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

作者： Han, Xuan Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Research Center of Military Computational Experiments and Parallel System National University of Defense Technology Changsha China

ISBN: (纸本)9781728128535

Cryptocurrency and blockchain technologies have developed in parallel in recent years, with technological breakthroughs in currency issuance, payment methods, and currency storage. However, the existing cryptocurrencies cannot replace fiat money. There is a huge gap between decentralized cryptocurrency and central bank digital currency, namely CBDC, in terms of monetary governance and circulation. In this paper, we propose the function and security requirements of CBDC, through a comprehensive analysis of the existing typical cryptocurrency and the prototype of the CBDC scheme. On this basis, we present a blockchain-based framework for CBDC with three layers, including supervisory layer, network layer and user layer, and describe the key business processes of the CBDC's entire lifecycle of issuance-circulation-withdrawal in detail. Finally, we take cross-border payment as an example to explain the transaction process of CBDC. We aim to provide theoretical guidance for CBDC design. © 2019 IEEE.

关键词： Blockchain

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Spiking Neuron Networks based Energy-Efficient Object Detection for Mobile Robot

Spiking Neuron Networks based Energy-Efficient Object Detect...

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Chinese automation Congress (CAC)

作者： Bo-Chao Liu Qian Yu Jing-Wen Gao Shuang Zhao Xiong-Chang Liu Yan-Feng Lu Beijing Information Science and Technology University Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Instrument Science and Optoelectronic Engineering Beijing Information Science and Technology University Beijing China The State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China Science and Technology on Underwater Vehicle Technology Laboratory Harbin Engineering University Harbin China

ISBN: (纸本)9781665426480

This paper focuses on the highly energy-efficient visual detection method, which first apples spiking neuron networks based object detection method in mobile robot. Visual perception is an important part of mobile robot, but the real-time visual perception is with high energy consumption that limits the battery running time of the robotic platform. In this paper, we bring in a Spiking-YOLO algorithm for the visual detection of the mobile robot, which combines the YOLO algorithm with the third generation artificial neural network-Spiking Neural Network (SNN). The Spiking-YOLO integrates the characteristics in real-time and high precision of the YOLO, and event-driven and low-powered nature of the SNN. We introduce the Spiking-YOLO model to the Robotnik's SUMMIT-XL mobile robot to perform the task of visual detection and the experimental verification is carried out on data sets. The experimental results show that this method has excellent energy-efficient and precision on the detection task of the robot.

关键词： Visualization Energy consumption Neurons Object detection Energy efficiency Real-time systems Mobile robots

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Optimize Student Learning via Random Forest-Based Adaptive Narrative Game

Optimize Student Learning via Random Forest-Based Adaptive N...

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Ryan Hare Ying Tang Wei Cui Joleen Liang Department of Electrical and Computer Engineering Rowan University Glassboro USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China Squirrel AI Learning Shanghai China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge relative to the problem presented to him through his game-playing behavior data, such as time taken to find solutions, errors in solutions, and emotional indicators. Hints, prompts, and/or individualized lessons are then offered to the player to guide their learning in a positive and productive direction. Our preliminary pilot study demonstrates that the model can make accurate classifications, from which proper assistance can then be provided to individual students as they play.

关键词： Games Databases Forestry Education Problem-solving Presses Artificial intelligence

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A decentralized social networking architecture enhanced by blockchain

A decentralized social networking architecture enhanced by b...

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

作者： Zeng, Shuai Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Research Center of Military Computational Experiments and Parallel System National University of Defense Technology Changsha China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728128535

The privacy issues become a major problem that should be resolved for the existing centralized online social networks, which have prompted researchers to consider the decentralization framework for online social networks. In this paper, we propose a novel decentralized social networking architecture enhanced by blockchain technology. We use a sharding framework to increase the system scalability, a blockchain system to ensure the data integrity and consistency, a reputation-based authority control method to improve the system security. © 2019 IEEE.

关键词： Blockchain

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Batch Normalization Masked Sparse Autoencoder for Robotic Grasping Detection

Batch Normalization Masked Sparse Autoencoder for Robotic Gr...

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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhenzhou Shao Ying Qu Guangli Ren Guohui Wang Yong Guan Zhiping Shi Jindong Tan Beijing Advanced Innovation Center for Imaging Technology and Beijing Key Laboratory of Light Industrial Robot and Safety Verification Capital Normal University Beijing China The Engineering College The University of Tennessee Knoxville TN USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

To improve the accuracy of the grasping detection, this paper proposes a novel detector with batch normalization masked evaluation model. It is designed with a two-layer sparse autoencoder, and a Batch Normalization based mask is incorporated into the second layer of the model to effectively reduce the features with weak correlation. The extracted features from such model are more distinctive, which guarantees the higher accuracy of the grasping detection. Extensive experiments show that the proposed evaluation model outperforms the state-of- the-art, and the recognition accuracy can reach 95.51% for robotic grasping detection.

关键词： Training Redundancy Grasping Detectors Feature extraction Intelligent robots Load modeling

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Renal Clearing Carbon Dots-Based Near-Infrared Fluorescent Super-Small Nanoprobe for Renal Imaging

SSRN

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SSRN 2022年

作者： Gao, Pengli Hui, Hui Guo, Chanjuan Liu, Yu Su, Ya Huang, Xiazi Shang, Wenting Jiang, Jingying Tian, Jie Guo, Kunxiong CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Biological Science and Medicine Engineering School of Engineering Medicine Beihang University No. 37 Xueyuan Road Beijing100191 China Key Laboratory of Big Data-Based Precision Medicine Beihang University Ministry of Industry and Information Technology of the People’s Republic of China No. 37 Xueyuan Road Beijing100191 China School of Engineering Medicine Beihang University No. 37 Xueyuan Road Beijing100191 China Department of Hepatobiliary Surgery Zhujiang Hospital Southern Medical University Guangzhou China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine China

Real-time renal imaging is indispensable for monitoring kidney-related disease processes and evaluating the nephrotoxicity of nanoprobes. Owing to their small size, tunable photoluminescence properties, and low biotoxicity, carbon dots (CDs) are widely employed as nanoprobes for imaging-guided disease diagnosis. However, the preparation of near-infrared CDs is a stupendous challenging. Here, CDs with near-infrared fluorescence (NIR-CDs ) were prepared by one-pot pyrolysis and were comprehensively evaluated for real-time renal imaging. The NIR-CDs possessed super-small size (1.8 ± 0.2 nm) and high quantum yield (14.3% in aqueous solution) at 696 nm. NIR-CDs exhibited high-efficiency renal clearance at 24 h post-treatment (97.47-99.27% ID) via glomerular filtration. The irreversible clearance of NIR-CDs enables effective real-time monitoring of kidney function in the normal and acute kidney injury (AKI) mouse models. Consequently, NIR-CDs can be utilized for imaging analyses of impaired kidney function, pioneering a serviceable guideline for neoapplications of NIR-CDs. © 2022, The Authors. All rights reserved.

关键词： Nanoprobes

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Programming by visual demonstration for pick-and-place tasks using robot skills

Programming by visual demonstration for pick-and-place tasks...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Hao, Peng Lu, Tao Cai, Yinghao Wang, Shuo 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 Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781728163215

In this paper, we present a vision-based robot programming system for pick-and-place tasks that can generate programs from human demonstrations. The system consists of a detection network and a program generation module. The detection network leverages convolutional pose machines to detect the key-points of the objects. The network is trained in a simulation environment in which the train set is collected and auto-labeled. To bridge the gap between reality and simulation, we propose a design method of transform function for mapping a real image to synthesized style. Compared with the unmapped results, the Mean Absolute Error (MAE) of the model completely trained with synthesized images is reduced by 23% and the False Negative Rate FNR (FNR) of the model fine-tuned by the real images is reduced by 42.5% after mapping. The program generation module provides a human-readable program based on the detection results to reproduce a real-world demonstration, in which a longshort memory (LSM) is designed to integrate current and historical information. The system is tested in the real world with a UR5 robot on the task of stacking colored cubes in different orders. © 2019 IEEE.

关键词： Demonstrations

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Attacking DNN-based Intrusion Detection Models

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IFAC-PapersOnLine 2020年第5期53卷 415-419页

作者： Xingwei Zhang Xiaolong Zheng Desheng Dash Wu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China and also with the Stockholm Business School Stockholm University SE-106 91 Stockholm Sweden

Intrusion detection plays an important role in public security domains. Dynamic deep neural network(DNN)-based intrusion detection models have been demonstrated to show effective performance for timely detecting network intrusions. While DNN-based intrusion detection models have shown powerful performance, in this paper, we verify that they could be easily attacked by well-designed small adversarial perturbations. We design an effective procedure to employ commonly used adversarial perturbations for attacking well-trained DNN detection models on NSL-KDD dataset. We further find that the performance of DNN models for recognizing real labels of abnormal data suffers more from attacks compared with that on normal samples.

关键词： public security intrusion detection deep neural networks adversarial perturbations

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3D Deep Learning for 3D Printing of Tooth Model

3D Deep Learning for 3D Printing of Tooth Model

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

作者： Liu, Yiwen Shang, Xiuqin Shen, Zhen Hu, Bin Wang, Zhihai Xiong, Gang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing University of Technology Faculty of Information Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Beijing University of Technology Faculty of Information Beijing China

ISBN: (纸本)9781728128535

Recently, with the development of both 3D sensors and 3D virtual network that bring the needs of interaction with the real world, many 3D applications burst out. However, it is difficult to understanding these three-dimensional scenes with a fixed program. Then, a data-driven method is required to process these 3D data, which brings a strong demand of 3D Deep Learning in 3D data. Towards this goal, with an end-to-end deep learning, the experiment is based on PointNet++, a well proposed method for feature extraction. The experiment optimizes the network structure and parameters to improve the classification results. Finally, the network is applied to tooth model for classification and identification so that the dental model can be found from different perspectives. © 2019 IEEE.

关键词： 3D printers

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Primitives generation policy learning without catastrophic forgetting for robotic manipulation 19

Primitives generation policy learning without catastrophic f...

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19th IEEE International Conference on Data Mining Workshops, ICDMW 2019

作者： Xiong, Fangzhou Liu, Zhiyong Huang, Kaizhu Yang, Xu Hussain, Amir State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science China China CAS Centre for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China Department of EEE Xi'an Jiaotong-Liverpool University China School of Computing Edinburgh Napier University United Kingdom

ISBN: (纸本)9781728146034

Catastrophic forgetting is a tough challenge when agent attempts to address different tasks sequentially without storing previous information, which gradually hinders the development of continual learning. Except for image classification tasks in continual learning, however, there are little reviews related to robotic manipulation. In this paper, we present a novel hierarchical architecture called Primitives Generation Policy Learning to enable continual learning. More specifically, a generative method by Variational Autoencoder is employed to generate state primitives from task space, then separate policy learning component is designed to learn torque control commands for different tasks sequentially. Furthermore, different task policies could be identified automatically by comparing reconstruction loss in the autoencoder. Experiment on robotic manipulation task shows that the proposed method exhibits substantially improved performance over some other continual learning methods. © 2019 IEEE.

关键词： Learning systems

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