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arXiv 2019年

作者： Gou, Chao Shen, Tianyu Zheng, Wenbo Xue, Huadan Yu, Hui Ji, Qiang Jin, Zhengyu Wang, Fei-Yue School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Radiology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100730 China School of Creative Technologies University of Portsmouth PortsmouthPO1 2DJ United Kingdom Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Qingdao Academy of Intelligent Industries Qingdao266000 China

Data-driven artificial intelligence technologies have made much progress in medical image analysis in the last decades. However, it still remains challenging due to its distinctive complexity of acquiring and annotating image data, extracting medical domain knowledge, and explaining the diagnostic decision for medical image analysis. In this paper, we propose a data-knowledge-driven framework termed as Parallel Medical Imaging (PMI) for intelligent medical image analysis based on the methodology of interactive ACP-based parallel intelligence. In the PMI framework, computational experiments with predictive learning in a data-driven way are conducted to extract medical knowledge for diagnostic decision support. Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge. Through the closed-loop optimization based on parallel execution, our proposed PMI framework can boost the generalization ability and alleviate the limitation of medical interpretation for diagnostic decisions. Furthermore, we illustrate the preliminary implementation of PMI method through the case studies of mammogram analysis and skin lesion image analysis. Experimental results on several public medical image datasets demonstrate the effectiveness of proposed PMI. © 2019, CC BY.

关键词： Medical imaging

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7.3: Compare Variation of User's EEG Owing to Parallax Cue

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SID Symposium Digest of Technical Papers 2018年第S1期49卷

作者： Haichen Hu Danli Wang Kang Yue Xinpan Yang Steve C. Chiu Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Science and Engineering Idaho State University Pocatello America

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user’s EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30-channel was recorded. The result suggests that significant variation of the user’s EEG data occurred in the Alpha (8–13Hz) and Beta(14–30Hz) frequency band by different parallax’s comparison. And we found not only parallax but also electrode position can impact the user’s EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ, PZ, OZ, O2 in international 10–20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further.

关键词： EEG Parallax RDS 3D

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Critical technologies in the cluster of virtual and augmented reality

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IOP Conference Series: Materials Science and Engineering 2021年第1期1019卷

作者： V Zhmud A Liapidevskiy V Avrmachuk V Sayapin J N Sedinkin W Hardt Department of Automation Novosibirsk State Technical University Novosibirsk Russia General Director of Novosibirsk Institute of Program Systems Novosibirsk Russia Department of Complex Information Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Director of R&D Center of Novosibirsk Institute of Program Systems Novosibirsk Russia Vice-Director on Development of Novosibirsk Institute of Program Systems Novosibirsk Russia Department of Computer Science Technical University of Chemnitz Chemnitz Germany

The program 'Digital Economy of the Russian Federation', launched in 2019, contains virtual and augmented reality technologies in its list. Each such technology consists of many assistive technologies called sub-technologies. These assistive technologies make up a large list, while failure to obtain the required results in at least one of these technologies may lead to the fact that the integral technology will not be created. This article discusses a list of only critical assistive technologies, that is, precisely those technologies whose creation is a critical factor for creating a generalized (integral) technology. It is on these technologies that the main emphasis should be placed not only in financing their development, but also in the thorough planning of goals and objectives, in the selection of performers and in the acceptance of research results.

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Preface

Communications and Control Engineering

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Communications and control Engineering 2011年第9780857292551期 vii-xi页

作者： Sun, Zhendong Ge, Shuzhi Sam College Automation Science & Engineering Center for Control and Optimization South China University of Technology Guangzhou510640 China Robotics Institue and Institute of Intelligent Systems and Information Technology University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering The National University of Singapore Singapore Singapore

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Path planning of mobile robot based on improved DDQN

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Journal of Physics: Conference Series 2021年第1期2024卷

作者： Yang yunxiao Wangjun Zhang Hualiang Dai shilong School of Computer Science Shenyang University of Chemical Technology Shenyang 110142 China. Industrial Control Network and System Laboratory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China. Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China.

Aiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm.

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2018年 620卷 v-vi页

作者： Omatu, Sigeru Rodríguez, Sara Villarrubia, Gabriel Faria, Pedro Sitek, Pawel Prieto, Javier Faculty of Engineering Department of Electronics Information and Communication Engineering Osaka Institute of Technology OsakaOsaka Japan Faculty of Science Department of Computer Science and Automation Control University of Salamanca Salamanca Spain GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Instituto Superior de Engenharia do Porto Porto Portugal Faculty of Electrical Engineering and Computer Science Department of Control and Management Systems Department of Information Systems Kielce University of Technology Kielce Poland

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Nonlinear analysis of dynamical complex networks 2014

Abstract and Applied Analysis

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Abstract and Applied Analysis 2014年第1期2014卷

作者： Wang, Zidong Shen, Bo Dong, Hongli Hu, Jun He, Xiao Ding, Derui Department of Computer Science Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Communication Systems and Networks (CSN) Research Group Faculty of Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia School of Information Science and Technology Donghua University Shanghai 200051 China College of Electrical and Information Engineering Northeast Petroleum University Daqing 163318 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150001 China Department of Applied Mathematics Harbin University of Science and Technology Harbin 150080 China Department of Automation Tsinghua University Beijing 100084 China Department of Applied Mathematics Anhui Polytechnic University Wuhu 241000 China

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Influence of Mobility Models in Precision Spray Aided by Wireless Sensor Networks

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Journal of Physics: Conference Series 2015年第1期574卷

作者： L B L Gonçalves F G Costa L A Neves J Ueyama G F D Zafalon C Montez A S R Pinto Department of Computer Science and Statistic São Paulo State University São José do Rio Preto SP 15054-000 Brazil Institute of Mathematics and Computer Science University of São Paulo São Carlos SP 13566-590 Brazil Department of Control Engineering and Automation Federal University of Santa Catarina Florianópolis SC 88040-900 Brazil

Precision Spray is a technique to increase performance of Precision Agriculture. This spray technique may be aided by a Wireless Sensor Network, however, for such approach, the communication between the agricultural input applicator vehicle and network is critical due to its proper functioning. Thus, this work analyzes how the number of nodes in a wireless sensor network, its type of distribution and different areas of scenario affects the performance of communication. We performed simulations to observe system's behavior changing to find the most fitted non-controlled mobility model to the system.

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Analysis of barriers to the development of Industrial Internet of Things technology and ways to overcome them

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IOP Conference Series: Materials Science and Engineering 2021年第1期1019卷

作者： V Zhmud A Liapidevskiy V Avrmachuk V Sayapin O Stukach H Roth Dept. of Automation Novosibirsk State Technical University Novosibirsk Russia Novosibirsk Institute of Program Systems Novosibirsk Russia Department of Complex Information Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Faculty of Computer Sciences of National Research University Higher School of Economics Moscow Russia Faculty of Natural Sciences and Technology University of Siegen Siegen Germany

As is known, in 2019 the national program 'Digital Economy of the Russian Federation' was launched. In the first phase, nine roadmaps were developed, of which six were approved by the Prime Minister. Participation in the development of these roadmaps provides a fresh perspective on some particular problems. The authors reveal some details of the approach to the implementation of the 'Industrial Internet of Things' direction. Some specific differences of the industrial Internet from the regular Internet are discussed using examples. Some conclusions are drawn about the directions for further research in this area.

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Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

arXiv

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology

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