In China, most of the high temperature coal stove combustion process needs people to rake the coal cinder. In this paper, a heterogeneous redundant robot is designed to rake coal cinder. The system consists of a six-axis robot, a robot's orbit, a rake and a rake's bracket orbit. In order to achieve controllable, safe and efficient work of raking coal cinder in high temperature coal furnace, in this study, the constraint conditions of each limit position are modelled mathematically, and the parameters of the robot system are optimised by an optimal solution of structural parameters under multi-variables and multi-constraints. Finally, the forward kinematics model of the whole heterogeneous redundant robot is established, and the feasibility of the scheme is verified by simulation.
{"title":"Kinematic and workspace analysis of redundant heterogeneous robot for flat slag of high temperature furnace","authors":"Haixing Wang, Hongsen Wang, Qunpo Liu, Zhuoran Zhang","doi":"10.1504/ijccps.2023.133726","DOIUrl":"https://doi.org/10.1504/ijccps.2023.133726","url":null,"abstract":"In China, most of the high temperature coal stove combustion process needs people to rake the coal cinder. In this paper, a heterogeneous redundant robot is designed to rake coal cinder. The system consists of a six-axis robot, a robot's orbit, a rake and a rake's bracket orbit. In order to achieve controllable, safe and efficient work of raking coal cinder in high temperature coal furnace, in this study, the constraint conditions of each limit position are modelled mathematically, and the parameters of the robot system are optimised by an optimal solution of structural parameters under multi-variables and multi-constraints. Finally, the forward kinematics model of the whole heterogeneous redundant robot is established, and the feasibility of the scheme is verified by simulation.","PeriodicalId":476892,"journal":{"name":"International journal of cybernetics and cyber-physical systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135912928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijccps.2023.133729
Haodong Wang, Fan Li, Zhifei Wang
{"title":"Analysis of hardware security protection strategy based on microcontroller","authors":"Haodong Wang, Fan Li, Zhifei Wang","doi":"10.1504/ijccps.2023.133729","DOIUrl":"https://doi.org/10.1504/ijccps.2023.133729","url":null,"abstract":"","PeriodicalId":476892,"journal":{"name":"International journal of cybernetics and cyber-physical systems","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135913191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijccps.2023.133730
Manli Wang, Bingbing Chen, Changsen Zhang
Face detection detects and locates faces in images for face recognition, face tracking, and analysis applications. The performance of many advanced face recognition models deteriorates significantly when applied to low-light environments, hence face detection from low-light images is challenging. To solve the problem, this paper proposes a face detection method based on feature recovery, which includes two modules: feature recovery and feature extraction. The feature recovery module can obtain the face feature recovery image, which is fused with the original low-light face image to obtain the face feature image. On this basis, the feature extraction is trained for face detection. Finally, a face detection method suitable for low-light is obtained. It solves the difficulty of face detection under low-light. The experiment results carried out the overall detection precision increased by 18% on the DARK FACE test set, which verified the effectiveness of the proposed method.
{"title":"Face detection algorithm under low-light based on feature recovery","authors":"Manli Wang, Bingbing Chen, Changsen Zhang","doi":"10.1504/ijccps.2023.133730","DOIUrl":"https://doi.org/10.1504/ijccps.2023.133730","url":null,"abstract":"Face detection detects and locates faces in images for face recognition, face tracking, and analysis applications. The performance of many advanced face recognition models deteriorates significantly when applied to low-light environments, hence face detection from low-light images is challenging. To solve the problem, this paper proposes a face detection method based on feature recovery, which includes two modules: feature recovery and feature extraction. The feature recovery module can obtain the face feature recovery image, which is fused with the original low-light face image to obtain the face feature image. On this basis, the feature extraction is trained for face detection. Finally, a face detection method suitable for low-light is obtained. It solves the difficulty of face detection under low-light. The experiment results carried out the overall detection precision increased by 18% on the DARK FACE test set, which verified the effectiveness of the proposed method.","PeriodicalId":476892,"journal":{"name":"International journal of cybernetics and cyber-physical systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135914591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijccps.2023.133732
Varghese Mathew Vaidyan, Akhilesh Tyagi
{"title":"On fuzzy inference based supervisory control decision model with quantum artificial intelligence electromagnetic prediction models","authors":"Varghese Mathew Vaidyan, Akhilesh Tyagi","doi":"10.1504/ijccps.2023.133732","DOIUrl":"https://doi.org/10.1504/ijccps.2023.133732","url":null,"abstract":"","PeriodicalId":476892,"journal":{"name":"International journal of cybernetics and cyber-physical systems","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135913189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article proposes a SRN algorithm of feature fusion to solve the problem of image motion blur. First, an Attention Residual Module (ARM) is designed to add channel attention between residual units to increase feature extraction capabilities. Second, a feature pyramid structure is constructed to improve the representation ability of the network. Then, a multi-scale coordinate attention feature fusion structure is built to improve the deblurring effect of the model. Finally, optimising the loss function improves the robustness of model to discrete points and increases the stability of the model. The testing is performed on the GOPRO dataset. Our algorithm is the best, with PSNR and SSIM reaching 34.72 dB and 0.97. Tested on the foreign object data set, the PSNR and SSIM of our algorithm have been greatly improved, and compared with other methods, it has a great advantage in detailed texture recovery.
{"title":"Image deblurring method based on feature fusion SRN","authors":"Junjia Bi, Lingxiao Yang, Jingwen Zhang, Jianjun Zhang","doi":"10.1504/ijccps.2023.133728","DOIUrl":"https://doi.org/10.1504/ijccps.2023.133728","url":null,"abstract":"This article proposes a SRN algorithm of feature fusion to solve the problem of image motion blur. First, an Attention Residual Module (ARM) is designed to add channel attention between residual units to increase feature extraction capabilities. Second, a feature pyramid structure is constructed to improve the representation ability of the network. Then, a multi-scale coordinate attention feature fusion structure is built to improve the deblurring effect of the model. Finally, optimising the loss function improves the robustness of model to discrete points and increases the stability of the model. The testing is performed on the GOPRO dataset. Our algorithm is the best, with PSNR and SSIM reaching 34.72 dB and 0.97. Tested on the foreign object data set, the PSNR and SSIM of our algorithm have been greatly improved, and compared with other methods, it has a great advantage in detailed texture recovery.","PeriodicalId":476892,"journal":{"name":"International journal of cybernetics and cyber-physical systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135912931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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