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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Hossain Shanto, Md Saeem Ulfat Tahsin, Md Jasim, Sarah Khan, Mohammad Ashrafuzzaman North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

Machine learning algorithms are becoming more sophisticated with time, their computational capabilities and resource consumption are also increasing. And with its escalating potential, machine learning enthusiasts constantly look for better and easier ways to run complex algorithms. Often, laptops and CPUs available for personal use have memory ranging from 4/8/12 GB and have comparatively lower computation capability. In such restricted environments, there is a trade-off between the accuracy of machine learning models and resource consumption. This barrier can be overcome by using cloud CPUs offered popularly by Google Colab and Kaggle. These popular platforms provide CPUs capable of training and evaluating machine learning algorithms. In this paper, we illustrate the performance of the CPUs offered by Google Colab and Kaggle and compare it with a local device (ASUS FX505GE) using KNN, Logistic Regression, and Random Forest algorithm. We could find that multithreading provides better performance in the case of machine learning algorithms. Random Forest takes comparatively longer for evaluation and training purposes because of the numerous calculations involved in its different phases. Even though KNN has no training phase, the impact of multi-processing cores is noticeable both in training and evaluation. However, Logistic Regression has the best performance with only a single core both in the training and evaluation period because of the simplicity of the model structure and requires fewer computational resources. © 2023 IEEE.

关键词： Program processors

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Model Predictive Control Utilizing Machine Learning Models within a Pinball-Based, Cyber-Physical Testbed

Model Predictive Control Utilizing Machine Learning Models w...

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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Veselovec, Xavier Koliba, Mayson Fuchs, Zachariah E. University of Cincinnati Department of Electrical and Computer Engineering OH United States

ISBN: (纸本)9798350338782

We examine the shot aiming problem within a physical pinball machine using model predictive control methods and machine learning based system models. A switched mode system model is developed and trained using data collected from an infrared beam-break sensor array that allows the estimation and prediction of future ball states. The trained model is then used within a model predictive controller to successfully aim shots within a physical pinball machine. The experimental results show that the controller performs with sufficient accuracy to hit standard pinball targets found in commercial pinball machines. © 2023 IEEE.

关键词： Model predictive control

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A Multi-EnerCage-HC System with Reactive Resonant Shielding

A Multi-EnerCage-HC System with Reactive Resonant Shielding

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Han, Yiming Zhao, Linran Jia, Yaoyao The University of Texas at Austin Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350300260

This paper presents a multi-EnerCage-homecage (multi-EHC) system designed to facilitate wireless high-throughput behavioral neuroscience studies on small freely moving animals, such as rodents. The multi-EHC system includes an array of EHC units that are placed side by side. Each EHC unit can independently enable wireless power and data communication with a wearable/implantable device (WID) implanted in or carried by the animal subject via a resonance-based multi-coil inductive link that operates at 13.56 MHz. However, when placing the EHC units closely, the strong cross-coupling among adjacent EHC units can cause frequency splitting, resulting in inefficient power transmission. Since the split frequencies cannot be converged by tuning the resonating capacitance of the inductive link, we implemented resonant reactive shielding coils between every two adjacent EHC units. These shielding coils can reduce the undesired cross-coupling, enabling the convergence of the split frequencies, so that each EHC unit can operate independently with reliable power and data transmission. We have validated the concept by building a two-EHC prototype. © 2023 IEEE.

关键词： cross-coupling frequency splitting neuroscience studies reactive resonant shielding wireless power transmission

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Non-Linear Programming Based Optimal Power Flow (N-OPF) for Bulk Transmission Grid: Modeling and Comparative Study

Non-Linear Programming Based Optimal Power Flow (N-OPF) for ...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Hasan, Md Shamim Kamalasadan, Sukumar Department of Electrical and Computer Engineering CharlotteNC28223 United States

ISBN: (纸本)9798350320169

Optimal power flow is generally formulated as a non-convex and non-linear programming (NLP) problem. Traditionally, the OPF problem is solved in transmission systems through approximations and/or relaxation techniques. Such methods lead to infeasible and inaccurate solutions. This paper provides a numerical analysis of the performance of different NLP OPF formulations, such as the Interior Point Method (IPM), Sequential Quadratic Programming (SQP), and Alternating Direction Method of Multipliers (ADMM) in terms of, feasibility, optimality, and scalability. The performances are evaluated using small (IEEE 9 bus and 14 bus), medium (IEEE 30 bus, 39 bus, and 57 bus), and large (IEEE 118 bus) test cases. © 2023 IEEE.

关键词： Buses

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Grid Impedance Shaping for Grid-Forming Inverters: A Soft Actor-Critic Deep Reinforcement Learning Algorithm 10

Grid Impedance Shaping for Grid-Forming Inverters: A Soft Ac...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Oshnoei, Arman Sorouri, Hoda Oshnoei, Soroush Teodorescu, Remus Blaabjerg, Frede Aalborg University Department of Energy Aalborg Denmark Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350351330

This paper proposed an advanced method for adjusting grid impedance in grid-forming inverters, utilizing the Soft Actor-Critic Deep Reinforcement Learning (SAC-DRL) algorithm. The approach contains a flexible strategy for controlling virtual impedance, supported by an equivalent grid impedance estimator. This facilitates accurate modifications of virtual impedance based on the grid's X/R ratio and the converter's power capacity, aiming to optimize power flow and maintain grid stability. A unique feature of this methodology is the division of virtual reactance into two segments: one adhering to standard control protocols and the other designated for precision enhancement via the SAC-DRL method. This strategy introduces a layer of intelligence to the system, strengthening its resilience against fluctuations in grid impedance. Experimental validations, executed on a laboratory setup, verify the robustness of this approach, highlighting its potential to significantly improve intelligent power grid management practices. © 2024 IEEE.

关键词： Reinforcement learning

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Encoder-Decoder Graph Convolutional Network for Automatic Timed-Up-and-Go and Sit-to-Stand Segmentation 48

Encoder-Decoder Graph Convolutional Network for Automatic Ti...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wen, Bo Du, Chen Nguyen, Truong Q. Uc San Diego Department of Electrical and Computer Engineering United States

ISBN: (纸本)9781728163277

Vision-based action segmentation is an important tool in human movement analysis. In this work, we present a novel Encoder-Decoder Graph Convolutional Network (ED-GCN) to perform auto-segmentation on two widely accepted clinical tests for human mobility and balance assessment: the "Timed-Up-and-Go"(TUG) test and the "Sit-to-Stand"(STS) test. For STS, we perform a fine-grained segmentation that further segments the stand up and sit down actions into more sub-phases. To the best of our knowledge, this is the first work that analyzes such subtle segmentation with biomedical significance. We also propose two novel metrics for action segmentation, which overcome some key drawbacks in the popular F1 and Edit scores. Experiment shows that our network has superior performance over state-of-the-art action segmentation networks in TUG and STS segmentation. © 2023 IEEE.

关键词： action segmentation automatic TUG segmentation graph convolutional network human movement evaluation

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Hopping-Aware Cluster Header Capability for Sensor Relocation in Mobile IoT Networks

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Intelligent Automation & Soft Computing 2023年第5期36卷 1613-1625页

作者： Moonseong Kim Jaeyoung Park Young-Joon Kim Woochan Lee Department of IT Convergence Software Seoul Theological UniversityBucheon14754Korea Department of Computer Engineering Hongik UniversitySeoul04066Korea Department of Electronic Engineering Gachon UniversitySeongnam13120Korea Department of Electrical Engineering Incheon National UniversityIncheon22012Korea

Mobile sensor nodes such as hopping sensors are of critical importance in data ***,the occurrence of sensing holes is unavoidable due to the energy limitation of the ***,it is evident that the relocation of mobile sensors is the most desirable method to recover the sensing *** previous research conducted by the authors so far demonstrated the most realistic hopping sensor relocation scheme,which is suitable for the distributed *** previous studies,the cluster header plays an essential role in detecting the sensing hole and requesting the neighboring cluster to recover the sensing hole that occurred in the sensor ***,the limitations of the cluster header in the previously proposed relocation protocol are not fully *** the cluster header jumps more frequently than non-header nodes,its energy con-sumption is relatively high compared to other ***,it is most likely to lead to header node failure and can lead to data loss on the *** this paper,the jumping ability and energy consumption of the cluster header are seriously *** ability to replace cluster headers in case of failure is also *** results show that the data collection time can be further increased,which demonstrates the validity of the proposed algorithms.

关键词： Hopping sensor mobileinternetofthings relocationprotocol header node numericalsimulation

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Cross-Coupling Effects of Voltage Control and Active Power Control on Small-Signal Stability of Virtual Synchronous Generator 11

Cross-Coupling Effects of Voltage Control and Active Power C...

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Xu, Jingzhe Zhou, Weihua Bahrani, Behrooz Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9788957083505

Inverter-based renewable energy resources, as a critical response to the net-zero energy transition, have been displacing conventional synchronous generation. However, this displacement has increased the grid's vulnerability to disturbances. The virtual synchronous generator (VSG) is a potential solution for grid strengthening. However, previous research has shown that VSG can also experience instability under certain grid conditions, which have been partially addressed in previous work. This paper aims to fill the gaps in these works by exploring the fundamental causation of instability through an updated comprehensive state-space modeling, parameter scanning, and consequent states participation factor analysis. It has been found that both voltage and active power controllers contribute to low-frequency instabilities in most cases. Furthermore, the two controllers are tightly coupled, although parameterising may improve decoupling. © 2023 The Korean Institute of Power Electronics.

关键词： Synchronous generators

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An End-To-End Deep Learning Approach for Atherosclerosis Detection using Autoencoders 5

An End-To-End Deep Learning Approach for Atherosclerosis Det...

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5th International Conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： Baalbaki, Jad M. Kadri, Sara S. El Awad, Karin A. Aridy, Carole H. Al Aridi, Majd S. Zein, Mohamad A. El Diab, Mohammad O. Sabbah, Maher M. Zantout, Rached. N Adla, Yasmine A. Abu Rafik Hariri University Electrical and Computer Engineering Department Mechrif Lebanon

ISBN: (纸本)9798350338492

Cardiovascular disease remains the primary cause of mortality worldwide. Early diagnosis of cardiovascular diseases, particularly atherosclerosis, has been the focus of extensive research. Gated Magnetic Resonance Imaging has enabled researchers to capture high-resolution images of the cardiovascular system, facilitating the detection of arterial wall plaque. The integration of machine learning techniques into the diagnostic process has significantly improved the accuracy of cardiovascular disease diagnosis. This paper proposes a deep learning approach that utilizes autoencoders to identify atherosclerosis in murine subjects from gated MRI images. The images underwent preprocessing using a Block Matching 3D (BM3D) filter to eliminate noise and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance contrast. To evaluate the performance of the proposed model, we trained and tested two different autoencoder architectures, stacked and convolutional. The models successfully distinguished healthy and diseased subjects based on input and reconstructed images. The Convolutional Autoencoder outperformed the Stacked Autoencoder by minimizing the reconstruction loss by 95%, demonstrating superior accuracy in reconstructing medical images. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Equipment Parameter Investigation for a Proposed Power Conductor Hot Spot Identification system 10

Equipment Parameter Investigation for a Proposed Power Condu...

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10th International Conference on Modern Power Systems, MPS 2023

作者： Polycarpou, Alexis Komodromos, Michael Kalli, Kyriacos Argyrou, Maria C. Ioannou, Andreas Computer Engineering and Informatics Frederick University Frederick Research Center Deparment of Electrical Nicosia Cyprus Cyprus University of Technology PhOSLab Department of Electrical Engineering/Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350326826

Reliability, efficiency and continuity of power energy supplied is an area which receives increasing attention as the main infrastructure of power transmission and distribution systems in many countries is ageing. Hot spot related cable failures and power interruptions have a big financial impact on the power provider. The operational parameters of a method to identify in real time hot spots on cables is investigated in this paper, which can result to timely fault prevention. This can be achieved through exploiting the optical sensing capabilities of the existing optical fibre on-grid network. The resulting parameters aim to be advantageous for the system as they increase accuracy and will enable the Remote, automated, continuous, and real time monitoring of the grid infrastructure integrity. © 2023 IEEE.

关键词： Cables

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