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arXiv 2023年

作者： Yu, Xuan Liu, Yili Mao, Sitong Zhou, Shunbo Xiong, Rong Liao, Yiyi Wang, Yue The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Huawei Cloud Computing Technologies Co. Ltd. Shenzhen China

LiDAR Mapping has been a long-standing problem in robotics. Recent progress in neural implicit representation has brought new opportunities to robotic mapping. In this paper, we propose the multi-volume neural feature fields, called NF-Atlas, which bridge the neural feature volumes with pose graph optimization. By regarding the neural feature volume as pose graph nodes and the relative pose between volumes as pose graph edges, the entire neural feature field becomes both locally rigid and globally elastic. Locally, the neural feature volume employs a sparse feature Octree and a small MLP to encode the signed distance function (SDF) of the submap with an option of semantics. Learning the map using this structure allows for end-to-end solving of maximum a posteriori (MAP) based probabilistic mapping. Globally, the map is built volume by volume independently, avoiding catastrophic forgetting when mapping incrementally. Furthermore, when a loop closure occurs, with the elastic pose graph based representation, only updating the origin of neural volumes is required without remapping. Finally, these functionalities of NF-Atlas are validated. Thanks to the sparsity and the optimization based formulation, NF-Atlas shows competitive performance in terms of accuracy, efficiency and memory usage on both simulation and real-world datasets. The project page is: https://***/NFAtlas/ Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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IEEE International Conference on systems, Man and Cybernetics

作者： Zibo Miao Yu Pan Qing Gao Guangdong Key Laboratory of Intelligent Morphing Mechanisms School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique.

关键词： Perturbation methods Design methodology Quantum optics Robustness Feedback control Closed loop systems Tuning Cybernetics

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Spatial Temporal Transpose Convolutional Networks for Action Recognition

Spatial Temporal Transpose Convolutional Networks for Action...

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International Conference on Computer and Communications (ICCC)

作者： Ying Bai Dongsheng Yang Hongliang Wang Changyi Deng Wei Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In recent years, the use of skeleton data for human action recognition has received increasing attention in fields such as intelligent monitoring, human-object interaction, and video retrieval. Among them, Modeling Human Skeleton as Spatial-Temporal Graph Convolutional Network (ST-GCN) has made significant progress. However, methods based on Graph Convolution (GCN) generally face two issues: on one hand, the receptive fields of the spatial and temporal graphs are not flexible enough, limiting their feature representation capabilities. On the other hand, the model is overly parameterized, leading to high computational costs. In this paper, we first propose a spatial-temporal transpose convolution module to overcome the inflexibility of the spatial-temporal graph receptive field. Secondly, we introduce 1 *1 convolutions and depth-wise separable convolution layers into the network model, effectively reducing the computational complexity of the model. On the two subsets of the large-scale skeleton action recognition dataset NTU RGB-D, experimental results show that the proposed method achieved Top1 accuracy metrics of 84.6% and 92.5% respectively, while the model size was reduced by 13.48 times compared to the classic ST-GCN method, and the computational complexity was reduced by approximately 17.18 times.

关键词： Hands Accuracy Convolution Computational modeling Benchmark testing Approximation algorithms Skeleton Partitioning algorithms Computational complexity Monitoring

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Experiences with Postdoctoral Training in Human Factors: Mentor and Trainee Perspectives

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023年第1期67卷 265 - 269页

作者： Kelly Caine Ken Catchpole Farzan Sasangohar Karen B. Chen Sudeep Hegde Dustin Souders Anthony McDonald Elease J. McLaurin Clemson University School of Computing Clemson SC USA Medical University of South Carolina Clinical Practice and Human Factors Charleston SC USA Texas A&M University Industrial and Systems Engineering Central SC USA North Carolina State University Edward P. Fitts Department of Industrial and Systems Engineering Raleigh NC USA Clemson University Department of Industrial Engineering Central SC USA Clemson University Psychology Department Clemson SC USA University of Wisconsin-Madison Department of Industrial and Systems Engineering Madison WI USA Oak Ridge Institute for Science & Education Fellowship Program Centers for Disease Control & Prevention Philadelphia PA USA

Postdoctoral training positions are becoming more common in the human factors and ergonomics (HFE) discipline. However, conversations related to training in the HFE discipline have largely focused on undergraduate and graduate education. This panel assembles both postdoctoral mentors and former trainees who collectively have a diverse set of HFE-related postdoctoral experiences. By panelists discussing their experiences, observations, and recommendations related to postdoctoral training with the audience, the panel session will support HFE faculty and students in making more informed decisions about if and how a postdoctoral experience (either as a mentor or trainee) could be a part of their career development in HFE.

关键词： education postdoctoral training human factors mentorship

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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A Vein Segmentation Method Based on Improved U-Net

A Vein Segmentation Method Based on Improved U-Net

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Information and Communication Technology (ICTech), International Conference of

作者： Hongliang Wang Liang Zhu Changyi Deng Pengfei Xiu Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China China Industrial Control Systems Cyber Emergency Response Team Institute of Software Beijing China

Recently, medical research has revealed that diffusion weighted imaging (DWI) is less sensitive than susceptibility-weighted imaging (SWI) for acute ischemic stroke. Brain vein analysis in SWI is very important for personalised therapy, quantitative diagnosis, and prognosis of acute ischemic stroke. Therefore, the accurate segmentation of veins in SWI images has important significance in supplementary diagnosis and medical research. However, vein segmentation in SWI is a challenging task because different veins show distinct variability and uneven intensity and are difficult to manually split. This paper proposes an improvement method based on U-net, by using dense connection and mixing loss function, the best Dice coefficient and the ASSD were obtained by other comparison methods. The method presented in this article can be used as a potential tool to accurately measure SWI cerebral veins and help physicians in decision making.

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Research on Cybersecurity Risk Prevention and control of New Infrastructure

Research on Cybersecurity Risk Prevention and Control of New...

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2021 International Conference on Computer Network Security and Software Engineering, CNSSE 2021

作者： Zhu, Lina Ma, Zhentao Huang, Haibo Yan, Min China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China College of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Sciences Beijing100190 China

Currently, the new infrastructure has entered the stage of intensive deployment. While driving the integration of innovative applications of new generation information and communication technologies such as 5G, and accelerating the process of digital transformation, new infrastructure is also facing the challenges of complex and diverse cyber attacks and increasingly severe security risks. This paper systematically analyzes the new infrastructure cybersecurity risks, summarizes the new challenges faced by new infrastructure, and proposes a risk prevention and control model METAD which combines monitoring and early warning, threat intelligence sharing, risk assessment, and intelligent defense. © Published under licence by IOP Publishing Ltd.

关键词： Cybersecurity

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Research on Key Technologies of industrial Internet Data Security 2

Research on Key Technologies of Industrial Internet Data Sec...

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2021 2nd International Conference on Computer Information and Big Data Applications, CIBDA 2021

作者： Yan, Min Huang, Haibo Li, Jun Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Sciences Beijng100190 China Department of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

At present, industrial Internet data is facing a severe security situation in storage, transmission, and use. This paper proposes data security management and security protection strategies by studying the data security risks and status quo of industrial Internet platforms. Among them, the security management strategy includes data classification and classification, data security assessment and data security threat emergency management. The security protection strategy deploys industrial data security protection from the technical aspects of collection, storage, transmission, processing, sharing, and deletion, providing a reference for further strengthening the industrial Internet data security. © Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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Robust optimization for quantum reinforcement learning control using partial observations

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Physical Review A 2022年第6期105卷 062443-062443页

作者： Chen Jiang Yu Pan Zheng-Guang Wu Qing Gao Daoyi Dong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China The School of Automation Science and Electrical Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China The School of Engineering and Information Technology University of New South Wales Canberra Australian Capital Territory 2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

关键词： Machine learning Quantum control Quantum engineering

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Distributed Adaptive Fault-Tolerant Output Regulation of Heterogeneous Multi-Agent systems With Coupling Uncertainties and Actuator Faults

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1098-1106页

作者： Chao Deng Weinan Gao Weiwei Che the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the Department of Electrical and Computer Engineering Allen E.Paulson College of Engineering and ComputingGeorgia Southern UniversityStatesboroGA 30460 USA the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027 the Institute of Complexity Science Qingdao UniversityQingdao 266071China

In this paper, we consider the distributed adaptive fault-tolerant output regulation problem for heterogeneous multiagent systems with matched system uncertainties and mismatched coupling uncertainties among subsystems under the influence of actuator faults. First, distributed finite-time observers are proposed for all subsystems to observe the state of the exosystem. Then, a novel fault-tolerant controller is designed to compensate for the influence of matched system uncertainties and actuator faults. By using the linear matrix inequality technique, a sufficient condition is provided to guarantee the solvability of the considered problem in the presence of mismatched coupling uncertainties. Moreover, it is shown that the system in closed-loop with the developed controller can achieve output regulation by using the Lyapunov stability theory and cyclic-small-gain ***, a numerical example is given to illustrate the effectiveness of the obtained result.

关键词： Adaptive control cyclic-small-gain fault-tolerant control multi-agent systems(MASs)

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