Yang Zhao, Qing Liu, Tong Shang, Yingqiang Shang, R. Xia
With the increasing scale of high-voltage cable equipment in domestic urban power grids, it is necessary to deepen the intelligent construction of transmission lines, solve the common problems encountered in big data processing and edge side application of high-voltage cables, and take edge IOT agent as the cutting point for technical research. By studying edge computing, AI image recognition and intelligent linkage control model of cable channel business application, intelligent management and control of high-voltage cable line status, risk early warning, differentiated operation and maintenance decision, etc. can be realized, and the intrinsic safety level and lean operation and maintenance management ability of cable lines and channel equipment can be improved.
{"title":"Research on multi-service local processing and application based on edge IoT agent","authors":"Yang Zhao, Qing Liu, Tong Shang, Yingqiang Shang, R. Xia","doi":"10.1117/12.2671189","DOIUrl":"https://doi.org/10.1117/12.2671189","url":null,"abstract":"With the increasing scale of high-voltage cable equipment in domestic urban power grids, it is necessary to deepen the intelligent construction of transmission lines, solve the common problems encountered in big data processing and edge side application of high-voltage cables, and take edge IOT agent as the cutting point for technical research. By studying edge computing, AI image recognition and intelligent linkage control model of cable channel business application, intelligent management and control of high-voltage cable line status, risk early warning, differentiated operation and maintenance decision, etc. can be realized, and the intrinsic safety level and lean operation and maintenance management ability of cable lines and channel equipment can be improved.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127394509","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}
Neurological diseases, including Alzheimer's disease and brain tumors, are the leading causes of death and disability worldwide. However, it is difficult for scientists to quantify the response of these deadly diseases to treatment. Existing neuron-based solutions have limited accuracy. Neuroblastoma cell lines have unique, irregular and concave morphology, which makes them show low precision scores in different cancer cell types. Based on this, this study proposes a new cell semantic segmentation network model. The model first enhances the original cell map, and then introduces the residual module and attention mechanism based on the classical U-Net network structure, which alleviates the problem of network degradation and improves the efficiency and effect of network training. The experimental results on the neuroblastoma cell line data set provided by Sartorius show that the segmentation accuracy of the proposed model is about fifteen percentage points higher than that of the classical U-Net model and one percentage point higher than that of the U-Net++ model.
{"title":"Research on neural cell image segmentation based on improved U-Net model","authors":"Zhehao Xiao","doi":"10.1117/12.2671318","DOIUrl":"https://doi.org/10.1117/12.2671318","url":null,"abstract":"Neurological diseases, including Alzheimer's disease and brain tumors, are the leading causes of death and disability worldwide. However, it is difficult for scientists to quantify the response of these deadly diseases to treatment. Existing neuron-based solutions have limited accuracy. Neuroblastoma cell lines have unique, irregular and concave morphology, which makes them show low precision scores in different cancer cell types. Based on this, this study proposes a new cell semantic segmentation network model. The model first enhances the original cell map, and then introduces the residual module and attention mechanism based on the classical U-Net network structure, which alleviates the problem of network degradation and improves the efficiency and effect of network training. The experimental results on the neuroblastoma cell line data set provided by Sartorius show that the segmentation accuracy of the proposed model is about fifteen percentage points higher than that of the classical U-Net model and one percentage point higher than that of the U-Net++ model.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122240802","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}
A more accurate target detection model is proposed in this research based on Yolov5 target detection algorithm, aiming at its low regression accuracy to the target boundary box. Firstly, coordinate attention mechanism is added to the backbone network to improve the position information of the perceived target in the underlying feature information. Secondly, GIOU is replaced with EIOU to improve the convergence speed. Finally, the feature extraction network is replaced with BiFPN to more efficiently fuse different feature information. Using PASCAL VOC 2007 and 2012 datasets and redividing the training set and verification set, this algorithm is better than the original algorithm mAP@0.5 increased by 2.9%, mAP@0.5:0.95 increased by 1.4%.
{"title":"Object detection algorithm based on improved Yolov5","authors":"Hua Wang, Jiang Yin, Shuang Zhang, Daishuang Hou","doi":"10.1117/12.2672682","DOIUrl":"https://doi.org/10.1117/12.2672682","url":null,"abstract":"A more accurate target detection model is proposed in this research based on Yolov5 target detection algorithm, aiming at its low regression accuracy to the target boundary box. Firstly, coordinate attention mechanism is added to the backbone network to improve the position information of the perceived target in the underlying feature information. Secondly, GIOU is replaced with EIOU to improve the convergence speed. Finally, the feature extraction network is replaced with BiFPN to more efficiently fuse different feature information. Using PASCAL VOC 2007 and 2012 datasets and redividing the training set and verification set, this algorithm is better than the original algorithm mAP@0.5 increased by 2.9%, mAP@0.5:0.95 increased by 1.4%.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132755097","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 study examines the literature on AI and libraries, examines the significant roles AI has played recently in industries related to libraries, and briefly describes relevant technical functions and their application characteristics in the library field. It begins with six key technologies: OCR, data mining, natural language processing, face recognition, knowledge mapping, and machine learning, and then makes a thorough analysis of each. Detailed analysis and summary of the results achieved in the practical application of AI, an analytical overview of the business functions related to AI in the library field on the development and reform of libraries and the current application status of various technologies, and the problems that libraries may encounter in the practical implementation of AI-related technologies are pointed out.
{"title":"Research on the application of artificial intelligence in the library sector","authors":"Zihan Xu","doi":"10.1117/12.2671477","DOIUrl":"https://doi.org/10.1117/12.2671477","url":null,"abstract":"This study examines the literature on AI and libraries, examines the significant roles AI has played recently in industries related to libraries, and briefly describes relevant technical functions and their application characteristics in the library field. It begins with six key technologies: OCR, data mining, natural language processing, face recognition, knowledge mapping, and machine learning, and then makes a thorough analysis of each. Detailed analysis and summary of the results achieved in the practical application of AI, an analytical overview of the business functions related to AI in the library field on the development and reform of libraries and the current application status of various technologies, and the problems that libraries may encounter in the practical implementation of AI-related technologies are pointed out.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128544848","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}
Chinese pattern design has not only witnessed the history of more than 5,000 years in China, but also impacted the aesthetic cognition of the East in the West. Chinese patterns have been beautiful since ancient times. In the past, it was created by the wisdom and hard work of the Chinese people, and now it should be inherited by the wisdom and hard work of the Chinese people. For the visualization platform of Chinese pattern design, better construction and improvement are needed. Therefore, in order to visualize information and achieve better results, if the memory occupancy is too high, the operation effect of the platform will be reduced. In order to improve the operation effect of the visualization platform, the construction of visual virtual reality platform of Chinese pattern design is proposed. Based on B/S mode, the software structure is established, and specific analysis is carried out, and the functional plate and visual effect design are improved. Through hardware and software design, the visual virtual reality platform of Chinese pattern design is constructed.
{"title":"Research on the construction of visual virtual reality platform for Chinese pattern design","authors":"Pin Gao, Hongming Bian, Y. Bao","doi":"10.1117/12.2671209","DOIUrl":"https://doi.org/10.1117/12.2671209","url":null,"abstract":"Chinese pattern design has not only witnessed the history of more than 5,000 years in China, but also impacted the aesthetic cognition of the East in the West. Chinese patterns have been beautiful since ancient times. In the past, it was created by the wisdom and hard work of the Chinese people, and now it should be inherited by the wisdom and hard work of the Chinese people. For the visualization platform of Chinese pattern design, better construction and improvement are needed. Therefore, in order to visualize information and achieve better results, if the memory occupancy is too high, the operation effect of the platform will be reduced. In order to improve the operation effect of the visualization platform, the construction of visual virtual reality platform of Chinese pattern design is proposed. Based on B/S mode, the software structure is established, and specific analysis is carried out, and the functional plate and visual effect design are improved. Through hardware and software design, the visual virtual reality platform of Chinese pattern design is constructed.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131625381","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}
Lane detection is a crucial environmental sensing technique that is used in advanced driving assistance systems and automatic driving. The research on this issue has significant practical value. Aiming the current lane detection algorithm could not solve the problems of the local receptive field and detail feature loss, we introduced the multi-head self-attention module in Transformer into the encoder and decoder to obtain the global receptive field while solving the problem of detail feature loss with the multi-level feature fusion decoder. The proposed algorithm has been compared with the ERFNet model in the CULane dataset, and the detection accuracy has improved by 3.9 percentage points. The detection accuracy in the Tusimple dataset is 96.51%. Introducing a multi-head self-attention module increases the feature selection effect of the attention mechanism in the coding and decoding process. It provides a new solution for the lane detection algorithm.
{"title":"Lane detection algorithm based on multi-head self-attention and multi-level feature fusion","authors":"Bobo Guo, Zanxia Qiang, Xianfu Bao, Yao Xu","doi":"10.1117/12.2671212","DOIUrl":"https://doi.org/10.1117/12.2671212","url":null,"abstract":"Lane detection is a crucial environmental sensing technique that is used in advanced driving assistance systems and automatic driving. The research on this issue has significant practical value. Aiming the current lane detection algorithm could not solve the problems of the local receptive field and detail feature loss, we introduced the multi-head self-attention module in Transformer into the encoder and decoder to obtain the global receptive field while solving the problem of detail feature loss with the multi-level feature fusion decoder. The proposed algorithm has been compared with the ERFNet model in the CULane dataset, and the detection accuracy has improved by 3.9 percentage points. The detection accuracy in the Tusimple dataset is 96.51%. Introducing a multi-head self-attention module increases the feature selection effect of the attention mechanism in the coding and decoding process. It provides a new solution for the lane detection algorithm.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"103 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131919013","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 paper proposes an end-to-end abnormal behavior detection network to detect strenuous movements in slow moving crowds, such as running, bicycling in transportation surveillance videos. The algorithm forms continuous video frames into a video packet and use the video packet feature extractor to obtain the spatio-temporal information. The implicit vector-based attention mechanism will work on the extracted video packet features to highlight the important features. We use fully connected layers to transform the space and reduce the computation. Finally, the packet-pooling maps the processed video packet features to the abnormal scores. The network input is flexible to cope with the form of video streams, and the network output is the abnormal score. The designed compound loss function will help the model improve the classification performance. This paper arranges several commonly used anomaly detection datasets and tests the algorithms on the integrated dataset. The experiment results show that the proposed algorithm has significant advantages in many objective metrics comparing with other anomaly detection algorithms.
{"title":"The anomaly behavior detection algorithm with video-packet attention in transportation surveillance videos","authors":"Liyuan Wang, S. Yu, Ling Ding, Yuanxu Wu, Yu Chen, Jinsheng Xiao","doi":"10.1117/12.2671205","DOIUrl":"https://doi.org/10.1117/12.2671205","url":null,"abstract":"This paper proposes an end-to-end abnormal behavior detection network to detect strenuous movements in slow moving crowds, such as running, bicycling in transportation surveillance videos. The algorithm forms continuous video frames into a video packet and use the video packet feature extractor to obtain the spatio-temporal information. The implicit vector-based attention mechanism will work on the extracted video packet features to highlight the important features. We use fully connected layers to transform the space and reduce the computation. Finally, the packet-pooling maps the processed video packet features to the abnormal scores. The network input is flexible to cope with the form of video streams, and the network output is the abnormal score. The designed compound loss function will help the model improve the classification performance. This paper arranges several commonly used anomaly detection datasets and tests the algorithms on the integrated dataset. The experiment results show that the proposed algorithm has significant advantages in many objective metrics comparing with other anomaly detection algorithms.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133216732","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}
Chunli Wang, Botao Zeng, Jin-Chao Gao, Ge Peng, Wei Yang
In recent years, the traffic image semantic segmentation plays a crucial role in automatic driving. The result of semantic segmentation will directly affect the car's understanding of the external scene. Thus, a semantic segmentation algorithm based on UNET network model is proposed for getting better results in traffic images segmentation. To prove the effectiveness of the proposed algorithm, highway driving dataset is used on the experiments. The experimental results show that the proposed network can achieve high precision image semantic segmentation in complex road scenes, and the segmentation accuracy is greatly improved compared with other network models.
{"title":"A traffic image semantic segmentation algorithm based on UNET","authors":"Chunli Wang, Botao Zeng, Jin-Chao Gao, Ge Peng, Wei Yang","doi":"10.1117/12.2671074","DOIUrl":"https://doi.org/10.1117/12.2671074","url":null,"abstract":"In recent years, the traffic image semantic segmentation plays a crucial role in automatic driving. The result of semantic segmentation will directly affect the car's understanding of the external scene. Thus, a semantic segmentation algorithm based on UNET network model is proposed for getting better results in traffic images segmentation. To prove the effectiveness of the proposed algorithm, highway driving dataset is used on the experiments. The experimental results show that the proposed network can achieve high precision image semantic segmentation in complex road scenes, and the segmentation accuracy is greatly improved compared with other network models.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133452036","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}
Sleep is the life instinct of human beings. It is not only of great significance to the physical and mental health of individuals, but also can be used as a natural means of regulating, restoring and enhancing bodily functions. There is a prominent contradiction between health needs and the backward status of daily sleep health management, and there is an urgent need to develop theories and methods from sleep structure conversion mechanism to sleep quality monitoring and intervention. In the past few years, artificial intelligence (AI) technology has rapidly emerged in the field of sleep medicine. The purpose of this article is to provide a brief overview of relevant terms, definitions and use cases of artificial intelligence in sleep medicine. AI has a variety of applications in sleep medicine, including sleep and respiratory event scoring in sleep labs, diagnosis and management of sleep disorders, and population health. Although still in its infancy, there are still challenges that hinder the ubiquity and broad clinical application of AI. Overcoming these challenges will help seamlessly integrate AI into sleep medicine and enhance clinical practice. AI is a powerful tool in healthcare that can improve patient care, enhance diagnostic capabilities, and enhance the management of sleep disorders. However, before existing machine learning algorithms can be incorporated into sleep clinics, these artificial intelligence devices need to be regulated and standardized.
{"title":"Application of artificial intelligence in sleep medicine","authors":"Qianfeng Chen, Maorong Hu","doi":"10.1117/12.2671764","DOIUrl":"https://doi.org/10.1117/12.2671764","url":null,"abstract":"Sleep is the life instinct of human beings. It is not only of great significance to the physical and mental health of individuals, but also can be used as a natural means of regulating, restoring and enhancing bodily functions. There is a prominent contradiction between health needs and the backward status of daily sleep health management, and there is an urgent need to develop theories and methods from sleep structure conversion mechanism to sleep quality monitoring and intervention. In the past few years, artificial intelligence (AI) technology has rapidly emerged in the field of sleep medicine. The purpose of this article is to provide a brief overview of relevant terms, definitions and use cases of artificial intelligence in sleep medicine. AI has a variety of applications in sleep medicine, including sleep and respiratory event scoring in sleep labs, diagnosis and management of sleep disorders, and population health. Although still in its infancy, there are still challenges that hinder the ubiquity and broad clinical application of AI. Overcoming these challenges will help seamlessly integrate AI into sleep medicine and enhance clinical practice. AI is a powerful tool in healthcare that can improve patient care, enhance diagnostic capabilities, and enhance the management of sleep disorders. However, before existing machine learning algorithms can be incorporated into sleep clinics, these artificial intelligence devices need to be regulated and standardized.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132036800","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}
Users of the simulation software not only need to model the capability of each unit, but also need to create a decision maker for the units in the simulation software, to control ships, aircrafts and ground units to cooperates to achieve one goal. In this paper a new approach is constructed to create the decision maker. We use reinforcement learning based on global critic and local actor. The invention constructs an air isomorphic formation command method based on multiagent PPO algorithm. The evaluation network uses global information, so that the algorithm has the ability to evaluate global information and guide the agent to select actions that are beneficial to the global environment state. The input of the action network is local information, so that the agent can focus on local information.
{"title":"Global critic and local actor for campaign-tactic combat management in the joint operation simulation software","authors":"Yabin Wang, Peng Cui, Youjiang Li","doi":"10.1117/12.2671217","DOIUrl":"https://doi.org/10.1117/12.2671217","url":null,"abstract":"Users of the simulation software not only need to model the capability of each unit, but also need to create a decision maker for the units in the simulation software, to control ships, aircrafts and ground units to cooperates to achieve one goal. In this paper a new approach is constructed to create the decision maker. We use reinforcement learning based on global critic and local actor. The invention constructs an air isomorphic formation command method based on multiagent PPO algorithm. The evaluation network uses global information, so that the algorithm has the ability to evaluate global information and guide the agent to select actions that are beneficial to the global environment state. The input of the action network is local information, so that the agent can focus on local information.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"36 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113992860","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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