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arXiv 2024年

作者： Banerjee, Shuvayan Srivastava, Radhendushka Saunderson, James Rajwade, Ajit IITB-Monash Research Academy Australia The Department of Mathematics IIT Bombay India The Department of Electrical and Computer Systems Engineering Monash University Australia The Department of Computer Science and Engineering IIT Bombay India

关键词： Specifications

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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

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

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Workshop on Smart Grid and Renewable Energy (SGRE)

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

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.

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Average Current Control Design of Active Power Decoupling Converter with Low Filter Inductance Based on Double Sampling Techniques for PV Microinverter

Average Current Control Design of Active Power Decoupling Co...

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Energy and Sustainable Development

作者： Yidi Shen Arafat Hasnain Daniel Zakzewski Rakesh Resalayyan Alireza Khaligh Department of Electrical and Computer Engineering and The Institute for Systems Research University of Maryland College Park College Park Maryland USA

ISBN: (数字)9798331517076

ISBN: (纸本)9798331517083

Active power decoupling (APD) converter is a power dense and reliable solution in dc-ac power conversion systems to balance the fluctuating ac-side power and constant dc-side power. In boost type APD converter, filter inductor with low inductance can be beneficial in achieving efficient and compact designs or allows the implementation of zero voltage switching (ZVS) based modulation. On the other hand, the reduced inductance increases the difficulty of controller design due to reduction of the average current control loop phase margin and higher requirement in low-pass-filter (LPF). This paper proposes an average current control based on double sampling techniques to reduce the phase delay of LPF and improve the current tracking performance under low inductance. The implementation of double sampling in the digital signal processor (DSP) and the design of control loops are discussed. The proposed approach is experimentally verified using a 40 V, 400 W APD converter prototype.

关键词： Current control Inductance Tracking loops Digital signal processors Prototypes Avalanche photodiodes Zero voltage switching Active filters Delays Inductors

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Downstream Task Performance of Pre-Trained Model For Outdoor Audio Event Classification 9

Downstream Task Performance of Pre-Trained Model For Outdoor...

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9th International Conference on Signal Processing and Intelligent systems, ICSPIS 2023

作者： Rochadiani, Theresia Herlina Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wawan Bina Nusantara University Binus Graduate Program- Computer Science Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350369922

Our surroundings' auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models - three, four, and five classes - with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Evaluation of Chest CT-Scan Anomaly Detection Models Using Principal Component Analysis 25

Evaluation of Chest CT-Scan Anomaly Detection Models Using P...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alyabrodi, Ala Al-Daja, Shrouq Injadat, MohammadNoor Moubayed, Abdallah Elrashidi, Ali Zarqa University Cyber Security Department Zarqa Jordan Zarqa University Data Science & Ai Department Zarqa Jordan King Fahd University of Petroleum & Minerals Research Center for Intelligent Secure Systems Computer Engineering Department and Interdisciplinary Dhahran Saudi Arabia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Machine learning-based improvements in anomaly detection, visualization, and segmentation are made possible by the growing digitization of medical imaging, which reduces the workload for medical specialists. Nevertheless, trustworthy labeled data is necessary for supervised machine learning and is sometimes expensive, time-consuming, or impossible to get. Consequently, techniques that need either little labeling (unsupervised methods) or partial labeling (semi-supervised methods) have been used increasingly been used. One approach that can be used to tackle medical imaging tasks like segmentation and classification is anomaly detection, which makes use of both unsupervised and semi-supervised methods. This study investigates the anomaly detection capabilities of deep learning and machine learning models trained on chest CT scans. To adopt this approach, firstly, by evaluating several feature extraction techniques (Principal Component Analysis (PCA), Autoencoder, and variance threshold), PCA achieves the best results on the selected dataset. After that, different machine learning and deep learning models (Isolation Forest, K-means, feed-forward neural networks, and convolutional neural networks) are assessed to detect anomalies using PCA for dimensionality reduction. The experiments show that the NN-Feedforward model with PCA outperformed the other models, also attains remarkably low false alarm rate, demonstrating its effectiveness in reducing false positives and precisely detecting anomalies. © 2024 IEEE.

关键词： computerized tomography

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Revolutionizing Road Safety: YOLOv8-Powered Driver Fatigue Detection

Revolutionizing Road Safety: YOLOv8-Powered Driver Fatigue D...

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Xie, Shuaiyue Chuah, Joong Huang Chai, Grace Mei Ting Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia Smart Manufacturing Systems Research Group University of Southampton Malaysia Johor Iskandar Puteri79100 Malaysia

ISBN: (纸本)9798350341072

Driver fatigue is a significant cause of road accidents. Effective real-time fatigue detection systems are necessary to improve road safety. Utilizing a lightweight and fast model and creating an effective fatigue judgment rule is important in detecting fatigue in real-time. In this research, we propose a fatigue detection model based on You Look Only Once version 8 (YOLOv8) model to detect driver's facial features and use percentage of eyelid closure over the pupil over time (PERCLOS) and Percentage of Open Mouth (POM) indicators to identify fatigue. This research is divided into two parts. The first part involves creating, labeling, and preprocessing the dataset. We use the YawDD dataset as the original dataset and manually label a new dataset using the Roboflow platform. We then preprocessed the new dataset to train the YOLOv8 model for face features detection. In the second part, we established a multi-indicator fatigue determination rule that uses PERCLOS and POM indicators and set a threshold to determine whether the driver is fatigue. By combining the YOLOv8 model with the fatigue determination rules based on PERCLOS and POM, we successfully built a model to identify a driver's fatigue. Finally, the fatigue detection model based on YOLOv8 achieves a high accuracy rate of 96.9% and a high speed of 11.4 ms/frame in face features detection, surpassing other traditional models and previous generations of YOLO models. This model can effectively detect fatigue in both driver-driving pictures and videos. © 2023 IEEE.

关键词： Roads and streets

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Criteria-based Unsupervised Feature Selection Techniques: A Comparative Study Over Chest CT Scan 25

Criteria-based Unsupervised Feature Selection Techniques: A ...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Daja, Shrouq Alyabrodi, Ala' Moubayed, Abdallah Injadat, MohammadNoor Elrashidi, Ali Zarqa University Data Science & Ai Department Zarqa Jordan Zarqa University Cyber Security Department Zarqa Jordan King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Intelligent Secure Systems Computer Engineering Department Dhahran Saudi Arabia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

research on classifying chest CT scans as normal or abnormal using machine learning and deep learning has garnered significant attention. To address this, various feature selection (FS) methods are employed to reduce dimensionality and identify key features. This paper evaluates criteria tailored to unsupervised binary datasets, such as feature subsets, redundancy, and diversity, by comparing Mutual Information-Based Methods (MI) with Clustering-Based Feature Selection (Agglomerative Clustering) ones. Additionally, it examines Graph Structure, Spectral Properties, and Quality criteria by comparing Graph-Based Methods with Spectral Feature Selection-be (SPEC). For feature reduction, Agglomerative Clustering may offer a slight advantage due to better handling of diversity and redundancy, though the difference is minimal. If these aspects are crucial, Agglomerative Clustering might be preferred. Conversely, for Graph-Based vs. SPEC, SPEC Method is more suitable for feature reduction due to its coherent feature selection and focus on capturing significant patterns related to medical interventions. © 2024 IEEE.

关键词： Contrastive Learning

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Distributed Control for a Microgrid Cluster: Implementation and Experimental Validation

Distributed Control for a Microgrid Cluster: Implementation ...

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International Conference on Environment and electrical engineering (EEEIC)

作者： David Carrasco-González Raúl Sarrias-Mena Pablo Horrillo-Quintero Ehsan Hosseini Francisco Llorens-Iborra Luis M. Fernández-Ramírez SURET Research Group Department of Electrical Engineering University of Cadiz Algeciras Spain SURET Research Group Department of Engineering in Automation Electronics and Computer Architecture and Networks University of Cadiz Algeciras Spain

ISBN: (数字)9798350355185

ISBN: (纸本)9798350355192

In response to growing energy demands and concerns about climate change, microgrid clusters (MGCs) are gaining widespread attention. Their ability to integrate technologies for energy consumption, generation, and storage in AC, DC or a compound of both offer a high degree of flexibility and resiliency. This research introduces a new control strategy for an MGC composed of two MGs interconnected to a local grid via a point of common coupling. The first one is a DC MG comprising an ultracapacitor (UC), wind turbine (WT), DC loads and hydrogen system. The second is an AC MG with a battery bank, AC loads and photovoltaic (PV) generator. The control strategy employs local controllers for each device and distributed control via two control agents, coordinating the power distribution among the energy storage systems (ESSs) of the MGC. This system is tested and validated on a real-time experimental setup, using two Raspberry Pi microcontrollers and an OPAL-RT unit, under different working conditions. The results obtained in this work show that the control strategy implemented in the MGC works correctly.

关键词： Energy consumption Microcontrollers Wind speed Decentralized control Hydrogen Microgrids Supercapacitors Real-time systems Wind turbines Solar radiation

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AQROM:A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

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Digital Communications and Networks 2024年第5期10卷 1405-1414页

作者： Wei Zhou Xing Jiang Qingsong Luo Bingli Guo Xiang Sun Fengyuan Sun Lingyu Meng School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China The 34th Research Institute of China Electronics Technology Group Corporation Guilin541004China Guangxi Key Laboratory of Optical Network and Optical Information Security Guilin541004China State Key Laboratory of Information Photonics and Optical Communications BeijingUniversity of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM87131USA

In Software-Defined Networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and *** SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this *** this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the *** can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic *** can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the *** simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.

关键词： Software-defined networks Asynchronous advantage actor-critic QoS-aware routing optimization mechanism

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Sleep Apnea detecting based on ECG and EEG Signals Using Discrete Wavelet Transform and Artificial Neural Network 3

Sleep Apnea detecting based on ECG and EEG Signals Using Dis...

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3rd International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2022

作者： Hammal, Abd Alrhman Bonney, Talal Al Nassan, Wafaa Obaideen, Khaled Electrical and Computer Engineering Aleppo University Syria Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Sustainable Energy and Power Systems Research Centre RISE University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839538186

Sleep apnea is a severe sleep disorder that happens when breathing stops and starts during slumber. If it goes untreated, it can cause serious health problems. In recent years, Sleep Apnea (SA) has been diagnosed using either Electrocardiogram (ECG) signals that refer the heart health or Electroencephalogram (EEG) signals that refer to brain health. This research aims to build an Artificial Neural Network (ANN) classification model to determine sleep disorders using ECG and EEG signals. Features Delta sub-band waves are extracted and processed using a multilayer perceptron (MLP) with two different topologies;hidden layers are the same. The results show that using ECG and EEG signals to classify sleep apnea achieves high accuracy of 99.97% compared to other recent related work. We anticipate our assay to be a starting point for reliable wearable diagnostic devices to control equipment that can help patients with sleep apnea avoid respiratory distress episodes. © IET Conference Proceedings 2022.

关键词： Neural networks

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