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arXiv 2023年

作者： Taga, Keisuke Kato, Yuzuru Yamazaki, Yoshihiro Kawahara, Yoshinobu Nakao, Hiroya Department of Physics School of Advanced Science and Engineering Waseda University Tokyo169-8555 Japan Department of Complex and Intelligent Systems School of Systems Information Science Future University Hakodate Hokkaido041-8655 Japan Graduate School of Information Science and Technology Osaka University Osaka565-0871 Japan Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8552 Japan

We apply Dynamic Mode Decomposition (DMD) to Elementary Cellular Automata (ECA). Three types of DMD methods are considered and the reproducibility of the system dynamics and Koopman eigenvalues from observed time series are investigated. While standard DMD fails to reproduce the system dynamics and Koopman eigenvalues associated with a given periodic orbit in some cases, Hankel DMD with delay-embedded time series improves reproducibility. However, Hankel DMD can still fail to reproduce all the Koopman eigenvalues in specific cases. We propose an Extended DMD method for ECA that uses nonlinearly transformed time series with discretized Walsh functions and show that it can completely reproduce the dynamics and Koopman eigenvalues. Linear-algebraic backgrounds for the reproducibility of the system dynamics and Koopman eigenvalues are also discussed. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic mode decomposition

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Error-state Kalman Filtering with Linearized State Constraints

TechRxiv

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TechRxiv 2023年

作者： Do, Hoang Viet Jung, Jae Hyung Park, Chan Gook Song, Jin Woo The Intelligent Navigation and Control Systems Laboratory Department of Convergence Engineering for Intelligent Drone School of Intelligent Mechatronics Engineering Sejong University Seoul05006 Korea Republic of The Department of Aerospace Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of The Navigation and Electronic System Laboratory Department of Aerospace Engineering Institute of Advanced Aerospace Technology Seoul National University Seoul08826 Korea Republic of

In recent years, the error-state Kalman filter (ErKF) has been extensively employed across various applications, including but not limited to robotics, aerospace, and localization. However, incorporating constraints into the ErKF framework when state constraint is necessary has remained a challenging task due to its intrinsic properties. This paper explores all possible ways to achieve this goal in the context of the estimate projection method. In particular, the constraint can be enforced before or after the ErKF’s correction step. We approach the problem from a mathematical perspective by deriving analytical solutions and discussing their statistical properties. We prove that the two mentioned methods are statistically identical for a linear system with linear constraints. Conversely, the filter’s behavior remains uncertain in the presence of linearized constraints. However, we provide a special case of the nonlinear constraint, wherein the results of the linear case remain valid. To support our theorem and verify the filter’s performance when the assumptions are invalidated, we present two Monte Carlo simulations under the increasing initialization error and the constraint’s incompleteness. The simulation results clearly confirm our insights and lead to the conclusion that constraining the error-state after the correction may offer superior outcomes compared to its competitor. © 2023, CC BY.

关键词： Linearization

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Unlocking Freeform Structured Surface Denoising with Small Sample Learning: Enhancing Performance Via Physics-Informed Loss and Detail-Driven Data Augmentation

SSRN

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SSRN 2024年

作者： Cui, Weixin Lou, Shan Zeng, Wenhan Kadirkamanathan, Visakan Qin, Yuchu Scott, Paul J. Jiang, Xiangqian EPSRC Future Advanced Metrology Hub School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom

Denoising plays a vital role in surface metrology and other engineering domains. Frequently, these noises manifest themselves as outliers and high-frequency artifacts. Traditional denoising techniques, such as Gaussian and partial differential equation-based diffusion filters, although effective, often involve a time-consuming calibration process, especially when addressing diverse topographical surfaces. The challenge intensifies for novice users in the presence of varied surface textures. This paper addresses these challenges by introducing a learning-based solution that aims to deliver the benefits of traditional filters without the need for manual tuning. Denoising convolutional neural network is employed as the basic architecture due to its effectiveness in tackling mix-level Gaussian noise and adapting to small training dataset. Acknowledging the constraints of limited datasets, prevalent in many engineering scenarios, we propose a novel physics-informed loss. This innovative loss marries filtering techniques with neural network training, tapping into data characteristics for enhanced model performance. Furthermore, we also embrace hybrid data augmentation techniques, going beyond conventional approaches by further generating customized noise maps to enhance recognition of complex components. Through extensive experiments, our proposed denoising model consistently outperforms meticulously calibrated traditional filters on average. Additionally, when juxtaposing our physics-informed loss with the conventional DnCNN loss across different dataset sizes, our approach consistently emerges superior, particularly with more constrained datasets. Furthermore, the new loss shows effectiveness in general image denoising, which proves the robustness and universality of this suggested method. These findings underscore the promise of our method as a general avenue to seamlessly weave domain expertise into neural networks, especially in data-scarce situations of most industr

关键词： Gaussian noise (electronic)

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基于自解耦三明治结构的横向运动栅场效应晶体管MEMS微力传感器

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engineering 2023年第2期21卷 61-74,M0004页

作者：高文迪乔智霞韩香广王小章 Adnan Shakoor 刘存朗卢德江杨萍赵立波王永录王久洪蒋庄德孙东 State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesOverseas Expertise Introduction Center for Micro/Nano Manufacturing and Nano Measurement Technologies Discipline InnovationXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150006China Eleventh Research Institute Sixth Academy of China Aerospace Science and Technology Co.Xi’an 710100China Department of Control and Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Department of Biomedical Engineering City University of Hong KongHong Kong 999077China

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵... 详细信息

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵列层和两个软光阻层SU-8组成。通过所提出的双差分传感结构,LMGFET横向工作在垂直干扰下的输出电流被大大消除,所提出传感器的相对输出误差从4.53%(传统差分结构)降低到0.01%。提出了一种实用的传感器制造工艺,并对其进行了模拟。基于LMGFET的力传感器的灵敏度为4.65μA·nN^(-1),可与垂直可移动栅极场效应晶体管(VMGFET)器件相媲美,但非线性度提高了0.78%,测量范围扩大了±5.10μN。这些分析为LMGFET器件的电气和结构参数提供了全面的设计优化,并证明了所提出的传感器在生物医学显微操作应用中具有良好的力传感潜力。

关键词：力传感器场效应晶体管横向运动三明治结构电气模型显微操作非线性度双差分

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Distributed No-Regret Learning for Stochastic Aggregative Games over Networks

Distributed No-Regret Learning for Stochastic Aggregative Ga...

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第41届中国控制会议

作者： Jinlong Lei Peng Yi Li Li Department of Control Science and Engineering Tongji University Institute for Advanced Study Tongji University Shanghai Research Institute for Intelligent Autonomous Systems

In this work,we propose a distributed no-regret learning for stochastic aggregative games over time-varying *** consider a finite set of players repeatedly playing the network aggregative game in stochastic regimes,where each player has an expectation-valued objective function depending on its own strategy and the aggregate of all player strategies,and the players can estimate gradients of their objective functions up to a zero-mean error with bounded *** consider the scenario in which players cannot directly obtain the aggregate value,but they are able to share their estimates of the aggregate with their neighbors without disclosing their own *** design a distributed learning algorithm based on the mirror descent and dynamical averaging *** then provide analysis for both the variational regret and the cost regret for aggregative games with player-specific problem being convex,and show that the expected regret bounds can be O(K) for specific *** analyses indicate the key correlations between the regret bounds,the network connectivity,and game structures,*** addition,we validate the almost sure convergence to the Nash equilibrium for the class of strictly monotone ***,we present preliminary numerics by applying the proposed scheme to the Nash-Cournot competition problem.

关键词： Distributed No-Regret Learning for Stochastic Aggregative Games over Networks

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A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment

A Modified Dempster Shafer Approach to Classification in Sur...

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Iranian Conference on Electrical engineering (ICEE)

作者： Arash Iranfar Mohammad Soleymannejad Behzad Moshiri Hamid D. Taghirad School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.

关键词：

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An Efficient Learning control Framework with Sim-to-Real for String-Type Artificial Muscle-Driven Robotic systems

arXiv

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arXiv 2024年

作者： Tao, Jiyue Zhang, Yunsong Rajendran, Sunil Kumar Zhang, Feitian The Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering the State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China The BSS Technologies Inc. Takoma ParkMD20912 United States

Robotic systems driven by artificial muscles present unique challenges due to the nonlinear dynamics of actuators and the complex designs of mechanical structures. Traditional model-based controllers often struggle to achieve desired control performance in such systems. Deep reinforcement learning (DRL), a trending machine learning technique widely adopted in robot control, offers a promising alternative. However, integrating DRL into these robotic systems faces significant challenges, including the requirement for large amounts of training data and the inevitable sim-to-real gap when deployed to real-world robots. This paper proposes an efficient reinforcement learning control framework with sim-to-real transfer to address these challenges. Bootstrap and augmentation enhancements are designed to improve the data efficiency of baseline DRL algorithms, while a sim-to-real transfer technique, namely randomization of muscle dynamics, is adopted to bridge the gap between simulation and real-world deployment. Extensive experiments and ablation studies are conducted utilizing two string-type artificial muscle-driven robotic systems including a two degree-of-freedom robotic eye and a parallel robotic wrist, the results of which demonstrate the effectiveness of the proposed learning control strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Material Identification Approach Based on Wi-Fi Signal

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Computers, Materials & Continua 2021年第12期69卷 3383-3397页

作者： Chao Li Fan Li Wei Du Lihua Yin Bin Wang Chonghua Wang Tianjie Luo Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510700China PCL Research Center of Cyberspace Security Peng Cheng LaboratoryShenzhen518052China Department of Computer Science and Computer Engineering University of ArkansasFayetteville72701USA College of Electrical Engineering Zhejiang UniversityHangzhou310058China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Material identification is a technology that can help to identify the type of target *** approaches depend on expensive instruments,complicated pre-treatments and professional *** is difficult to find a substantial yet effective material identification method to meet the daily use *** this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level *** practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi *** that,the amplitude ratios and phase differences can be more stably utilized to classify the *** implement our system and evaluate its ability to identify materials in indoor *** result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

关键词： Internet of Things Wi-Fi signal channel state information material identification noise elimination

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Damping Linearization of Ankle Prosthesis Based on Electro-Hydrostatic Actuation

Damping Linearization of Ankle Prosthesis Based on Electro-H...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Dong, Dengpeng Wang, Xingjian Tian, Xinyu Wang, Shaoping Wei, Yi Gavrilov, Alexander I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

The research object of this paper is the ankle prosthesis system based on electro-hydrostatic actuation. In the passive state, the prosthesis uses PWM wave to drive a high speed ON/OFF valve (HOV), making it equivalent to a proportional valve with a certain opening size to adjust the damping of the system and achieve a compliant control. However, due to the mechanical delay characteristics of the HOV, there are obvious nonlinear regions in the low duty cycle and high duty cycle regions, which affect the control performance of the system. This paper will discuss this problem. Using the control strategies in PWM-PFM to widen the linear region of the prosthetic system damping control, improve the system's ability to adjust the damping, and facilitate the prosthesis to better achieve active compliance control. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Linearization

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Cooperative neural-adaptive fault-tolerant output regulation for heterogeneous nonlinear uncertain multiagent systems with disturbance

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Science China(Information Sciences) 2021年第7期64卷 181-192页

作者： Shanling DONG Guanrong CHEN Meiqin LIU Zheng-Guang WU College of Electrical Engineering Zhejiang University Department of Electrical Engineering City University of Hong Kong National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University Institute for Advanced Study Chengdu University

This paper investigates the cooperative output regulation problem for heterogeneous nonlinear uncertain multiagent networked systems subject to actuator failure, bounded matched or mismatched disturbances or disturbances produced by a given linear exosystem. Accurate information about nonlinearity,actuator failure and disturbance may be completely unknown. First, a distributed finite-time observer is designed to estimate the dynamics of the exosystem on a finite-time interval over a communication ***, a neural-adaptive control protocol is proposed. It is shown that(i) closed-loop multiagent systems are asymptotically stable, with output synchronization errors that tend to zero in the absence of mismatched disturbance, and(ii) the states of the closed-loop multiagent systems and the output synchronization errors are bounded in the presence of mismatched disturbance. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control strategy.

关键词： cooperative output regulation actuator failure matched/mismatched disturbance distributed finite-time observer neural-adaptive control

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