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Sliding Mode control in Power Converters and Drives: A Review

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 392-406页

作者： Ligang Wu Jianxing Liu Sergio Vazquez Sudip K.Mazumder IEEE Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Electronic Engineering Department Universidad de Sevilla41092 SevillaSpain Electrical and Computer Engineering Department University of Illinois ChicagoChicagoIL 60607 USA

Sliding mode control(SMC)has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched *** aim of this paper is to present a review of SMC describing the key developments and examining the new trends and challenges for its application to power electronic *** fundamental theory of SMC is briefly reviewed and the key technical problems associated with the implementation of SMC to power converters and drives,such chattering phenomenon and variable switching frequency,are discussed and *** recent developments in SMC systems,future challenges and perspectives of SMC for power converters are discussed.

关键词： Motor drive power electronics systems sliding mode control

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CNN Framework for Accurate Brain Tumour Segmentation from Enhanced MRI Slices

CNN Framework for Accurate Brain Tumour Segmentation from En...

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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Mohan, Ramya Hasson, Feras N Fadhil, Hilal A. Arunmozhi, Rama Rajinikanth, Venkatesan Department of Computer Science Engineering Tamil Nadu Chennai602105 India National University of Science and Technology Department of Electrical and Computer Engineering Oman Sohar University Department of Electrical and Computer Engineering Oman

ISBN: (纸本)9798350334050

The brain tumor (BT) is a severe condition caused by abnormal cell growth. If left untreated, the BT may result in a variety of harsh conditions, including death. As a consequence of the significance of automatic BT detection, several schemes have been developed and implemented in the literature to accurately assess the BT. We propose a method for accurately segmenting brain images from MRI slices in this study. This proposal includes a number of phases, including;(i) collecting and resizing images, (ii) enhancing the image using a selected scheme, (iii) segmenting using ResUnet, and (iv) evaluating and validating the performance. This study examines several brain MRi slices with or without the skull region, and the results are evaluated separately. Based on these outcomes, it is concluded that the proposed ResUnet together with CLAHE provides a significant improvement over other methods concerning Jaccard (>92%), Dice (>95%), and accuracy (>98%). © 2023 IEEE.

关键词： Tumors

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Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

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computers, Materials & Continua 2023年第11期77卷 1855-1872页

作者： Heba M.El-Hoseny Heba F.Elsepae Wael A.Mohamed Ayman S.Selmy Department of Computer Science the Higher Future Institute for Specialized Technological StudiesObour11828Egypt Department of Electrical Engineering Benha Faculty of EngineeringBenha UniversityBenha13511Egypt

Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision *** methods of diagnosing and treating the disease are time-consuming and ***,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic *** advanced techniques offer higher accuracy and *** Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease ***,these technological advancements hold great potential for improving the management of diabetic *** study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these *** robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its *** outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance *** study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of *** manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

关键词： No diabetic retinopathy(NDR) convolution layers(CNV layers) transfer learning data cleansing convolutional neural networks a visual geometry group(VGG16)

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An Integrated control Framework for Safe and Ergonomic Human-Drone Interaction in Industrial Warehouses

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3570-3585页

作者： Proia, Silvia Cavone, Graziana Scarabaggio, Paolo Carli, Raffaele Dotoli, Mariagrazia University of Modena and Reggio Emilia Department of Sciences and Methods for Engineering Modena41121 Italy University Roma Tre Department of Civil Computer Science and Aeronautical Technologies Engineering Rome00154 Italy Polytechnic of Bari Department of Electrical and Information Engineering Bari70126 Italy

This study introduces a novel control framework for human-drone interaction (HDI) in industrial warehouses, targeting pick-and-delivery operations. The goals are to enhance operator safety as well as well-being and, at the same time, to improve efficiency and reduce production costs. To these aims, the speed and separation monitoring (SSM) operation method is employed for the first time in HDI, drawing an analogy to the safety requirements outlined in collaborative robots' ISO standards. The so-called protective separation distance is used to ensure the safety of operators engaged in collaborative tasks with drones. In addition, we employ the rapid upper limb assessment (RULA) method to evaluate the ergonomic posture of operators during interactions with drones. To validate the proposed approach in a realistic industrial setting, a quadrotor is deployed for pick-and-delivery tasks along a predefined trajectory from the picking bay to the palletizing area, where the interaction between the drone and a moving operator takes place. The drone navigates toward the interaction space while avoiding collisions with shelves and other drones in motion. The control strategy for the drone cruise navigation integrates simultaneously the time-variant artificial potential field (APF) technique for trajectory planning and the iterative linear quadratic regulator (LQR) controller for trajectory tracking. Differently, in the descent phase, the receding horizon LQR algorithm is employed to follow a trajectory planned in accordance with the SSM, which starts from the approach point at the border of the interaction space and ends in the volume with the operator's minimum RULA. The presented control strategy facilitates drone management by adapting the drone's position to changes in the operator's position while satisfying HDI safety requirements. The results of the proposed HDI framework simulations for the case study demonstrate the effectiveness of the method in ensuring a safe and er

关键词： ISO Standards

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3D layout of the Spidergon-Donut on-chip interconnection network

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International Journal of High Performance Systems Architecture 2023年第3期11卷 137-147页

作者： Sibai, Fadi N. Asaduzzaman, Abu Elmoursy, Ali Department of Electrical and Computer Engineering Gulf University for Science and Technology P.O. Box 7207 Hawally32093 Kuwait Department of Electrical and Computer Engineering Wichita State University WichitaKS67260 United States Department of Electrical and Computer Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates

3D integration promises to resolve many of the heat and die size limitations of 2D integrated circuits. A critical step in the design of 3D many-cores and MPSOCs is the layout of their 3D network-on-chip (NoC). In this paper, we explore and present multiple 3D layouts of the Spidergon-Donut (SD) NoC and estimate their longest wire lengths and cost requirements. For a total of 64 cores, the 4×2×8 and 2×4×8 placements result in the best longest wire delays, with the former higher 3D integration costs, while the second requiring larger chip area and through-silicon-vias (TSV) array costs. Such study helps in guiding 3D integration direction and weighing 3D NoC layout and placement alternatives. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Network-on-chip

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Enhancing Trustworthiness and Interoperability of Electronic Voting Systems through Blockchain Bridges

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第4期4卷 749-760页

作者： Rexha, Blerim Neziri, Vehbi Dervishi, Ramadan Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosovo10000 Department of Computer Science and Engineering UBT Higher Education Institution Prishtina Kosovo10000 Department of Computer Science Universum Higher Education Institution Prishtina Kosovo10000

Decentralized applications leveraging blockchain technology are gaining widespread adoption within the decentralized applications ecosystem. Interoperability, a fundamental concept facilitating seamless data and processing power exchange across diverse blockchain networks, is paramount in this context. The primary objective of this paper is to explore the transformative potential of "blockchain bridges" in facilitating secure and transparent electronic voting processes across multiple blockchain networks. The study employs a comprehensive analysis of various approaches, including atomic exchanges, sidechains, cross-chain bridges, token wrappers, and interledger protocols. The selection of a specific method is guided by the unique requirements and privacy considerations of the electronic voting use case. The application of two distinct blockchains serves as a practical demonstration, illustrating the principles of blockchain bridges in real-world scenarios. The research reveals that blockchain bridges not only streamline the exchange of data between diverse blockchain networks but also establish a dual decentralization paradigm. This paradigm enables the creation of openly maintained, purpose-specific, decentralized ledgers for electronic voting. The integration of blockchain bridges significantly reduces the risk of fraud, instilling greater confidence in the accuracy of election results. Thus, by presenting a comprehensive array of approaches and emphasizing their practical application, this research contributes to advancing the understanding and implementation of blockchain technology in the critical domain of electronic voting. © 2023, Ital Publication. All rights reserved.

关键词： Blockchain

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ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning 3

ConvoWaste: An Automatic Waste Segregation Machine Using Dee...

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3rd International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2023

作者： Nafiz, Md. Shahariar Das, Shuvra Smaran Morol, Md. Kishor Al Juabir, Abdullah Nandi, Dip Department of Electrical and Electronic Engineering Bangladesh Department of Computer Science Bangladesh

ISBN: (纸本)9798350346435

Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and to boost up the circular economy. This paper proposes a machine to segregate the waste into the different parts with the help of smart object detection algorithm using ConvoWaste in the field of Deep Convolutional Neural Network (DCNN), and image processing technique. In this paper, the deep learning and image processing techniques are applied to classify the waste precisely and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an android app in order to dump the separated waste in a desired place by its automation properties. The use of this system can aid the process of recycling resources that were initially destined to become waste, utilizing natural resources and turning these resources back into the usable products. Thus, the system helps to fulfill the criteria of circular economy through the resource optimization and extraction. Finally, the system is made to provide the services at a low cost with higher accuracy level in terms of the technological advancement in the field of Artificial Intelligence (AI). We have got 98% accuracy for our ConvoWaste deep learning model. © 2023 IEEE.

关键词： Object detection

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Intelligent obstacle avoidance algorithm for safe urban monitoring with autonomous mobile drones

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Journal of Electronic science and Technology 2024年第4期22卷 19-36页

作者： Didar Yedilkhan Abzal E.Kyzyrkanov Zarina A.Kutpanova Shadi Aljawarneh Sabyrzhan K.Atanov Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Automation and Control Systems L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Software Engineering Jordan University of Science and TechnologyIrbid22110Jordan

The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone *** deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition *** this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring *** primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as *** algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone ***,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the ***,the algorithm identifies observers within the swarm based on the current *** further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy *** approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular *** integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle *** proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive *** results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.

关键词： Drone swarms Fuzzy logic Intelligent solution Smart city Urban monitoring

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Integrated On-Board Battery Charger with Galvanic Isolation based on 6-Phase SPM Synchronous Machine for Electric Aircraft 50

Integrated On-Board Battery Charger with Galvanic Isolation ...

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

作者： Metwly, Mohamed Y. He, JiangBiao Abdel-Khalik, Ayman S. Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States Department of Electrical Engineering Alexandria University Alexandria Egypt

ISBN: (纸本)9781665464543

Aircraft electrification has received increasing attention due to the significant benefits, such as low carbon emissions, high energy efficiency, low operation cost, and reduced acoustic noise. Limitations on both the low energy density with the present battery technologies and slow battery charging speed highly affect the widespread adoption of electric aircraft, especially for megawatt-scale large aircraft. Thus, this paper presents an isolated integrated on-board battery charger (IOBC) using an existing six-phase surface-mounted permanent magnet (SPM) synchronous propulsion machine for MW-scale electric aircraft. Besides exploiting the existing powertrain apparatuses, i.e., the six-phase motor and inverter, in the propulsion and charging modes of operation, the proposed charger offers galvanic isolation to meet safety standards and code compliance. Moreover, zero average torque production during charging and unity power factor operation at the grid side can be simultaneously achieved. The proposed charger concept entails simple hardware reconfiguration to switch between the propulsion and charging modes of operation. Furthermore, this paper adopts a 36-slot/34-pole fractional slot concentrated winding (FSCW)-based SPM machine, which is designed and optimized based on the magnetic equivalent circuit (MEC) model and the multi-objective genetic algorithm (MOGA) design optimization approach, respectively. Eventually, theoretical findings are verified based on 2D and 3D finite element (FE) simulations. © 2024 IEEE.

关键词： Permanent magnets

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Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

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Intelligent Automation & Soft Computing 2023年第4期36卷 299-312页

作者： Abdullah A.Asiri Bilal Khan Fazal Muhammad Shams ur Rahman Hassan A.Alshamrani Khalaf A.Alshamrani Muhammad Irfan Fawaz F.Alqhtani Department of Radiological Sciences College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Computer Science City University of Science and Information TechnologyPeshawarPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science Engineering University of Engineering and TechnologyMardan23200Pakistan Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause *** technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of ***(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic *** diseases that cause death need to be identiﬁed through such techniques and technologies to overcome the mortality *** brain tumor is one of the most common causes of *** have already proposed various models for the classiﬁcation and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classiﬁcation process with improved efﬁ***,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving *** the Kaggle dataset,these strategies are tested using classiﬁcation accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation *** results show that SVM outperforms other algorithms,with 95.3%accuracy.

关键词： MRI images brain tumor machine learning-based classiﬁcation

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