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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Julien Mellet Andrea Berra Achilleas Santi Seisa Viswa Sankaranarayanan Udayanga G.W.K.N. Gamage Miguel Ángel Trujillo Soto Guillermo Heredia George Nikolakopoulos Vincenzo Lippiello Fabio Ruggiero Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Department of Computer Robotics and AI Team Electrical and Space Engineering LuleåUIniversity of Technology Luleå Sweden Department of Electrical Engineering and Photonics Automation and Control Group Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors' integrity. Notably, the arm's damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for Micro Aerial Vehicle (MAV) in real environments subject to perturbations during interaction.

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement

Distributed Optimization with Finite Bit Adaptive Quantizati...

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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Artificial Intelligence Thrust of the Information Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node’s local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network’s bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm’s operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm’s unique characteristics.

关键词： Adaptation models Quantization (signal) Heuristic algorithms Bandwidth Distributed algorithms Optimization Convergence

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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Conformal Prediction for Distribution-free Optimal control of Linear Stochastic Systems

arXiv

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arXiv 2024年

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Thomas Lord Department of Computer Science The Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States The School of Electronics Electrical Engineering and Computer Science Queen's University Northern Ireland Belfast United Kingdom

We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves designing a feedback controller to maintain an error system within a prediction region (PR). We define PRs as sublevel sets of a nonconformity score over error trajectories, enabling the handling of joint chance constraints. We propose two methods to design feedback control and PRs: one through direct optimization over error trajectory samples, and the other indirectly using the S-procedure with a disturbance ellipsoid obtained from data. By tightening constraints with PRs, we solve a relaxed problem to synthesize a feedback policy. Our method ensures reliable probabilistic guarantees based on marginal coverage, independent of data size. © 2024, CC BY.

关键词： Optimal control systems

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Sensitivity and Distance Based Performance Analysis for Batteryless Tags with Transmit Beamforming and Ambient Backscattering

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China Communications 2022年第2期19卷 109-117页

作者： Minzheng Jia Chaochao Yao Wei Liu Ruyi Ye Tutun Juhana Bo Ai Department of Information Engineering Beijing Polytechnic CollegeBeijing 100042China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Communication and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China Chinese National Institute of Standalization Beijing 100088China School of Electrical Engineering and Informatics Institut Teknologi BandungIndonesia State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China

Ambient backscatter is a new green technology for Internet of Things(IoT)that utilizes surrounding wireless signals to enable batteryless devices to communicate with other *** battery-free devices first harvest energy from ambient wireless signals and then backscatter the signals for ***,sensitivity and distance are two important parameters for system ***,most existing studies on ambient backscatter communication systems do not consider the impact of the sensitivity of the energy-harvesting nodes and the distances between these *** this paper,we first provide a literature review for ambient communication technology and then take sensitivity and distance as two key parameters and investigate the sensitivity and distance based performance for ambient backscatter communication ***,we establish the mathematical model based on distances between transceivers and backscattering nodes,extract a parameter that can differentiate the direct path and the backscattering path,evaluate the effects of transmit beamforming,design an energy detector for the reader,and analyze the outage probability of energy harvesting at the tag and the bit error rate(BER)at the *** are then provided to corroborate the proposed studies.

关键词： ambient backscatter battery-free tag Internet of Things(IoT) outage probability performance analysis sensitivity transmission distance

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Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

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High-Speed Railway 2024年第3期2卷 143-152页

作者： Xiaolin Luo Tao Tang Le Wang Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China Department of Electrical and Computer Engineering University of CaliforniaRiversideCA 92521USA Beijing Zongheng Electro-Mechanical Technology Development Co. Ltd.Beijing 100081China School of Automation and Intelligence Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe *** guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB *** this case,the minimal safety distance is usually numerically calculated without an analytic ***,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing *** solve this problem,we propose a Distributed Economic Model Predictive control(DEMPC)approach with computation efficiency and theoretical ***,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved *** theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient ***,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among ***,experimental results demonstrate the performance and advantages of the proposed approaches.

关键词： Virtually coupled train set Space-time separation Economic model predictive control Distributed model predictive control Emergency braking configuration

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Visual Tracking Nonlinear Model Predictive control Method for Autonomous Wind Turbine Inspection

arXiv

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arXiv 2023年

作者： Amer, Abdelhakim Mehndiratta, Mohit le Fevre Sejersen, Jonas Pham, Huy Xuan Kayacan, Erdal The Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark GIM Robotics Espoo Finland The Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

Automated visual inspection of on-and offshore wind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes are open-sourced. © 2023, CC BY.

关键词： Inspection

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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A pseudo-labelled ensemble inference for fast and efficient real field photovoltaic system's power prediction

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Optik 2023年第1期287卷

作者： Dhingra, Bhavya Tyagi, Shivam Verma, Aryan Tomar, Anuradha Rani, Poonam Department of Electrical Engineering Netaji Subhas University of Technology Dwarka Delhi110078 India Department of Computer Science Netaji Subhas University of Technology Dwarka Delhi110078 India Department of Instrumentation and Control Engineering Netaji Subhas University of TechnologyDwarka Delhi110078 India

Clean energy solutions like solar power plants have been at the forefront of the collaborative global efforts of the world towards the sustainable development of society. This research paper explores the use of machine learning in predicting photovoltaic (PV) power generation. An accurate and reliable prediction of solar power generation is crucial for the efficient integration of clean energy solutions into the existing power grid. The most common approach for PV forecasting is the statistical approach, which captures the local weather, seasonal, and geographical effects on the PV system but has limited accuracy due to limited data. The physical approach uses complex physical models to simulate solar radiation and predict energy output, but it is time-consuming and unsuitable for short-term forecasting. This study proposed a 3-stage machine learning architecture that uses pseudo labelling to increase accuracy and efficiency in predicting power generated by PV modules. The architecture uses a light gradient boosting machine (LGBM) and Cuda random forest (cuRF) together, which improved the model performance and achieved a high accuracy score of 99.98% R2. This study addresses missing data points and improves accuracy using imputation and weighted ensemble techniques with a year's worth of institutional data. The proposed model outperforms other models tested in this study. © 2023

关键词： Machine learning

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Probabilistic Tube-based control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic

Probabilistic Tube-based Control Synthesis of Stochastic Mul...

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IEEE Conference on Decision and control

作者： Eleftherios E. Vlahakis Lars Lindemann Pantelis Sopasakis Dimos V. Dimarogonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles CA USA School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Northern Ireland UK

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical.

关键词： control design Scalability Linearity Probabilistic logic Scheduling Optimization Multi-agent systems

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