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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wei Li Gang Xiong Hao Tao Yunjun Han Fang Wang Xisong Dong Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan China School of Control Science and Engineering (CSE) Shandong University Jinan China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios.

关键词： Industries Heuristic algorithms Digital twin Conferences Knowledge based systems Reinforcement learning Markov processes

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Feature Aggregation Attention Network for Single Image Dehazing

Feature Aggregation Attention Network for Single Image Dehaz...

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IEEE International Conference on Image Processing

作者： Lan Yan Wenbo Zheng Chao Gou Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Software Engineering Xi’an Jiaotong University Xi’an China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Due to its ill-posed nature, single image dehazing is a challenging problem. In this paper, we propose an end-to-end feature aggregation attention network (FAAN) for single image dehazing. It incorporates the idea of attention mechanism and residual learning and can adaptively aggregate different level features. In particular, in the proposed FANN, we design a novel block structure consisting of feature attention module, smoothed dilated convolution and local residual learning. The local residual learning allows the less useful information to be bypassed through multiple skip connections. The feature attention module is designed to assign more weight to important features. The smoothed dilated convolution is adopted to enlarge the receptive field without the negative influence of gridding artifacts. The experiments on the RESIDE dataset show that the proposed approach acquires state-of-the-art performance in both qualitative and quantitative measures.

关键词： Convolution Atmospheric modeling Kernel Image color analysis Task analysis Training

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DH-Mammo PET: a dual-head positron emission mammography system for breast imaging

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PHYSICS IN MEDICINE AND BIOLOGY 2022年第20期67卷 205004-205004页

作者： Shi, Yu Wang, Yirong Zhou, Jianwei Zhu, Yuzhen Lyu, Xudong Hui, Hui Wen, Bo Liu, Yanyun Li, Lei Li, Juntao Meng, Fanzhen Kang, Fei Zhu, Shouping Xidian Univ Sch Life Sci & Technol Xian 710126 Shaanxi Peoples R China Minist Educ Engn Res Ctr Mol & Neuro Imaging Xian 710126 Shaanxi Peoples R China Xidian Univ Sch Life Sci & Technol Int Joint Res Ctr Adv Med Imaging & Intelligent D Xian 710126 Shaanxi Peoples R China Xidian Univ Sch Life Sci & Technol Xian Key Lab Intelligent Sensing & Regulat Trans Xian 710126 Shaanxi Peoples R China Fourth Mil Med Univ Xijing Hosp Dept Nucl Med Xian 710032 Shaanxi Peoples R China Raycan Technol Co Ltd Suzhou Suzhou 215163 Jiangsu Peoples R China Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100080 Peoples R China Hebei Med Univ Sch Med Imaging Shijiazhuang 050017 Hebei Peoples R China

Objective. To develop a simultaneous positron emission tomography-Optical (OPET) breast imaging dual-head PET subsystem, called DH-Mammo PET, for accurate, early diagnosis and efficacy assessment of breast cancer with high resolution and sensitivity. Approach. We developed a breast-dedicated PET based on LYSO crystal, silicon photomultiplier array and multi-voltage threshold sampling technique. It consists of two detector heads, each with a detection area of 216 mm x 145.5 mm. The distance between the detector heads is fixed at 120 mm. In order to extract coincidences and correct data, GPU-based software coincidence processing, random, scatter, normalization, gap-filling and attenuation corrections were applied in turn. The images were reconstructed using maximum likelihood expectation maximization with depth of interaction (DOI) modeling. The performance of DH-Mammo PET was evaluated referring to NEMA NU 4-2008, NU 2-2007 and Chinese industry recommended standard YY/T 1835-2022. Besides, several clinical patient images of DH-Mammo PET were compared with those of a whole-body PET/CT. Main results. The energy resolution was 14.5%, and time resolution was < 1.31 ns. Indicated by the Na-22 point source imaging, its spatial resolution was 2.60 mm (5.40 mm), 1.00 mm (1.04 mm), and 0.96 mm (0.93 mm) in the X, Y and Z directions, respectively, using the system response matrix with (without) DOI modeling. Indicated by the Derenzo phantom imaging, the spatial resolution was similar to 3.0 mm, <1.2 mm, and X, Y and Z directions. The system sensitivity was 6.87%, 4.89% and 3.37% with an energy window of 100-800, 250-750 and 350-650 keV, respectively. The scatter fraction was 26.43%, and the peak NECR was 162.6 kcps at 24.1 MBq for the modified rat-like phantom. As for the recovery coefficients, they ranged from 0.15 to 1.04 for rods between 1 and 5 mm obtained with a NEMA image quality phantom. The spill-over ratio for the air-filled and water-filled chamber was 0.05 and 0.11,

关键词： performance evaluation mammography dual-head PET system PET-optical

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A Collision-Free Person-Following Approach Based on Path Planning

A Collision-Free Person-Following Approach Based on Path Pla...

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IEEE International Conference on Mechatronics and Automation

作者： Lei Pang Zhiqiang Cao Junzhi Yu Weimin Zhang Xuechao Chen State Key Laboratory of Management and Control for Complex Systems Institute of Automation School of Artificial Intelligence University of Chinese Academy of Sciences BIC-ESAT College of Engineering Peking University Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Person following is a crucial capability for mobile robots in tasks of human assistance and cooperation. In this paper, a person-following approach based on path planning is proposed for mobile robots, which takes person following and obstacle avoidance into account in a unified framework. The proposed approach is comprised of a sensing module and a planning module. The sensing module with a camera and a 3D LiDAR is employed to locate the target person and perceive obstacles. For the planning module, the combination of global planner and local planner effectively outputs collision-free paths and control commands for a mobile robot to keep following the target person. Experiments were conducted to evaluate the performance of the proposed approach.

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Prediction of human assessments of dairy odor utilizing a fast gas chromatograph and neural networks

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2019年 157卷 541-548页

作者： Chang, Fangle Heinemann, Paul H. Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Innovat Ctr Parallel Agr Qingdao 266109 Peoples R China Penn State Univ Dept Agr & Biol Engn 105 Agr Engn Bldg University Pk PA 16802 USA

A method based on hedonic tone was developed and applied to evaluate human assessments of odor emitted from dairy operations using a fast gas chromatograph and neural networks. A general pleasantness scale ranging from -11 (extremely unpleasant) to +11 (extremely pleasant) was used to collect human responses. The panelists were able to identify the difference between various samples, and gave individually consistent responses for the same sample. The measurements of a fast gas chromatograph, called the zNose, were trained using Artificial Neural Networks (ANNs) to predict the human assessments. Three ANNs, Levenberg-Marquardt Back-propagation Neural Network (LMBNN), Scaled Conjugate Gradient Back-propagation (CGBNN), and Resilient Back-propagation Neural Network (RPBNN), were applied to connect human assessments and instrument measurements. In separate validation, zNose-LMBNN model showed superiority in four criteria, Mean Square Error (MSE), Correlation Coefficient (R), probability within 10% range to target, and probability within 5% range to target. The optimal model outputs represented human response as high as 67% within the 10% range and 44% within the 5% range of the targets. In addition, the model outputs have a good linear relationship with the targets (R = 0.53).

关键词： Dairy Human assessments Hedonic tone zNose Artificial Neural Networks

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A Quantized controller of Linear systems with Disturbances under DoS Attacks

A Quantized Controller of Linear Systems with Disturbances u...

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International Conference on control and Automation (ICCA)

作者： Wenjie Liu Jian Sun Jie Chen State Key Lab of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Tongji University Shanghai China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper considers the problem of stabilizing linear time-invariant systems with quantized state measurements under both external disturbances and Denial-of-Service (DoS) attacks. An element-based impulsive controller that can stabilize systems when quantization, DoS attacks and bounded disturbances coexist is proposed. A sufficient condition for stability characterized by the parameters of DoS attacks, bit-rate and the quantizer's saturation time is obtained. Moreover, analysis shows that under an appropriate design of parameters of the controller, the resultant saturation time always remains small. A numerical example is given to confirm the validity of the proposed method.

关键词： Denial-of-service attack Quantization (signal) Encoding Decoding Bandwidth Switched systems Eigenvalues and eigenfunctions

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Gauss-Newton unrolled neural networks and data-driven priors for regularized PSSE with robustness

arXiv

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arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A Blockchain-based Framework for Collab.rative Production in Distributed and Social Manufacturing

A Blockchain-based Framework for Collaborative Production in...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Ouyang, Liwei Yuan, Yong Wang, Fei-Yue 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 Qingdao Academy of Intelligent Industries Qingdao266109 China

ISBN: (纸本)9781728128535

In recent years, novel collab.rative production paradigms, such as (re-)distributed manufacturing and social manufacturing, have attracted intensive attention due to their potential of further changing the existing manufacturing modes and industrial structures. Blockchain, as a new distributed computing architecture, is expected to be successfully used in manufacturing, and help solve issues related to interoperability and collab.ration, security and supervision, marketization and protocol, democratic organization and global value chain governance, and so on. In this paper, a blockchain-based framework for collab.rative production is proposed, aiming at providing novel ideas for the design, implementation and optimization of collab.rative production, and also inspiring the upgrading and transformation of manufacturing industries. © 2019 IEEE.

关键词： Smart contract

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Collab.rative Optimization of Cyber Physical Social systems for Urban Transportation Based on Knowledge Automation 3rd

Collaborative Optimization of Cyber Physical Social Systems ...

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3rd IFAC Workshop pn Cyber-Physical and Human systems (CPHS)

作者： Xiong, Gang Chen, Xiaoyu Shuo, Nan Lv, Yisheng Zhu, Fenghua Qu, Tianci Ye, Peijun Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan 523808 Peoples R China Tokyo Metropolitan Univ Grad Sch Syst Design Tokyo 1910065 Japan

The integration of signals from physical, social and cyber spaces, known as Cyber-PhysicalSocial systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collab.rative optimized among the three sub -systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collab.rative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation. Copyright (C) 2020 The Authors.

关键词： CPSS knowledge automation urban transportation Collab.rative Optimization

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Webly Supervised Knowledge Embedding Model for Visual Reasoning

Webly Supervised Knowledge Embedding Model for Visual Reason...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenbo Zheng Lan Yan Chao Gou Fei-Yue Wang School of Software Engineering Xi'an Jiaotong University The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Intelligent Systems Engineering Sun Yat-sen University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Visual reasoning between visual image and natural language description is a long-standing challenge in computer vision. While recent approaches offer a great promise by compositionality or relational computing, most of them are oppressed by the challenge of training with datasets containing only a limited number of images with ground-truth texts. Besides, it is extremely time-consuming and difficult to build a larger dataset by annotating millions of images with text descriptions that may very likely lead to a biased model. Inspired by the majority success of webly supervised learning, we utilize readily-availab.e web images with its noisy annotations for learning a robust representation. Our key idea is to presume on web images and corresponding tags along with fully annotated datasets in learning with knowledge embedding. We present a two-stage approach for the task that can augment knowledge through an effective embedding model with weakly supervised web data. This approach learns not only knowledge-based embeddings derived from key-value memory networks to make joint and full use of textual and visual information but also exploits the knowledge to improve the performance with knowledge-based representation learning for applying other general reasoning tasks. Experimental results on two benchmarks show that the proposed approach significantly improves performance compared with the state-of-the-art methods and guarantees the robustness of our model against visual reasoning tasks and other reasoning tasks.

关键词： Visualization Cognition Knowledge based systems Task analysis Knowledge engineering Modulation Robustness

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