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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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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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Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease

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IEEE Access 2019年 7卷 120315-120325页

作者： Ar Junejo Yin Shen Asif Ali Laghari Xiaobo Zhang Hao Luo School of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan School of Automation Guandong University of Technology Guangzhou China

Today, with the development of industry and mechanized life style, the prevalence of the disease is rising steadily as well. Observing at the trend and lifecycle style, its predict that after ten years around 23.6 million people die because of Cardiovascular Disease (CVD). For that reason, aim to use Deep Learning Techniques (DLTs), to analysis stable CVD that would give valuable awareness to decrease misdiagnosis in the Robust Healthcare Industry (RHI). An objective of this paper is first, Molecular diagnosis (MD), and second using Deep Learning Techniques DLTs, to synthesis and characterize to accumulate (raw information) from CVD patients, those who admitted the emergency section between January (2018 to December 2019). We are using Artificial Neural Network (ANN), model characterize to predict CVD patients and configuration, Feature selection (FS), Mean Square Error (MSE), accuracy, sensitivity. The ANN accuracy is 98.4, K-nearest neighbor (KNN) accuracy is 98.01%, Naïve Bayes (NB), accuracy is 96.99%. Decision tree (DT), accuracy is 87.81%. Our robust data driven model explore the efficient accuracy rate to predict CVD patients. The ANN model in term of their efficient in disease analysis, and prognosis of the RHI.

关键词： Heart Blood Diseases Arteries Mathematical model Artificial neural networks

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A Multivariate Time Series Classification based Multiple Fault Diagnosis Method for Hydraulic Systems

A Multivariate Time Series Classification based Multiple Fau...

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第三十八届中国控制会议

作者： Xiaohang Zhao Ke Zhang Yi Chai Complex system safety and control Key Laboratory of Ministry of Education School of Automation Chongqing University IEEE Department of Electrical and computer engineering Duke University

Hydraulic systems is a class of nonlinear complex *** are many typical characteristics with the systems:multiple functional components,multiple operation modes,space-time coupling work,and monitoring signals for faults are multivariate time series data,*** of the characteristics,fault diagnosis for Hydraulic systems is not *** fault diagnosis methods mostly ignore the multivariable timing characteristics of monitoring signals,it has made many detection and diagnosis(especially for multiple fault)can not keep high accuracy,and some of the methods are not even be able to multiple fault *** at the problem,a multivariate time series classification based diagnosis method is ***,extracting timing characteristics(transformed features)from the time series data collected via sensors by 1-NN ***,training the transformed features by multi-class OVO-SVM to classify multivariate time *** of the method contains single fault and multiple faults conditions,the results show that the method has high accuracy,it can complete multiple-faults classification.

关键词： fault diagnosis hydraulic systems multivariate time series analysis classification

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Deep Learning Based Automatic Crack Detection and Segmentation for Unmanned Aerial Vehicle Inspections

Deep Learning Based Automatic Crack Detection and Segmentati...

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IEEE International Conference on Robotics and Biomimetics

作者： Kangcheng Liu Xiaodong Han Ben M. Chen Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong College of Computer and Control Engineering Minjiang University Fuzhou China

Automatic crack detection and segmentation plays a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and the performance of it is compared with other state of the art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark-dataset on the internet for crack detection and segmentation, which is open-sourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmark-dataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed for the crack inspection tasks of various concrete structures.

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On PID control for synchronization of complex dynamical network with delayed nodes

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Science China(Technological Sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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Abstraction-Based Synthesis of Opacity-Enforcing controllers using Alternating Simulation Relations

Abstraction-Based Synthesis of Opacity-Enforcing Controllers...

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IEEE Conference on Decision and control

作者： Junyao Hou Xiang Yin Shaoyuan Li Majid Zamani Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Computer Science Department University of Colorado Boulder CO USA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Opacity is an important information-flow security property that captures the plausible deniability for some "secret" of a system. In this paper, we investigate the problem of synthesizing controllers that enforce opacity for labeled transition systems (LTS). Most of the existing works on synthesis of opacity-enforcing controllers are based on the original system model, which may contain a large number of states. To mitigate the complexity of the controller synthesis procedure, we propose an abstraction-based approach for controller synthesis. Specifically, we propose notion of opacity-preserving alternating (bi)simulation relation for the purpose of abstraction. We show that, if the abstract system is opacity-preserving alternatingly simulated by the original system which may be significantly smaller, then we can synthesize an opacity-enforcing controller based on the abstract system and then refine it back to a controller enforcing opacity of the original system. We investigate both initial-state opacity and infinite-step opacity. We also show the effectiveness of the proposed approach by a set of examples.

关键词： Computational modeling control system synthesis control system security Closed loop systems

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Delays in Model Reduction of Chemical Reaction Networks

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IFAC-PapersOnLine 2018年第14期51卷 100-105页

作者： Lipták, György Hangos, Katalin M. Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences P.O. Box 63 BudapestH-1518 Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary

A novel engineering model reduction method is proposed in this paper that can be applied to a chemical reaction network (CRN) with chains of linear reactions. The reduced model is a delayed CRN with possibly different delays but with less state variables than the original model. As the first step of the model reduction, a decomposition method is also developed to transform chains with joint reactions into independent chains of linear reactions. The well known example of McKeithan's network is used as a case study to illustrate the basic concepts and the design method. © 2018

关键词： Reduction

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Characteristic barriers to the implementation of the roadmap for augmented reality technology development program

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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 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

The paper gives the author's vision of what the main barriers to the creation and implementation of new technologies in the field of virtual and augmented reality may exist during the implementation of the Digital Economy of the Russian Federation program. Since some of the authors took part in the development of roadmaps for this program, there is a two-sided vision of the problem. Authors can put themselves in the shoes of both the funding organization for this project and the campaign that is developing concretely in this area. Almost two years have passed since this program was launched. The conclusions of the article can be useful both for organizations funding this program and for grant recipients

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Research and development of a stereo encoder of a FM-transmitter based on FPGA 1st

Research and development of a stereo encoder of a FM-transmi...

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1st International Conference on computer Science, Engineering and Education Applications, ICCSEEA2018

作者： Artem, Volokyta Volodymyr, Shymkovych Ivan, Volokyta Vladyslav, Vasyliev Department of Computer Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 37 Prosp. Peremohy Kyiv Ukraine Department of Automation and Control in Technical Systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 37 Prosp. Peremohy Kyiv Ukraine Royal Caribbean International FL United States

ISBN: (纸本)9783319910079

This paper describes the research and development of the digital stereo encoder FM transmitter including its implementation on Field Programmable Gate Array (FPGA). The development of structural schemes of the digital part of the FM transmitter exciter, the stereo encoder and the digital synthesizer of the sine wave on the FPGA is also described. Paper includes a description of development of a digital synthesizer of a sinusoidal signal on a FPGA, which uses a tabular method to generate the sinusoidal wave, fourth part of its period is written to memory and the rest of the period is calculated using the first part. To generate five sinusoids of different frequencies in a stereo encoder, only one table in memory used. The simulation of the stereocoder on the FPGA in carried out in the specialized computer-Aided Design (CAD). The graphs of the left and right balance signals, the sum and difference of these signals, the signal of difference of the two sine waves which have their amplitude modulated and with a carrier signal of 19 kHz and the complex stereo signal are constructed. After receiving a complex stereo signal its frequency modulation is carried out with a carrier signal of 100 MHz, the FPGA chip outputs the frequency code of modulated signal. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Digital signal processing

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