Tian Ling, Shuo Tian, Songyuheng Gao, Zhixue Xing, J. Lai, Zhenzhai Li
In order to reduce the incidence of traffic accidents, the use of computer vision to identify vehicles and passers-by in the process of driving can achieve the effect of assisting driving. This paper mainly introduces the performance improvement brought by the introduction of the SPP module in YOLO-V3 for object recognition. Model training is performed on the VOC dataset based on YOLO-V3-SPP. Finally, 300 photos were used to test the accuracy of the algorithm. The results show that the recognition accuracy of YOLO-V3-SPP for vehicles and pedestrians can reach 94.19% and 90.68%, and the accuracy of YOLO-V3 is improved by nearly ten under the same equipment. percentage point. The research on this technology can effectively reduce the probability of traffic accidents and provide reference value for the future driving safety warning field.
{"title":"An improvement of vehicle and passerby recognition based on YOLO-V3 algorithm","authors":"Tian Ling, Shuo Tian, Songyuheng Gao, Zhixue Xing, J. Lai, Zhenzhai Li","doi":"10.1117/12.2671229","DOIUrl":"https://doi.org/10.1117/12.2671229","url":null,"abstract":"In order to reduce the incidence of traffic accidents, the use of computer vision to identify vehicles and passers-by in the process of driving can achieve the effect of assisting driving. This paper mainly introduces the performance improvement brought by the introduction of the SPP module in YOLO-V3 for object recognition. Model training is performed on the VOC dataset based on YOLO-V3-SPP. Finally, 300 photos were used to test the accuracy of the algorithm. The results show that the recognition accuracy of YOLO-V3-SPP for vehicles and pedestrians can reach 94.19% and 90.68%, and the accuracy of YOLO-V3 is improved by nearly ten under the same equipment. percentage point. The research on this technology can effectively reduce the probability of traffic accidents and provide reference value for the future driving safety warning field.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"90 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":"124998359","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}
Aiming at the problem of tracking and controlling the motion path of industrial robots in the process of research, design and development, this paper will take the common six-axis industrial robots as the research object, take advantage of the application advantages of VR technology, 3D modeling technology and Web3D interactive technology, take 3ds Max as the modeling tool and Unity3D virtual reality engine as the development platform, and build a virtual reality simulation experiment system of industrial robots from the perspective of visual interaction between virtual robots and real robots, so as to provide a comprehensive and feasible solution for the research of virtual motion simulation and control of industrial robots. The whole system adopts B/S architecture and completes the design and deployment of the whole function according to MVC mode in APS.NET environment, so as to support users with different roles to test the functions of each component module of industrial robot in virtual reality environment, and also simulate the trajectory planning and motion effect control of industrial robot in different scenes. The system will greatly improve the research and development efficiency of industrial robots, increase the efficiency and flexibility of industrial robots, break through the limitations of traditional testing methods on time and space, and provide experience and reference for the intelligent development of industrial robots.
{"title":"Application of virtual reality technology in motion simulation and control of industrial robot","authors":"Wei Zhao","doi":"10.1117/12.2672640","DOIUrl":"https://doi.org/10.1117/12.2672640","url":null,"abstract":"Aiming at the problem of tracking and controlling the motion path of industrial robots in the process of research, design and development, this paper will take the common six-axis industrial robots as the research object, take advantage of the application advantages of VR technology, 3D modeling technology and Web3D interactive technology, take 3ds Max as the modeling tool and Unity3D virtual reality engine as the development platform, and build a virtual reality simulation experiment system of industrial robots from the perspective of visual interaction between virtual robots and real robots, so as to provide a comprehensive and feasible solution for the research of virtual motion simulation and control of industrial robots. The whole system adopts B/S architecture and completes the design and deployment of the whole function according to MVC mode in APS.NET environment, so as to support users with different roles to test the functions of each component module of industrial robot in virtual reality environment, and also simulate the trajectory planning and motion effect control of industrial robot in different scenes. The system will greatly improve the research and development efficiency of industrial robots, increase the efficiency and flexibility of industrial robots, break through the limitations of traditional testing methods on time and space, and provide experience and reference for the intelligent development of industrial robots.","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":"122662286","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}
Haoqing Li, Xiaohao Huang, C. Pan, Chunlei Yang, Jinbao Wang
As a key indicator in ship design, many major incidents of ship sinking are related to the ship's damaged stability. The process of calculating the damaged stability becomes more and more complex and time-consuming on account of more and more stringent specification standards. A two-stage design step is used in this article to realize the calculation of ship’s damaged stability under various watertight bulkhead fast. Firstly, a multi-layer feed-forward neural network model was designed for the predictive regression of a ship's damaged stability using the location of the watertight bulkhead as a variable. Secondly, the relationship between each watertight bulkhead variant and the damaged stability A-value is analyzed. After that, with hydrostatic curve calculation based on the inlet simulation and the interaction between watertight bulkheads considered, a multilayer feed-forward neural network model based on the attention mechanism is designed, which could predict the regression of the damaged stability A-value and analyze bulkhead weights. Finally, the validity of the model was verified by the data, in which the mean value of the prediction error MAE (mean absolute error) was at 2.67×10-4 and the computation time was greatly reduced.
{"title":"Development of neural network model based on attention mechanism applied to the prediction of ship damaged stability","authors":"Haoqing Li, Xiaohao Huang, C. Pan, Chunlei Yang, Jinbao Wang","doi":"10.1117/12.2671282","DOIUrl":"https://doi.org/10.1117/12.2671282","url":null,"abstract":"As a key indicator in ship design, many major incidents of ship sinking are related to the ship's damaged stability. The process of calculating the damaged stability becomes more and more complex and time-consuming on account of more and more stringent specification standards. A two-stage design step is used in this article to realize the calculation of ship’s damaged stability under various watertight bulkhead fast. Firstly, a multi-layer feed-forward neural network model was designed for the predictive regression of a ship's damaged stability using the location of the watertight bulkhead as a variable. Secondly, the relationship between each watertight bulkhead variant and the damaged stability A-value is analyzed. After that, with hydrostatic curve calculation based on the inlet simulation and the interaction between watertight bulkheads considered, a multilayer feed-forward neural network model based on the attention mechanism is designed, which could predict the regression of the damaged stability A-value and analyze bulkhead weights. Finally, the validity of the model was verified by the data, in which the mean value of the prediction error MAE (mean absolute error) was at 2.67×10-4 and the computation time was greatly reduced.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"9 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":"127745547","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, the academic community has carried out some research on knowledge reasoning using Reinforcement Learning (RL), which has achieved good results in multi-hop reasoning. However, these methods often need to manually design the reward function to adapt to a specific dataset. For different datasets, the reward function in RL-based methods needs to be manually adjusted to obtain good performance. To solve this problem, an agent training model combined with Generative Adversarial Networks (GAN) is proposed. The model consists of two modules: a generative adversarial inference engine and a sampler. The sampler uses a policy-based bidirectional breadth-first search method to find the demonstration path, and the agent uses the reward considering the information of the neighborhood entities as the initial reward function. After sufficient adversarial training between the agent and the discriminator, the policy-based agent can find evidence paths that match the demonstration distribution and synthesize these evidence paths to make predictions. Experiments show that the model achieves better results in both fact prediction and link prediction tasks.
{"title":"Reinforcement learning multi-hop reasoning method with GAN network","authors":"Zhicai Gao, Xiaoze Gong, Yongli Wang","doi":"10.1117/12.2671176","DOIUrl":"https://doi.org/10.1117/12.2671176","url":null,"abstract":"At present, the academic community has carried out some research on knowledge reasoning using Reinforcement Learning (RL), which has achieved good results in multi-hop reasoning. However, these methods often need to manually design the reward function to adapt to a specific dataset. For different datasets, the reward function in RL-based methods needs to be manually adjusted to obtain good performance. To solve this problem, an agent training model combined with Generative Adversarial Networks (GAN) is proposed. The model consists of two modules: a generative adversarial inference engine and a sampler. The sampler uses a policy-based bidirectional breadth-first search method to find the demonstration path, and the agent uses the reward considering the information of the neighborhood entities as the initial reward function. After sufficient adversarial training between the agent and the discriminator, the policy-based agent can find evidence paths that match the demonstration distribution and synthesize these evidence paths to make predictions. Experiments show that the model achieves better results in both fact prediction and link prediction tasks.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"11 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":"128138263","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 view of the large volume and complex structure of electromagnetic space big data, it is difficult to store and retrieve spectrum data using traditional databases and knowledge graph. Due to the abstractness and space-time characteristics of electromagnetic spectrum data, the use of event forms can better represent the spectrum data, and also make people and machines better understand. Based on the knowledge graph and the concept of events, this paper constructs the spectrum event knowledge graph (EMS-DEKG) and compares several methods of spectrum data retrieval through experiments, which shows that the EMS-DEKG method improves the stability and timeliness of electromagnetic space big data storage and retrieval.
{"title":"Research on the construction and application of event based electromagnetic space big data knowledge graph","authors":"Dongsheng Li, Bing Ma, Yuanzhong Ren, K. Li","doi":"10.1117/12.2671438","DOIUrl":"https://doi.org/10.1117/12.2671438","url":null,"abstract":"In view of the large volume and complex structure of electromagnetic space big data, it is difficult to store and retrieve spectrum data using traditional databases and knowledge graph. Due to the abstractness and space-time characteristics of electromagnetic spectrum data, the use of event forms can better represent the spectrum data, and also make people and machines better understand. Based on the knowledge graph and the concept of events, this paper constructs the spectrum event knowledge graph (EMS-DEKG) and compares several methods of spectrum data retrieval through experiments, which shows that the EMS-DEKG method improves the stability and timeliness of electromagnetic space big data storage and retrieval.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"30 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":"133658006","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}
Juan Fang, Qiangang Zheng, Wei-ming Liu, Haibo Zhang
With the development of Reinforcement Learning (RL), it becomes able to solve the continuous action space problem and shows strong ability in dealing with complex nonlinear control problem. Based on the Deep Deterministic Policy Gradient (DDPG) algorithm, a novel scheme of aeroengine acceleration controller is proposed in this paper. According to the characteristics of the engine acceleration stage, the reward function is constructed, and the state parameters are updated in the form of sliding window to reduce the sensitivity of the network to noise. DDPG adopts actor-critic framework, critic calculates value function by the deep neural network, actor outputs action command and forms a closed-loop control system with the engine. The method is verified by digital simulation at ground condition and the results demonstrate that compared with the traditional PID controller, the acceleration time of DDPG controller is reduced by 41.56%. Additionally, the network converges within 400 steps.
{"title":"Optimization control with multi-constraint of aeroengine acceleration process based on reinforcement learning","authors":"Juan Fang, Qiangang Zheng, Wei-ming Liu, Haibo Zhang","doi":"10.1117/12.2671152","DOIUrl":"https://doi.org/10.1117/12.2671152","url":null,"abstract":"With the development of Reinforcement Learning (RL), it becomes able to solve the continuous action space problem and shows strong ability in dealing with complex nonlinear control problem. Based on the Deep Deterministic Policy Gradient (DDPG) algorithm, a novel scheme of aeroengine acceleration controller is proposed in this paper. According to the characteristics of the engine acceleration stage, the reward function is constructed, and the state parameters are updated in the form of sliding window to reduce the sensitivity of the network to noise. DDPG adopts actor-critic framework, critic calculates value function by the deep neural network, actor outputs action command and forms a closed-loop control system with the engine. The method is verified by digital simulation at ground condition and the results demonstrate that compared with the traditional PID controller, the acceleration time of DDPG controller is reduced by 41.56%. Additionally, the network converges within 400 steps.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"8 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":"131926190","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}
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}
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}
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}
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