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IEEE International Conference on Robotics and Biomimetics

作者： Danil Belov Artem Erkhov Farit Khabibullin Elisaveta Pestova Sergei Satsevich Ilya Osokin Pavel Osinenko Dzmitry Tsetserukou Intelligent Space Robotics Laboratory and Artificial Intelligence in Dynamic Action Laboratory Skolkovo Institute of Science and Technology Moscow Russia Cognitive Modeling Center Moscow Institute of Physics and Technology Dolgoprudny Russia

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This paper is dedicated to the development of a novel adaptive torsion spring mechanism for optimizing energy consumption in legged robots. By adjusting the equilibrium position and stiffness of the spring, the system improves energy efficiency during cyclic movements, such as walking and jumping. The adaptive compliance mechanism, consisting of a torsion spring combined with a worm gear driven by a servo actuator, compensates for motion-induced torque and reduces motor load. Simulation results demonstrate a significant reduction in power consumption, highlighting the effectiveness of this approach in enhancing robotic locomotion.

关键词： Legged locomotion Knee Energy consumption Power demand Torque Simulation Trajectory Springs Servomotors Robots

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User Identity Protection in EEG-based Brain-Computer Interfaces

arXiv

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arXiv 2024年

作者： Meng, Lubin Jiang, Xue Huang, Jian Li, Wei Luo, Hanbin Wu, Dongrui The Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals;however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs. © 2024, CC BY.

关键词： Differential privacy

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Deterministic-like data-driven discovery of stochastic differential equations via the Feynman–Kac formalism

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The European Physical Journal Special Topics 2024年第3期234卷 721-741页

作者： Ma, Chaoxiang Huang, Cheng Cheng, Cheng Li, Xiuting The College of Informatics Huazhong Agriculture University and the China–Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan People’s Republic of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Ministry of Education Key Lab of Intelligent Control and Image Processing Wuhan China

This paper develops a data-driven deterministic identification architecture for discovering stochastic differential equations (SDEs) directly from data. The architecture first generates deterministic data for stochastic processes using the Feynman–Kac formula, and gives a parabolic partial differential equation (PDE) associated with the SDE. Then, a sparse regression model is proposed to discover drift and diffusion terms in SDEs using PDE data-driven techniques, where a large candidate library of potential terms only for the drift and diffusion coefficients in SDEs need be constructed. To simultaneously infer the drift and diffusion terms, we proposed a sequential thresholded reweighted least-squares algorithm to solve the constructed sparse regression model. The main advantage of the proposed method is that on the one hand, theoretical and numerical identification results of PDEs can be used for SDEs, on the score, our SDE identification problem is translated into the parameter estimation problem of PDEs, on the other hand, the proposed algorithm is easily executed and can enhance the sparsity and accuracy. Through several classical SDEs and ordinary differential equations, the effectiveness of the proposed data-driven method is demonstrated, and several comparison experiments with state-of-the-art approaches is provided to illustrate the superiority of the developed algorithm.

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Crysformer:An attention-based graph neural network for properties prediction of crystals

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Chinese Physics B 2023年第9期32卷 15-20页

作者：王田陈家辉滕婧史金钢曾新华 Hichem Snoussi Institute of Artificial Intelligence SKLSDEBeihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100191China School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Academy for Engineering and Technology Fudan UniversityShanghai 200433China Charles Delaunay Institute University of Technology of TroyesTroyes Cedex 10004France

We present a novel approach for the prediction of crystal material properties that is distinct from the computationally complex and expensive density functional theory(DFT)-based ***,we utilize an attention-based graph neural network that yields high-accuracy *** approach employs two attention mechanisms that allow for message passing on the crystal graphs,which in turn enable the model to selectively attend to pertinent atoms and their local environments,thereby improving *** conduct comprehensive experiments to validate our approach,which demonstrates that our method surpasses existing methods in terms of predictive *** results suggest that deep learning,particularly attention-based networks,holds significant promise for predicting crystal material properties,with implications for material discovery and the refined intelligent systems.

关键词： deep learning property prediction crystal attention networks

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Noncooperative Target Finite-Time Surrounding control of spacecraft Formation

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space(science & technology) 2024年第1期4卷 260-270页

作者： Lifan Zhou Yanning Guo Yu Zhang Panxing Huang Pengyu Wang School of Astronautics Harbin Institute of TechnologyHarbin 150001China Beijing Institute of Control Engineering Beijing 100190China National Key Laboratory of Science and Technology on Space Intelligent Control Beijing 100190China Korea Advanced Institute of Science and Technology Daejeon 34141Republic of Korea

This paper proposes a novel distributed control method for surrounding a noncooperative target that has maneuverability by spacecraft formation.A relative orbit error dynamic model between the target and the formation is established dependent on a reference spacecraft under the 2-body *** estimate and compensate for the target’s control input rapidly,a novel finite-time extended state observer is *** is stable in the sense of fast finite-time uniformly ultimately bounded stability.A fast terminal sliding mode controller is proposed for finite-time convergence of the *** examples are implemented to show the effectiveness of proposed algorithm.

关键词： finite time surrounding control distributed control method relative orbit error dynamic model reference spacecraft spacecraft formationa estimate compensate spacecraft formation noncooperative target

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The engineering of circular causality for specialization and design of complex systems:cad2CAS and casCAD2

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Frontiers of Information technology & Electronic Engineering 2024年第2期25卷 323-332页

作者： Xiaolong LIANG Rui QIN Juanjuan LI Fei-Yue WANG Faculty of Innovation Engineering Macao University of Science and TechnologyMacao999078China State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of SciencesBeijing100190China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing100190China

The emergence of decentralized autonomous organizations and operations(DAOs),equipped with innovative mechanisms,enables new possibilities for transforming traditional social collaborative *** are the underlying drive... 详细信息

关键词： complexity. circular equipped

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External Attention Mechanism-Based Modulation Classification

External Attention Mechanism-Based Modulation Classification

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2022 International Conference on Scientific Computation and Applied Statistics, ICSCAS 2022

作者： Tao, Xueying Shao, Huaizong Li, Qiang Pan, Ye Fu, Zhongqi School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China Peng Cheng Laboratory Shenzhen China

This paper considers the modulation classification of radio frequency (RF) signals. An external attention mechanism-based convolution neural network (EACNN) is proposed. Thanks to the external attention layers, the EACNN network can capture the potential correlations of different modulation data, which helps reduce computational consumption and memory costs efficiently during training. Moreover, to account for the variation of the signals induced by channel fading, we further propose a customized batch normalization (BN) layer in EACNN to improve the classification accuracy with less training time. Numerical experiments on RML2016.a dataset shows that the proposed method outperforms the baseline method CNN2 by 7% in terms of classification accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Fading channels

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Simulation of Nanosecond Microwave Pulse Amplification Based on a Gyrotron Traveling Wave Tube

Simulation of Nanosecond Microwave Pulse Amplification Based...

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Ruoyang Pan Zhiyuan Zhang Guo Liu Weijie Wang Yelei Yao Wei Jiang Zewei Wu Youlei Pu Jianxun Wang Yong Luo School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

The paper presents the simulation results for nanosecond pulse amplification utilizing a TE 11 mode Ku-band gyrotron traveling wave tube (gyro-TWT). This study evaluates the output performance across various input pulse widths within the 15–17 GHz frequency range. Simulations show that the minimum output pulse width is 0.8 ns, with an output power of 183.9 kW. Moreover, when input pulse widths exceed 2 ns, it is observed that the tube can attain a saturated output power of 293.1 kW, with negligible disparity between input and output pulse widths. The gyro-TWT can serve as a nanosecond pulse source, with potential applications in fields such as military radar, biomedicine, and microwave chemistry.

关键词： Pulse generation Traveling wave tubes Simulation Power amplifiers Gyrotrons Radar imaging Microwave amplifiers

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Microstructure and properties evolution of plasma sprayed Al_(2)O_(3)-Y_(2)O_(3) composite coatings during high temperature and thermal shock treatment

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Journal of Rare Earths 2021年第6期39卷 718-727,I0004页

作者： Xingyu Wang Xiaolong Wang Xiaowen Sun Yong Yang Chen Zhang Yuduo Ma Yuhang Cui Yanchun Dong Dianran Yan You Wang Key Lab.for New Type of Functional Materials in Hebei Province State Key Laboratory of Reliability and Intelligence of Electrical EquipmentTianjin Key Laboratory of Materials Laminating Fabrication and Interface Control TechnologySchool of Materials Science and EngineeringHebei University of TechnologyTianjin 300132China Department of Materials Science Harbin Institute of TechnologyHarbin 150001China

Al_(2)O_(3)-Y_(2)O_(3) composite powder with TiO_(2) additive was plasma sprayed to prepare Al_(2)O_(3)-Y_(2)O_(3) composite *** micro structure and properties evolution of the Al_(2)O_(3)-Y_(2)O_(3) coatings during high temperature and thermal shock resistance were *** results show that the micro structure of the Al_(2)O_(3)-Y_(2)O_(3)-TiO_(2) coating is more uniform than that of the Al_(2)O_(3)-Y_(2)O_(3) ***,amorphous phase is formed in the two *** Al_(2)O_(3)-Y_(2)O_(3)(-TiO_(2)) coatings were heat treated for 2 h at temperatures of 800,1000 and 1200℃,*** is found that the microstructure and properties of the two coatings have no obvious change at 800℃.Some of the amorphous phase is crystallized at1000℃,and meanwhile Y_(2)O_(3) and Al_(2)O_(3) react to form YAG phase and YAM *** 1200℃,all of the amorphous phases are *** heat treatment,the micro hardness of the two coatings is *** thermal shock resistance of the Al_(2)O_(3)-Y_(2)O_(3) system coatings can be improved by using TC4 titanium alloy as substrate and with NiCrAlY bonding ***,the Al_(2)O_(3)-Y_(2)O_(3)-TiO_(2) coating exhibits better thermal shock resistance due to the addition of TiO_(2).

关键词： Al_(2)O_(3)-Y_(2)O_(3)coating TiO_(2) Plasma spraying Thermal shock resistance Microstructure evolution

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Distributed Secure Consensus Estimation for Power Systems Against False Data Injection Attacks

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International Journal of Robust and Nonlinear control 2025年

作者： Cheng, Zhijian Ren, Hongru Qin, Jiahu Lu, Renquan School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control and Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China Department of Automation University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

As a result of the rapid growth of distributed state estimation in modern power systems, power networks are facing increasingly serious security problems, thus requiring the advancement of defense techniques against cyber attacks. This paper is devoted to investigating distributed consensus state estimation with a defense mechanism against false data injection (FDI) attacks for power systems. By introducing a transformation matrix, the local subsystem model associated with the mixed remote terminal unit and phasor measurement unit measurements is constructed. Taking into account historical estimation information on state variables without being attacked, a defense mechanism constructed by secure historical estimation value is presented to protect the estimator against FDI attacks while maintaining accuracy in estimation. Then a distributed Kalman consensus filter (DKCF) is hosted to estimate the dynamic states of power systems under FDI attack protector. Considering scalability in large-scale power systems, a suboptimal DKCF with the designed protector is developed. By means of the Lyapunov-based approach, a sufficient condition is provided to ensure that the proposed estimator equipped with the defense mechanism is stable. Finally, the proposed distributed state estimation algorithms are validated on an IEEE benchmark 14-bus power system. © 2025 John Wiley & Sons Ltd.

关键词： Cyber attacks

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