CAAI Transactions on Intelligence Technology最新文献

{"title":"Learning feature alignment and dual correlation for few-shot image classification","authors":"Xilang Huang,&nbsp;Seon Han Choi","doi":"10.1049/cit2.12273","DOIUrl":"10.1049/cit2.12273","url":null,"abstract":"<p>Few-shot image classification is the task of classifying novel classes using extremely limited labelled samples. To perform classification using the limited samples, one solution is to learn the feature alignment (FA) information between the labelled and unlabelled sample features. Most FA methods use the feature mean as the class prototype and calculate the correlation between prototype and unlabelled features to learn an alignment strategy. However, mean prototypes tend to degenerate informative features because spatial features at the same position may not be equally important for the final classification, leading to inaccurate correlation calculations. Therefore, the authors propose an effective intraclass FA strategy that aggregates semantically similar spatial features from an adaptive reference prototype in low-dimensional feature space to obtain an informative prototype feature map for precise correlation computation. Moreover, a dual correlation module to learn the hard and soft correlations was developed by the authors. This module combines the correlation information between the prototype and unlabelled features in both the original and learnable feature spaces, aiming to produce a comprehensive cross-correlation between the prototypes and unlabelled features. Using both FA and cross-attention modules, our model can maintain informative class features and capture important shared features for classification. Experimental results on three few-shot classification benchmarks show that the proposed method outperformed related methods and resulted in a 3% performance boost in the 1-shot setting by inserting the proposed module into the related methods.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 2","pages":"303-318"},"PeriodicalIF":5.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Exploring the spatiotemporal relationship between green infrastructure and urban heat island under multi-source remote sensing imagery: A case study of Fuzhou City","authors":"Tingting Hong,&nbsp;Xiaohui Huang,&nbsp;Guangjian Chen,&nbsp;Yiwei Yang,&nbsp;Lijia Chen","doi":"10.1049/cit2.12272","DOIUrl":"10.1049/cit2.12272","url":null,"abstract":"<p>Green Infrastructure (GI) has garnered increasing attention from various regions due to its potential to mitigate urban heat island (UHI), which has been exacerbated by global climate change. This study focuses on the central area of Fuzhou city, one of the “furnace” cities, and aims to explore the correlation between the GI pattern and land surface temperature (LST) in the spring and autumn seasons. The research adopts a multiscale approach, starting from the urban scale and using urban geographic spatial characteristics, multispectral remote sensing data, and morphological spatial pattern analysis (MSPA). Significant MSPA elements were tested and combined with LST to conduct a geographic weighted regression (GWR) experiment. The findings reveal that the UHI in the central area of Fuzhou city has a spatial characteristic of “high temperature in the middle and low temperature around,” which is coupled with a “central scattered and peripheral concentrated” distribution of GI. This suggests that remote sensing data can effectively be utilised for UHI inversion. Additionally, the study finds that the complexity of GI, whether from the perspective of the overall GI pattern or the classification study based on the proportion of the core area, has an impact on the alleviation of UHI in both seasons. In conclusion, this study underscores the importance of a reasonable layout of urban green infrastructure for mitigating UHI.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"8 4","pages":"1337-1349"},"PeriodicalIF":5.1,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138604706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Guest Editorial: Special issue on explainable AI empowered for indoor positioning and indoor navigation","authors":"Ki-Il Kim,&nbsp;Aswani Kumar Cherukuri,&nbsp;Xue Jun Li,&nbsp;Tanveer Ahmad,&nbsp;Muhammad Rafiq,&nbsp;Shehzad Ashraf Chaudhry","doi":"10.1049/cit2.12274","DOIUrl":"https://doi.org/10.1049/cit2.12274","url":null,"abstract":"&lt;p&gt;The convergence of Internet of Things (IoT), vehicularad hoc network (VANET), and mobile ad hoc network relies on sensor networks to gather data from nodes or objects. These networks involve nodes, gateways, and anchors, operating on limited battery power, mainly used in broadcasting. IoT applications, like healthcare, smart cities, and transportation, often need position data and face challenges in delay sensitivity. Localisation is important in ITS and VANETs, influencing autonomous vehicles, collision warning systems, and road information dissemination. A robust localisation system, often combining GPS with techniques like Dead Reckoning and Image/Video Localisation, is essential for accuracy and security. Artificial intelligence (AI) integration, particularly in machine learning, enhances indoor wireless localisation effectiveness. Advancements in wireless communication (WSN, IoT, and massive MIMO) transform dense environments into programmable entities, but pose challenges in aligning self-learning AI with sensor tech for accuracy and budget considerations. We seek original research on sensor localisation, fusion, protocols, and positioning algorithms, inviting contributions from industry and academia to address these evolving challenges.&lt;/p&gt;&lt;p&gt;This special issue titled ‘Sensing, Communication, and Localization in WSN, IoT &amp; VANET’ appears in the CAAI Transactions on Intelligence Technology. We encourage contributions addressing localisation accuracy, network coverage, upper and lower bounding, lane and vehicle detection, and related topics.&lt;/p&gt;&lt;p&gt;In the first paper, (Hamil et al.) explore how smartphone sensors and IoT devices aid in rescuing individuals during emergencies like fires in tall buildings. It introduces a pioneering Sensor Management and Data Fusion-Wireless Data Exchange fusion scheme, leveraging an evolutionary algorithm within complex multi-storey buildings. This scheme aims to diversify particle sets effectively, capturing the user's real-time state using wearable device sensors. The authors further explore how smartphones sensors utilise data for object movement alongside Bluetooth Low Energy beacon based localisation with the help of Sensor Management security and Data Fusion-Wireless Data Exchange scheme. The effectiveness of this scheme and its impact on a smartphone user's real-time state within indoor settings were assessed through various experiments in controlled environments.&lt;/p&gt;&lt;p&gt;In the second paper, (Khan J et al.) proposed a novel method to fine-tune alpha-beta filter parameters using a feed-forward backpropagation neural network. This model, comprising the alpha-beta filter as the core predictor and a feedforward artificial neural network as the learning element, uses temperature and humidity sensor data for precise predictions from noisy readings. By integrating the feed-forward backpropagation neural network significantly boosts prediction accuracy, slashing both roots mean square error (RMSE) and mea","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"8 4","pages":"1101-1103"},"PeriodicalIF":5.1,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138678834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"A verifiable essential secret image sharing scheme based on HLRs (VESIS-(t, s, k, n))","authors":"Massoud Hadian Dehkordi,&nbsp;Seyed Taghi Farahi,&nbsp;Samaneh Mashhadi","doi":"10.1049/cit2.12271","DOIUrl":"10.1049/cit2.12271","url":null,"abstract":"<p>In traditional secret image sharing schemes, a secret image is shared among shareholders who have the same position. But if the shareholders have two different positions, essential and non-essential, it is necessary to use essential secret image sharing schemes. In this article, a verifiable essential secret image sharing scheme based on HLRs is proposed. Shareholder's share consists of two parts. The first part is produced by the shareholders, which prevents the fraud of dealers. The second part is a shadow image that is produced by using HLRs and the first part of share. The verification of the first part of the shares is done for the first time by using multilinear and bilinear maps. Also, for verifying shadow images, Bloom Filters are used for the first time. The proposed scheme is more efficient than similar schemes, and for the first part of the shares, has formal security.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 2","pages":"388-410"},"PeriodicalIF":5.1,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments","authors":"Zhiqian Zhou, Junkai Ren, Zhiwen Zeng, Junhao Xiao, Xinglong Zhang, Xian Guo, Zongtan Zhou, Huimin Lu","doi":"10.1049/cit2.12269","DOIUrl":"https://doi.org/10.1049/cit2.12269","url":null,"abstract":"Abstract When deploying mobile robots in real‐world scenarios, such as airports, train stations, hospitals, and schools, collisions with pedestrians are intolerable and catastrophic. Motion safety becomes one of the most fundamental requirements for mobile robots. However, until now, efficient and safe robot navigation in such dynamic environments is still an open problem. The critical reason is that the inconsistency between navigation efficiency and motion safety is greatly intensified by the high dynamics and uncertainties of pedestrians. To face the challenge, this paper proposes a safe deep reinforcement learning algorithm named Conflict‐Averse Safe Reinforcement Learning (CASRL) for autonomous robot navigation in dynamic environments. Specifically, it first separates the collision avoidance sub‐task from the overall navigation task and maintains a safety critic to evaluate the safety/risk of actions. Later, it constructs two task‐specific but model‐agnostic policy gradients for goal‐reaching and collision avoidance sub‐tasks to eliminate their mutual interference. Then, it further performs a conflict‐averse gradient manipulation to address the inconsistency between two sub‐tasks. Finally, extensive experiments are performed to evaluate the superiority of CASRL. Simulation results show an average 8.2% performance improvement over the vanilla baseline in eight groups of dynamic environments, which is further extended to 13.4% in the most challenging group. Besides, forty real‐world experiments fully illustrated that the CASRL could be successfully deployed on a real robot.","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135146697","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":"Transfer learning from T1-weighted to T2-weighted Magnetic resonance sequences for brain image segmentation","authors":"Imene Mecheter,&nbsp;Maysam Abbod,&nbsp;Habib Zaidi,&nbsp;Abbes Amira","doi":"10.1049/cit2.12270","DOIUrl":"10.1049/cit2.12270","url":null,"abstract":"<p>Magnetic resonance (MR) imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body. The segmentation of MR images plays a crucial role in medical image analysis, as it enables accurate diagnosis, treatment planning, and monitoring of various diseases and conditions. Due to the lack of sufficient medical images, it is challenging to achieve an accurate segmentation, especially with the application of deep learning networks. The aim of this work is to study transfer learning from T1-weighted (T1-w) to T2-weighted (T2-w) MR sequences to enhance bone segmentation with minimal required computation resources. With the use of an excitation-based convolutional neural networks, four transfer learning mechanisms are proposed: transfer learning without fine tuning, open fine tuning, conservative fine tuning, and hybrid transfer learning. Moreover, a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique. The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources. The segmentation results are evaluated using 14 clinical 3D brain MR and CT images. The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393 ± 0.0007. Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation, it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 1","pages":"26-39"},"PeriodicalIF":5.1,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135645979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Guest Editorial: Special issue on recurrent dynamic neural networks: Theory and applications","authors":"Long Jin,&nbsp;Predrag S. Stanimirović","doi":"10.1049/cit2.12266","DOIUrl":"https://doi.org/10.1049/cit2.12266","url":null,"abstract":"&lt;p&gt;Recurrent dynamic neural network has been proven to be a powerful tool in the online solving of problems with considerable complexity and has been applied to various fields. In recent years, various recurrent dynamic neural networks have been developed to solve complex time-varying problems, such as time-varying matrix inversion, time-varying nonlinear optimisation, motion control of manipulators and so on. However, some thorny issues remain, including, but not limited to, sensitivity to noises, slow convergent speed, and high computational complexity.&lt;/p&gt;&lt;p&gt;We envisioned this Special Issue could provide a platform for researchers in this area to publish their latest research ideas. This call received 25 high-quality submissions. After passing through the peer review process, eight high-quality papers were accepted for publication.&lt;/p&gt;&lt;p&gt;In the first paper (Ren et al.), the authors give an overview of the latest process of weakly supervised learning in medical image analysis, including incomplete, inexact and inaccurate supervision, and introduce the related works on different applications for medical image analysis. Related concepts are illustrated to help readers get an overview ranging from supervised to unsupervised learning within the scope of machine learning. Furthermore, the challenges and future works of weakly supervised learning in medical image analysis are discussed.&lt;/p&gt;&lt;p&gt;In the second paper (Gheisari et al.), the ways, advantages, drawbacks, architectures, and methods of deep learning (DL) are investigated in order to have a straightforward and clear understanding of it from different views. Moreover, the existing related methods are compared with each other, and the application of DL is described in some applications, such as medical image analysis, handwriting recognition and so on.&lt;/p&gt;&lt;p&gt;In the third paper (Shi et al.), an advanced continuous-time recurrent neural network (RNN) model based on a double integral RNN design formula is proposed for solving continuous time-varying matrix inversion, which has an incomparable disturbance-suppression property. For digital hardware applications, the corresponding advanced discrete-time RNN model is proposed based on the discretisation formulas. As a result of theoretical analysis, it is demonstrated that the advanced continuous-time RNN model and the corresponding advanced discrete-time RNN model have global and exponential convergence performance, and they are excellent for suppressing different disturbances. Finally, inspiring experiments, including two numerical experiments and a practical experiment, are presented to demonstrate the effectiveness and superiority of the advanced discrete-time RNN model for solving discrete time-varying matrix inversion with disturbance-suppression.&lt;/p&gt;&lt;p&gt;In the fourth paper (Li Z. and Li S.), for the first time, a novel recursive recurrent network model is proposed to solve the kinematic control issue for manipulators with different levels of physi","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"8 3","pages":"547-548"},"PeriodicalIF":5.1,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50122144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"D2LFS2Net: Multi-class skin lesion diagnosis using deep learning and variance-controlled Marine Predator optimisation: An application for precision medicine","authors":"Veena Dillshad,&nbsp;Muhammad Attique Khan,&nbsp;Muhammad Nazir,&nbsp;Oumaima Saidani,&nbsp;Nazik Alturki,&nbsp;Seifedine Kadry","doi":"10.1049/cit2.12267","DOIUrl":"10.1049/cit2.12267","url":null,"abstract":"<p>In computer vision applications like surveillance and remote sensing, to mention a few, deep learning has had considerable success. Medical imaging still faces a number of difficulties, including intra-class similarity, a scarcity of training data, and poor contrast skin lesions, notably in the case of skin cancer. An optimisation-aided deep learning-based system is proposed for accurate multi-class skin lesion identification. The sequential procedures of the proposed system start with preprocessing and end with categorisation. The preprocessing step is where a hybrid contrast enhancement technique is initially proposed for lesion identification with healthy regions. Instead of flipping and rotating data, the outputs from the middle phases of the hybrid enhanced technique are employed for data augmentation in the next step. Next, two pre-trained deep learning models, MobileNetV2 and NasNet Mobile, are trained using deep transfer learning on the upgraded enriched dataset. Later, a dual-threshold serial approach is employed to obtain and combine the features of both models. The next step was the variance-controlled Marine Predator methodology, which the authors proposed as a superior optimisation method. The top features from the fused feature vector are classified using machine learning classifiers. The experimental strategy provided enhanced accuracy of 94.4% using the publicly available dataset HAM10000. Additionally, the proposed framework is evaluated compared to current approaches, with remarkable results.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 1","pages":"207-222"},"PeriodicalIF":8.4,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86453594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Machine learning and human-machine trust in healthcare: A systematic survey","authors":"Han Lin,&nbsp;Jiatong Han,&nbsp;Pingping Wu,&nbsp;Jiangyan Wang,&nbsp;Juan Tu,&nbsp;Hao Tang,&nbsp;Liuning Zhu","doi":"10.1049/cit2.12268","DOIUrl":"10.1049/cit2.12268","url":null,"abstract":"<p>As human-machine interaction (HMI) in healthcare continues to evolve, the issue of trust in HMI in healthcare has been raised and explored. It is critical for the development and safety of healthcare that humans have proper trust in medical machines. Intelligent machines that have applied machine learning (ML) technologies continue to penetrate deeper into the medical environment, which also places higher demands on intelligent healthcare. In order to make machines play a role in HMI in healthcare more effectively and make human-machine cooperation more harmonious, the authors need to build good human-machine trust (HMT) in healthcare. This article provides a systematic overview of the prominent research on ML and HMT in healthcare. In addition, this study explores and analyses ML and three important factors that influence HMT in healthcare, and then proposes a HMT model in healthcare. Finally, general trends are summarised and issues to consider addressing in future research on HMT in healthcare are identified.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 2","pages":"286-302"},"PeriodicalIF":5.1,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77282377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Lateral interaction by Laplacian-based graph smoothing for deep neural networks","authors":"Jianhui Chen,&nbsp;Zuoren Wang,&nbsp;Cheng-Lin Liu","doi":"10.1049/cit2.12265","DOIUrl":"10.1049/cit2.12265","url":null,"abstract":"<p>Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions. Linear self-organising map (SOM) introduces lateral interaction in a general form in which signals of any modality can be used. Some approaches directly incorporate SOM learning rules into neural networks, but incur complex operations and poor extendibility. The efficient way to implement lateral interaction in deep neural networks is not well established. The use of Laplacian Matrix-based Smoothing (LS) regularisation is proposed for implementing lateral interaction in a concise form. The authors’ derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS-regulated k-means, and they both show the topology-preserving capability. The authors also verify that LS-regularisation can be used in conjunction with the end-to-end training paradigm in deep auto-encoders. Additionally, the benefits of LS-regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated. Furthermore, the topologically ordered structure introduced by LS-regularisation in feature extractor can improve the generalisation performance on classification tasks. Overall, LS-regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"8 4","pages":"1590-1607"},"PeriodicalIF":5.1,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74297058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}