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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic systems over a Lossy Communication Channel

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Journal of systems Science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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Efficient Model Training in Edge Networks with Hierarchical Split Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Songge Wu, Wen Song, Lingyang Shen, Xuemin Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China Pengcheng Laboratory Frontier Research Center Shenzhen518055 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Xiangtan University Hunan Institute of Advanced Sensing and Information Technology Xiangtan China University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada

In this paper, we propose an efficient model training scheme, named Group-based Hierarchical Split Learning (GHSL), which can accelerate the artificial intelligence (AI) training process in edge networks in a "first-sequential-then-parallel" manner. Specifically, the proposed scheme hierarchically splits an AI model into a user-side and server-side model, while dividing a number of users into multiple groups. Users in each group train user-side models with the interaction of the shared server-side model sequentially;different groups perform the above training process parallelly;the AI models of each group are aggregated into a global model. We also carry out the convergence analysis for the proposed scheme over non-independent and identically distributed data, which reveals that the convergence rate depends on user grouping. Furthermore, we propose a data-driven two-stage user grouping algorithm to minimize the overall training delay, taking user resource heterogeneity and the black-box training process into account. The proposed algorithm first utilizes the Gaussian process regression approach to determine the number of groups, and then employs the coalition game theory to determine the optimal user grouping decision. Comprehensive simulation results demonstrate that the proposed scheme can reduce training delay, user-side computational workload, and communication overhead by up to 19%, 53%, and 54%, respectively, comparing to state-of-the-art benchmarks. © 2002-2012 IEEE.

关键词： Game theory

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Modular Multilevel DC-DC Converter With Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

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CPSS Transactions on Power Electronics and Applications 2022年第1期7卷 37-48页

作者： Shanshan Zhao Yu Chen Shenghui Cui Jingxin Hu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Department of Electrical and Computer Engineering Seoul National University Korea Institute for Power Generation and Storage Systems E. ON Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype.

关键词： Multilevel converters Semiconductor devices Prototypes DC-DC power converters Transformers Valves Power electronics

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

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IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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Outage and DMT Analysis of Partition-based Schemes for RIS-aided MIMO Fading Channels

arXiv

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arXiv 2023年

作者： Nicolaides, Andreas Psomas, Constantinos Kraidy, Ghassan M. Yang, Sheng Krikidis, Ioannis The IRIDA Research Centre for Communication Technologies Department of Electrical and Computer Engineering University of Cyprus Cyprus The Department of Electronic Systems Norwegian University of Science and Technology Norway The Laboratory of Signals and Systems CentraleSupélec Paris-Saclay University France

In this paper, we investigate the performance of multiple-input multiple-output (MIMO) fading channels assisted by a reconfigurable intelligent surface (RIS), through the employment of partition-based RIS schemes. The proposed schemes are implemented without requiring any channel state information knowledge at the transmitter side;this characteristic makes them attractive for practical applications. In particular, the RIS elements are partitioned into sub-surfaces, which are periodically modified in an efficient way to assist the communication. Under this framework, we propose two low-complexity partition-based schemes, where each sub-surface is adjusted by following an amplitude-based or a phase-based approach. Specifically, the activate-reflect (AR) scheme activates each sub-surface consecutively, by changing the reflection amplitude of the corresponding elements. On the other hand, the flip-reflect (FR) scheme adjusts periodically the phase shift of the elements at each sub-surface. Through the sequential reconfiguration of each sub-surface, an equivalent parallel channel in the time domain is produced. We analyze the performance of each scheme in terms of outage probability and provide expressions for the achieved diversity-multiplexing tradeoff. Our results show that the asymptotic performance of the considered network under the partition-based schemes can be significantly enhanced in terms of diversity gain compared to the conventional case, where a single partition is considered. Moreover, the FR scheme always achieves the maximum multiplexing gain, while for the AR scheme this maximum gain can be achieved only under certain conditions with respect to the number of elements in each sub-surface. © 2023, CC BY.

关键词： Fading channels

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Aluminum Nitride Ring Resonator-Coupled Spin Defects in Hexagonal Boron Nitride for Integrated Quantum Photonics

Aluminum Nitride Ring Resonator-Coupled Spin Defects in Hexa...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Roberts, Henry C. Waseem, Aadil Wicker, Scott A. Abudayyeh, Hamza Kim, Dong Seob Liu, Zhida Randolph, Steven Li, Xiaoqin Li, Xiuling Department of Electrical and Computer Engineering Microelectronics Research Center The University of Texas at Austin 10100 Harry Ransom Trail AustinTX78758 United States Department of Physics Center for Complex Quantum Systems Center for Dynamics Control of Materials Texas Materials Institute The University of Texas at Austin 2515 Speedway AustinTX78712 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

We report a new approach to generate waveguide-coupled emission from deterministically implanted boron vacancy spin defects in hBN using single-crystal AlN-on-sapphire ring resonators. This facilitates the eventual development of hBN-based integrated quantum technologies. © Optica Publishing Group 2024

关键词： Single crystals

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Aluminum Nitride Ring Resonator-Coupled Spin Defects in Hexagonal Boron Nitride for Integrated Quantum Photonics

Aluminum Nitride Ring Resonator-Coupled Spin Defects in Hexa...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Roberts, Henry C. Waseem, Aadil Wicker, Scott A. Abudayyeh, Hamza Kim, Dong Seob Liu, Zhida Randolph, Steven Li, Xiaoqin Li, Xiuling Department of Electrical and Computer Engineering Microelectronics Research Center The University of Texas at Austin 10100 Harry Ransom Trail AustinTX78758 United States Department of Physics Center for Complex Quantum Systems Center for Dynamics and Control of Materials Texas Materials Institute The University of Texas at Austin 2515 Speedway AustinTX78712 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

ISBN: (纸本)9781957171395

关键词： Aluminum nitride

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State Derivative Normalization for Continuous-Time Deep Neural Networks

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IFAC-PapersOnLine 2024年第15期58卷 253-258页

作者： Jonas Weigand Gerben I. Beintema Jonas Ulmen Daniel Görges Roland Tóth Maarten Schoukens Martin Ruskowski Chair of Machine Tools and Control Systems RPTU Kaiserslautern and the German Research Center for Artificial Intelligence Kaiserslautern Germany Control Systems (CS) Group at the Department of Electrical Engineering Eindhoven University of Technology Netherlands. R. Tóth is also affiliated to the Systems and Control Laboratory at the Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature.

关键词： System Identification Continuous-Time Neural Ordinary Differential Equations Nonlinear State-Space

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A Cybertwin-Based Trading Mechanism for Personalized Transmission Services in Cloud Native Networks

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IEEE Wireless Communications Letters 2025年

作者： Zeng, Zhiyong Liang, Dandan Qin, Meng Sun, Ruijin Liang, Hui Zhang, Wei Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China Pengcheng Laboratory Shenzhen518055 China Pengcheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518055 China Xidian University Guangzhou institute of technology Guangzhou510555 China Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia

The Cybertwin-based cloud native network (CCNN) with a cloud-edge-end architecture is to provide personalized user services, which includes both wireless access networks and cloud networks. However, the limited availability of resources and intense user competition present significant challenges. While prior studies have contributed to this issue, they overlook the feasibility of using resources as the trading object in a user-centric approach, particularly in wireless networks. To address this gap, this letter proposes a Cybertwin-based Personalized Transmission Service Trading Mechanism (CPTSTM), employing a one-to-many concurrent bilateral negotiation model for dynamic multi-provider trading. An optimization problem is formulated to minimize user costs by modeling the negotiation as a sequential decision-making process, accounting for individual rationality and system uncertainties. Given restricted and hybrid action spaces, we introduce a Constrained Parameterized Deep Q-learning Network (CPDQN)-based negotiation strategy integrating offer evaluation, acceptance, and bidding strategies. Simulation results confirm the feasibility of the proposed mechanism and demonstrate that the CPDQN-based algorithm outperforms existing methods in terms of trade success rate (nearly 100%) and the number of negotiation rounds (reduced by 75%) while maintaining comparable user utility across various scenarios. © 2012 IEEE.

关键词： Cloud platforms

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Emergent Ferromagnetism in CaRuO₃/CaMnO₃ (111)-Oriented Superlattices

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Nano Letters 2024年第8期24卷 2567-2573页

作者： Kane, Margaret Bhandari, Churna Holtz, Megan E. Balakrishnan, Purnima P. Grutter, Alexander J. Fitzsimmons, Michael Yang, Chao-Yao Satpathy, Sashi Paudyal, Durga Suzuki, Yuri Department of Materials Science and Engineering Stanford University Stanford 94305 CA United States Ames National Laboratory Iowa State University Ames 50011 IA United States Metallurgical and Materials Engineering Colorado School of Mines Golden 80401 CO United States NIST Center for Neutron Research National Institute of Standards and Technology Gaithersburg 20899 MD United States Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge 37830 TN United States Department of Material Science and Engineering National Yang Ming Chiao Tung University Hsinchu City 30100 Taiwan Department of Physics & Astronomy University of Missouri Columbia 65211 MO United States Department of Applied Physics Stanford University Stanford 94305 CA United States Geballe Laboratory for Advanced Materials Stanford University Stanford 94305 CA United States Department of Electrical and Computer Engineering Iowa State University Ames 50011 IA United States

The boundary between CaRuO3 and CaMnO3 is an ideal test bed for emergent magnetic ground states stabilized through interfacial electron interactions. In this system, nominally antiferromagnetic and paramagnetic materi... 详细信息

The boundary between CaRuO₃ and CaMnO₃ is an ideal test bed for emergent magnetic ground states stabilized through interfacial electron interactions. In this system, nominally antiferromagnetic and paramagnetic materials combine to yield interfacial ferromagnetism in CaMnO₃ due to electron leakage across the interface. In this work, we show that the crystal symmetry at the surface is a critical factor determining the nature of the interfacial interactions. Specifically, by growing CaRuO₃/CaMnO₃ heterostructures along the (111) instead of the (001) crystallographic axis, we achieve a 3-fold enhancement of the magnetization and involve the CaRuO₃ layers in the ferromagnetism, which now spans both constituent materials. The stabilization of a net magnetic moment in CaRuO₃ through strain effects has been long-sought but never consistently achieved, and our observations demonstrate the importance of interface engineering in the development of new functional heterostructures. © 2024 American Chemical Society.

关键词： complex oxides emergent magnetism manganites ruthenatess superlattices

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