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European Conference on Power Electronics and Applications

作者： Mahmoud Saeidi Ahmad Ali Nazeri Rufad Zilic Peter Zacharias Centre of Competence for Distributed Electric Power Technology Faculty of Electrical Engineering/Computer Science University of Kassel Kassel Germany

ISBN: (数字)9789075815399

ISBN: (纸本)9781665487009

The voltage gradient is increased to reduce the switching losses in the wide-bandgap (WBG) semiconductors, which causes the higher power density of the system. Fast rise and fall times during switching of WBG semiconductors result in capacitive non-linearities and displacement currents through power module parasitic capacitances, which needs an appropriate filter design During the switching in the power module, the capacitive current flows through the parasitic capacitance of the power module, which causes the disturbances in the system. This leads to the need for a more oversized filter design, which then increases the overall cost and volume of the system and reduces the efficiency of the system. This paper proposes a novel technique to suppress the capacitive displacement currents without switching speed reduction. The proposed method reduces the volume of the common-mode choke (CMC) and addresses the electromagnetic interference (EMI) and electromagnetic compatibility (EMC) issues. The system was experimentally tested using the commercial 1700 V silicon carbide (SiC) half-bridge power module BSM300D12P2E001 to validate the proposed scheme.

关键词： Costs Silicon carbide Electromagnetic interference Multichip modules Switching loss Switches Voltage

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Prediction of multi class drugs: A perspective for designing drug with many uses 2

Prediction of multi class drugs: A perspective for designing...

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2nd International Conference on Artificial Intelligence and Signal Processing, AISP 2022

作者： Vaidya, Pankaj Chauhan, Shweta Jaiswal, Varun School of Electrical and Computer Science Engineering Shoolini University Solan India Faculty of Applied Sciences and Biotechnology Shoolini University Solan India Department of Food and Nutrition College of BioNano Technology Gachon University Seongnam-si Gyeonggi-do13120 Korea Republic of

ISBN: (纸本)9781665442909

The drug-like molecule which could treat multiple diseases is commercially more viable and can act on multiple biological pathways. Such drug candidates can also be more important in the treatment of complex diseases like cancer. Traditional methods are not focused on the development of such drugs, but computational method can be developed to predict multiple disease potential of drug-like molecules. Computational methods have been extremely successful in drug discovery through prediction of drug potential of the drug-like molecules such as toxicity, physiological effects, binding energy and binding pose with the receptor. Computational methods to predict multiple disease potential of the drug-like molecules are not worked out so far in spite of the high importance of such drugs and it can also expedite the drug repurposing. Hence, information of approved drugs used for the treatment of single and multiple diseases was included to develop the machine learning-based model for the prediction of multiple disease potential of the drug-like molecules. Molecular descriptors were used as the features and optimally selected for support vector machine-based prediction models. The fairly high accuracy of developed method justifies the importance of selected method and approach. The developed method is expected to expedite the drug discovery process through the prediction of multi-drug potential of drug-like molecules. © 2022 IEEE.

关键词： Diseases

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Optimal scheduling of battery energy storage in microgrid to minimize electricity and fuel costs

Optimal scheduling of battery energy storage in microgrid to...

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International Multidisciplinary Conference on computer and Energy science (SpliTech)

作者： Zvonimir Š imić Danijel Topić Marina Dubravac Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia

ISBN: (纸本)9781665488280

Optimal scheduling of the battery energy storage (BES) in a microgrid is presented in this paper. The microgrid model in this paper consists of a photovoltaic (PV) power plant, a wind generator (WG), battery energy storage (BES), load, and diesel generator (DG). BES is required to store the generation excess from renewable energy sources (RES). BES also can store energy from the main grid but optimization is required to decide when to charge and discharge the battery. The optimization goal is to minimize electricity costs when the main grid is available and fuel costs when it is not available. The optimization problem in this paper is a linear programming (LP) problem that is modeled and solved with Python Gurobi. Optimization results are presented for different cases. A DG is used only when the main grid is not available due to the high fuel price. Electricity cost from DG is 5.28 times higher than the cost from the grid for the first case and 4.36 times higher for the second case. There is no generation excess in the second case so the battery is not used when the main grid is not available.

关键词： Wind energy generation Costs Wind speed Simulation Microgrids Optimal scheduling Wind power generation

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Diagnostic Tools for Detecting Autism Spectrum Disorder: A Review 1

Diagnostic Tools for Detecting Autism Spectrum Disorder: A R...

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1st International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Rezaee, Khosro Khosravi, Mohammad R. Zadeh, Hossein Ghayoumi Moghimi, Mohammad Kazem Samara, Ghassan Attar, Hani Almatarneh, Sattam Meybod University Deprtement of Biomedical Enginering Meybod Iran Weifang University of Science and Technology Shandong Weifang China Vali-E-Asr University of Rafsanjan Department of Electrical Engineering Rafsanjan Iran University of Sistan and Baluchestan Department of Communication Engineering Zahedan Iran Zerqa University Department of Computer Science Zerqa Jordan Zarqa University Faculty of Enineering Department of Energy Engineering Zerqa Jordan Zarqa University Faculty of Information Technology Data Science and Artificial Intelligence Department Zerqa Jordan

ISBN: (纸本)9798350332742

The autism spectrum disorder (ASD) is a developmental disability caused by abnormalities in the brain. Different methods have been used to diagnose ASD in the past, each with its own advantages and disadvantages. Prior research has focused mainly on algorithms and processing methods, rather than identification tools, to improve the automated diagnosis of ASD. This article identifies and describes diagnostic tools in the ASD literature, including facial features, EEG recordings, speech signals, and neuroimaging, in order to assist researchers interested in developing statistical, computational, and sound clinical approaches to ASD data mining. Accordingly, based on the responses obtained, this review study intends to assess several aspects of the key automatic diagnostic tools for autism spectrum disorders, including diagnostic accuracy, privacy protection, uncertainty, cost, efficiency, absence of methodological interference, and prospective clinical and therapeutic conditions. © 2022 IEEE.

关键词： Neuroimaging

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques

Early Fault Detection in Rolling Element Bearings Using Sign...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Ali Saeed M. Usman Akram Muazzam A. Khan M. Belal Afsar Department of Computer Science ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Department of Computer and Software Engineering National University of Sciences & Technology Islamabad Pakistan Faculty of Electrical Engineering and Information Technology Otto-von-Guericke University Maddeburg DE

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-to-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method.

关键词： Vibrations Fault diagnosis Low voltage Smart cities Rolling bearings Signal processing Motors Robots Monitoring Investment

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Design and Performance Analysis of a Modified Proportional Multi-Resonant (PMR) Controller for Three-Phase Voltage-Source Inverters

Design and Performance Analysis of a Modified Proportional M...

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European Conference on Power Electronics and Applications

作者： Ahmad Ali Nazeri Mahmoud Saeidi Peter Zacharias Faculty of Electrical Engineering / Computer Science Centre of Competence for Distributed Electric Power Technology University of Kassel Kassel Germany

ISBN: (数字)9789075815399

ISBN: (纸本)9781665487009

The three-phase voltage source inverter (VSI) can be operated in grid-connected and/or stand-alone mode where the VSI is connected to the grid and/or critical loads at the point of common coupling (PCC). Proper voltage control is needed for the output voltage regulation in stand-alone operation and current control is needed for the grid current control. This paper presents a step-by-step design procedure, an extensive system stability analysis, and methods of discretization for the current control of three-phase power converters in the synchronous $(dq)$ and stationary reference frame (SRF). A proportional-integral multi-resonant (PI-MR) controller in the synchronous reference frame (SynRF) is implemented for the regulation of the inner current loop. Moreover, the inverter inductor current controller in a stationary frame is proposed to provide active damping, and improve transients, and steady-state performance. The traditional PI-MR controller is compared for different load conditions with the modified practical proportional multi-resonant (PMR) controller in parallel with the harmonic compensators of orders 5th, 7th, 11th, and 13th to reduce low-order load current harmonics. The PMR controller shows superior performance with lower total harmonic distortion (THD) than the conventional PI and PI-MR controllers for highly nonlinear load conditions. Moreover, the modified PMR controller has almost zero steady-state error, improved tracking of the reference signal, and better disturbance rejection compared to the conventional PI-MR control. A comprehensive design guideline of the proposed controller with a wider range of system stability margin is analyzed with harmonic damping of the three-phase VSI. Proper discretization methods for each controller have been outlined. The system is simulated in MATLAB/Simulink environment and experimentally implemented on a TMS320F28335 floating-point digital signal processor (DSP) for a 7.5 kW inverter to validate the performance

关键词： Current control Damping Total harmonic distortion Digital signal processors Power system stability Harmonic analysis Stability analysis

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AI-based Flexible Online Laboratory Learning System for Post-COVID-19 Era: Requirements and Design

AI-based Flexible Online Laboratory Learning System for Post...

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Elmesalawy, Mahmoud M. Atia, Ayman Yousef, Ahmed Mohamed Fahmy El-Haleem, Ahmed M. Abd Anany, Mohamed G. Elmosilhy, Noha A. Salama, Abdullah I. Hamdy, Alaa Zoghby, Helmy M. El Din, Eman Serag El Helwan University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligent Computer Science Department Cairo Egypt Giza Egypt Fayoum University Faculty of Specific Education Education Technology Department Fayoum Egypt Electronics and Communications Engineering Department Cairo Egypt Helwan University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Helwan University Faculty of Engineering Electrical Power and Machines Engineering Department Cairo Egypt Network Planning Department Cairo Egypt

ISBN: (纸本)9781665412438

The worldwide outbreak due to COVID-19 pandemic has led to a great interest in e-learning. However, the lack of suitable online laboratory management systems has posed a particular challenge for sectors that need laboratory activities such as engineering, science and technology. In this paper, the requirements and design for a flexible AI-based laboratory learning system (LLS) that can support online laboratory experimentations are presented. The elicitation of the LLS design requirements is decided based on a conducted survey for a set of LLS features. The LLS is designed with the flexibility to support various types of online experimentations such as virtual or remote controlled experiments using either desktop or web applications. The virtualization technique is used to manage the laboratory resources and allow multiple users to access the LLS. Moreover, the proposed LLS introduces the use of AI techniques to provide efficient virtual lab assistant and adaptive assessment process. © 2021 IEEE.

关键词： COVID-19

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A Robust Technique for Steganography of Enhanced Audio Signals

A Robust Technique for Steganography of Enhanced Audio Signa...

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Electronic engineering (ICEEM), International Conference on

作者： Marwa A. Nasr Walid El-Shafai El-Sayed M. El-Rabaie Adel S. El-Fishawy Moawad I. Dessouky Nariman Abdel-Salam Fathi E. Abd El-Samie Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science College of Engineering Security Engineering Laboratory Prince Sultan University Riyadh Saudi Arabia Communications and Electronics Engineering Department Faculty of Engineering Canadian International College (CIC) Giza Egypt

Audio steganography is a tool for concealing data (a secret message) inside an audio signal (a carrier). It is regarded as an essential approach for information security. This paper presents a proposed technique for audio signal steganography, which is implemented in the wavelet domain, with a pre-processing enhancement step. First, adaptive Wiener filtering is implemented to reduce the noise on audio signals. Then, the Inverse Short-Time Fourier Transform (ISTFT) is applied to convert the target image from the spectrogram-like nature to the 1-D signal-like nature. The embedding process is performed on high-frequency coefficients in the wavelet domain to keep the audio signals unchanged for human perception. The reverse process is implemented at the receiver. Hence, the embedded coefficients are extracted to recover the embedded image through Short-Time Fourier Transform (STFT). The recovered audio signal is also obtained after the removal of embedded coefficients. The major goal is to enhance the performance of the audio steganography technique, while retaining the audio signal quality. The outcomes show that the suggested technique successfully allows audio steganography and enhances the resultant audio signal quality at different Signal-to-Noise Ratio (SNR) levels.

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PhysioZoo: The Open Digital Physiological Biomarkers Resource

PhysioZoo: The Open Digital Physiological Biomarkers Resourc...

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computers in Cardiology (CinC)

作者： Joachim A Behar Jeremy Levy Eran Zvuloni Sheina Gendelman Aviv Rosenberg Shany Biton Raphael Derman Jonathan A Sobel Alexandra Alexandrovich Peter H Charlton Márton Á Goda Faculty of Biomedical Engineering Technion Technion-IIT Haifa Israel The Faculty of Electrical & Computer Engineering Technion-IIT Haifa Israel Faculty of Computer Science Technion Technion-IIT Haifa Israel Department of Anesthesiology Rambam Medical Center Haifa Israel University of Geneva Geneva Switzerland University of Cambridge Cambridge United Kingdom

PhysioZoo is a collaborative platform designed for the analysis of continuous physiological time series. The platform currently comprises four modules, each consisting of a library, a user interface, and a set of tutorials: (1) PhysioZoo HRV, dedicated to studying heart rate variability (HRV) in humans and other mammals; (2) PhysioZoo SPO2, which focuses on the analysis of digital oximetry biomarkers (OBM) using continuous oximetry (SPO2) measurements from humans; (3) PhysioZoo ECG, dedicated to the analysis of electrocardiogram (ECG) time series; (4) PhysioZoo PPG, designed to study photoplethys-mography (PPG) time series. In this proceeding, we introduce the PhysioZoo platform as an open resource for digital physiological biomarkers engineering, facilitating streamlined analysis and data visualization of physiological time series while ensuring the reproducibility of published experiments. We welcome researchers to contribute new libraries for the analysis of various physiological time series, such as electroencephalography, blood pressure, and phonocardiography. You can access the resource at ***. We encourage researchers to explore and utilize this platform to advance their studies in the field of continuous physiological time-series analysis.

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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computer Systems science & engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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