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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Dead-time Compensation Method for Bus-clamping Modulated Voltage Source Inverter

Dead-time Compensation Method for Bus-clamping Modulated Vol...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Ghaderloo, Reza Asrar Shen, Yidi Singhabahu, Chanaka Resalayyan, Rakesh Khaligh, Alireza University of Maryland Maryland Power Electronics Laboratory Department of Electrical and Computer Engineering The Institute for Systems Research College ParkMD20742 United States

ISBN: (纸本)9798350316445

Bus-clamping Pulse Width Modulation (PWM) is an effective method to reduce the switching loss in a three-phase voltage source inverter (VSI). In bus-clamping PWM scheme, the phase legs are switched using high frequency PWM signals for two-third of the line cycle, while for the remaining duration of cycle, the pole voltage is clamped to either positive or negative rail of the DC bus. In PWM operation of a half bridge, a dead-time is applied between the gate signals of complementary switches to ensure safe and reliable operation. However, introduction of dead-time leads to poor power quality, increased Total Harmonic Distortion (THD) and variation in actual voltage compared to the intended pole voltage. Moreover, when the bus-clamping technique is used, the PWM has both high frequency switching region and clamped region in a line cycle, and consequently, the undesired effects of dead-time are further aggravated. Therefore, in order to enhance the quality of output voltage, this paper presents a dead-time compensation strategy for a VSI operating with bus-clamping PWM. The proposed method calculates the required compensation term to be added on the modulation signal considering wide range of operating conditions. Additionally, the compensation includes a new strategy for low current conditions near zero-crossing to avoid distortion. The proposed method is verified by simulation and experiments in a three-phase VSI with a switching frequency of 100 kHz and a fundamental frequency of 60Hz. © 2023 IEEE.

关键词： Bus-clamping Dead-time Pulse-width Modulation Voltage Source Inverter

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Live Demonstration: A fully embedded adaptive real-time hand gesture classifier leveraging HD-sEMG and deep learning

Live Demonstration: A fully embedded adaptive real-time hand...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Isabel, Xavier Labbe, Thomas Chamberland, Felix Buteau, Etienne Campbell, Evan Cote-Allard, Ulysse Tam, Simon Fortier, Paul Campeau-Lecours, Alexandre Boukadoum, Mounir Scheme, Erik Gosselin, Benoit Laval University Electrical Engineering and Computer Engineering Department Canada University of New-Brunswick Institute of Biomedical Engineering Canada University of Oslo Department off Technology Systems Norway Laval University Mechanical Engineering Department Canada University of Quebec at Montreal Computer Science Department Canada

ISBN: (纸本)9798350300260

In a fully immersive virtual reality setting, visitors will actively participate in our demonstration, engaging in an interactive videogame utilizing our cutting-edge HD-sEMG Bracelet. Prior to this, they will undergo state-of-the-art training to personalize their machine learning gesture recognition model. The gameplay will be broadcast on a computer monitor, allowing onlookers to observe while the demonstrator explains the core concept of our work. By wearing the bracelet, the player gains control over their limb in a game inspired by Simon Says. Throughout the demonstration, visitors will gain firsthand experience and knowledge about the forthcoming advantages of HD-sEMG for control applications, such as prosthesis control. They will also discover how the EMaGer HD-sEMG Bracelet capitalizes on its uniform and high electrode density to enhance the reliability of hand gesture recognition models utilized in myoelectric prosthesis control. © 2023 IEEE.

关键词： Armband Control Convolution Deep Learning Edge Computing Electromyography Gesture HD-EMG Machine Learning Myoelectric Neural Network

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Repairing Reed-Solomon Codes over Prime Fields via Exponential Sums

Repairing Reed-Solomon Codes over Prime Fields via Exponenti...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Con, Roni Shutty, Noah Tamo, Itzhak Wootters, Mary Tel Aviv University Blavatnik School of Computer Science Israel Stanford University Stanford Institute for Theoretical Physics United States Tel Aviv University Department of Electrical Engineering-Systems Israel Stanford University Department of Computer Science and Electrical Engineering United States

ISBN: (纸本)9781665475549

This paper presents several repair schemes for lowrate Reed Solomon (RS) codes over prime fields that can repair any node by downloading a constant number of bits from each surviving node. The resulting total bandwidth is higher than the bandwidth incurred during the trivial repair;however, this is still interesting in the context of leakage-resilient secret sharing. In that language, our results give attacks that show that k-out-of-n Shamir's Secret Sharing over prime fields for small k is not leakage resilient, even if the parties only leak a constant number of bits. To the best of our knowledge, these are the first such *** another application, we provide decoding schemes for RS codes over prime fields, where the entire RS codeword is recovered by transmitting a constant number of bits from each *** results follow from a novel connection between exponential sums and repair of RS codes. In particular, we show that nontrivial bounds on certain exponential sums imply the existence of efficient nonlinear repair schemes for RS codes over prime fields. © 2023 IEEE.

关键词： Reed-Solomon codes

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Distributed Nash equilibrium for pursuit-evasion game with one evader and multiple pursuers

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Science China(Information Sciences) 2025年

作者： Libing WANG Zhixin LIU Shuo YUAN Zhiqiang PU Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering Wayne State University Institute of Automation Chinese Academy of Sciences

This paper investigates the pursuit-evasion problem involving one evader and multiple pursuers with limited sensing capability, where the evader tries to maximize the distance with the pursuers, while the pursuers have different objectives based on whether they can receive the information of the evader. The subgroup of pursuers who can observe the evader(called leaders) tries to be close to the evader, and the other subgroup of pursuers(called followers) tries to synchronize with their neighbors. When the subgraph formed by all leaders is complete, sufficient conditions are given to guarantee that the pursuers capture the evader and the pursuit-evasion game composed of the evader and leaders reaches Nash equilibrium. Furthermore, for the incomplete subgraph case, the distributed observers are proposed to estimate the relative positions between the evader and all leaders. It is shown that the distributed control strategy based on the observers converges exponentially to the Nash equilibrium solution, and makes the pursuers capture the evader. Finally, simulation examples are provided to verify the effectiveness of the proposed strategies.

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Tolerant Tracking Control Protocol for PMSM Based on Policy Iteration Algorithm and Fault Compensation 16th

Tolerant Tracking Control Protocol for PMSM Based on Polic...

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16th International Conference on Social robotics, ICSR + InnoBiz 2024

作者： Yan, Shuya Li, Xiaocong Qian, Huaming Ma, Jun Al Mamun, Abdullah College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Guangzhou China Information Science and Technology Eastern Institute of Technology Ningbo China

ISBN: (纸本)9789819611508

In robotics applications, ensuring reliable performance in the presence of actuator faults is essential for maintaining system safety and reliability. This paper presents a tolerant tracking control method for permanent magnet synchronous motors (PMSMs) based on adaptive dynamic programming and fault compensation. The method simultaneously considers tracking accuracy and energy consumption through a policy iteration algorithm. In the optimality analysis of the algorithm, more relaxed conditions are provided to demonstrate that the performance function can converge to a near-optimal value within a finite number of iterations. In practical implementation, an actor-critic network is used to approximate the performance function and control protocol, alongside a fault detection mechanism based on an expanded time horizon, which achieves fault detection from arbitrary initial values. The effectiveness of the proposed algorithm is verified using a high-fidelity PMSM model in Simulink. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Permanent magnets

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Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1014-1030页

作者： Zaipeng Xie Cheng Ji Chentai Qiao WenZhan Song Zewen Li Yufeng Zhang Yujing Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina Center for Cyber‐Physical Systems University of GeorgiaAthensGeorgiaUSA Information Networking Institute Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual ***,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent *** tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration *** rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment *** addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent *** was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty *** experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

关键词： artificial intelligence techniques decision making intelligent multi‐agent systems

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Design and Modeling of a Two-stage PV Inverter for Single Phase and Three Phase Applications

Design and Modeling of a Two-stage PV Inverter for Single Ph...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Shen, Yidi Ghaderloo, Reza Asrar Singhabahu, Chanaka Resalayyan, Rakesh Khaligh, Alireza Maryland Power Electronics Laboratory Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

ISBN: (纸本)9798350316445

This paper presents the modeling and design of a 1kW two-stage photovoltaic (PV) inverter compatible with both single phase and three phase grid. The topology consists of a cascade of dual-active-bridge (DAB) based dc-dc stage and a two-level voltage source inverter (VSI) stage. Two-level VSI based dc-ac stage is designed and the modulation schemes are compared considering operation for both single phase and three phase output connection. A 3 leg interleaved hybrid pulse width modulation (3 leg-IHPWM) is proposed for single phase operation to reduce power loss and current total harmonic distortion (THD) with full utilization of the third switching leg. Sizing of dc link capacitance is analyzed for both single phase power decoupling and three phase load unbalance. Circuit design and component selection are conducted including dc link voltage selection, output filter sizing, DAB dc-dc stage transformer sizing and modulation optimization for zero-voltage-switching (ZVS) ability and control flexibility. The proposed design is verified through hardware prototype and experimental testing. © 2023 IEEE.

关键词： Single Phase Three Phase Two-stage Inverter Voltage Source Inverter

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Smart Robot: Artificial Intelligent Cleaning Robot with Wifi Boundary Sensing

Smart Robot: Artificial Intelligent Cleaning Robot with Wifi...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Tannous, Gresmarteneanos Ame, Ibrahim Kian, Nafiseh Yousefi Al-Turjman, Fadi Mlimwa, Cliford Peter Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Research Centre for AI and IoT Nicosia Cyprus Research Centre for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Cyprus Department of Computer Engineering Cyprus International University Nicosia Cyprus

ISBN: (纸本)9783031782756

The environment surrounds our daily lives, whether we want to or not. When we have a clean environment, we can enjoy very healthy lifestyles. However, when we have dirty environments, it can cause a myriad of diseases, health risks, and worse, global pandemics like plagues. This became very apparent during the years of the COVID-19 pandemic when we had to sanitize our hands on an hourly basis after touching suspicious items that might have had human contact. This study entails a way to keep our environment clean by picking up trash and items that are left on the floor by other people. A smart robot that uses artificial intelligence to be able to not only move around and understand its surroundings, but also know if there is an object that is identified as trash and needs to be removed from that area. The smart robot is fitted with a high-definition camera for object detection and can see them from 3–4 m. From the image, it will scan the angle direction of the object, change its course, and follow it. After reaching the said location, it will begin its procedure of picking up and storing it in its trash compartment. The robot can be tuned to only clean a certain area and not cross its boundary by comparing fingerprints of registered Wi-Fi RSSIs. This method is most accurate for indoor boundary setting. For outdoors, a GPS method is available to track and bind the robot within a map circumference. Finally, all these metrics and location services can be viewed on a custom-made mobile application that complements the robot's features. What ties all this together is a cloud-based server that feeds the app data from the robot and stores it for future review. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wi-Fi

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Autonomous Drifting Based on Maximal Safety Probability Learning

Autonomous Drifting Based on Maximal Safety Probability Lear...

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International Conference on Intelligent Transportation

作者： Hikaru Hoshino Jiaxing Li Arnav Menon John M. Dolan Yorie Nakahira Department of Electrical Materials and Engineering University of Hyoto Department of Electrical and Computer Engineering Carnegie Mellon University Robotics Institute Carnegie Mellon University

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This paper proposes a novel learning-based framework for autonomous driving based on the concept of maximal safety probability. Efficient learning requires rewards that are informative of desirable/undesirable states, but such rewards are challenging to design manually due to the difficulty of differentiating better states among many safe states. On the other hand, learning policies that maximize safety probability does not require laborious reward shaping but is numerically challenging because the algorithms must optimize policies based on binary rewards sparse in time. Here, we show that physics-informed reinforcement learning can efficiently learn this form of maximally safe policy. Unlike existing drift control methods, our approach does not require a specific reference trajectory or complex reward shaping, and can learn safe behaviors only from sparse binary rewards. This is enabled by the use of the physics loss that plays an analogous role to reward shaping. The effectiveness of the proposed approach is demonstrated through lane keeping in a normal cornering scenario and safe drifting in a high-speed racing scenario.

关键词： Hands Accuracy Reinforcement learning Aerospace electronics Safety Trajectory Autonomous vehicles Physics Intelligent transportation systems Testing

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