IET Cyber-Physical Systems: Theory and Applications最新文献_第5页

Optimal congestion management in network routers subject to constraints, disturbances, and noise using the model predictive control approach 利用模型预测控制方法优化受约束、干扰和噪声影响的网络路由器的拥塞管理

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-10-12 DOI: 10.1049/cps2.12081

Bijan Nasiri, Farhad Bayat, MohammadAli Mohammadkhani, Andrzej Bartoszewicz

引用次数: 0

Multiple dependence representation of attention graph convolutional network relation extraction model 注意力图卷积网络关系提取模型的多重依赖性表示法

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-10-04 DOI: 10.1049/cps2.12080

Zhao Liangfu, Xiong Yujie, Gao Yongbin, Yu Wenjun

{"title":"Multiple dependence representation of attention graph convolutional network relation extraction model","authors":"Zhao Liangfu, Xiong Yujie, Gao Yongbin, Yu Wenjun","doi":"10.1049/cps2.12080","DOIUrl":"10.1049/cps2.12080","url":null,"abstract":"Dependency analysis can better help neural network to capture semantic features in sentences, so as to extract entity relation. Currently, hard pruning strategies and soft pruning strategies based on dependency tree structure coding have been proposed to balance beneficial additional information and adverse interference in extraction tasks. A new model based on graph convolutional networks, which uses a variety of representations describing dependency trees from different perspectives and combining these representations to obtain a better sentence representation for relation classification is proposed. A newly defined module is added, and this module uses the attention mechanism to capture deeper semantic features from the context representation as the global semantic features of the input text, thus helping the model to capture deeper semantic information at the sentence level for relational extraction tasks. In order to get more information about a given entity pair from the input sentence, the authors also model implicit co-references (references) to entities. This model can extract semantic features related to the relationship between entities from sentences to the maximum extent. The results show that the model in this paper achieves good results on SemEval2010-Task8 and KBP37 datasets.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"247-257"},"PeriodicalIF":1.7,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Selective real-time adversarial perturbations against deep reinforcement learning agents 针对深度强化学习代理的选择性实时对抗扰动

IF 1.5

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-22 DOI: 10.1049/cps2.12065

Hongjin Yao, Yisheng Li, Yunpeng Sun, Zhichao Lian

{"title":"Selective real-time adversarial perturbations against deep reinforcement learning agents","authors":"Hongjin Yao, Yisheng Li, Yunpeng Sun, Zhichao Lian","doi":"10.1049/cps2.12065","DOIUrl":"10.1049/cps2.12065","url":null,"abstract":"Recent work has shown that deep reinforcement learning (DRL) is vulnerable to adversarial attacks, so that exploiting vulnerabilities in DRL systems through adversarial attack techniques has become a necessary prerequisite for building robust DRL systems. Compared to traditional deep learning systems, DRL systems are characterised by long sequential decisions rather than one-step decision, so attackers must perform multi-step attacks on them. To successfully attack a DRL system, the number of attacks must be minimised to avoid detecting by the victim agent and to ensure the effectiveness of the attack. Some selective attack methods proposed in recent researches, that is, attacking an agent at partial time steps, are not applicable to real-time attack scenarios, although they can avoid detecting by the victim agent. A real-time selective attack method that is applicable to environments with discrete action spaces is proposed. Firstly, the optimal attack threshold T for performing selective attacks in the environment Env is determined. Then, the observation states corresponding to when the value of the action preference function of the victim agent in multiple eposides exceeds the threshold T are added to the training set according to this threshold. Finally, a universal perturbation is generated based on this training set, and it is used to perform real-time selective attacks on the victim agent. Comparative experiments show that our attack method can perform real-time attacks while maintaining the attack effect and stealthiness.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 1","pages":"41-49"},"PeriodicalIF":1.5,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feature attention gated context aggregation network for single image dehazing and its application on unmanned aerial vehicle images 用于单一图像去毛刺的特征注意门控上下文聚合网络及其在无人机图像上的应用

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-20 DOI: 10.1049/cps2.12076

Yongquan Wu, Xuan Zhao, Xinsheng Zhang, Tao Long, Ping Luo

{"title":"Feature attention gated context aggregation network for single image dehazing and its application on unmanned aerial vehicle images","authors":"Yongquan Wu, Xuan Zhao, Xinsheng Zhang, Tao Long, Ping Luo","doi":"10.1049/cps2.12076","DOIUrl":"10.1049/cps2.12076","url":null,"abstract":"Single-image dehazing is a highly challenging ill-posed task in the field of computer vision. To address this, a new image dehazing model with feature attention, named feature attention gated context aggregation network (FAGCA-Net), is proposed to tackle the issues of incomplete or over-dehazing caused by the original model's inability to handle non-uniform haze density distributions. A feature attention module that combines channel attention and spatial attention is introduced. Additionally, the authors propose a new extended attention convolutional block, which not only addresses the grid artefacts caused by the extended convolution but also provides added flexibility in handling different types of feature information. At the same time, in addition to the input image itself, incorporating the dark channel and edge channel of the image as the final input of the model is helpful for the model learning process. To demonstrate the robustness of the new model, it is applied to two completely different dehazing datasets, and it achieves significant dehazing performance improvement over the original model. Finally, to verify the effectiveness of the model in practical production processes, the authors apply it as an image preprocessing step to a set of UAV (Unmanned Aerial Vehicle) images of foreign objects. The result shows that the UAV images after being processed by FAGCA-Net for haze removal have a better impact on subsequent usage.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"218-227"},"PeriodicalIF":1.7,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136375950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting covert channel attacks on cyber-physical systems 检测对网络物理系统的隐蔽通道攻击

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-20 DOI: 10.1049/cps2.12078

Hongwei Li, Danai Chasaki

引用次数: 0

Feature selection algorithm for substation main equipment defect text mining based on natural language processing 基于自然语言处理的变电站主设备缺陷文本挖掘特征选择算法

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-20 DOI: 10.1049/cps2.12079

Xiaoqing Mai, Tianhu Zhang, Changwu Hu, Yan Zhang

引用次数: 0

A swin transformer based bird nest detection approach with unmanned aerial vehicle images for power distribution and pole towers 基于无人机图像的配电和杆塔鸟巢检测方法

IF 1.5

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-19 DOI: 10.1049/cps2.12073

Yue Meng, Yu Song, Yuquan Chen, Xin Zhang, Mei Wu, Biao Du

引用次数: 0

A multilevel segmentation method of asymmetric semantics based on deep learning 基于深度学习的非对称语义多级分割方法

IF 1.5

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-15 DOI: 10.1049/cps2.12075

Angxin Liu, Yongbiao Yang

{"title":"A multilevel segmentation method of asymmetric semantics based on deep learning","authors":"Angxin Liu, Yongbiao Yang","doi":"10.1049/cps2.12075","DOIUrl":"10.1049/cps2.12075","url":null,"abstract":"An asymmetric semantic multi-level segmentation method based on depth learning is proposed in order to improve the precision and effect of semantic segmentation. A ‘content tree’ structure and an adjacency matrix are constructed to represent the parent-child relationship between each image sub region in a complete image. Through multiple combinations of spatial attention mechanism and channel attention mechanism, the similarity semantic features of the target object can be selectively aggregated, so as to enhance its feature expression and avoid the impact of significant objects. The asymmetric semantic segmentation model asymmetric pyramid feature convolutional network (APFCN) is constructed, and the path feature extraction and parameter adjustment are realised through APFCN. On the basis of APFCN network, a full convolution network is introduced for end-to-end image semantic segmentation. Combining the advantages of convolution network in extracting image features and the advantages of short-term and short-term memory network in solving long-term dependence, an end-to-end hybrid depth network is constructed for image semantic multi-level segmentation. The experimental results show that the mean intersection over Union value and mean pixel accuracy value are higher than that of the literature method, both of which are increased by more than 3%, and the segmentation effect is good.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 2","pages":"194-205"},"PeriodicalIF":1.5,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135437827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsafe behaviour detection with the improved YOLOv5 model 使用改进的 YOLOv5 模型检测不安全行为

IF 1.5

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-15 DOI: 10.1049/cps2.12070

Li Ying, Zhao Lei, Geng Junwei, Hu Jinhui, Ma Lei, Zhao Zilong

{"title":"Unsafe behaviour detection with the improved YOLOv5 model","authors":"Li Ying, Zhao Lei, Geng Junwei, Hu Jinhui, Ma Lei, Zhao Zilong","doi":"10.1049/cps2.12070","DOIUrl":"10.1049/cps2.12070","url":null,"abstract":"In industrial environments, workers should wear workwear for safety considerations. For the same reason, smoking is also prohibited. Due to the supervision of monitoring devices, workers have reduced smoking behaviours and started wearing workwear. To meet the requirements for detecting these behaviours in real-time monitoring videos with high speed and accuracy, the authors proposed an improved YOLOv5 model with the Triplet Attention mechanism. This mechanism strengthens the connection between channel and spatial dimensions, focuses the network on important parts, and improves feature extraction. Compared to the original YOLOv5 model, the addition of the mechanism increases the parameters by only 0.04%. The recall rate of the YOLOv5 model is enhanced while its prediction speed is maintained with only a minimal increase in parameters. Experiment results show that, compared to the original model, the improved YOLOv5 has a recall rate of 78.8%, 91%, and 89.3% for detecting smoking behaviour, not wearing helmets, and inappropriate workwear, respectively.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 1","pages":"87-98"},"PeriodicalIF":1.5,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135438294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of design frameworks for human-cyber-physical systems moving from industry 4 to 5 从工业 4 到工业 5 的人-网络-物理系统设计框架综述

IF 1.5

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-09-14 DOI: 10.1049/cps2.12077

Katherine van-Lopik, Steven Hayward, Rebecca Grant, Laura McGirr, Paul Goodall, Yan Jin, Mark Price, Andrew A. West, Paul P. Conway

引用次数: 0