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International Conference on Communication Technology (ICCT)

作者： Junpeng Yin Yusun Fu Haobo Huang Jinhui Tang Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering the Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Spatial audio is an important component of the metaverse. With the rapid increase of the spatial resolution and the number of objects, the data volume of the spatial side information has grown larger. However, the low bit rate transmission of spatial side information in the object-based audio coding methods leads to the low-quality reconstruction of spatial side information with limited bandwidth. To address this issue, this paper constructs a cascade design of spatial audio coding and unequal error protection (UEP) rateless codes. Furthermore, the further protection of specific spatial side information is proposed for different scenarios, which effectively addresses the limitation of traditional spatial audio coding in side information protection. For the situation where the code length K of different importance levels is different, we propose an adaptive degree distribution (ADD), adding K to the selection probability as an influencing factor, which has better performance than other degree distribution for short code length. ADD is used to replace the multiple degree distribution strategy of the traditional unequal error protection scheme, and the block error rate (BER) is reduced nearly 10 times compared with the traditional scheme.

关键词： Codes Error analysis Metaverse Distribution strategy Spatial audio Bit rate Symbols Communications technology Protection Spatial resolution

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Lite-HRPE: A 6DoF Object Pose Estimation Method for Resource-Limited Platforms

Lite-HRPE: A 6DoF Object Pose Estimation Method for Resource...

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International Conference on control and Automation (ICCA)

作者： Xin Liu Qi Guan Shibei Xue Dezong Zhao Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China James Watt School of Engineering University of Glasgow Glasgow U.K.

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Accurately estimating the six-degree-of-freedom pose of objects is essential for intelligent robotics. Although significant progress has been made in this area, most studies fail to account for specific hardware limitations for model deployment, which remains a major challenge for resource-constrained scenarios. To address this issue, we propose Lite-HRPE, a lightweight RGB-based pose estimation method, which leverages a multi-branch parallel structure to extract spatial and semantic information of key points for pose estimation. Additionally, Lite-HRPE adopts the G-block and G-neck modular structure and streamlines the original feature extraction network to realize a compact network structure. This allows Lite-HRPE to strike a balance between pose estimation accuracy, parameter count, computational load, and runtime speed. Our evaluation on public datasets shows that Lite-HRPE achieves a 95.7% accuracy with only 10.8% of the number of parameters and 11.8% of the FLOPs compared to Hybridpose.

关键词： Training Accuracy Runtime Pose estimation Semantics Feature extraction Real-time systems

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A New Island Genetic Algorithm for Path Planning Problems *

A New Island Genetic Algorithm for Path Planning Problems *

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Jinghao Zhang Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Department of Automation Tsinghua University Beijing P. R. China

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling.

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Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Shi, Yang Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China The Department of Mechanical Engineering University of Victoria VictoriaBC V8P 5C2 Canada

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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image retrieval based on color features integrated with anisotropic directionality

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Journal of Systems Engineering and Electronics 2010年第1期000卷

作者： Jing Bai Xiaohua Wang and Licheng Jiao key laboratory of intelligent Perception and image Understanding of ministry of education of China Institute of intelligent Information processing Xidian University Xi’an 710071 P. R. China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian UniversityXi'an 710071P. R. China

A novel image retrieval approach based on color features and anisotropic directional information is proposed for content based image retrieval systems (CBIR). The color feature is described by the color histogram (CH), which is translation and rotation invariant. However, the CH does not contain spatial information which is very important for the image retrieval. To overcome this shortcoming, the subband energy of the lifting directionlet transform (L-DT) is proposed to describe the directional information, in which L-DT is characterized by multi-direction and anisotropic basis functions compared with the wavelet transform. A global similarity measure is designed to implement the fusion of both color feature and anisotropic directionality for the retrieval process. The retrieval experiments using a set of COREL images demonstrate that the higher query precision and better visual effect can be achieved.

关键词： image retrieval color histogram directionlet transform anisotropic similarity

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A high-order disturbance observer based sliding mode velocity control of mobile wheeled inverted pendulum systems

A high-order disturbance observer based sliding mode velocit...

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World Congress on intelligent control and Automation (WCICA)

作者： Songhyok Ri Jian Huang Chunjing Tao Myonghyok Ri Yongsok Ri Daeho Han School of Automation Huazhong University of Science and Technology Wuhan CHINA Department of Control Science University of Sciences Pyongyang D.P.R. of Korea Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan CHINA R&D department National Research Center for Rehabilitation Technical Aids Beijing Automation school Kim Chaek University of Technology Pyongyang D.P.R. of Korea Department of Phisics University of Sciences Pyongyang D.P.R. of Korea

ISBN: (纸本)9781467384155

In this paper, a novel disturbance observer (DO) for the Mobile Wheeled Inverted Pendulum (MWIP) system is proposed. A choice method of optimal gain matrices is also proposed for a given robust gain, which can improve the estimation precision of the DO. Combining the proposed DO and Sliding Mode control (SMC), a new sliding mode velocity control method is designed for the MWIP system. The convergency of the DO is proved by Lyapunov theorem. And the stability of the closed-loop system is achieved through the appropriate selection of sliding surface coefficients. The effectiveness of all proposed methods is verified by simulation results for the MWIP system.

关键词： Wheels Automation Velocity control Uncertainty Observers Robustness

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Adaptive Fuzzy Tracking control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems

arXiv

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arXiv 2022年

作者： Mao, Bing Wu, Xiaoqun Liu, Hui Xu, Yuhua Lü, Jinhu The School of Mathematics and Statistics Wuhan University Hubei430072 China Hubei Key Laboratory of Computational Science Wuhan University Hubei430072 China Research Center of Complex Network Wuhan University Hubei430072 China The School of Automation Huazhong University of Science and Technology Hubei430074 China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei430074 China The School of Statistics and Data Science Nanjing Audit University Jiangsu211815 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples. Copyright © 2022, The Authors. All rights reserved.

关键词： Fuzzy control

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Robust Filtering for Networked Systems with Random Transmission Delays and Packet Dropouts

Robust Filtering for Networked Systems with Random Transmiss...

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2011 American control Conference (ACC)

作者： Yunze Cai Hua O. Wang Kazuo Tanaka Xiaoming Xu Department of Mechanical Engineering Boston University Boston MA USA Department of Automation and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiaotong University Shanghai China Department of Mechanical Systems and Intelligent Systems University of Electro-Communications Tokyo Japan Department of Automation Shanghai Jiaotong University Shanghai China University of Shanghai for Science and Technology Shanghai China

This paper addresses a robust H_(infinity) filtering problem for networked systems that are subject to both random transmission delays and packet dropouts. To start with, a data transmission model is established by employing random series with Bernoulli distributions. A sufficient condition for robust stability with H_(infinity) constraints is derived for the filtering error system. The robust filter is designed in terms of the feasibility of a linear matrix inequality (LMI). The numerical examples are provided to show the effectiveness of the data transmission model and the proposed filtering method.

关键词： Delay Filtering Robustness Data models Symmetric matrices Hidden Markov models Markov processes

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Protecting privacy of users in brain-computer interface applications

arXiv

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arXiv 2019年

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C.A. School of Engineering and Technology University of Washington TacomaWA98402 United States Department of Computer Science Bar-Ilan University Israel School of Engineering and Technology University of Washington TacomaWA98402 United States Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e. such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Global Finite Time Stabilization of SISO Hurwitz Linear Systems Subject to Actuator Saturation: The Case of Real Eigenvalues

Global Finite Time Stabilization of SISO Hurwitz Linear Syst...

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American control Conference (ACC)

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A.

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper considers the global finite time stabilization of linear systems subject to actuator saturation. We show that a controllable SISO system whose eigenvalues are real and negative can be globally stabilized in finite time by a homogeneous feedback law. To obviate numerical issues, this homogeneous feedback law is implemented by discretizing a compact set. Numerical simulation validates the effectiveness of the proposed approach.

关键词： Linear systems Actuators Numerical simulation control systems Eigenvalues and eigenfunctions

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