Dynamic systems are ubiquitous in nature. The analysis of the stability and performance of dynamic systems has been a research hotspot in control science and operations research for a long time. In this paper, we construct and analyze an actual sequential decision-making problem of dynamic system. The Model-Free reinforcement learning algorithms are used to optimize this problem. The problem is analyzed in detail through adaptive control theory and information theory, also the extreme performance of the algorithm is pointed out. In this paper, we select three classic Model-Free reinforcement learning algorithms, DQN, DQN-PER, and PPO, to compare and analyze their performance on the timing series decision problem we construct.
{"title":"Application Research of Model-Free Reinforcement Learning under the Condition of Conditional Transfer Function with Coupling Factors","authors":"Xiaoya Yang, Youtian Guo, Rui Wang, Xiaohui Hu","doi":"10.1145/3430199.3430210","DOIUrl":"https://doi.org/10.1145/3430199.3430210","url":null,"abstract":"Dynamic systems are ubiquitous in nature. The analysis of the stability and performance of dynamic systems has been a research hotspot in control science and operations research for a long time. In this paper, we construct and analyze an actual sequential decision-making problem of dynamic system. The Model-Free reinforcement learning algorithms are used to optimize this problem. The problem is analyzed in detail through adaptive control theory and information theory, also the extreme performance of the algorithm is pointed out. In this paper, we select three classic Model-Free reinforcement learning algorithms, DQN, DQN-PER, and PPO, to compare and analyze their performance on the timing series decision problem we construct.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127149822","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}
Jingjing Xiao, Dongyue Si, Yanfang Wu, Meng Li, J. Yin, H. Ding
This paper presents a multi-view learning based method for left atrial cavity segmentation in 3D Late Gadolinium Enhanced Magnetic Resonance Imaging (LGE-MRI). Segmenting left atrium is challenging due to the low intensity contrast, motion artifacts, and extremely thin atrial walls. Since the spatial consistency of the atrium could help to alleviate the segmentation ambiguity caused by those problems, the proposed method consists of three deep convolutional streams which construct 3D segmentation likelihood maps from different views, i.e., axial view, coronal view, and sagittal view. Then, those likelihood maps will be fused and contribute to a final 3D segmentation map, where the method further inspects the 3D connectivity of the labeled pixels and discards the disconnected regions that don't belong to the atrium. The proposed method is tested on a publicly available dataset, where 80 scans are for training and 20 scans are for testing. Compared to the other state-of-the-art algorithms, the proposed method demonstrates a considerable improvement, which shows the advantages of using multi-view information.
{"title":"Multi-view Learning for 3D LGE-MRI Left Atrial Cavity Segmentation","authors":"Jingjing Xiao, Dongyue Si, Yanfang Wu, Meng Li, J. Yin, H. Ding","doi":"10.1145/3430199.3430203","DOIUrl":"https://doi.org/10.1145/3430199.3430203","url":null,"abstract":"This paper presents a multi-view learning based method for left atrial cavity segmentation in 3D Late Gadolinium Enhanced Magnetic Resonance Imaging (LGE-MRI). Segmenting left atrium is challenging due to the low intensity contrast, motion artifacts, and extremely thin atrial walls. Since the spatial consistency of the atrium could help to alleviate the segmentation ambiguity caused by those problems, the proposed method consists of three deep convolutional streams which construct 3D segmentation likelihood maps from different views, i.e., axial view, coronal view, and sagittal view. Then, those likelihood maps will be fused and contribute to a final 3D segmentation map, where the method further inspects the 3D connectivity of the labeled pixels and discards the disconnected regions that don't belong to the atrium. The proposed method is tested on a publicly available dataset, where 80 scans are for training and 20 scans are for testing. Compared to the other state-of-the-art algorithms, the proposed method demonstrates a considerable improvement, which shows the advantages of using multi-view information.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114990151","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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