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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zhang, Tiandong Wang, Rui Wang, Yu Wang, Shuo Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence 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)9781665436786

This paper presents a novel deep reinforcement learning (DRL) method to solve the locomotion control problem of the biomimetic underwater vehicle (BUV) with hybrid propulsion, in order to meet the challenge of intractable multi-fins coordination and the complex hydrodynamic model. The system overview of the BUV, named RoboDact, with two flexible long fins and a double-joint fishtail as hybrid propulsion, is introduced. After that, the locomotion control problem is modeled as a Markov decision process (MDP) to be solved. Therefore, the locomotion control method based on soft actor-critic (SAC, a novel DRL algorithm) is proposed. The simulation environment is established based on the kinetic model for interaction. Finally, the feasibility and effectiveness of the proposed control method is demonstrated after extensive simulations. It will provide rich insights into the coordination control of biomimetic underwater vehicles. © 2021 IEEE.

关键词： Reinforcement learning

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Optimal Discrete-Time Sliding-Mode control Based on Recurrent Neural Network: A Singular Value Approach

Research Square

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Research Square 2021年

作者： Toshani, Hamid Farrokhi, Mohammad School of Electrical Engineering Iran University of Science and Technology Narmak FarjamSt. Tehran16846 Iran Center of Excellence for Modeling and Control of Complex Systems Iran University of Science and Technology Narmak Farjam St. Tehran16846 Iran

In this paper, a strategy involving the combination of optimal discrete-time sliding-mode control and recurrent neural networks is proposed for a class of uncertain discrete-time linear systems. First, a performance index based on the reaching law and the control signal is defined. Then, the constrained quadratic programming problem is formulated considering the limitations on the control signal as the static constraint. The dynamic and algebraic model of the neural network is derived based on the optimization conditions of the quadratic problem and their relationship with the projection theory. The proposed method prevents the chattering by selecting proper parameters of the twisting reaching law. The convergence of the neural network is analysed using the Lyapunov stability theory. A singular-value-based analysis is employed for robustness of the proposed method. The stability conditions of the discrete-time closed-loop system is analysed by studying eigenvalues of the closed-loop matrix using the singular value approach. The performance of the proposed algorithm is assessed in simulated example in terms of chattering elimination, solution feasibility, and encountering uncertainties and is compared with the recently proposed DSMC methods in the literature. © 2021, CC BY.

关键词： Recurrent neural networks

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Models of Battle Dynamics in Strategic Computer Games 16

Models of Battle Dynamics in Strategic Computer Games

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Laryushin, Ivan Dmitrievich Pashin, Dmitrii Sergeevich Koltochenko, Yaroslav Alekseevich Ereshko, Felix Ivanovich Gaponov-Grekhov Institute of Applied Physics Ras Plasma Physics Department Nizhny Novgorod Russia State University of Nizhny Novgorod Faculty of Physics National Research Lobachevsky Nizhny Novgorod Russia Moscow Institute of Physics and Technology Department of Mathematical Modeling of Complex Systems and Optimization Moscow Russia Federal Research Center 'Computer Science and Control' of the Ras Department of Information and Computing Systems Moscow Russia

ISBN: (纸本)9798350337907

A set of new models that describe the dynamics of battles in strategic computer games is presented. These models not only have descriptive functions, but also allow solving problems of optimizing army control under given conditions, as well as calculating the most efficient composition of the army for the planned operation within a computer game. © 2023 IEEE.

关键词： Computer games

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Real-Time Marker Localization Learning for GelStereo Tactile...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Liu, Shujuan Cui, Shaowei Zhang, Chaofan Cai, Yinghao Wang, Shuo University of Science and Technology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Institute of Automation Beijing100190 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better realtime performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks. © 2022 IEEE.

关键词： Chemical detection

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Color-sensitive person re-identification 28

Color-sensitive person re-identification

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28th International Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Wang, Guan'an Yang, Yang Y. Cheng, Jian Wang, Jinqiao Hou, Zengguang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9780999241141

Recent deep Re-ID models mainly focus on learning high-level semantic features, while failing to explicitly explore color information which is one of the most important cues for person Re-ID model. In this paper, we propose a novel Color-Sensitive Re-ID to take full advantage of color information. On one hand, we train our model with real and fake images. By using the extra fake images, more color information can be exploited and it can avoid over-fitting during training. On the other hand, we also train our model with images of the same person with different colors. By doing so, features can be forced to focus on the color difference in regions. To generate fake images with specified colors, we propose a novel Color Translation GAN (CTGAN) to learn mappings between different clothing colors and preserve identity consistency among the same person with the clothing color. Extensive evaluations on two benchmark datasets show that our approach significantly outperforms state-of-the-art Re-ID models. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

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"Why not looking backward?" a robust two-step method to automatically terminate Bayesian optimization 23

"Why not looking backward?" a robust two-step method to auto...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Shuang Li Ke Li Wei Li Control and Simulation Center Harbin Institute of Technology China and National Key Laboratory of Modeling and Simulation for Complex Systems China Department of Computer Science University of Exeter Exeter UK

Bayesian Optimization (BO) is a powerful method for tackling expensive blackbox optimization problems. As a sequential model-based optimization strategy, BO iteratively explores promising solutions until a predetermined budget, either iterations or time, is exhausted. The decision on when to terminate BO significantly influences both the quality of solutions and its computational efficiency. In this paper, we propose a simple, yet theoretically grounded, two-step method for automatically terminating BO. Our core concept is to proactively identify if the search is within a convex region by examining previously observed samples. BO is halted once the local regret within this convex region falls below a predetermined threshold. To enhance numerical stability, we propose an approximation method for calculating the termination indicator by solving a bilevel optimization problem. We conduct extensive empirical studies on diverse benchmark problems, including synthetic functions, reinforcement learning, and hyperparameter optimization. Experimental results demonstrate that our proposed method saves up to ≈ 80% computational budget yet is with an order of magnitude smaller performance degradation, comparing against the other peer methods. In addition, our proposed termination method is robust in terms of the setting of its termination criterion.

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Overcoming catastrophic forgetting with self-adaptive identifiers 25th

Overcoming catastrophic forgetting with self-adaptive identi...

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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Xiong, Fangzhou Liu, Zhiyong Yang, Xu The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science Beijing100190 China Beijing100049 China Centre for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China Cloud Computing Center Chinese Academy of Sciences DongGuanGuangDong523808 China

ISBN: (纸本)9783030041816

Catastrophic forgetting is a tough issue when the agent faces the sequential multi-task learning scenario without storing previous task information. It gradually becomes an obstacle to achieve artificial general intelligence which is generally believed to behave like a human with continuous learning capability. In this paper, we propose to utilize the variational Bayesian inference method to overcome catastrophic forgetting. By pruning the neural network according to the mean and variance of weights, parameters are vastly reduced, which mitigates the storage problem of double parameters required in variational Bayesian inference. Based on this lightweight version, autoencoders trained on different tasks are employed to self-adaptively match the corresponding task parameters to tackle sequential multi-task learning problem. We show experimentally on several fundamental datasets that the proposed method can perform substantial improvements without catastrophic forgetting over other classic methods especially in the setting where the probability distributions between tasks present more different. © Springer Nature Switzerland AG 2018.

关键词： Probability distributions

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Optical detection using multi-wavelength modulation

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Key Engineering Materials 2008年 381-382卷 325-328页

作者： Ng, B.P. Luen, S.H. Zhang, Y. Soh, Y.C. Singapore Institute of Manufacturing Technology 71 Nanyang Avenue Singapore 638075 Singapore Center for Modeling and Control of Complex Systems School of Electrical and Electronics Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Singapore

In this paper, we proposed a method to attain a high sensitivity of weak optical signal detection by using the multi-wavelength modulation principle. In this method, the optical phase carrying the measurand is obtained by averaging the phases obtained in various orders of heterodyne harmonics. The employment of higher heterodyne harmonics reduces the drift effect in optical detection. The proposed method is demonstrated on the nano-scale displacement measurement by using a single channel fiber interferometer. In the experiment, the displacement is simulated by moving a mirror with a piezoelectric tube. The results show that more repeatable measurements with a resolution of 35nm can be obtained by using the proposed method.

关键词： Interferometers

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An improved active homodyne detector

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Key Engineering Materials 2008年 381-382卷 329-332页

作者： Seek, H.L. Zhang, Y. Soh, Y.C. Singapore Institute of Manufacturing Technology 71 Nanyang Avenue Singapore 638075 Singapore Center for Modeling and Control of Complex Systems School of Electrical and Electronics Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Singapore

This paper proposes an improved active homodyne detection technique to compensate for the drift in homodyne measurement. The technique is implemented on a fiber based Michelson interferometer where a computer controller is designed to drive a piezoelectric cylinder attached to one arm of the interferometer. It is shown that the proposed technique can efficiently stabilize the interferometric output.

关键词： Michelson interferometers

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Hydrodynamic modeling and Parameter Identification of a Bionic Underwater Vehicle:RobDact

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Cyborg and Bionic systems 2022年第1期2022卷 164-176页

作者： Qiyuan Cao Rui Wang Tiandong Zhang Yu Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of A utomationChinese Academy of SciencesBeijingChina School of Artific ial Itelligence University of Chinese Academy of Sciences BeijingChina Center for Excellence in Brain Science and Itelligence Technology Chinese Academy of SciencesShanghaiChina

In this paper,the hydrodynamic modeling and parameter identifcation of the RobDact,a bionic underwater vehide inspired byDactylopteridae,are carried out based on computational fluid dynamics(CFD)and force measurement ***,thePaper briely describes the RobDact,then establishes the kinematis model and rigid body dynamics model of the RobDactaccording to the hydrodynamic force and moment *** CFD simulations,the hydrodynamic force of theRobDact at diferent speeds is obtained,and then,the hydrodynamic model parameters are ***,themeasurement platform is developed to obtain the relationship between the thrust generated by the RobDact and the inputfluctuation parameters,Finally,combining the rigid body dynamics model and the fin'thrust mapping model,thehydrodynamic model of the RobDact at diferent motion states is constructed.

关键词： dynamics(CFD) equations hydrodynamic

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