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2024 International Conference on Power Electronics and Energy System, ICPEES 2024

作者： Pasaribu, Faisal Irsan Sara, Ira Devi Tarmizi, Tarmizi Nasaruddin, Nasaruddin Doctoral Program School of Engineering Universitas Syiah Kuala Aceh23111 Indonesia Department Electrical and Computer Engineering Universitas Syiah Kuala Aceh23111 Indonesia

Good power quality and high-power efficiency are important aspects of power system management. Harmonic, as an unwanted frequency component in electrical signals, can interfere with power quality and reduce electrical efficiency. A passive harmonic filter is a device used to reduce or filter harmonics in electrical power systems. This research proposes technology Internet of Things with an automatic control system that moves the step filter to solve the harmonic problem, with the aim of improving the quality of power and electrical efficiency. The step filter functions to identify and isolate the harmonic, thus enabling appropriate controls to suppress the impact of the harmonic. The proposed system uses sophisticated control algorithms using sensors that can read current, voltage, frequency, and power factor to adjust the step filter parameters dynamically, following changes in load conditions and harmonizing. Simulation of the control network using the proteus software is subsequently undertaken in further research to design the control device. Using an automatic control set in the control simulation results reduced the need for manual intervention and let the system adjust the step filter automatically in response to changing network conditions. This showed that this method worked to lower harmonic distortion, increases the power factor through the filter used, and make the system more energy efficient overall. The study emphasizes the importance of applying sophisticated control strategies to manage harmonics efficiently, which ultimately paves the way to a cleaner and more reliable power grid. © Published under licence by IOP Publishing Ltd.

关键词： Power quality

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EMC2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence 41

EMC2: Efficient MCMC Negative Sampling for Contrastive Learn...

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41st International Conference on Machine Learning, ICML 2024

作者： Yau, Chung-Yiu Wai, Hoi-To Raman, Parameswaran Sarkar, Soumajyoti Hong, Mingyi Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Amazon Web Services United States Department of Electrical and Computer Engineering University of Minnesota United States

A key challenge in contrastive learning is to generate negative samples from a large sample set to contrast with positive samples, for learning better encoding of the data. These negative samples often follow a softmax distribution which are dynamically updated during the training process. However, sampling from this distribution is non-trivial due to the high computational costs in computing the partition function. In this paper, we propose an Efficient Markov Chain Monte Carlo negative sampling method for Contrastive learning (EMC2). We follow the global contrastive learning loss as introduced in (Yuan et al., 2022), and propose EMC2 which utilizes an adaptive Metropolis-Hastings subroutine to generate hardness-aware negative samples in an online fashion during the optimization. We prove that EMC2 finds an O(1/√T)-stationary point of the global contrastive loss in T iterations. Compared to prior works, EMC2 is the first algorithm that exhibits global convergence (to stationarity) regardless of the choice of batch size while exhibiting low computation and memory cost. Numerical experiments validate that EMC2 is effective with small batch training and achieves comparable or better performance than baseline algorithms. We report the results for pre-training image encoders on STL-10 and Imagenet-100. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Giant phonon-skyrmion coupling in ferromagnet/heavy metal heterostructures

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Physical Review B 2024年第6期110卷 064423-064423页

作者： Egor Savostin Vitaliy Lomakin Program in Materials Science and Engineering Center for Memory and Recording Research Department of Electrical and Computer Engineering

The strong impact of the strain-induced Dzyaloshinskii-Moriya interaction (SIDMI) on the magnetization dynamics of skyrmions in nanomagnetic structures is demonstrated. The effects of SIDMI are characterized by skyrmion equations (SEs) of motion and magnetoelastic (ME) equations. The study is performed on a model system of MgO/CoFe/Pt stacked on a piezoelectric substrate. The results demonstrate a major nonlinear amplification in both the first- and higher-harmonic magnitudes of the skyrmion breathing mode due to SIDMI. Remarkably, this enhancement can trigger a skyrmion collapse, enabling its deletion with ultraweak strain-induced excitations. The SIDMI effect is shown to be much more significant than the conventional ME effect. These findings open different avenues for the efficient manipulation of nanomagnetic structures through strain.

关键词： Exchange interaction Magnetic interactions Magnetization dynamics Magnetoacoustic effect Micromagnetism Spin dynamics

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Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics 46

Deep Learning-Based Estimation of Arterial Stiffness from PP...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Abrisham, Kiana Pilevar Alipour, Khalil Tarvirdizadeh, Bahram Ghamari, Mohammad Laboratory Department of Mechatronics Engineering School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Tehran Iran Kettering University Department of Electrical and Computer Engineering FlintMI United States

ISBN: (纸本)9798350371499

Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wave Velocity (cf-PWV) - the gold standard in this field - is vital for early detection and management of CVDs. This study introduces a novel approach, utilizing photoplethysmogram (PPG) signal spectrograms as inputs for deep learning models to estimate cf-PWV, a significant advancement over traditional methods. Employing a modified ResNet-18 architecture, we analyze PPG signals from digital, radial, and brachial arteries of a simulated dataset of 4374 healthy adults. Our methodology's innovation lies in its direct use of finely tuned spectrogram images, bypassing the complex feature extraction processes. This approach achieved R2 (correlation coefficient) values of up to 0.9902 for the digital artery, 0.9898 for the radial artery, and 0.9825 for the brachial artery, coupled with significantly lower Mean Absolute Percentage Errors (MAPE) of approximately 1.61% for the digital, 1.87% for the radial, and 2.08% for the brachial artery. These findings highlight the efficacy of PPG spectrograms, especially from the digital artery, in providing an accurate, user-friendly, and non-invasive method for cf-PWV estimation, thereby enhancing the capabilities of non-invasive cardiovascular diagnostics. © 2024 IEEE.

关键词： Photoplethysmography

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Joint Hybrid Beamforming and RIS Phase Shift Design for RIS-Enabled Mmwave ISAC System

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9149-9164页

作者： Wang, Lu Abanto-Leon, Luis F. Asadi, Arash Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum Germany Technical University of Darmstadt Department of Computer Science Darmstadt64289 Germany TU Delft Embedded Systems Group DelftCD 2628 Netherlands

Empowering cellular networks with augmented sensing capabilities is a key research area in sixth generation (6 G) communication systems. Recently, we have witnessed a plethora of efforts to devise solutions that integrate sensing capabilities into communication systems, i.e., integrated sensing and communication (ISAC), especially at millimeter-wave (mmWave) frequencies with large bandwidths. Despite the challenges posed by severe path loss and blockage at mmWave bands, this work incorporates reconfigurable intelligent surfaces (RISs) into ISAC systems to enhance both sensing and communication. To comply with the waveform utilized in current cellular systems, this paper studies mmWave orthogonal frequency-division multiplexing (OFDM) ISAC systems in the presence of RISs. Specifically, we jointly design the hybrid beamforming and RIS phase shifts to guarantee the sensing functionalities via minimizing the beampattern mean squared error at RIS, subject to signal-to-interference-plus-noise (SINR) and power constraints. The non-convexity of the investigated problem poses a challenge which we address by proposing a solution based on the penalty method and manifold-based alternating direction method of multipliers (ADMM). Simulation results demonstrate that both sensing and communication capabilities improve when the RIS is adequately designed. In addition, we discuss the tradeoff between sensing and communication. © 2025 IEEE.

关键词： Mean square error

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Mutual Inductance Extraction and Power Allocation of a Planar Omnidirectional WPT System

Mutual Inductance Extraction and Power Allocation of a Plana...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yu, Xipei Feng, Junjie Zhu, Liyan Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering Blacksburg United States

ISBN: (纸本)9798350376067

In recent times, planar omnidirectional wireless power transfer has emerged as a prominent technology, especially in consumer electronics application. However, in such scenario, the presence of multiple receivers with angular misalignment often results in unstable operation due to variations in mutual inductance. To address this challenge, this article undertakes the modelling of several receiver coils and proposes a method to extract mutual inductance between them utilizing additional DC/DC converters. This extraction process serves to mitigate these effects, thereby enabling effective power allocation among multiple receivers Finally, the experimental validation is conducted. at 6.78MHz. When charging the mobile phone and electronic watch receivers, the overall efficiency is 68%-77% and 56%-66%, considering the orientation and angular misalignment. © 2024 IEEE.

关键词： Inductive power transmission

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Improving Information Freshness in Edge-Assisted Smart Grids: An AoI-Aware Routing Strategy for Neighborhood Area Networks 22

Improving Information Freshness in Edge-Assisted Smart Grids...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Farag, Hossam Kotb, Mostafa Stefanović, Čedomir Gidlund, Mikael Department of Electronic Systems Aalborg University Denmark Department of Informatics Universität Hamburg Germany Department of Electrical Engineering Aswan University Egypt Mathematics Department Aswan University Egypt Department of Computer and Electrical Engineering Mid Sweden University Sweden

ISBN: (纸本)9798331527471

The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high responsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sensing information received at the edge controller, captured by the Age of Information (AoI) metric, is vital to maintain system stability, where outdated information may lead to incorrect responses to grid conditions, potentially causing inefficiencies or system disruptions. However, the timeliness of the transmitted updates is mainly compromised by the delay and congestion within the routing links in the Neighborhood Area Network (NAN). In this work, we develop an intelligent routing strategy to improve the AoI of the Routing Protocol for Low-power and lossy networks (RPL), which is the common routing protocol for smart grids. Our proposed method is based on an AoI-aware parent selection mechanism, by which a node becomes attached to the parent with the highest probability of delivering a packet within a predefined AoI threshold. The prediction is made based on a supervised machine learning model, trained using the collection of heterogeneous routing metrics. The performance of the proposed method is evaluated via extensive discrete-event simulations and the results show its potency to improve the peak AoI and the AoI violation probability compared to the standard RPL. © 2024 IEEE.

关键词： Edge computing

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Selection of Three-phase Inverter Topology and Optimization of the PCB Layout of a T-type Bridge-Leg for Power Dense Motor Drive Application 4

Selection of Three-phase Inverter Topology and Optimization ...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

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

ISBN: (纸本)9798350306262

The topology selection plays a key role in minimizing the losses and improving the output waveform quality of an inverter. In addition, increasing the switching frequency of an inverter help to reduce the size of EMI filters resulting in power density improvement. Use of wide-bandgap device (like SiC devices) enables high switching frequency operation in power electronic converters. However, due to low gate charge and small junction capacitance of SiC devices, the SiC-based inverter is more likely to be influenced by side-effects of fast switching transition like undesired ringing, larger voltage overshoot and mistriggerig due to miller-effect. Parasitic inductances of multiple power loops and gate loops within the inverter leg play a key role in these undesired effects, however, a proper design of PCB-layout can reduce these effects. In this paper, a comparison is conducted between a T-type and 2-level inverter topologies for motor drive applications. Furthermore, a placement configuration of components along with the PCB layout are proposed for a T-type leg to improve its switching transition behavior by reducing the power and gate loop parasitic inductances. The parasitic inductances of proposed configuration are estimated using FEA simulation, and the estimated values are used to simulate the switching transition behavior of a T-type leg. Finally, a Double Pulse Test (DPT) is executed to demonstrate the accurate switching transition behaviour of a T-type leg which is designed with the proposed PCB layout and component placement. © 2024 IEEE.

关键词： Silicon carbide

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DDDAS-Based Learning for Edge Computing at 5G and Beyond 5G 4th

DDDAS-Based Learning for Edge Computing at 5G and Beyond 5G

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4th International Conference on Dynamic Data Driven Applications systems, DDDAS 2022

作者： Runsewe, Temitope Yavuz, Abdurrahman Celik, Nurcin Saad, Walid Department of Industrial and Systems Engineering University of Miami MiamiFL33146 United States Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

ISBN: (纸本)9783031526695

The emerging and foreseen advancements in 5G and Beyond 5G (B5G) networking infrastructures enhance both communications capabilities and the flexibility of executing computational tasks in a distributed manner, including those encountered in edge computing (EC). Both, 5G & B5G and EC environments present complexities and dynamicity in their multi-level and multimodal infrastructures. DDDAS-based design and adaptive and optimized management of the respective 5G, B5G, and EC infrastructures are needed, to tackle the stochasticity inherent in these complex and dynamic systems, and to provide quality solutions for respective requirements. In fact, both emerging communication and computational technologies and infrastructure systems can benefit from their symbiotic relationship in optimizing their corresponding adaptive and optimized management. EC enabled by 5G (and future B5G) allows efficient distributed execution of computational tasks. DDDAS-based methods can support the adaptivity in bandwidth and energy efficiencies in 5G and B5G communications. On the other hand, EC has become a very attractive feature for critical infrastructure such as energy grids as it allows for secure and efficient real-time data processing. In order to fully exploit the advantages of EC, the communication network should be able to tackle the changing requirements related to the task management within edge servers. Thus, leveraging the DDDAS paradigm, we jointly optimize the scheduling and offloading of computational tasks in an EC-enabled microgrid considering both physical constraints of microgrid and network requirements. The results showcase the superiority of the proposed DDDAS-based approaches in terms of network utilization and operational efficiencies achieved, with microgrids as case example. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Energy efficiency

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Employing Feature engineering for River Stage Forecasting to Improve Hybrid Model Performance 32

Employing Feature Engineering for River Stage Forecasting to...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Mihel, Anna Maria Pecnik, Spela Vrbancic, Grega Lerga, Jonatan Krvavica, Nino University of Rijeka Faculty of Engineering Department of Computer Engineering Rijeka Croatia University of Maribor Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science Maribor Slovenia University of Rijeka Faculty of Civil Engineering Department of Hydrotechnics and Geotechnics Rijeka Croatia

ISBN: (纸本)9789532901382

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data. © 2024 University of Split, FESB.

关键词： Mean square error

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