My country is currently in a new era of industrial revolution and technological development, among which the most representative is the rapid development of information technology. And the action plan has been formulated, and it is pointed out that it is necessary to deeply integrate the real economy and artificial intelligence, the Internet and digital China, as well as the smart society and big data. As the most critical part of community governance, the complexity and difficulty of community sports center governance is relatively high. Therefore, one of the key goals that all sectors of society are currently concerned about is how to build and develop community sports through more advanced technical means center. The innovative governance community sports center must effectively combine and integrate the current advanced technologies such as the Internet of Things and big data, so that the level of community governance can be effectively improved. This paper draws on the academic background of “governance” in the field of public management. Different from social management, “governance” mainly reflects the administrative aspect, the cooperation between the government and all sectors of society, while the traditional social management mainly highlights the state’s administrative management. It reflects the concept that the government’s management power is inviolable and unshareable, which is also very different from the development direction of the gradual liberalization of the administrative power advocated by the Chinese government. Therefore, the effective way to solve various social problems at this stage is to change the concept of social management to community governance, and this approach has also been supported and valued by the government in recent years. The research shows that the management efficiency of community sports facilities can be effectively improved by optimizing the community sports governance system, making full use of the convenience brought by information resources, and using its own advanced technology.
{"title":"Dilemma and countermeasures of community sports center governance under the background of big data and Internet of Things","authors":"J. Zha","doi":"10.3233/jcm-226785","DOIUrl":"https://doi.org/10.3233/jcm-226785","url":null,"abstract":"My country is currently in a new era of industrial revolution and technological development, among which the most representative is the rapid development of information technology. And the action plan has been formulated, and it is pointed out that it is necessary to deeply integrate the real economy and artificial intelligence, the Internet and digital China, as well as the smart society and big data. As the most critical part of community governance, the complexity and difficulty of community sports center governance is relatively high. Therefore, one of the key goals that all sectors of society are currently concerned about is how to build and develop community sports through more advanced technical means center. The innovative governance community sports center must effectively combine and integrate the current advanced technologies such as the Internet of Things and big data, so that the level of community governance can be effectively improved. This paper draws on the academic background of “governance” in the field of public management. Different from social management, “governance” mainly reflects the administrative aspect, the cooperation between the government and all sectors of society, while the traditional social management mainly highlights the state’s administrative management. It reflects the concept that the government’s management power is inviolable and unshareable, which is also very different from the development direction of the gradual liberalization of the administrative power advocated by the Chinese government. Therefore, the effective way to solve various social problems at this stage is to change the concept of social management to community governance, and this approach has also been supported and valued by the government in recent years. The research shows that the management efficiency of community sports facilities can be effectively improved by optimizing the community sports governance system, making full use of the convenience brought by information resources, and using its own advanced technology.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"112 1","pages":"2069-2081"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84204790","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}
In the post-targeted poverty alleviation era, rural revitalization has become a common action of the whole society, strengthen the rural ecological environment governance, and the construction of beautiful countryside needs to be promoted urgently. Agricultural development, rural prosperity and farmers’ prosperity are inseparable from the support of a good ecological environment. From ecological, production, life and new energy four aspects of the rural ecological environment development evaluation index system, and then the principal component analysis screening important influence index, on the basis of the genetic algorithm and BP neural network improvement model, 31 provinces during much starker choices-and graver consequences-in rural ecological environment development, and the BP neural network and GA-BP neural network evaluation results. The results show that: (1) Generally speaking, during the 13th Five-Year Plan period, my country’s rural ecological environment development index has gradually improved, but the change range is small, the average value has risen from 0.2257 to 0.2431; The number of provinces with excellent development levels has risen from 5 to 7, and the development of rural ecological environment in Beijing, Tianjin and other provinces has risen to excellent; (2) The development of regional rural ecological environment has increased or decreased, and about three-quarters of the provinces have improved the development of rural ecological environment; (3) The development of rural ecological environment is uneven, and the difference gradually expands; (4) Compared with BP neural network, GA-BP neural network has fast convergence speed, small training, verification and overall errors, high fitting degree, and has a good evaluation effect. The research conclusions can provide a basis for the evaluation and improvement of rural ecological environment development.
{"title":"Evaluation of rural ecological environment development based on PCA-GA-BP model","authors":"Yongxin Wang","doi":"10.3233/jcm-226786","DOIUrl":"https://doi.org/10.3233/jcm-226786","url":null,"abstract":"In the post-targeted poverty alleviation era, rural revitalization has become a common action of the whole society, strengthen the rural ecological environment governance, and the construction of beautiful countryside needs to be promoted urgently. Agricultural development, rural prosperity and farmers’ prosperity are inseparable from the support of a good ecological environment. From ecological, production, life and new energy four aspects of the rural ecological environment development evaluation index system, and then the principal component analysis screening important influence index, on the basis of the genetic algorithm and BP neural network improvement model, 31 provinces during much starker choices-and graver consequences-in rural ecological environment development, and the BP neural network and GA-BP neural network evaluation results. The results show that: (1) Generally speaking, during the 13th Five-Year Plan period, my country’s rural ecological environment development index has gradually improved, but the change range is small, the average value has risen from 0.2257 to 0.2431; The number of provinces with excellent development levels has risen from 5 to 7, and the development of rural ecological environment in Beijing, Tianjin and other provinces has risen to excellent; (2) The development of regional rural ecological environment has increased or decreased, and about three-quarters of the provinces have improved the development of rural ecological environment; (3) The development of rural ecological environment is uneven, and the difference gradually expands; (4) Compared with BP neural network, GA-BP neural network has fast convergence speed, small training, verification and overall errors, high fitting degree, and has a good evaluation effect. The research conclusions can provide a basis for the evaluation and improvement of rural ecological environment development.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"38 1","pages":"1869-1882"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86420387","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}
With the rapid growth in the number of motor vehicles worldwide, the general public is beginning to attach importance to the quality inspection of wheels before they leave the factory. The current wheel defect detection systems are often cumbersome to operate and have low practical performance. Therefore, this research will use dynamic image segmentation, image texture feature extraction and Back Propagation neural network classification based on wheel image defect feature analysis algorithm to achieve automatic intelligent detection of automotive wheel defects. In this study, an intelligent detection system for automotive wheel defects is also designed, and finally the performance of the detection system is tested experimentally to illustrate its practicality. The experimental results show that the proposed intelligent detection system for automotive wheel defects based on image texture features identifies defects in wheel castings with a correct rate of 96% and a false positive rate of only 2%. This illustrates that the detection system proposed in this study has a high recognition rate and can provide a useful reference for the automotive industry inspection.
{"title":"Damage detection method of automobile hub based on image texture feature","authors":"Ying Wang","doi":"10.3233/jcm-226789","DOIUrl":"https://doi.org/10.3233/jcm-226789","url":null,"abstract":"With the rapid growth in the number of motor vehicles worldwide, the general public is beginning to attach importance to the quality inspection of wheels before they leave the factory. The current wheel defect detection systems are often cumbersome to operate and have low practical performance. Therefore, this research will use dynamic image segmentation, image texture feature extraction and Back Propagation neural network classification based on wheel image defect feature analysis algorithm to achieve automatic intelligent detection of automotive wheel defects. In this study, an intelligent detection system for automotive wheel defects is also designed, and finally the performance of the detection system is tested experimentally to illustrate its practicality. The experimental results show that the proposed intelligent detection system for automotive wheel defects based on image texture features identifies defects in wheel castings with a correct rate of 96% and a false positive rate of only 2%. This illustrates that the detection system proposed in this study has a high recognition rate and can provide a useful reference for the automotive industry inspection.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"66 1","pages":"1941-1953"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75833911","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}
The development of science and technology requires UAV to improve the accuracy of path planning to better apply in the military field and serve the people. The research proposes to use the social spider algorithm to optimize the ant colony algorithm, and jointly build an IACA to deal with the optimal selection problem of UAV path planning. Firstly, the swarm spider algorithm is used to make a reasonable division and planning of the UAV’s flight field. Secondly, the AC is used to adjust and control the UAV’s state and path. Then, the IACA is formed to carry out performance simulation and comparison experiments on the optimal path planning of the UAV to verify the superiority of the research algorithm. The results show that the maximum number of iterations of the original AC and the IACA is 100, but the IACA under the route planning optimization reaches the convergence state in 32 generations; Moreover, when the number of iterations is about 20 generations, there will be a stable fitness value, which saves time for the experiment to find the optimal path. In the simulation experiment, it is assumed that three UAVs will form a formation to conduct the experiment, and the multiple UAVs will be subject to global track planning and repeated rolling time domain track planning. The autonomous operation time of multiple UAVs through the assembly point is (5.30 s, 5.79 s, 9.29 s). The distance between UAVs during flight is predicted. It is found that the nearest distance is 2.3309 m near t= 6.65 s, which is in line with the safety distance standard. Under the improved algorithm, the speed in all directions is also relatively gentle. All the above results show that the improved algorithm can effectively improve the iteration speed and save time.
{"title":"Optimal trajectory planning algorithm for autonomous flight of multiple UAVs in small areas","authors":"Yi Tang, Z. Wang","doi":"10.3233/jcm-226800","DOIUrl":"https://doi.org/10.3233/jcm-226800","url":null,"abstract":"The development of science and technology requires UAV to improve the accuracy of path planning to better apply in the military field and serve the people. The research proposes to use the social spider algorithm to optimize the ant colony algorithm, and jointly build an IACA to deal with the optimal selection problem of UAV path planning. Firstly, the swarm spider algorithm is used to make a reasonable division and planning of the UAV’s flight field. Secondly, the AC is used to adjust and control the UAV’s state and path. Then, the IACA is formed to carry out performance simulation and comparison experiments on the optimal path planning of the UAV to verify the superiority of the research algorithm. The results show that the maximum number of iterations of the original AC and the IACA is 100, but the IACA under the route planning optimization reaches the convergence state in 32 generations; Moreover, when the number of iterations is about 20 generations, there will be a stable fitness value, which saves time for the experiment to find the optimal path. In the simulation experiment, it is assumed that three UAVs will form a formation to conduct the experiment, and the multiple UAVs will be subject to global track planning and repeated rolling time domain track planning. The autonomous operation time of multiple UAVs through the assembly point is (5.30 s, 5.79 s, 9.29 s). The distance between UAVs during flight is predicted. It is found that the nearest distance is 2.3309 m near t= 6.65 s, which is in line with the safety distance standard. Under the improved algorithm, the speed in all directions is also relatively gentle. All the above results show that the improved algorithm can effectively improve the iteration speed and save time.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"37 1","pages":"2193-2204"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82534976","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}
With the rapid developments of data restores and big data technologies in recent years, it aims at applying new technologies in the new stage of smart hospital development and improving scientific and proper management level of hospitals. Based on the constructions of hospital data centers and applications of Internet of things with big data technologies, hospitals can achieve intelligent operations and managements in daily work. With the aids of constructions of the data centers and mass data brought by smart hospital service models, smart management decision-making supports are effectively provided for hospital managers. Through continuous progresses of smart hospital constructions, the operations and managements of hospitals are increasingly dependent based on the supports of the data from the data center in hospital, which form the key in the smart operation and management of hospital in future.
{"title":"Research and exploration of data centre construction in smart hospital stage","authors":"Xin Xia, Yunlong Ma, Ye Luo, Jianwei Lu","doi":"10.3233/jcm-226768","DOIUrl":"https://doi.org/10.3233/jcm-226768","url":null,"abstract":"With the rapid developments of data restores and big data technologies in recent years, it aims at applying new technologies in the new stage of smart hospital development and improving scientific and proper management level of hospitals. Based on the constructions of hospital data centers and applications of Internet of things with big data technologies, hospitals can achieve intelligent operations and managements in daily work. With the aids of constructions of the data centers and mass data brought by smart hospital service models, smart management decision-making supports are effectively provided for hospital managers. Through continuous progresses of smart hospital constructions, the operations and managements of hospitals are increasingly dependent based on the supports of the data from the data center in hospital, which form the key in the smart operation and management of hospital in future.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"7 1","pages":"1847-1858"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81811767","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}
At present, car ownership is expanding, and parking facilities are insufficient. This problem has plagued people’s lives and hindered the development of cities. The stereo garage has become the main way to solve the parking problem. But the existing stereo garage is low in intelligence and low in vehicle entry efficiency. Therefore, in this study, a new vehicle entry strategy for the road stacking stereo garage is designed. GA algorithm is innovatively improved and applied to vehicle strategy optimization. By taking the dual objective function as the fitness function of the algorithm, the access strategy is optimized. Using MATLAB software to simulate and verify each access strategy and its improvement effect. This study provides guidance and data support for seeking the best vehicle access strategy. It has good practical application value for vehicle access in 3D garage.
{"title":"Automatic access control method of PLC stacker crane based on GA optimization algorithm","authors":"Xiao-ling Liu","doi":"10.3233/jcm-226792","DOIUrl":"https://doi.org/10.3233/jcm-226792","url":null,"abstract":"At present, car ownership is expanding, and parking facilities are insufficient. This problem has plagued people’s lives and hindered the development of cities. The stereo garage has become the main way to solve the parking problem. But the existing stereo garage is low in intelligence and low in vehicle entry efficiency. Therefore, in this study, a new vehicle entry strategy for the road stacking stereo garage is designed. GA algorithm is innovatively improved and applied to vehicle strategy optimization. By taking the dual objective function as the fitness function of the algorithm, the access strategy is optimized. Using MATLAB software to simulate and verify each access strategy and its improvement effect. This study provides guidance and data support for seeking the best vehicle access strategy. It has good practical application value for vehicle access in 3D garage.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"44 1","pages":"2179-2192"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82831925","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}
Event extraction, as one of the difficult tasks of information extraction, can quickly obtain valuable information from the massive information on the Internet. This paper proposes a joint event extraction model based on RoBERTa-wwm-ext and gating mechanism for document-level long text data, which not only uses the prior knowledge from event types and pre-trained language models, but also uses gated fusion module to aggregate information in the event argument extraction tasks to enhance entity representation and splices entity type embedding, thereby enhancing the correlation among events, arguments and argument roles in the text, and improving the recognition accuracy of the arguments of each event in the document. Finally, the effectiveness of the model is verified on the public dataset.
{"title":"A joint event extraction model based on RoBERTa-wwm-ext and gating mechanism","authors":"Baosheng Yin, Hua Wu, Weiyi Kong","doi":"10.3233/jcm-226772","DOIUrl":"https://doi.org/10.3233/jcm-226772","url":null,"abstract":"Event extraction, as one of the difficult tasks of information extraction, can quickly obtain valuable information from the massive information on the Internet. This paper proposes a joint event extraction model based on RoBERTa-wwm-ext and gating mechanism for document-level long text data, which not only uses the prior knowledge from event types and pre-trained language models, but also uses gated fusion module to aggregate information in the event argument extraction tasks to enhance entity representation and splices entity type embedding, thereby enhancing the correlation among events, arguments and argument roles in the text, and improving the recognition accuracy of the arguments of each event in the document. Finally, the effectiveness of the model is verified on the public dataset.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"72 1","pages":"2101-2112"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80217156","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}
Chunyan Zhu, Lan Zhang, Jialin Liu, Xue Bai, Mengting Hu
In this paper, a model of “intake quantity of water in the base period – the number of students using water” is constructed in typical colleges and universities, and a typical case study and analysis are carried out. The model takes into account the type of college students and the time in school, which provides a scientific calculation method for the calculation of water-saving amount in colleges and universities, and plays an important role in promoting the construction of water-saving colleges and universities and the construction of water-saving society.
{"title":"Calculation model and case study of water saving in typical colleges and universities","authors":"Chunyan Zhu, Lan Zhang, Jialin Liu, Xue Bai, Mengting Hu","doi":"10.3233/jcm226782","DOIUrl":"https://doi.org/10.3233/jcm226782","url":null,"abstract":"In this paper, a model of “intake quantity of water in the base period – the number of students using water” is constructed in typical colleges and universities, and a typical case study and analysis are carried out. The model takes into account the type of college students and the time in school, which provides a scientific calculation method for the calculation of water-saving amount in colleges and universities, and plays an important role in promoting the construction of water-saving colleges and universities and the construction of water-saving society.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"36 1","pages":"1999-2008"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88273564","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}
In public management, intelligent face recognition detection technology plays a very crucial role, which can greatly improve the efficiency of public management and reduce the workload of staff. To address the shortcomings of traditional face detection algorithms such as low detection efficiency and easy overfitting, a face detection model based on convolutional neural network (CNN) was proposed in this study, and the structure of CNN was optimized to enhance the accuracy and efficiency of the proposed face detection model. To solve the face detection errors caused by illumination differences, a light compensation strategy was proposed to pre-process the data; meanwhile, a Gaussian curvature filtering algorithm was used to enhance the face image and improve the subsequent detection accuracy. On this basis, a face detection model based on improved CNN was designed in this study. Experiments showed that the accuracy of the model reached 99.86% with high accuracy and efficiency, indicating that such method can improve the efficiency of public management and has good application prospects in access control and check-in systems.
{"title":"Application of improved CNN-based face detection technology in public administration","authors":"Zhao Zhao","doi":"10.3233/jcm226780","DOIUrl":"https://doi.org/10.3233/jcm226780","url":null,"abstract":"In public management, intelligent face recognition detection technology plays a very crucial role, which can greatly improve the efficiency of public management and reduce the workload of staff. To address the shortcomings of traditional face detection algorithms such as low detection efficiency and easy overfitting, a face detection model based on convolutional neural network (CNN) was proposed in this study, and the structure of CNN was optimized to enhance the accuracy and efficiency of the proposed face detection model. To solve the face detection errors caused by illumination differences, a light compensation strategy was proposed to pre-process the data; meanwhile, a Gaussian curvature filtering algorithm was used to enhance the face image and improve the subsequent detection accuracy. On this basis, a face detection model based on improved CNN was designed in this study. Experiments showed that the accuracy of the model reached 99.86% with high accuracy and efficiency, indicating that such method can improve the efficiency of public management and has good application prospects in access control and check-in systems.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"98 13 1","pages":"1985-1997"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87710705","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}
With the improvement of computer computing power and the development of artificial intelligence technology, face recognition technology has made a major breakthrough, and has been popularized and applied in all areas of life. However, different face structure and pose will affect the accuracy of face recognition. To overcome the problem, a low rank joint sparse representation algorithm for face recognition is proposed. The low rank features of images are extracted by structure independent and pairwise rank decomposition methods. The extracted low rank features of the first level image and the low rank features of the second level image are sparsely represented. Finally, the residual rate model is used to classify the images, and the final result of face recognition is obtained. The experimental results show that the proposed SRP algorithm has a recognition accuracy of more than 92% in two different face recognition tests. In the mixed multi face pose test, PRS algorithm performs best in the recognition of 1, 2, 3, 4, and 5 multi face pose types, with recognition rates of 95%, 94%, 93%, 91%, and 90% respectively. The algorithm also has excellent recognition performance and robustness in identifying harsh environments such as fuzzy environments. The research content focuses on complex face recognition scenes, innovatively uses low rank to complete the extraction of face feature data, and combines sparse selection of classification features to improve the overall effect of face recognition. It has important reference value for improving the overall security and recognition rate of face recognition.
{"title":"Face recognition technology based on low-rank joint sparse representation algorithm","authors":"Hongsheng Wang, Jingjing Cai","doi":"10.3233/jcm-226778","DOIUrl":"https://doi.org/10.3233/jcm-226778","url":null,"abstract":"With the improvement of computer computing power and the development of artificial intelligence technology, face recognition technology has made a major breakthrough, and has been popularized and applied in all areas of life. However, different face structure and pose will affect the accuracy of face recognition. To overcome the problem, a low rank joint sparse representation algorithm for face recognition is proposed. The low rank features of images are extracted by structure independent and pairwise rank decomposition methods. The extracted low rank features of the first level image and the low rank features of the second level image are sparsely represented. Finally, the residual rate model is used to classify the images, and the final result of face recognition is obtained. The experimental results show that the proposed SRP algorithm has a recognition accuracy of more than 92% in two different face recognition tests. In the mixed multi face pose test, PRS algorithm performs best in the recognition of 1, 2, 3, 4, and 5 multi face pose types, with recognition rates of 95%, 94%, 93%, 91%, and 90% respectively. The algorithm also has excellent recognition performance and robustness in identifying harsh environments such as fuzzy environments. The research content focuses on complex face recognition scenes, innovatively uses low rank to complete the extraction of face feature data, and combines sparse selection of classification features to improve the overall effect of face recognition. It has important reference value for improving the overall security and recognition rate of face recognition.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"1 1","pages":"2045-2058"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83998748","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：