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Application of soft computing algorithms for hybrid modular multilevel inverters

Measurement: Sensors 2024年 31卷

作者： Gireesh Kumar, Devineni Venkata Sireesha, Nagineni Bhoopal, Neerudi Kumar Gatla, Ranjith Kotb, Hossam AboRas, Kareem M. Elrashidi, Ali Alqarni, Mohammed Yasin Ghadi, Yazeed Oubelaid, Adel Department of Electrical & Electronics Engineering B V Raju Institute of Technology Narsapur502313 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad500043 India Department of Electrical & Electronics Engineering Institute of Aeronautical Engineering Hyderabad500043 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria21544 Egypt Electrical Engineering Department University of Business and Technology Ar Rawdah Jeddah23435 Saudi Arabia Engineering Mathematics Department Faculty of Engineering Alexandria University Alexandria21544 Egypt Department of Computer Science and Software Engineering Al Ain University Abu Dhabi15322 United Arab Emirates Laboratoire de Technologie Industrielle et de L'Information Faculté de Technologie Université de Bejaia Bejaia06000 Algeria

This study utilized Harris Hawk optimization to analyze the harmonic distortions of a symmetric capacitor based multilevel converter. The proposed multilevel converter uses identical and symmetric DC sources in its input. The proposed topology for a multilevel converter uses fewer switches than typical cascaded designs. It can output 9-level outputs and can be scaled up to higher levels. The proposed multilevel converter can be improved by implementing a low frequency pulse width modulation technique to reduce the stress on the switches. The optimization of the switching angles using Harris Hawk was able to solve the non-linear aspects of the Selective Harmonic Elimination Pulse Width Modulation. The output voltage's THD was compared with that of other optimization methods, such as ant colony and particle swarm. The comparison shows that the output THD of the Harris Hawk optimizer is lower than that of other methods, which is about 5 %, according to the standards of IEEE-519. © 2023

关键词： Pulse width modulation

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Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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Improved Traffic Filtering for IoT Security 5

Improved Traffic Filtering for IoT Security

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Ovili, Henry Peter Okpor, Margaret Nwanze, N.E. Olayinka, Tosin Ojei, Emmanuel Nwankwo, Wilson Delta State University of Science and Technology Department of Information Systems & Technology Ozoro Nigeria Delta State University of Science and Technology Department of Cyber Security Ozoro Nigeria Delta State University of Science and Technology Faculty of Engineering Marine Engineering Department Ozoro Nigeria Wellspring University Department of Computing Benin City Nigeria Delta State University of Science and Technology Department of Software Engineering Ozoro Nigeria

ISBN: (纸本)9798331542559

This research presents an advanced traffic filtering technique using a Support Vector Machine (SVM) model to enhance IoT network security, achieving 85% accuracy, with high precision (87%) and recall (96%) in identifying legitimate traffic. However, the model struggles with neutral traffic, highlighting the need for further refinement, and offers a scalable solution for real-time dynamic analysis, with future work focusing on improving neutral traffic classification and integrating additional features to enhance performance. © 2024 IEEE.

关键词： Anomaly detection

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Economic and environmental analysis of a solar-powered ev charging system in india—a case study 2nd

Economic and environmental analysis of a solar-powered ev ch...

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2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shrivastava, Prashant Soon, Teykok Mekhilef, Saad Ahmad, Furkan Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Power Electronics and Renewable Energy Laboratory Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia School of Software and Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Indian Institute of Technology New Delhi Delhi110016 India

ISBN: (纸本)9789811607486

With the growing environmental concern, the demand for electric vehicle is increasing in India. However, the limited existing charging facilities slow down the rate of adoption in the market. Presently, the solar-based charging system is gaining popularity, due to its low greenhouse gas emission. In this study, a case study for different solar charging systems suited in Delhi is presented. The economic and environmental analysis of different solar-based EV charging systems utilized for charging three types of commercial EVs such as 2, 3, and 4-W is explained. The cost of energy required for 100 km drive is lower with vehicle integrating PV charging system;however, it can act as a supportive system for driving range improvement. The analysis shows that the economically solar rooftop charging system is suitable for all types of EVs. The CO2 emission produced by all types of solar-based EV charging systems is lower than coal-based charging systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Solar energy

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification

A Novel Attention-based Explainable Deep Learning Framework ...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Gladys W. Muoka Ding Yi Chiagoziem Chima Ukwuoma Moindi Duke Martin Ahmet Arif Aydin Mugahed A. Al-Antari School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China Department of Computer Science Unicaf University Lilongwe Lilongwe Malawi Department of Computer Engineering Faculty of Engineering İnönü University Turkey Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul Korea

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study’s primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics.

关键词：

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Classification Algorithms with a Complex Structure in Pattern Recognition

Classification Algorithms with a Complex Structure in Patter...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Akram Kh. Nishanov Oybek Allamov Askar T. Rakhmanov Janar Yusupova Ortik B. Ruzibaev Department of Systematic and Practical Programming Technical Sciences Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al Khwarizmi Urgench Uzbekistan Department of systematic and practical programming Tashkent university of information technologies named after muhammad al-Khwarizmi Tashkent Uzbekistan Faculty of Software Engineering Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The article considers in the intellectual processing of information, moving from a large unit of measurement to a smaller, more important unit of measurement, selecting sets of informative symbols and classifying symbols, developing modern recognition systems that help specialists in the field, as well as evaluating the states of objects to be classified, developing a classification algorithm for multi-class cases of educational samples. The development of a multi-class algorithm for symbol classification serves to develop the theory of symbol recognition. It deals with the construction of a decision rule construction algorithm. Its main purpose is to determine which of the given classes the recognized object belongs to. The complexity or simplicity of the proposed algorithm depends on the number of classes participating in the study sample. If the number of classes is two, then the formulation of decision rules is simple. That is why the classification algorithms proposed by most authors are solved for cases where the number of classes is three or more, the decisive rule in the matter of classification of objects is solved by making two classes. The construction of a multi-class decision rule for classification, which takes into account all classes at the same time, is a complex and urgent issue. In the article, in the classification of objects, a multi-level complex structured decision rule was developed based on special inequalities, and two-, three- and multi-class classification algorithms were developed.

关键词： Training Productivity Measurement units Atmospheric measurements Symbols Particle measurements Classification algorithms

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Algorithmic Cluster Expansions for Quantum Problems

arXiv

引用

arXiv 2023年

作者： Mann, Ryan L. Minko, Romy M. Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney SydneyNSW2007 Australia School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

We establish a general framework for developing approximation algorithms for a class of counting problems. Our framework is based on the cluster expansion of abstract polymer models formalism of Kotecký and Preiss. We apply our framework to obtain efficient algorithms for (1) approximating probability amplitudes of a class of quantum circuits close to the identity, (2) approximating expectation values of a class of quantum circuits with operators close to the identity, (3) approximating partition functions of a class of quantum spin systems at high temperature, and (4) approximating thermal expectation values of a class of quantum spin systems at high temperature with positive-semidefinite operators. Further, we obtain hardness of approximation results for approximating probability amplitudes of quantum circuits and partition functions of quantum spin systems. This establishes a computational complexity transition for these problems and shows that our algorithmic conditions are optimal under complexity-theoretic assumptions. Finally, we show that our algorithmic condition is almost optimal for expectation values and optimal for thermal expectation values in the sense of zero freeness. Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

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Fast target-aware learning for few-shot video object segmentation

引用

Science China(information Sciences) 2022年第8期65卷 71-86页

作者： Yadang CHEN Chuanyan HAO Zhi-Xin YANG Enhua WU Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and Technology School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of Macau State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of Sciences Faculty of Science and Technology University of Macau

Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video *** proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained.

关键词： video object segmentation few-shot target-aware meta knowledge bilevel-optimization

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Systematizing the Meta-Analytical Process in software engineering 21

Systematizing the Meta-Analytical Process in Software Engine...

引用

Proceedings of the 2021 European Symposium on software engineering

作者： Letizia Jaccheri Zamira Kholmatova Giancarlo Succi Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer Science Lab for Industrializing the Production of Software Innopolis University Russia

ISBN: (纸本)9781450385060

The generalization of knowledge is a necessary part of every scientific field. Meta-analysis is already advocated as a tool for generalization in different areas such as medicine, psychology, business, and this process is already standardized for them. software engineering started using meta-analysis as a tool for aggregating results from families of experiments, but not so long for generalization of results coming from different studies, and for this purpose, the meta-analytical approach is not yet clarified. In this paper, we attempt to systematize the application of meta-analysis as a secondary study to the software engineering field suggesting our preliminary protocol. To see the reliability of the proposed protocol we conducted several studies using it. Following even uniform protocol with these studies, we identified the issues preventing the wide usage of meta-analysis in software engineering and proposed our solutions for them.

关键词： Meta-analysis in software engineering Evidence-based software engineering Research synthesis in software engineering

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Personal Trajectory Verification Based on Blockchain and Zero-knowledge Proof 2

Personal Trajectory Verification Based on Blockchain and Zer...

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2nd International Conference on Computer Science and Management technology, ICCSMT 2021

作者： Lin, Shaofu Li, Yunhui Chen, Huamin Li, Jingwen Jia, Xiaofeng College of Software Engineering Beijing University of Technology Faculty of Information Technology Beijing China Chinatelecom Research Institute China Telecom Co. Ltd Beijing China Beijing Bigdata Centre Beijing China

ISBN: (纸本)9781665420631

With the development of location-based services based on smart terminals and positioning technologies, location forgery and privacy leakage become an important issue, making it difficult for the existing methods of proof of location to guarantee the accuracy, authenticity and privacy of user trajectory information. By using the decentralized, non-tamperable and traceable characteristics of blockchain and combining with zero-knowledge proof method, this paper proposes a personal trajectory verification algorithm to provide a solution to solve the limitation of traditional location proof method for trajectory verification problems. © 2021 IEEE.

关键词： Location based services

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