计算机科学最新文献

{"title":"Crime prediction against women in surveillance videos using deep learning models: A review","authors":"Kanchan Ganesh Dhuri ,&nbsp;Sunita Patil","doi":"10.1016/j.compeleceng.2025.110651","DOIUrl":"10.1016/j.compeleceng.2025.110651","url":null,"abstract":"<div><div>In recent years, violence against women has gained prominence in Indian debate. Ensuring secure public transportation environments is crucial for enhancing women’s active engagement in the economic, educational and social domains. Although various studies have been undertaken on the issue in recent years, the majority of present research lacks substantial in-depth analysis. Furthermore, a notable shortcoming found in these studies is the insufficient attention paid to the possibility of cyber-attacks on security video systems. This study provides a detailed overview of recent advances in deep learning-based models for identifying antisocial conduct against women in surveillance films. It assesses cutting-edge approaches and publicly available audio-visual datasets used in model training, with a particular emphasis on their relevance to real-world transportation scenarios. The study investigates the function of intelligent video surveillance systems and the integration of multi-modal data in crime detection. Important issues are noted, including model generalizability, dataset constraints and ethical considerations. The results offer crucial insights into the design of reliable, automated safety systems, making them a fundamental resource for academics and system developers. This study impacts improving public safety and lowering gender-based violence by providing guidance for the creation of scalable and effective crime detection mechanisms. It establishes the foundation for upcoming advancements in deep learning used in public transit security and urban surveillance.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110651"},"PeriodicalIF":4.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049327","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":"Dynamic mask network based on spiking neural convolutional model for missing modality brain tumor segmentation","authors":"Junjie Li,&nbsp;Rui Cai,&nbsp;Bing Li,&nbsp;Hong Peng","doi":"10.1016/j.engappai.2025.112215","DOIUrl":"10.1016/j.engappai.2025.112215","url":null,"abstract":"<div><div>Brain tumor segmentation is a medical image processing task aimed at accurately locating and isolating tumor regions from brain scan images (e.g., Magnetic Resonance Imaging, MRI) in order to help doctors in diagnosis, treatment planning and surgical navigation. Automatic brain tumor segmentation is extremely challenging due to incomplete feature representation in the case of missing modalities and insufficient inter-modal information interaction. To this end, this paper proposes a novel dynamic threshold mask Transformer network for the missing modality brain tumor segmentation task, which is designed based on the nonlinear spiking neural convolutional model. The network consists of four independent encoders and a shared decoder to extract the features of each modality and perform shared representation learning. Among them, the dynamic threshold mask Transformer introduces learnable embedding vectors, generates dynamic masks on top of static masks to achieve fine-grained feature filtering, and enhances the ability of inter-modal information interaction. The adaptive gating weighting module and the channel cross spiking neural P attention module fuse modal features layer by layer in both spatial and channel dimensions to strengthen the modeling capability of local and global features. We conducted extensive comparative experiments on different missing modal cases in the BraTS2020 and BraTS2018 datasets. The experimental results show that the method effectively improves the robustness of missing modalities and the performance of brain tumor segmentation while maintaining the computational efficiency, and has good generalization ability and practicality.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"161 ","pages":"Article 112215"},"PeriodicalIF":8.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Time-divisioned joint bi-static sensing and MU-MIMO communications","authors":"Enhao Wang,&nbsp;Yun Xiao,&nbsp;Yunfei Chen,&nbsp;Aissa Ikhlef,&nbsp;Hongjian Sun","doi":"10.1016/j.phycom.2025.102834","DOIUrl":"10.1016/j.phycom.2025.102834","url":null,"abstract":"<div><div>Joint sensing and communications (JSAC) has been regarded as a key technology in the sixth-generation (6G) wireless communications system. Most existing works on JSAC consider a mono-static setting with a single user. In this work, a multi-user multiple-input multiple-output (MU-MIMO) JSAC system using bi-static sensing with time division is studied. Multi-user interference degrades communications but improves sensing. Both uplink and downlink are investigated. Time division is employed to avoid interference between communications and sensing. A tradeoff measure is optimized with respect to the number of users and time division parameter. Numerical results show that the optimum number of users and time division coefficient exist to maximize both channel capacity for communications and detection probability for sensing.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102834"},"PeriodicalIF":2.2,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Road surface damage detection based on enhanced YOLOv8","authors":"Wenjin Chen ,&nbsp;Jia Sheng Yang ,&nbsp;Chenbo Xia ,&nbsp;Yaosong Li ,&nbsp;Xu Xiao","doi":"10.1016/j.compind.2025.104363","DOIUrl":"10.1016/j.compind.2025.104363","url":null,"abstract":"<div><div>The Road Damage Detection System (RDDS) is crucial in intelligent transportation networks, enhancing driving safety, comfort, and overall traffic efficiency. A key factor in the system's performance is the effectiveness of the underlying detection algorithm. Currently, the YOLOv8 algorithm is widely applied in defect detection, but it faces challenges due to the varying scales of road damage. Specifically, the convolutional downsampling module in the backbone network often has a limited receptive field, reducing its ability to capture global information, while the multi-scale feature fusion network may lose critical local defect details and deep location information. These limitations hinder YOLOv8’s performance in detecting pavement defects. To address these issues, we propose an enhanced algorithm, YOLOv8 with Context Capture and Slimneck Structure (YOLOv8-CCS), which targets multi-scale defect characteristics and the prevalence of small-sized targets in road damage detection. To overcome the limited receptive field and improve global context awareness, we have integrated an enhanced context-guided module downsampling component (E-ContextGuidedBlock_Down), which expands the receptive field and improves context capture. Additionally, we replace the existing multi-scale fusion network with Ghost Shuffle Convolution (GSConv)-Slimneck and introduce the Enhanced VoVNet-based Ghost Shuffle Cross Stage Partial (VoVGSCSP-E) module in specific layers. To further enhance feature extraction and minimize information loss during fusion, we incorporate the Content-Aware ReAssembly of Features (CARAFE) upsampling module and a weighted feature fusion method. Finally, the Multi-Level Context Attention Bottleneck (MLCABOT) module is added between the backbone network and the multi-scale feature fusion network, improving the connectivity and overall feature extraction capability. In validation, our proposed method outperformed YOLOv8 by 3 %, 4.7 % and 3.8 % on the RDD-2022, ROAD-MAS and Unmanned Aerial Vehicle Asphalt Pavement Distress Dataset (UAPD) datasets, respectively. It also achieved the highest F1 score among comparable detection models and ranked among the top three in inference speed. These results highlight the potential of YOLOv8-CCS for real-time road damage detection, providing a more accurate and comprehensive solution for urban pavement management. Such a system, equipped with an advanced detection algorithm, can significantly improve road maintenance efficiency and enhance driving safety.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104363"},"PeriodicalIF":9.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Quantify the joint effect of mobility and urban environment on computation offloading to multi-UAV MEC network: Sojourn time","authors":"Basheer Ameen Raddwan ,&nbsp;Ibrahim Ahmed Al-Baltah","doi":"10.1016/j.adhoc.2025.104019","DOIUrl":"10.1016/j.adhoc.2025.104019","url":null,"abstract":"<div><div>The integration of Multi-Access or Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) offers transformative prospects for 5G/6G networks, especially with compute offloading in urban settings. The combined effect of the mobility of airborne MECs (airMECs) and urban environmental dynamics has not been explored in current research, which has shown that the optimization strategy may be impractical in densely populated areas due to the restricted computational resources available on airMECs. In this paper, we analyze the combined mobility of users and airMECs while considering urban dynamics constraints to quantify sojourn time, which serves as a valuable input for mobility characterization in planning. We introduce novel analytical and statistical approaches to quantify sojourn time for both omnidirectional and directional antenna scenarios. We develop the statistical approach by simulating three-dimensional mobility in numerous urban configurations and gather ray-tracing line-of-sight data for two scenarios. The first scenario excludes buildings to verify the statistical approach against the analytical one, whereas the second scenario includes buildings to assess their influence. Following the quantification of the suggested environment-dependent sojourn time, we present a stochastic task size quantification as an application example. Additionally, we present many evaluations to ensure the accuracy and the practicality of the proposed models. For example, we analyze the circular directionality of sojourn time using the Von Mises probability distribution, examining task sizes with offloading time, and assessing handover times for a designated task size. The findings indicate that environmental dynamics significantly influence sojourn time and computational offloading, necessitating explicit consideration in airMEC network planning and MEC application design. This study offers critical insights for developing resilient, adaptive networks that support computation-intensive applications in dynamic urban environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104019"},"PeriodicalIF":4.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050133","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":"DroidReach++: Exploring the reachability of native code in android applications","authors":"Luca Borzacchiello ,&nbsp;Matteo Cornacchia ,&nbsp;Davide Maiorca ,&nbsp;Giorgio Giacinto ,&nbsp;Emilio Coppa","doi":"10.1016/j.cose.2025.104657","DOIUrl":"10.1016/j.cose.2025.104657","url":null,"abstract":"<div><div>Modern Android applications often incorporate numerous native C/C++ libraries to efficiently handle CPU-intensive tasks or interact at a low level with specific hardware, such as performing specialized GPU rendering. Recent research on Android security has revealed that these libraries are frequently adopted by third-party developers and may pose security risks if not regularly updated, as publicly disclosed vulnerabilities in outdated libraries can be exploited by malicious actors. To determine whether these known vulnerabilities represent an immediate and tangible threat, it is essential to assess whether the vulnerable functions can be executed during application runtime – a research problem commonly known as <em>function reachability</em>. In this article, we introduce <span>DroidReach++</span>, a novel static analysis approach for evaluating the reachability of native function calls in Android applications. Our framework overcomes the limitations of existing state-of-the-art methods by combining heuristics with symbolic execution, enabling a more precise reconstruction of Inter-procedural Control-Flow Graphs (ICFGs). When applied to the top 500 applications from the Google Play Store, <span>DroidReach++</span> identifies a significantly higher number of execution paths compared to previous techniques. Finally, two case studies demonstrate how <span>DroidReach++</span> serves as an effective tool for vulnerability assessment.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"159 ","pages":"Article 104657"},"PeriodicalIF":5.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Towards the future of pedestrian–AV interaction: Human perception vs. LLM insights on Smart Pole Interaction Unit in shared spaces","authors":"Vishal Chauhan ,&nbsp;Anubhav ,&nbsp;Chia-Ming Chang ,&nbsp;Xiang Su ,&nbsp;Jin Nakazato ,&nbsp;Ehsan Javanmardi ,&nbsp;Alex Orsholits ,&nbsp;Takeo Igarashi ,&nbsp;Kantaro Fujiwara ,&nbsp;Manabu Tsukada","doi":"10.1016/j.ijhcs.2025.103628","DOIUrl":"10.1016/j.ijhcs.2025.103628","url":null,"abstract":"<div><div>As autonomous vehicles (AVs) reshape urban mobility, establishing effective communication between pedestrians and self-driving vehicles has become a critical safety imperative. This work investigates the integration of Smart Pole Interaction Units (SPIUs) as external human–machine interfaces (eHMIs) in shared spaces and introduces an innovative approach to enhance pedestrian–AV interactions. To provide subjective evidence on SPIU usability, we conduct a group design study (“Humans”) involving 25 participants (aged 18–40). We evaluate user preferences and interaction patterns using group discussion materials, revealing that 90% of the participants strongly prefer real-time multi-AV interactions facilitated by SPIU over conventional eHMI systems, where a pedestrian must look at multiple AVs individually. Furthermore, they emphasize inclusive design through multi-sensory communication channels—visual, auditory, and tactile signals—specifically addressing the needs of vulnerable road users (VRUs), including those with impairments. To complement these non-expert, real-world insights, we employ three leading Large Language Models (LLMs) (ChatGPT-4, Gemini-Pro, and Claude 3.5 Sonnet) as “experts” due to their extensive training data. Using the advantages of the multimodal vision-language processing capabilities of these LLMs, identical questions (text and images) used in human discussions are posed to generate text responses for pedestrian–AV interaction scenarios. Responses generated from LLMs and recorded conversations from human group discussions are used to extract the most frequent words. A keyword frequency analysis from both humans and LLMs is performed with three categories, Context, Safety, and Important. Our findings indicate that LLMs employ safety-related keywords 30% more frequently than human participants, suggesting a more structured, safety-centric approach. Among LLMs, ChatGPT-4 demonstrates superior response latency, Claude shows a closer alignment with human responses, and Gemini-Pro provides structured and contextually relevant insights. Our results from “Humans” and “LLMs” establish SPIU as a promising system for facilitating trust-building and safety-ensuring interactions among pedestrians, AVs, and delivery robots. Integrating diverse stakeholder feedback, we propose a prototype SPIU design to advance pedestrian–AV interactions in shared urban spaces, positioning SPIU as crucial infrastructure hubs for safe and trustworthy navigation.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103628"},"PeriodicalIF":5.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Hybrid fleet composition and scheduling for road-based cross-border logistics under cost differentiation: A bi-level programming approach","authors":"Zhi Tang ,&nbsp;Ting Qu ,&nbsp;Yanghua Pan ,&nbsp;George Q. Huang","doi":"10.1016/j.eswa.2025.129636","DOIUrl":"10.1016/j.eswa.2025.129636","url":null,"abstract":"<div><div>Under the dual pressure of explosive growth in cross-border e-commerce demand and increasing timeliness requirements from overseas customers, cross-border logistics service providers are compelled to establish logistics facilities and deploy fleets across multiple regions to ensure rapid response. However, during freight transportation, the lack of effective management over these complex and heterogeneous fleets—particularly in terms of fleet composition and routing decisions—has led to high transportation costs and low operational efficiency. This study is grounded in the practical operational context of cross-border logistics in the Guangdong–Hong Kong–Macau Greater Bay Area and models a multi-level, multi-node cross-border transportation network. To minimize the overall operational cost, the problem is addressed from two interrelated decision-making perspectives: fleet composition at the strategic level and routing planning at the operational level. Thus, a bi-level programming model is proposed to systematically capture the hierarchical structure and the logical relationship between these two decision layers. Furthermore, the model incorporates cost differences among trucks with different functional capabilities to reflect the significant disparity in logistics cost structures between domestic and overseas operations. To address the above multi-objective mixed-integer linear programming (MILP) problem, a tailored Non-dominated Sorting Genetic Algorithm II (MNSGA-II) is developed. Several key components of the algorithm are modified and enhanced to improve its search efficiency and solution quality in handling the problem’s complexity. Comparative experiments against classical algorithms demonstrate the superior solution quality and robustness of the proposed approach. The influence of cost differentials on composition and scheduling decisions is further analyzed, providing practical insights for the strategic planning of cross-border logistics systems.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129636"},"PeriodicalIF":7.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Automatic Code Generation Techniques: A Systematic Literature Review","authors":"Maha Alharbi,&nbsp;Mohammad Alshayeb","doi":"10.1007/s10515-025-00551-3","DOIUrl":"10.1007/s10515-025-00551-3","url":null,"abstract":"<div><p>As modern software systems become complex and the demand for rapid development cycles increases, automatic code generation techniques have attained a prominent focus in academic research and industrial practice. These techniques can significantly reduce human error, increase productivity, and ensure consistency across large codebases. However, the task of generating code automatically presents significant challenges. In this study, we investigate, identify, and analyze the existing automatic techniques for generating code from various input formats, highlighting their efficiencies and areas for potential improvement. A Systematic Literature Review (SLR) is conducted to systematically summarize and review 76 primary studies related to automatic code generation in the software engineering domain. The selected studies are investigated from several dimensions: paradigms, techniques, input types, intermediate representations, tool support, targeted programming languages, and validation methods, including performance metrics, datasets, and benchmarking status. Our investigation identified 12 main techniques, categorized into five paradigms, where the Model-to-Code paradigm and model-driven techniques are the most prevalent. Notably, 57% of the studies utilized Java, and a limited number of studies showed multilingual support. Furthermore, 72% of the selected studies did not compare their results with existing techniques, and 17% lacked validation of the proposed techniques. We also noticed a lack of detailed information about the datasets used in the validation process, where 52% of the studies omitted these details. This SLR provides several recommendations to enhance methodological rigor in future research, and it highlights opportunities for leveraging emerging technologies to improve the efficiency of the identified automatic code generation techniques.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"33 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Deep Learning and IoT Fusion for Crop Health Monitoring: A High-Accuracy, Edge-Optimised Model for Smart Farming","authors":"Thomas Kinyanjui Njoroge,&nbsp;Edwin Juma Omol,&nbsp;Vincent Omollo Nyangaresi","doi":"10.1049/ipr2.70208","DOIUrl":"https://doi.org/10.1049/ipr2.70208","url":null,"abstract":"<p>Crop diseases and adverse field conditions threaten global food security, particularly in resource-limited regions. Current deep-learning models for disease detection suffer from insufficient accuracy, high prediction instability under field noise, and a lack of integration with environmental context. To address these limitations, we present a hybrid deep learning architecture combining EfficientNetV2, MobileNetV2, and Vision Transformers, augmented with attention mechanisms and multiscale feature fusion. Optimised for edge deployment via TensorFlow Lite and integrated with IoT sensors for real-time soil and field monitoring, the model achieved state-of-the-art performance with 99.2% accuracy, 0.993 precision, 0.993 recall, and a near-perfect AUC of 0.999998, outperforming benchmarks like DenseNet50 (88.4%) and ShuffleNet (95.8%). Training on 76 classes (22 diseases) demonstrated rapid convergence and robustness, with validation accuracy reaching 98.7% and minimal overfitting. Statistical validation confirmed superior stability, with 69% lower prediction variance (0.000010) than DenseNet50 (0.000035), ensuring reliable performance under real-world noise. Bayesian testing showed a 100% probability of superiority over DenseNet50 and 85.1% over ShuffleNet, while field trials on 249 real-world images achieved 97.97% accuracy, highlighting strong generalisation. IoT integration reduced false diagnoses by 92% through environmental correlation, and edge optimisation enabled real-time inference via a 30.4 MB mobile application (0.094-second latency). This work advances precision agriculture through a scalable, cloud-independent framework that unifies hybrid deep learning with edge-compatible IoT sensing. By addressing critical gaps in accuracy, stability, and contextual awareness, the system enhances crop health management in low-resource settings, offering a statistically validated tool for sustainable farming practices.</p>","PeriodicalId":56303,"journal":{"name":"IET Image Processing","volume":"19 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ipr2.70208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145038070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}