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CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction

CEFHRI: A Communication Efficient Federated Learning Framewo...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Umar Khalid Hasan Iqbal Saeed Vahidian Jing Hua Chen Chen Center For Research in Computer Vision University of Central Florida Orlando FL USA Dept. of Computer Science Wayne State University Detroit MI USA Dept. of Electrical and Computer Engineering Duke University Durham NC USA

Human-robot interaction (HRI) is a rapidly growing field that encompasses social and industrial applications. Machine learning plays a vital role in industrial HRI by enhancing the adaptability and autonomy of robots in complex environments. However, data privacy is a crucial concern in the interaction between humans and robots, as companies need to protect sensitive data while machine learning algorithms require access to large datasets. Federated Learning (FL) offers a solution by enabling the distributed training of models without sharing raw data. Despite extensive research on Federated learning (FL) for tasks such as natural language processing (NLP) and image classification, the question of how to use FL for HRI remains an open research problem. The traditional FL approach involves transmitting large neural network parameter matrices between the server and clients, which can lead to high communication costs and often becomes a bottleneck in FL. This paper proposes a communication-efficient FL framework for human-robot interaction (CEFHRI) to address the challenges of data heterogeneity and communication costs. The framework leverages pre-trained models and introduces a trainable spatiotemporal adapter for video understanding tasks in HRI. Experimental results on three human-robot interaction benchmark datasets: HRI30, InHARD, and COIN demonstrate the superiority of CEFHRI over full fine-tuning in terms of communication costs. The proposed methodology provides a secure and efficient approach to HRI federated learning, particularly in industrial environments with data privacy concerns and limited communication bandwidth. Our code is available at https://***/umarkhalidAI/CEFHRI-Efficient-Federated-Learning.

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Diagnosis of Epileptic Seizures in TLE Patients by Improved Cepstrum Analysis

Diagnosis of Epileptic Seizures in TLE Patients by Improved ...

引用

electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Bahar Tajadini Hani Attar Saeid Reza Seyd Nejad Soheila Rezakhani Reza Boostani CSE & IT Dept. CSE&IT Dept. Electrical and Computer Engineering Faculty Shiraz University Shiraz Iran Faculty of Engineering Zarqa University Zarqa Jordan College of Business Administration University of Business and Technology Jeddah Saudi Arabia Electrical Engineering Dept. Electrical Engineering Dept. Shahid Bahonar University Kerman Iran Neurology Dept. Neurology Dept. Neuroscience Research Center School of Medicine Medical Sciences University Kerman Iran CSE & IT Dept. CSE&IT Dept. Electrical and Computer Engineering Faculty School of Engineering Shiraz University Shiraz Iran

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Epilepsy is a common chronic brain disease characterized by recurrent seizures. The most effective way to detection the arrival of an epileptic seizure is EEG analysis. Various methods have been proposed in the detection of epilepsy. The article proposes the combination of the AutoRegressive and Cepstrum methods, taking into account the periodic characteristics of the amplitude and frequency of epileptic seizures compared to seizure-free periods in brain signals. Once the signal is received and pre-processed, the AR model of the signal is calculated and the Cepstrum is obtained on the modeled signal, then the Cepstrum coefficients are averaged within the given interval, and a detection of epileptic seizure is made by thresholding the averaged coefficients. TLE patients were selected in this study due to their higher prevalence compared to other types of epilepsy. The proposed detection algorithm was examined on 11 EEG patients with temporal lobe epilepsy. The results indicate that the proposed method has an accuracy of 98.3% and a false positive rate of 0.5%, which demonstrates its superiority over existing methods, especially considering the presence of various artifacts in the available data. Moreover, owing to its low computational complexity, the proposed method can be integrated into real-time implant detection systems.

关键词： Temporal lobe Accuracy Sensitivity Epilepsy Cepstrum Training data Surgery

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A Decision-Dependent Chance-Constrained Planning Model for Distribution Networks Under Extreme Weather Events

A Decision-Dependent Chance-Constrained Planning Model for D...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Zhou, Anping Sun, Hongbo Kitamura, Shoichi Nikovski, Daniel Dept. of Electrical and Computer Engineering Southern Methodist University DallasTX75205 United States Data Analytics Group Mitsubishi Electric Research Laboratories CambridgeMA02139 United States Advanced Technology R&D Center Mitsubishi Electric Corporation Amagasaki661-8661 Japan

ISBN: (纸本)9798350396782

Extreme weather events have posed tremendous challenges to the operation of distribution networks. In this paper, we propose a decision-dependent chance-constrained model for the optimal planning of diesel generators, renewable distributed generations (RDGs), energy storage systems, and switches under contingency. A promising moment-based ambiguity set that incorporates the information of decision variables is employed to depict the uncertainty arising from RDGs. By leveraging effective approximation methods such as the Bonferroni approximation method to handle the considered joint chance constraints, the proposed model is transformed into a tractable mixed-integer second-order conic programming problem, which means it can easily be implemented. Numerical experiments are put forward on the IEEE 33-bus test system to validate the effectiveness of the developed approach. © 2023 IEEE.

关键词： Chance constraint contingency distribution network distributionally robust optimization renewable energy

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Towards Automated Nanoenergetic Reaction Characterization with Computational Vision

Towards Automated Nanoenergetic Reaction Characterization wi...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Timothy B. Gaines J. Alex Hurt James M. Keller Grant J. Scott Camden I. Boyle Matthew R. Maschmann Stanton R. Price Dept. of Electrical Engineering and Computer Science University of Missouri Columbia Dept. of Mechanical and Aerospace Engineering University of Missouri Columbia U.S. Army Engineer Research and Development Center Vicksburg Mississippi

In order to utilize aluminum-based nanoenergetic materials effectively, the mechanisms by which reactive aluminum fuel escapes a passivating aluminum shell must be better understood. These reactions can be classified based upon pre- and post-reaction imagery taken of the material. To aide in quantitatively understanding these reactions we investigate creating an improved reaction classifier through leveraging larger, state-of-the-art deep learning models. Additionally, we experiment with various pre-training methods and image analogs to enable effective training of large models even with a small dataset. Image features used from the past experimentation may be used in conjunction with reaction imagery to aid in reaction classification.

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Persian Web Document Retrieval Corpus

Persian Web Document Retrieval Corpus

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Iran Workshop on Communication and Information Theory (IWCIT)

作者： Erfan Zinvandi Morteza Alikhani Zahra Pourbahman Reza Kazemi Arash Amini Electrical Engineering Department Sharif University of Technology Tehran Iran Computer Engineering Department Amirkabir University of Technology Tehran Iran Electronic Research Center Sharif University of Technology Tehran Iran Electrical Engineering Dept. Sharif University of Technology Tehran Iran

ISBN: (数字)9798350387094

ISBN: (纸本)9798350387100

In this paper, we present a Persian dataset designed specifically for the task of text information retrieval through the web. Unlike most available datasets, we incorporate real-world user queries in this dataset distinguishing it from the existing ones in the field. We believe this dataset serves as a valuable resource for researchers and practitioners involved in the development and evaluation of information retrieval systems tailored for the Persian language. Besides the 951 anonymized Persian queries collected by the Zarebin search engine, this dataset consists of documents from the web retrieved based on the queries by both Google and Zarebin search engines. Another interesting feature is that the relevance of each retrieved document to the original query is human annotated. Overall, this corpus consists of 18568 pairs of query and document, each with a relevant/irrelevant tag 1 .

关键词： Conferences Search engines Internet Task analysis Information theory

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On the Stimulation Artifact Reduction during Electrophysiological Recording of Compound Nerve Action Potentials *

On the Stimulation Artifact Reduction during Electrophysiolo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Raphael Panskus Lukas Holzapfel Wouter A. Serdijn Vasiliki Giagka Dept. of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology The Netherlands Dept. of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Microintegration IZM Berlin Germany Neuroscience Dept. Erasmus Medical Center Rotterdam the Netherlands

Recording neuronal activity triggered by electrical impulses is a powerful tool in neuroscience research and neural engineering. It is often applied in acute electrophysiological experimental settings to record compound nerve action potentials. However, the elicited neural response is often distorted by electrical stimulus artifacts, complicating subsequent analysis. In this work, we present a model to better understand the effect of the selected amplifier configuration and the location of the ground electrode in a practical electrophysiological nerve setup. Simulation results show that the stimulus artifact can be reduced by more than an order of magnitude if the placement of the ground electrode, its impedance, and the amplifier configuration are optimized. We experimentally demonstrate the effects in three different settings, in-vivo and in-vitro.

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An Improved Fault Current Bypass-Based DC Solid-State Circuit Breaker for DC Microgrid Applications

An Improved Fault Current Bypass-Based DC Solid-State Circui...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Jiale Zhou Qiang Mu Xiuhu Sun Tiancan Pang Yao Wang Tiefu Zhao Dept. of Electrical and Computer Engineering University of North Carolina at Charlotte Charlotte NC USA School of Electrical Engineering Hebei University of Technology Tianjin China Dept. of ECE North Carolina Battery Complexity Autonomous Vehicle and Electrification (BATT CAVE) Research Center UNC Charlotte Charlotte NC USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Solid-state circuit breakers (SSCBs) have gained traction in the protection field due to their fast fault interruption and arcless operation. This paper proposes an improved fault current bypass-based DC SSCB for DC microgrids. The proposed topology features fast fault isolation and cost-effectiveness. Initially, the superiority of the proposed topology is analyzed in comparison to two prior-art fault current bypass-based topologies. Next, the operating principles and key design considerations are presented for the proposed topology. Finally, the effectiveness of the proposed topology is verified through both simulation and experimental results. The proposed topology is the most cost-effective solution, with 99.82% efficiency and a 15% cost reduction for power semiconductor selection compared to the other two traditional fault current bypass-based topologies. Experimental results from a 400 VDC/80 A prototype demonstrate the feasibility of the proposed topology, showing that the clamping voltage of the metal oxide varistor (MOV) is lower than the DC voltage.

关键词： Varistors Costs Voltage Microgrids Switches Topology Solid state circuits Clamps Circuit faults Fault currents

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Overview of Ancillary Services in the Nigerian Power Sector 2

Overview of Ancillary Services in the Nigerian Power Sector

引用

2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Saka, Bemdoo Hussein, Suleiman U. Ishola, Biliaminu A. Mohammed, Yekini S. Hembe, Nanen D. Mustapha, Umar M. Thomas, Sadiq Nile University of Nigeria Dept. of Electrical/Electronics Eng. Abuja Nigeria Centre for Satellite Tech. Development Nat. Space Research and Dev. Agency Abuja Nigeria National Control Center Transmission Company of Nigeria Osogbo Abuja Nigeria Federal Polytechnic Nasarawa Dept. of Electrical/Electronics Eng. Nasarawa Nigeria Nasarawa State University Department of Geography Nasarawa Nigeria Advanced UAV Laboratory Nat. Space Research and Dev. Agency Abuja Nigeria Nile University of Nigeria Dept. of Computer Engineering Abuja Nigeria

ISBN: (纸本)9798350358834

An overview of the ancillary services in the Nigerian power sector is provided in this work. These services are essential in the power system because they are additional services required by the grid during critical times to enable the power grid to operate in a stable, safe, and reliable manner. The ancillary services provided as stipulated in the Grid Code are black start, frequency control, operating reserves, and voltage control. The System Operator procures the relevant ancillary service depending on the requirement of the grid at a particular time. The paper further presents some ways of improving the viability of ancillary services for the energy grid of Nigeria. These include making incentives more attractive for suppliers of ancillary services, utilizing more hydroelectric power plants for generation and load balancing, integrating distributed generation to provide support to the reactive power, and making necessary investments in the grid. © 2023 IEEE.

关键词： Hydroelectric power plants

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Unsupervised Muscle Fatigue Clustering Based on sEMG Signals using DeepCNN Autoencoders

Unsupervised Muscle Fatigue Clustering Based on sEMG Signals...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Mohammad Javad Javanbakht Mohammad Reza Fouji Amin Akhavan Saffar Mohammad- R. Akbarzadeh-T Alireza Akbarzadeh Dept. of Electrical Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran Dept. of Computer Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran Dept. of Electrical Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran Dept. of Mechanical Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran

Muscle fatigue assessment is key for optimizing physiotherapy, rehabilitation, and exercise. Muscle fatigue detection not only helps in reducing rehabilitation discomfort but also assists athletes in working out efficiently. The main motivation behind this research is to address the pressing need for a precise and reliable tool to assess muscle fatigue during physical activities. In addition to enhancing therapeutic interventions, this approach improves microtears-induced muscle strengthening by advancing the understanding of muscle fatigue levels. Previous studies have mainly employed supervised approaches to detect fatigue by processing surface electromyography (sEMG) signals through traditional methods in the Time, Frequency, and Time-Frequency domains. These approaches are vulnerable to challenges, such as signal sensitivity, noise interference, and inter-individual variability. Thus, the proposed method uses the spring energy model of the sEMG signal because of its excellent approximation with muscle characteristics. In order to handle noisy and ambiguous data, this paper employs a self-supervised Deep Convolutional Neural Network (DeepCNN) Autoencoder for feature extraction and K-means clustering due to their strong performance. Five healthy participants carried out two experiments using a knee rehabilitation robot (FUM-Physio). The results showcase a well-ordered clustering that closely mirrors the progression of muscle fatigue during these experiments. This indicates the robustness and generality of the proposed method.

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HERA: A Novel Heuristic Resource Allocator for Multi-SDM Multi-User Settings

HERA: A Novel Heuristic Resource Allocator for Multi-SDM Mul...

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IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Christos Antoniadis Kostas Katsalis Dimitrios Tyrovolas Sotiris A. Tegos Sotiris Ioannidis Panagiotis D. Diamantoulakis George K. Karagiannidis Christos Liaskos Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece DOCOMO Euro-Labs Munich Germany Dept. of Electrical and Computer Engineering Technical University of Crete Chania Greece Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon Computer Science Engineering Department University of Ioannina Ioannina Greece Institute of Computer Science Foundation for Research and Technology Hellas Greece

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

The advent of Programmable Wireless Environments (PWEs) has transformed the wireless propagation phenomenon into a software-defined resource, leveraging Software-defined metasurfaces (SDMs). These new technologies have shown that wireless waves can be routed within a space, contrary to the regular, chaotic wireless propagation, yielding considerable benefits to communication efficiency, and even completely new applications. A new topic has risen, in the context of modeling these capabilities as network resources, and allocating them to users. In this context, the present paper contributes HERA, a heuristic resource allocator that receives a setup with users and SDMs as input, and produces the necessary configuration of the latter, for efficient shared performance. Simulation results demonstrate considerable efficiency in this task, while the heuristic nature of HERA sets the basis for a flexible definition of complex user objectives in the future.

关键词： Wireless communication Chaotic communication Simulation Metasurfaces Delays Particle swarm optimization Standards Optimization Context modeling

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