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A Novel Transformer-less Buck-Boost Inverter with Coupled Inductor for Photovoltaic Systems

A Novel Transformer-less Buck-Boost Inverter with Coupled In...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Yunfeng Xu Weimin Wu Houqing Wang Frede Blaabjerg Mohamed Orabi Liang Yuan Dept. of Electrical Engineering Shanghai Maritime University Shanghai China Dept. of Electrical Engineering Anhui University of Science and Technology Huainan China Dept. of Electrical Engineering University of Arkansas Arkansas United States Dept. of Energy Technology Aalborg University Aalborg Denmark Dept. of Electrical Engineering Aswan University Aswan Egypt Dept. of Safety Science and Engineering Anhui University of Science and Technology Huainan China

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Among several proposed inverter topologies, the improved inverter based on the Aalborg inverter which can track the maximum energy from two independent photovoltaic arrays was proposed as a high efficiency, high power density, and dual-mode time-sharing inverter based on MOSFET switches. In order to further reduce the cost, improve the power density, and extend the lifetime, a novel dual-mode time-sharing inverter with features of high efficiency, a low number of semiconductor devices, leakage current suppression, the wide variation of input DC voltage, and various operating modes are proposed in this article. Besides, the principle of operation is demonstrated through the equivalent circuits. Then, the theoretical analysis shows that the proposed inverter will be converted from Buck and Boost mode to Buck-boost mode when the electrolytic capacitor ages. The lifetime of the inverter is extended and the cost is reduced. Finally, theoretical analysis and the correctness of the topology are confirmed through PSIM-Software simulations.

关键词： Power system measurements Density measurement Semiconductor devices Capacitors Switches Transformers Inverters Topology Leakage currents Voltage control

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A Physics-Based Temperature Dependent Analytical Model For 2DEG Density in AlGaN/GaN HEMT Devices 14

A Physics-Based Temperature Dependent Analytical Model For 2...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Nabila, Kashfia Tajmim Hudgins, Jerry L. University of Nebraska-Lincoln Dept. of Electrical and Computer Engineering Lincoln United States

ISBN: (纸本)9798331516116

Accurate modeling of the temperature dependent two-dimensional electron gas (2DEG) is very crucial in applied power electronics, given its critical role in High Electron Mobility Transistor (HEMT) characteristics. In view of this, the temperature dependency of 2 DEG has been analyzed for different gate voltages and modeled for the temperature range from 300K to 500K. Relying on the device physics, the temperature dependence of various parameters including bandgap variation, fermi level energy, barrier height reduction, threshold voltage changes and their effects on electron sheet charge density (2DEG) has been included. This model will also integrate the effects of sub-band energy variations, polarization changes, and material property adjustment due to temperature changes. The goal is to provide a robust analytical framework that can predict device behavior accurately across a spectrum of operational gate voltage and temperature conditions. This anticipated model will facilitate the development of power electronics that are more reliable and efficient, capable of operating effectively in diverse and demanding environments. © 2025 IEEE.

关键词： Threshold voltage

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Self-supervised Multimodal Speech Representations for the Assessment of Schizophrenia Symptoms

Self-supervised Multimodal Speech Representations for the As...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Gowtham Premananth Carol Espy-Wilson Dept. of Electrical and Computer Engineering University of Maryland College Park MD USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal schizophrenia assessment systems have gained traction over the last few years. This work introduces a schizophrenia assessment system to discern between prominent symptom classes of schizophrenia and predict an overall schizophrenia severity score. We develop a Vector Quantized Variational Auto-Encoder (VQ-VAE) based Multimodal Representation Learning (MRL) model to produce task-agnostic speech representations from vocal Tract Variables (TVs) and Facial Action Units (FAUs). These representations are then used in a Multi-Task Learning (MTL) based downstream prediction model to obtain class labels and an overall severity score. The proposed framework outperforms the previous works on the multi-class classification task across all evaluation metrics (Weighted F1 score, AUC-ROC score, and Weighted Accuracy). Additionally, it estimates the schizophrenia severity score, a task not addressed by earlier approaches.

关键词： Representation learning Measurement TV Schizophrenia Signal processing Predictive models Multitasking Vectors Acoustics Speech processing

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Analysis of Power Deployment Characteristics and Costs of Energy Storage Systems for Frequency Regulation in Renewable-Rich Power Grid 11

Analysis of Power Deployment Characteristics and Costs of En...

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11th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2024

作者： Kazme, Hafiz Zubyrul Basumatary, Khwrwmdao Shukla, Anup Lulea University of Technology Dept. of Engineering Science and Mathematics Sweden Indian Institute of Technology Dept. of Electrical Engineering India

The implementation of renewable energy sources such as solar and wind for electricity production has picked up an enormous pace in recent years, which not only gives rise to a more sustainable process of electricity production but also minimizes carbon dioxide emissions. However, the intermittent behaviour of the sources coupled with the absence of kinetic energy contribution to the electrical grid introduces a negative impact on the frequency stability of the grid. Hence, the grid requires further reinforcements in its infrastructure, and one such promising solution lies in the usage of grid-connected Energy Storage Systems (ESS) as a source of Frequency Containment Reserve for Disturbance (FCR-D) ancillary service for frequency regulation in Europe. This paper presents an extension of our previous work [1] to realize ESS as a favourable candidate for FCR-D service. In our previous work, we showed the benefits of ESS in terms of performance and cost compared to hydro turbines. However, the extended research takes a deeper dive into the characteristics of ESS and shows how the deployment characteristics, such as delay and power level, affect the frequency trajectory of an electrical grid with variable renewable energy penetration (REP) levels after an N-1 disturbance. The research found that ESS adhering to FCR-D deployment characteristics for the same disturbance yields diminishing impact at higher peak power levels, and with an increase in delay of deployment, the peak level must be increased by around 0.8MW to 5.08MW for per second increase in delay as the REP level of the grid rises to 30%. The study also found that the deployment delay must be reduced by up to 33.33% to keep the same level of impact of the disturbance. Finally, the total cost of FCR-D support may increase by up to 148.8% depending on the FCR-D deployment and grid characteristics. © 2024 IEEE.

关键词： Wind power integration

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Assessment of Asthma BAL Cytokines using Machine Learning Techniques 2

Assessment of Asthma BAL Cytokines using Machine Learning Te...

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2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing, PCEMS 2023

作者： Naik, Poornanand Purushottam Padukudru, Mahesh A. Rajan, Jeny National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal India J.S.S. Medical College JSSAHER Dept. of Respiratory Medicine Mysore India

ISBN: (纸本)9798350310719

Asthma is a chronic respiratory disorder characterised by airway inflammation and constriction, leading to difficulty in breathing and recurrent attacks of wheezing, coughing, and shortness of breath. In asthma, various cytokines, including interleukins (IL-4, IL-5, and IL-13) and tumor necrosis factoralpha (TNF-alpha), have been found to be increased in the airways of individuals. These cytokines are involved in the recruitment and activation of immune cells, such as eosinophils and T-lymphocytes, which contribute to the inflammation and airway hyperresponsiveness. Dysregulation of cytokine production and signaling has been implicated in the pathogenesis of asthma and may be targeted by therapies to alleviate symptoms and improve outcomes in individuals with this disease. We propose a predictive binary and multi-class machine learning model analysis that efficiently classify the asthma and healthy control patients by detecting cytokines in bronchoalveolar lavage (BAL) fluid which achieved better F1-score than existing approaches. © 2023 IEEE.

关键词： Machine learning

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An Investigation on Early Detection and Analysis of Bearing Fault in PMSG Based Wind Turbine-Generator System Using Fuzzy Logic and IoT

An Investigation on Early Detection and Analysis of Bearing ...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Arnab Chatterjee Arindam Kumar Sil Dept. of Electrical Engineering Indian Institute of Technology Roorkee Roorkee India Dept. of Electrical Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

One of the dominant, commercially available sources of renewable energy is wind energy. Condition monitoring is a very important aspect to take care of various faults in Wind Turbine-Generator (WTG). This paper describes the early detection and analysis of bearing fault in WTG with Permanent Magnet Synchronous Generator (PMSG) with the help of fuzzy logic system and Internet of Things (IoT) technology. The system analyses the frequency components of the mechanical torque signal during fault and compares them with the possible frequency ranges for different types of bearing faults and based on that it makes the decision about the nature of the fault with the help of fuzzy logic. The system also analyses the frequency components of the electromagnetic torque signal during fault and identifies the pattern of the available frequency components for fault detection. Thermal analysis process with phase current of PMSG has been shown to ensure the fault detection results. An IoT based system has been developed and tested for the fault detection, and it notifies the occurrence of fault to the user in real-time. It follows a particular analytical approach, described in this paper, which helps the IoT platform to detect the occurrence of fault, and it sends an email notification to the user in real-time.

关键词： Vibrations Fuzzy logic Renewable energy sources Torque Fault detection Real-time systems Permanent magnets

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A new multi-source light detection benchmark and semi-supervised focal light detection 24

A new multi-source light detection benchmark and semi-superv...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jae-Yong Baek Yong-Sang Yoo Seung-Hwan Bae Inha University Dept. of Electrical and Computer Engineering Vision and Learning Lab.

ISBN: (纸本)9798331314385

This paper addresses a multi-source light detection (LD) problem from vehicles, traffic signals, and streetlights under driving scenarios. Albeit it is crucial for autonomous driving and night vision, this problem has not been yet focused on as much as other object detection (OD). One of the main reasons is the absence of a public available LD benchmark dataset. Therefore, we construct a new large LD dataset consisting of different light sources via heavy annotation:YouTube Driving Light Detection dataset (YDLD). Compared to the existing LD datasets, our dataset has much more images and box annotations for multi-source lights. We also provide rigorous statistical analysis and transfer learning comparison of other well-known detection benchmark datasets to prove the generality of our *** the recent object detectors, we achieve the extensive comparison results on YDLD. However, they tend to yield the low mAP scores due to the intrinsic challenges of LD caused by very tiny size and similar appearance. To resolve those, we design a novel lightness focal loss which penalizes miss-classified samples more and a lightness spatial attention prior by reflecting a global scene context. In addition, we develop a semi-supervised focal light detection (SS-FLD) by embedding our lightness focal loss into the semi-supervised object detection (SSOD). We prove that our methods can consistently boost mAP to the variety of types of recent detectors on YDLD. We will open both YDLD and SS-FLD code at https://***/YDLD-dataset/YDLD.

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Machine Learning Classifiers Based on HoG Features Extracted from Locomotive Neutral Section Images 8

Machine Learning Classifiers Based on HoG Features Extracted...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： McIneka, Christopher Thembinkosi Pillay, Nelendran Durban University of Technology Dept. of Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9781665491068

This paper presents a comparative study on machine learning algorithms for neutral section image classification. The classifiers are trained by employing the Histogram of Oriented Gradient features that are extracted from the neutral section dataset [1]. A neutral section is a phase break that is used on the Transnet freight rail system to separate the single-phase supply from the 25kV three-phase overhead traction supply. The 25kV is a stepped-down voltage from an 8SkV three-phase supply coming from the national grid. While the main purpose of the neutral section is to separate phase voltages, electric locomotives can traverse through these phases by switching On and Off. This auto-switching is possible through induction magnets installed in between the rails and with magnet detection sensors installed underneath the locomotives. However, a computer vision model has been developed, trained, and tested with a neutral section dataset containing images having open and close markers [1]. This paper, therefore, utilises this dataset to provide performance comparison on several machine learning classification algorithms viz. Decision Tree, Discriminant Analysis, Support Vector Machine, K-Nearest Neighbors, Ensemble, Naïve Bayes, and Convolutional Neural Network. A confusion matrix, F1-measure and computation time are employed to measure the performance of each classifier. The MATLAB Classification Learner application was used to obtain the results. The results show that the Linear Support Vector Machine performs best when considering performance and prediction speed. The Linear Support Vector Machine achieved a training accuracy of 93.40% with a test accuracy reaching 94% at a prediction speed of 75 objects per second (computation time). © 2022 IEEE.

关键词： MATLAB

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A First Exploration of Fine-Grain Coherence for Integrity Metadata

A First Exploration of Fine-Grain Coherence for Integrity Me...

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Secure and Private Execution Environment Design (SEED), International Symposium on

作者： Per Ekemark Alberto Ros Konstantinos Sagonas Stefanos Kaxiras Dept. of Information Technology Uppsala University Uppsala Sweden Computer Engineering Dept. University of Murcia Murcia Spain School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (数字)9798331505653

ISBN: (纸本)9798331505660

Memory integrity protection is intended for secure execution, and it is typically associated with programs running on a single core. However, with the emergence of multi-processor systems-on-chip and chiplets, extending memory integrity protection to cache-coherent multiprocessors becomes essential. In this work, we explore for the first time the design space for maintaining coherence in fine-grain integrity metadata at the block level. We discuss various policies for updating the integrity tree using the underlying coherence protocol, and examine how these policies affect coherence traffic. We introduce the concepts of proactive and reactive update initiation, and discuss their implications for data and integrity-tree blocks. We also investigate the trade-offs between eager and lazy update propagation policies, focusing on coherence transactions such as invalidations and downgrades to analyse the pros and cons of different approaches. What we observe is that for some benchmarks the choice between the eager and the lazy update initiation policy does not make much difference, while for many other benchmarks one policy is better over the other, depending on how the benchmark shares its data.

关键词： Systematics Protocols Chiplets Focusing Coherence Benchmark testing Metadata Space exploration Protection

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Egyptian Hieroglyphics Handwriting Character Classification Using CNN with Explainable AI: SHAP and LIME

Egyptian Hieroglyphics Handwriting Character Classification ...

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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir M D. Shaek Ahmed Nurul Bashar Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Egyptian hieroglyphics, one of the oldest writing and human communication systems. In our modern life, it's challenging to interpret this language due to its complex visual symbols. In this study, we propose a Convolutional Neural Network (CNN) model for classifying Egyptian hieroglyphic handwriting characters. The dataset provides 18 different class of handwritten hieroglyph character images. To enhance model interpretability, we apply explainable AI techniques, specifically SHAP and LIME, to identify regions that influence model predictions. The results that our custom CNN model achieves train accuracy 90.62%, validation accuracy 88.25%, and test accuracy of 84.5%, with specific characters showing high classification performance. Also, AUC score reflects 0.99 to 1.00 for each of the classes.

关键词： Support vector machines Visualization Accuracy Translation Explainable AI Computational modeling Symbols Writing Predictive models Convolutional neural networks

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