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Performance Analysis of Single-Phase Inverter Using SEPIC Converter 1

6th International Conference on electrical, Control and computer engineering, InECCE 2021

作者： Zunnurain, Iman Alyzza Yumi, Koh Mun Faisal, Mohd Haris Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University of Malaysia Kuala Lumpur53100 Malaysia School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia UTM Johor Bahru Johor81310 Malaysia

ISBN: (数字)9789811686900

ISBN: (纸本)9789811686894

This paper presents the topology of integration of DC/DC SEPIC Converter with the full bridge DC/AC Inverter. The proposed topology can level up the small DC voltage into a higher AC voltage by connecting the circuit in parallel connection. SEPIC converter is chosen to be connected to the full bridge inverter as to testify its performance when it is connected in series between two power electronic circuits. The proposed topology is simulated using MATLAB and validate the results through the hardware implementation. The FPGA controller is selected to provide the pulsation for the main circuit for controlling the switching process. The performance of the converter including total harmonic distortion is analyzed and presented in this paper. The final harmonic reading for this proposed technique are 1.7% for THD voltage and 5.9% for THD current. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Topology

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IM-Diff: Implicit Multi-Contrast Diffusion Model for Arbitrary Scale MRI Super-Resolution

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4200-4212页

作者： Liu, Lanqing Zou, Jing Xu, Cheng Wang, Kang Lyu, Jun Xu, Xuemiao Hu, Zhanli Qin, Jing The Hong Kong Polytechnic University Centre for Smart Health School of Nursing 999077 Hong Kong Nanyang Technological University School of Mechanical and Aerospace Engineering 639798 Singapore Harvard Medical School Department of Psychiatry Brigham and Women's Hospital BostonMA02115 United States South China University of Technology Guangdong Engineering Center for Large Model and GenAI Technology School of Computer Science and Engineering Guangzhou510640 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Lauterbur Research Center for Biomedical Imaging Shenzhen518172 China CAS SIAT-PolyU Multi-modal Medical Molecular Imaging Joint Laboratory 999077 Hong Kong

Diffusion models have garnered significant attention for MRI Super-Resolution (SR) and have achieved promising results. However, existing diffusion-based SR models face two formidable challenges: 1) insufficient exploitation of complementary information from multi-contrast images, which hinders the faithful reconstruction of texture details and anatomical structures;and 2) reliance on fixed magnification factors, such as 2× or 4×, which is impractical for clinical scenarios that require arbitrary scale magnification. To circumvent these issues, this paper introduces IM-Diff, an implicit multi-contrast diffusion model for arbitrary-scale MRI SR, leveraging the merits of both multi-contrast information and the continuous nature of implicit neural representation (INR). Firstly, we propose an innovative hierarchical multi-contrast fusion (HMF) module with reference-aware cross Mamba (RCM) to effectively incorporate target-relevant information from the reference image into the target image, while ensuring a substantial receptive field with computational efficiency. Secondly, we introduce multiple wavelet INR magnification (WINRM) modules into the denoising process by integrating the wavelet implicit neural non-linearity, enabling effective learning of continuous representations of MR images. The involved wavelet activation enhances space-frequency concentration, further bolstering representation accuracy and robustness in INR. Extensive experiments on three public datasets demonstrate the superiority of our method over existing state-of-the-art SR models across various magnification factors. © 2013 IEEE.

关键词： Image enhancement

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A GaN MMIC Inverse Class-F Power Amplifier Using Co-Designed Bias Network and LC Resonator

A GaN MMIC Inverse Class-F Power Amplifier Using Co-Designed...

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Global Symposium on Millimeter Waves, GSMM

作者： Jie Shi Xiaohu Fang Xinyu Zhou Xu Yan Wei Lin Luyu Zhao School of Micro-electronics Southern University of Science and Technology Shenzhen China The Department of Electronic Engineering The Hong Kong Polytechnic University Hong Kong China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong China School of Electronic and Information Engineering Anhui University Hefei China

ISBN: (数字)9798350384956

ISBN: (纸本)9798350384963

This paper proposed a novel methodology to design compact and fully integrated inverse class-F (class-F-1) power amplifier (PA). In this method, a bias network (BN) is utilized to provide alternated open-circuit and short-circuit harmonic impedances and assist with fundamental matching. Then, a matching network (MN) formed by a parallel LC resonator is adopted to achieve isolation at 2nd harmonic and simultaneously help with impedance transformation at fundamental frequency. To validate the proposed method, a high efficiency class-F-1 PA operating at 5.8 GHz is designed using a commercial 0.25 μm GaN-on-SiC technology. Simulation results indicate that the proposed class-F-1 PA can provide a peak power-added efficiency (PAE) of 71% at saturation output power of 36.5 dBm.

关键词： Simulation Design methodology Impedance matching Power amplifiers Resonant frequency Voltage Harmonic analysis

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Workload Prediction of Virtual Machines Using Integrated Deep Learning Approaches Over Cloud Data Centers 1st

Workload Prediction of Virtual Machines Using Integrated Dee...

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Leka, Habte Lejebo Fengli, Zhang Kenea, Ayantu Tesfaye Sharma, Durga P. Tegene, Abebe Tamrat School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama1888 Ethiopia AMUIT MOEFDRE Under UNDP & MAISM RTU Kota Kota India

ISBN: (纸本)9789811954023

Exponential growth in the use of cloud computing services makes it difficult to forecast loads of virtual machines (VMs). Accurate virtual machine (VM) workload forecasting is the most critical task in appropriately managing cloud resources such as memory and central processing units while minimizing energy usage. To address this problem, an integrated deep learning model is proposed in this research paper. The model employs two popular neural networks: a bidirectional Long Short-Term Memory Network (BiLSTM) with a convolutional neural networks (CNN). The CNN component pulls high-level attributes from all VM workload data, whereas the BiLSTM component forecasts future VM workload. The experimental results reveal that the suggested model outperforms commonly used workload prediction methods in terms of forecasting accuracy of VMs workloads in cloud computing environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting 6

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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6th IEEE Symposium on computers and Informatics, ISCI 2024

作者： Omar, Mas Yakub, Fitri Talip, Mohamad Sofian Abu Maslan, Mohd Nazmin Sinha, Vijay Kumar Muljono, Muljono Universiti Teknologi Malaysia Wee Laboratory Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Department of Electronic System Engineering Kuala Lumpur Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Universiti Teknikal Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Melaka Malaysia Chitkara University School of Engineering & Technology Department of Computer Science & Engineering Himachal Pradesh India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350353853

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement System. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications. © 2024 IEEE.

关键词： Mean square error

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Cloud-Based Intelligent Patient Triage Systems with SVM Classifiers for Emergency departments 1

Cloud-Based Intelligent Patient Triage Systems with SVM Clas...

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1st International Conference on Innovations in Communications, electrical and computer engineering, ICICEC 2024

作者： Shibu, N.V. Sujatha, M. Suresh, M. Xavier Sindhuja, K. Raj, T. Saju Srinivasan, C. SRM Institute of Science and Technology Department of Computing Technologies School of Computing Kattankulathur Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit Bhopal University Department of Physics School of Advanced Sciences and Languages Madhya Pradesh Bhopal India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Efficient patient triage in emergency departments is crucial for the timely and successful allocation of resources and treatment in the constantly changing healthcare environment. To improve patient triage systems, this research suggests a new strategy that makes use of cloud computing and support vector machine (SVM) classifiers. The proposed solution endeavors to enhance patient outcomes in emergency departments by optimizing resource allocation via the use of cloud infrastructure's scalability and accessibility in conjunction with SVM algorithms' strong classification capabilities. Quickly classifying patients according to severity, the system integrates and analyzes data in real-time, allowing healthcare providers to prioritize high-risk situations and distribute resources appropriately. Healthcare practitioners can work together more efficiently and share patient data with cloud-based architecture, which promotes a more comprehensive approach to treatment. Results from the experiments prove that the system works as advertised and can be easily scaled up, highlighting its revolutionary potential to change the way emergency healthcare facilities handle patient triage. The growing body of evidence demonstrates the value of intelligent systems for healthcare delivery optimization and emergency patient care improvement via the use of technology. © 2024 IEEE.

关键词： Data-driven triage Emergency medicine Healthcare efficiency Healthcare optimization Real-time decision support Resource management

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Retraction Note: Polynomial cryptographic optical steganography substitution model for the telehealth system with multimedia data

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Rajaram, Gnanajeyaraman Dash, Satyabrata Arslan, Farrukh Venu, Dunde Ahmed, Mohammed Altaf Lydia, E. Laxmi Department of Applied Machine Learning Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Satyabrata Dash Ramachandra College of Engineering Eluru India School of Electrical and Computer Engineering Purdue University USA West Lafayette USA Department of Electrical Engineering University of Engineering and Technology Lahore Pakistan Department of ECE Kakatiya Institute of Technology and Science Warangal India Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Computer Science and Engineering Vignan’s Institute of Information Technology (Autonomous) Vishakhapatnam India

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Multilayer Perceptron Optimization of ECG Peaks for Cardiac Abnormality Detection 12

Multilayer Perceptron Optimization of ECG Peaks for Cardiac ...

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12th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2022

作者： Syam Ahmad Jamil, Addzrul Hi-Fi Kadir, Jailani Abdul Jamil, Johanis Mohd Hashim, Fakroul Ridzuan Shaharuddin, Shazreen Makmor, Nazrul Fariq Polytechnic of Seberang Perai Department of Electrical Engineering Penang Malaysia Polytechnic of Tuanku Syed Sirajuddin Department of Electrical Engineering Perlis Malaysia Polytechnic of Tuanku Syed Sirajuddin Department of Mathematics Science & Computer Perlis Malaysia National Defence University of Malaysia Faculty of Engineering Kuala Lumpur Malaysia National Defence University of Malaysia Faculty of Medicine & Defense Health Kuala Lumpur Malaysia

ISBN: (数字)9781665483391

ISBN: (纸本)9781665483391

The development of artificial neural networks (ANNs) was founded on computer alterations of human biology (the concept of neurons). The practicality of applying ANNs to various problems has been the subject of numerous studies, particularly in the field of biomedical engineering. Medical and educational decision-making regularly use applications to ANNs. Using a range of reference data, the ANNs used in the current study were trained to recognise cardiac abnormalities. Typically referred to as reference parameters, electrocardiogram (ECG) signal amplitude and duration are employed as input parameters for cardiac issues. An ECG complex consists of a P peak, QRS wave, and T peak. The amplitude and length of each P peak, QRS wave, and T peak are measured, resulting in a total of six input parameters for the artificial neural network. The artificial neural network (ANN) structure in this study is a multilayer perceptron (MLP), and the training techniques are Bayesian Regularization (BayR), Lavenberg Marquardt (LevM), and Backpropagation (BackP). The influence of the Tansig activation function on the MLP structure. The MLP network that achieved the highest accuracy (94.44%) utilising the BayR training method and Logsig activation function surpassed all others. © 2022 IEEE.

关键词： Electrocardiograms

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Dissipativity-Based Distributed Control and Communication Topology Co-Design for Voltage Regulation and Current Sharing in DC Microgrids

arXiv

引用

arXiv 2025年

作者： Najafirad, Mohammad Javad Welikala, Shirantha Department of Electrical and Computer Engineering School of Engineering and Science Stevens Institute of Technology HobokenNJ07030 United States

This paper presents a novel dissipativity-based distributed droop-free control approach for voltage regulation and current sharing in DC microgrids (MGs) comprised of an interconnected set of distributed generators (DGs), loads, and power lines. First, we describe the closed-loop DC MG as a networked system where the DGs and lines (i.e., subsystems) are interconnected via a static interconnection matrix. This interconnection matrix demonstrates how the inputs, outputs, and disturbances of DGs and lines are connected in a DC MG. Each DG has a local controller and a distributed global controller. To design the controllers, we use the dissipativity properties of the subsystems and formulate a linear matrix inequality (LMI) problem. To support the feasibility of this problem, we identify a set of necessary local and global conditions that we then enforce in a specifically developed LMI-based local controller design process. In contrast to existing DC MG control solutions, our approach proposes a unified framework for co-designing the distributed controller and communication topology. As the co-design process is LMI-based, it can be efficiently implemented and evaluated using existing convex optimization tools. The effectiveness of the proposed solution was verified by simulating an islanded DC MG in a MATLAB/Simulink environment under different scenarios, such as load changes and topological constraint changes, and then comparing the performance with a recent droop control algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms

arXiv

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arXiv 2024年

作者： Chen, Weiqin Squillante, Mark S. Wu, Chai Wah Paternain, Santiago Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States Mathematical Sciences Department IBM Research

We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as convergence and optimality of our analog of the Bellman operator and Q-learning, a new control-policy-variable gradient theorem, and a specific gradient ascent algorithm based on this theorem within the context of a specific control-theoretic framework. We empirically evaluate the performance of our control-theoretic approach on several classical reinforcement learning tasks, demonstrating significant improvements in solution quality, sample complexity, and running time of our approach over state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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