping along predefined flight routes. A comparative analysis was conducted against the latest YOLO variants, including YOLOv9t, YOLOv10n, and YOLOv11n, demonstrating that YOLOv8n provides the best trade-off between accuracy and real-time processing efficiency for UAV-based mobility monitoring. Unlike traditional methods that rely on batch processing, this system facilitates immediate data transmission to relevant regulatory bodies, and IoT networks, enabling responsive traffic management and decision-making. The study also underscores the transformative potential of UAVs as mobile computing and communication platforms, advocating for their broader adoption in real-time traffic mobility analysis within smart city infrastructures.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110313"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783632","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}

{"title":"Three-phased multi-scale residual-dense modified-U-Net architecture for deep image steganography","authors":"Suyash Sachdeva ,&nbsp;Ujjwal Sharma ,&nbsp;Priyanshu Rajput ,&nbsp;Riya Singhal ,&nbsp;K. Madhu Kiran ,&nbsp;Rohit Dhiman","doi":"10.1016/j.compeleceng.2025.110299","DOIUrl":"10.1016/j.compeleceng.2025.110299","url":null,"abstract":"<div><div>Recent advancements in deep image steganography have shown promise, yet many existing approaches fail to address the inherently lossy nature of neural networks, limiting their effectiveness. To overcome this limitation, we propose a novel architecture that integrates residual dense multi-scale JBs and FEBs within a three-phased U-Net framework, composed of three interconnected sub-networks: the secret encoder, encoder, and decoder. This modular design enhances data encoding and facilitates easy adaptation to different datasets, offering greater flexibility. By incorporating multi-scale processing to minimize information loss and leveraging high inter-connectivity to improve feature hiding. Additionally, a custom loss function, combining seven distinct components, is employed to guide the model’s learning process. The effectiveness of this architecture is demonstrated on CIFAR-10, CIFAR-100, and CelebA datasets, where it achieves an average improvement of 4.76 in peak signal-to-noise ratio (PSNR) and 0.0292 in structural similarity index measure (SSIM), which highlight the potential proposed architecture to elevate the field of image steganography.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110299"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738040","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}

{"title":"An interactive multiobjective energy management approach for grid-connected microgrids","authors":"Anjali Mohan,&nbsp;Reena Khanna,&nbsp;Karthik Thirumala,&nbsp;G. Saravana Ilango","doi":"10.1016/j.compeleceng.2025.110310","DOIUrl":"10.1016/j.compeleceng.2025.110310","url":null,"abstract":"<div><div>This paper presents a novel <em>interactive</em> Fuzzy-based Hybrid Augmented ε-Constraint and Compromise Programming (F-AUGMECON-CP) for the energy management of AC microgrid (MG). The considered multi-objective optimization (MOO) paradigm aims to minimize operating costs, greenhouse gas emissions, and grid dependency of the MG. The proposed work introduces a coordinated approach to sequentially incorporate the MG operator's weight preference (MGOs-P) for the three objectives and execute the MOO problem. This is done via three steps: (i) Fuzzy logic-based battery scheduling by developing three fuzzy inference systems to handle the uncertainties in forecasted parameters (ii) Generation of a non-dominated pareto-front considering the MGOs-P of the objectives using the AUGMECON-CP approach (multi-objective decision-making technique) (iii) Selection of the best pareto-optimal solution (POS) using VIKOR approach (multi-criteria decision-making technique). Four case studies investigate the impact of varying the MGOs-P of the objectives. The efficacy of the <em>interactive</em> approach is corroborated by conducting a comparative analysis with two other approaches: <em>posteriori</em> (Fuzzy-based AUGMECON) and <em>priori</em> (Fuzzy-based CP). The proposed approach presents an improved precision of the best POS obtained, reduced percentage deviation of objectives from individual optimum points, and the least computational complexity, making it a highly robust and adaptable method for any considered MOO problem.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110310"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767178","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}

{"title":"An effective Multi-Modality Feature Synergy and Feature Enhancer for multimodal intent recognition","authors":"Yichao Xia ,&nbsp;Jinmiao Song ,&nbsp;Shenwei Tian ,&nbsp;Qimeng Yang ,&nbsp;Xin Fan ,&nbsp;Zhezhe Zhu","doi":"10.1016/j.compeleceng.2025.110301","DOIUrl":"10.1016/j.compeleceng.2025.110301","url":null,"abstract":"<div><div>Multimodal intent recognition is a critical task that aims to accurately capture and interpret a user’s true intentions by integrating various sensory inputs such as facial expressions, body language, and vocal emotions. In complex and dynamic real-world multimodal interaction scenarios, deepening the understanding of human language and behavior becomes essential. Although multimodal data is rich in information, enhancing the representation of data features and efficiently integrating multimodal information to improve intent recognition performance remains a significant technical challenge. To address the aforementioned issue, a Video Feature Enhancer (VFE) module, combined with a Multi-Modality Feature Synergy (MFS) method, is proposed. The Video Feature Enhancer module employs a feature-weighting strategy based on energy optimization, along with an attention mechanism across channel spaces, to enhance the representational capability of video features. The Multi-Modality Feature Synergy method uses multi-level textual feature guidance and multimodal association learning to effectively integrate and optimize the feature representations of video and audio modalities. The Multi-Modality Feature Synergy method also suppresses non-essential information, facilitating the fusion of complementary information across different modalities, ultimately improving multimodal intent recognition performance. In the experimental evaluation, significant performance improvements are demonstrated over existing state-of-the-art methods on two benchmark datasets. On the MIntRec dataset, accuracy (ACC) is improved by 0.6%, weighted F1 score (WF1) by 1.21%, and weighted precision (WP) by 1.7%, while recall (R) increases by 1.8%. On the MELD-DA dataset, a 0.9% improvement in ACC is achieved, a significant increase of 1.15% in WF1 and 1.34% in WP, and also a 0.21% improvement in R is shown. Furthermore, through ablation studies, the substantial contributions of both the Video Feature Enhancer module and the Multi-Modality Feature Synergy method are validated in enhancing modality-specific feature representations and improving intent recognition accuracy.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110301"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760343","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}