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Multi-Objective Phase Shift Control Optimization for Dual Active Bridge Converters

Multi-Objective Phase Shift Control Optimization for Dual Ac...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Davila, Antonella Haro Hasan, Syed Imam Uzum, Alper Elrayyah, Ali Sozer, Yilmaz The University of Akron Dept. of Electrical and Computer Engineering AkronOH United States

ISBN: (纸本)9798350376067

This comprehensive analysis evaluates multiobjective optimization of the phase shift modulation techniques to evaluate the optimized phase shift at various operating points for a Dual Active Bridge converter. By simulating scenarios to understand the behavior of a system under various conditions for different modulation techniques, this study provides insights into different outcomes to optimize the system and identify the most effective phase-shift combinations. The system's performance is assessed based on two key metrics: the inductor RMS current and Zero Voltage Switching (ZVS) utilization. The analysis focuses on predicting the RMS inductor current for various phase-shift modulations, including Single-Phase Shift, Dual Phase Shift, Extended Phase Shift, and Triple Phase Shift modulation. Besides, peak current is crucial for assessing ZVS conditions, facilitating the optimization of the final set of phase-shift modulations. © 2024 IEEE.

关键词： Zero voltage switching

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A Framework for Evaluating Model Trustworthiness in Classification of Very High Resolution Histopathology Images

A Framework for Evaluating Model Trustworthiness in Classifi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Nouyed, Mohammad Iqbal Doretto, Gianfranco Adjeroh, Donald A. West Virginia University Lane Dept. of Computer Science and Electrical Engineering MorgantownWV26506 United States

ISBN: (纸本)9798350386226

In computer vision, one approach to explaining a deep learning model's decision is to show regions of visual evidence upon which the model makes a decision. Typically, this evidence is represented in the form of a saliency map which conveys how much an image region is contributing to the model's decision. For a model to be trustworthy, it is expected that this saliency region should provide relevant information. In this work, we use model "trustworthiness"or "rationale"to describe how much relevant information the model is using to determine the image class. For medical images, this information connects to biological relevance. For very high resolution histopathology image applications, such as gigapixel whole-slide image classification, where patch-based multiple-instance based learning approach is taken to determine the patch label, this biological relevance has to be determined both at the patch and the image level. In this work, we present a novel patch-based model trustworthiness evaluation framework for very high resolution histopathology images. Our trustworthiness framework takes two approaches: spatial overlap based and feature based evaluation. For the overlap based approach, we check overlap with the annotation provided with the database to see if they have biological relevance, since for tumor positive patches only high probability regions from within the annotated regions are likely to be relevant. For feature based approach, we train an interpretability model using the sub-patches of the training set, extract features and cluster them. Then based on the distance from these clusters we determine if there is any biological rationale behind the prediction. Finally, we propose four patch-level and four image-level rationale metrics that evaluate the biological relevance of the information used by the classifier to decide on the patch class. Our experiment using the CAMELYON16 dataset shows the efficacy of this approach for model trustworthiness evaluation

关键词： Deep learning

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Machine Learning Approaches for Survival Prediction in Covid-19 Patients: A Comparative Analysis

Machine Learning Approaches for Survival Prediction in Covid...

引用

2024 International Conference on Advances in Computing, Communication, electrical, and Smart Systems, iCACCESS 2024

作者： Islam, Md. Muhiminul Shuvo, M.I.R. Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350350289

The COVID-19 pandemic has underscored the significance of accurately predicting patient survival in order to promptly administer efficient medical care. The augmentation of biological and healthcare service data volume has been found to enhance the precision of disease prognosis, survival prediction, and other clinical assessments. Numerous biological characteristics serve as the underlying factors contributing to the etiology of numerous diseases. Therefore, it is imperative to have precise medical data that possesses appropriate characteristics in order to facilitate an analysis that exhibits exceptional clinical accuracy. In order to effectively analyze data, it is imperative to employ a machine learning model that is both exact and accurate in predicting sickness or survival outcomes. An expeditious and accurate assessment of the disease's magnitude is crucial during a particular phase of a pandemic, such as the Covid-19 outbreak. The primary aim of this research is to employ machine learning methodologies in order to forecast the survival outcomes of individuals diagnosed with COVID-19. This will be accomplished by using a publicly available dataset comprising various medical attributes pertaining to 383,499 COVID-19 patients, which was collected and made accessible by the Directorate General of Epidemiology, Secretariat of Health in Mexico. Various machine learning techniques, including Regression methods, Artificial Neural Networks, Random Forest Classifier, Support Vector Machine, AdaBoost, and XGBoost, are employed on the dataset that has undergone diverse preprocessing procedures. The experimental findings demonstrated that the system yielded numerous advantages in comparison to previous efforts in the same field. © 2024 IEEE.

关键词： COVID-19

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Addressing the Long-Tailed Data Distribution in Bone Marrow Cell Identification through Class Balance Deep Classification Model 2

Addressing the Long-Tailed Data Distribution in Bone Marrow ...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Ghosh, Rakesh Shankar Shill, Pintu Chandra Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

The accurate classification of bone marrow cells has long been a critical component of diagnosing blood diseases. However, traditional methods rely on subjective human interpretation and lack standardized quantitative criteria. To address this challenge, this research proposes a Class Balance Deep Classification Model (CBDCM) designed to classify bone marrow cells using EfficientNet to handle the long-tailed data distribution problem. In order to validate the propose model by applying it to proper dataset and analyzing the experimental outcomes of the propose method. Notably, CBDCM attains impressive precision, sensitivity, and specificity values of 86.14%, 87.53%, and 99.45%, respectively. Simulation results show that CBDCM outperforms, demonstrating superior performance in a detailed comparison with both deep neural networks and traditional machine learning techniques. The findings of this study, with significant implications for addressing class imbalance in datasets, hold promise for standardizing the classification of bone marrow cells, potentially revolutionizing the diagnosis of blood diseases. © 2024 IEEE.

关键词： bone marrow cell CBDCM data imbalance deep learning EfficientNet gaussian smoothing

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Effective Polyp Segmentation in Medical Imaging: A Semi-Supervised Approach with Progressive Supervision 3

Effective Polyp Segmentation in Medical Imaging: A Semi-Supe...

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3rd International Conference on Big Data, Information and computer Network, BDICN 2024

作者： Ye, Shunlong Ji, Zexuan Nanjing University of Science and Technology Dept. of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350363821

Medical imaging plays a pivotal role in diagnosing and monitoring various diseases, and accurate polyp segmentation is essential for early disease detection and treatment planning. However, the scarcity of well-Annotated medical imaging data hampers the development of effective segmentation models. Semi-supervised learning stands out as a critical approach to address this issue. Traditional semi-supervised medical image segmentation models leverage strong-weak perturbation consistency losses to provide supervision signals. While these losses are effective in promoting consistency, they often exhibit a drawback, that is the dominance of strong perturbations during early training stages. This dominance hinders the model's ability to extract meaningful features, ultimately impacting its overall performance. In response to these challenges, we introduce a novel training method applied to polyp segmentation. We employ a progressive approach, gradually introducing strong-weak perturbation supervision signals. This gradual guidance empowers the model to focus on learning relevant features from the beginning of training, mitigating the issue of early dominance and enhancing its performance. Our method is evaluated on two widely-used polyp segmentation datasets and surpasses state-of-The-Art methods, demonstrating substantial improvements in segmentation accuracy. © 2024 IEEE.

关键词： Self-supervised learning

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AI-Based Effective Virtual Inertia Estimation in a Meshed Network of Inverters 50

AI-Based Effective Virtual Inertia Estimation in a Meshed Ne...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Alvand, Hamideh Zare, Alireza Shadmand, Mohammad B. Mazumder, Sudip K. Dept. of Electrical & Computer Engineering University of Illinois Chicago IL United States

ISBN: (纸本)9781665464543

Mass penetration of renewable energy sources (RES) has drastically triggered the integration of clusters of VSG-based grid-forming inverters (GFMIs) to enhance grid stability and facilitates the transition towards power electronics dominated grid (PEDG). Although high number of GFMIs provides a smoother frequency response due to higher emulated virtual inertia, considering the complex dynamic of the PEDG, estimating the emulated virtual inertia by the primary control layer of the GFMIs introduces extensive mathematical modeling and computational burden. To address the shortcomings of conventional model-based approaches, this paper proposes a data-driven methodology based on convolution neural network (CNN) to estimate effective virtual inertia by analyzing the explanatory frequency response of a cluster of GFMIs. The neural network (NN) is trained offline and evaluated on a sample cluster of 100% GFMIs through multiple case studies to validate the effectiveness of the proposed methodology. The application of the proposed data-driven tool can further facilitate network analysis by considering the corresponding aggregated model to enhance the stability analysis of the PEDG. © 2024 IEEE.

关键词： Frequency response

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User Authentication and Vital Signs Extraction from Low-Frame-Rate and Monochrome No-Contact Fingerprint Captures 58

User Authentication and Vital Signs Extraction from Low-Fram...

引用

58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Olugbenle, Olaoluwayimika Drake, Logan Venkataswamy, Naveenkumar G. Rahman, Arfina Afolayanka, Yemi Imtiaz, Masudul Banavar, Mahesh K. Clarkson University Dept. of Electrical and Computer Engineering PotsdamNY United States

ISBN: (纸本)9798350354058

We present our work on leveraging low-frame-rate monochrome (blue light) videos of fingertips, captured with an off-the-shelf fingerprint capture device, to extract vital signs and identify users. These videos utilize photoplethysmography (PPG), commonly used to measure vital signs like heart rate. While prior research predominantly utilizes high-frame-rate, multi-wavelength PPG sensors (e.g., infrared, red, or RGB), our preliminary findings demonstrate that both user identification and vital sign extraction are achievable with the low-frame-rate data we collected. Preliminary results are promising, with low error rates for both heart rate estimation and user authentication. These results indicate promise for effective biometric systems. We anticipate further optimization will enhance accuracy and advance healthcare and security. © 2024 IEEE.

关键词： Photoplethysmography

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Wearable Device With Speech and Voice Recognition for Hearing-Impaired People 21

Wearable Device With Speech and Voice Recognition for Hearin...

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21st International Learning and Technology Conference, L and T 2024

作者： Tharwat, Marwa Wardak, Yasmin Balbaid, Shumokh Radin, Ejlal King Abdulaziz University Dept. of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Hearing impairment is a widespread health condition that affects millions of people worldwide. It refers to the lack of or low functionality of the hearing organs, resulting in difficulties in effective communication and participation in everyday conversations. This limitation can negatively impact various aspects of the social and personal lives of the hearing-impaired individuals. Moreover, the high cost and limited availability of assistive devices further restrict accessibility for those with limited financial resources. To address these challenges, this study develops an affordable wearable device equipped with speech and voice recognition technologies to enhance the accessibility of spoken language for individuals with hearing impairment. OpenAI Whisper is employed for real-time conversion of spoken words into written text. Voice recognition, utilizing Mel Frequency Cepstral Coefficients (MFCCs) and Random Forest (RF) classifier, identifies and distinguishes speakers within the user's family and friends zone. The recognized spoken words from the speech recognition process and the speaker's identity from the voice recognition process are combined and displayed on a screen to the hearing impaired. By combining speech and voice recognition technologies, the proposed device breaks communication barriers for individuals with hearing impairment and enhance their overall communication experience. © 2024 IEEE.

关键词： Speech recognition

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Optimization of Transformer Design Parameters of a 20kW SiC-Based Dual-Active Bridge Converter for Enhanced Efficiency

Optimization of Transformer Design Parameters of a 20kW SiC-...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Park, Gayoung Kim, Hwigon Cui, Shenghui Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350376067

This paper proposes a comprehensive optimization method to determine the optimal design parameters of a high-frequency (HF) transformer in a dual-active bridge (DAB) converter. To achieve high efficiency under heavy load conditions, it is essential to minimize the conduction loss of the DAB. Not only the modulation scheme but also the turns ratio and leakage inductance of the HF transformer are critical parameters that influence the operation and efficiency of the DAB. To address this issue, this paper presents a design method based on nonlinear optimization to derive the optimal combination of HF transformer and modulation parameters that minimizes conduction loss. By adopting a zero-voltage switching (ZVS)-enhanced triple-phase-shift (TPS) modulation scheme, both conduction loss and switching loss are minimized, while the optimal modulation parameters are determined simultaneously. Experimental demonstration of the optimized parameters is performed using a SiC-based 20 kW DAB converter hardware. © 2024 IEEE.

关键词： High frequency transformers

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Lightweight Federated Learning based White Space Detector for Cognitive Radios

Lightweight Federated Learning based White Space Detector fo...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Galib, Md Mehedi Hassan Younis, Mohamed University of Maryland Baltimore County Dept. of Computer Science and Electrical and Engineering BaltimoreMD21250 United States

ISBN: (纸本)9798350351255

In cognitive communication, detecting white space is critical for preventing interference with primary user's transmissions. Rather than modifying the radio transceivers, several machine learning (ML)-based detection techniques have been proposed, where a model is trained offline and then employed to infer white spaces in real-time. Despite their viability for spectrum sensing, these techniques are computationally complex, especially when deep neural network (DNN) models are pursued. Moreover, training a centralized model requires transmitting massive data to the fusion center (FC), which ultimately results in congestion on the transmission channel rather than flagging opportunities for cognitive transmissions. This paper opts to fill the technical gap by proposing: (1) a federated learning model that enables effective design of spectrum monitors in a distributed manner, and (2) a spiking neural network (SNN)-based lightweight white space detector that solves the issue of computationally expensive DNN models and is suitable for resource-constrained devices. The SNN-based federated learning (SFL) model employs secondary users to train their respective SNN models using local data (spatial locality) and sends the gradient of SNN to FC. FC combines the individual SNN models and sends the aggregated model back to each edge node. Validation using live LTE data has demonstrated the effectiveness of SFL with a detection accuracy of 91.16%. © 2024 IEEE.

关键词： Radio transceivers

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