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Comprehensive analysis of faults and diagnosis techniques in cascaded multi-level inverters

Heliyon 2024年第21期10卷 e39901页

作者： Gatla, Ranjith Kumar Kumar, Devineni Gireesh Shashavali, Palthur Dsnm, Rao Kotb, Hossam Alkuhayli, Abdulaziz Ghadi, Yazeed Yasin Mbasso, Wulfran Fendzi Department of Electrical & Electronics Engineering Institute of Aeronautical Engineering Telangana Dundigal 500043 India Department of Electrical & Electronics Engineering B V Raju Institute of Technology Telangana Narsapur 502313 India Department of EEE S K University College of Engineering & Technology Andhra Pradesh Ananthapuramu 515003 India Department of EEE Gokaraju Rangaraju Institute of Engineering & Technology Telangana Bachupally 500090 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria 21544 Egypt Electrical Engineering Department College of Engineering King Saud University Riyadh 11421 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 15322 United Arab Emirates Laboratory of Technology and Applied Sciences University Institute of Technology University of Douala PO Box: 8698 Douala Cameroon

Reliability is a crucial factor to consider for multi-level inverters (MLIs) used in industrial applications. With the increasing number of power semiconductor devices, the potential for defects to significantly degrade the overall system is heightened. A highly effective fault-detection technique is required to minimize the impact of faults. This paper provides a comprehensive overview of the fundamental principles of multi-level inverters and the various sorts of faults that can occur in multi-level inverters. This study provides a comprehensive analysis of five-level cascaded H-bridge multilevel inverters (MLIs) under both normal and defective conditions. The paper outlines a fault-detection method that utilizes total harmonic distortion and a normalized output voltage factor. In addition, the paper discusses a fault-isolation strategy that relies on reducing amplitude modulation. This method leads to the development of a fault-tolerant inverter. The utilization of level-shifted pulse-width modulation (LSPWM) technology is employed for the purpose of switching operations. LSPWM is the most appropriate technique for MLIs that require a low amount of computational resources. The fault-diagnosis approach given is suitable for MLI-based drives, grid-connected operations, and other applications. This paper presents a comprehensive examination of the 5L-CMLI (5-Level Cascaded Multi-Level Inverter) under various fault scenarios in CMLI. Subsequently, various fault diagnosis approaches will be examined, including their advantages and disadvantages. The paper discusses several defects that can occur in the Insulated Gate Bipolar Transistor (IGBT) of a Current Mode Logic Inverter (CMLI), and also presents a design for a reliable fault diagnosis system. Furthermore, this analysis examines several fault detection strategies in CMLI, categorized according to open-loop and closed-loop dynamic systems fault classifications. © 2024 The Authors

关键词： Fault detection Multi-level inverters Open-loop and closed-loop systems Total harmonic distortion

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Embedding 3D models in offline physical environments

Embedding 3D models in offline physical environments

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作者： Ertugrul, Egemen Zhang, Han Zhu, Fang Lu, Ping Li, Ping Sheng, Bin Wu, Enhua Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space-efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user-friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low-cost autonomous robots, and 3D printing. © 2020 John Wiley & Sons, Ltd.

关键词： Digital storage

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I3CL: Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection

arXiv

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arXiv 2021年

作者： Du, Bo Ye, Jian Zhang, Jing Liu, Juhua Tao, Dacheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Computer Science Faculty of Engineering The University of Sydney Sydney Australia Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University Wuhan China Jd Explore Academy China

Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i.e., 1) fracture detections at the gaps in a text instance;and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context. To address these issues, we propose a novel method named Intraand Inter-Instance Collaborative Learning (I3CL). Specifically, to address the first issue, we design an effective convolutional module with multiple receptive fields, which is able to collaboratively learn better character and gap feature representations at local and long ranges inside a text instance. To address the second issue, we devise an instance-based transformer module to exploit the dependencies between different text instances and a global context module to exploit the semantic context from the shared background, which are able to collaboratively learn more discriminative text feature representation. In this way, I3CL can effectively exploit the intra- and inter-instance dependencies together in a unified end-to-end trainable framework. Besides, to make full use of the unlabeled data, we design an effective semi-supervised learning method to leverage the pseudo labels via an ensemble strategy. Without bells and whistles, experimental results show that the proposed I3CL sets new state-of-the-art results on three challenging public benchmarks, i.e., an F-measure of 77.5% on ArT, 86.9% on Total-Text, and 86.4% on CTW-1500. Notably, our I3CL with the ResNeSt-101 backbone ranked the 1stplace on the ArT leaderboard. Code is available at ***/ViTAETransformer/ViTAE-Transformer-Scene-Text-Detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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A Summary Evaluation Method Combining Linguistic Quality and Semantic Similarity

A Summary Evaluation Method Combining Linguistic Quality and...

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2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Wang, Xingwen Liu, Bo Shen, Libin Li, Yong Gu, Rentao Qu, Guangzhi Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing Laboratory of Advanced Information Networks Beijing100876 China Oakland University Computer Science and Engineering Dept. RochesterMI48309 United States

ISBN: (纸本)9781728176246

Summary evaluation method is crucial to promote the development of summarization technologies. However, most of the existing summary evaluation methods seldom consider the content integrity and readability of summaries simultaneously. This paper proposed a Linguistic Quality and Semantic Similarity Model (LQSSM) to evaluate generated summaries more comprehensively. Considering the readability of summaries, a linguistic quality evaluation network (LQEN) is proposed to evaluate summaries automatically. Meanwhile, a semantic similarity evaluation network (SSEN) is introduced to directly measure the informativeness between the summary and original . Additionally, there is also a comprehensive evaluation using fusion indicators. The LQSSM and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) methods were used to score the summaries generated by five models. The results showed that the proposed method gave further feedback from different angles on the basis of reaching the level of other methods. © 2020 IEEE.

关键词： Semantics

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Indoor Air Quality (IAQ) Sensing Device Prototype: software-Hardware Architecture and Performance

Indoor Air Quality (IAQ) Sensing Device Prototype: Software-...

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International Conference on Green engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019

作者： Nor Azhar, A. Rahmah, M. Roslina, A.H. Fauziah, Z. Department of Computer Science Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia Department of Graphic and Multimedia Technology Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia Department of Software Engineering Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia

In a green environment, air quality is among the prominent aspects to be considered to prevent pollution and in turn maintain safe air. Sick Building Syndrome (SBS) and Building Related Illness (BRI) are the problems that must be encountered by building occupants. Existing devices are quite expensive, and more attention is needed on monitoring air quality. Therefore, this paper discusses a software-hardware architecture proposal to overcome the existing IAQ data retrieval performance problem. The objective of this work is to analyse the most recent IAQ sensing device developed by the researcher, to evaluate the performance attribute, and to implement the software-hardware architecture based on the Performance-Driven software Architecture Refactoring (PDSAR) method. In order to assess the overall performance of the architecture proposed, the prototype of the Indoor Air Quality (IAQ) sensing device was developed. The testing was performed during a period of six months. The results demonstrate that the average performance for 540 readings is 76.67%. This shows that the architecture can be accepted as a generic IAQ sensing device development structure. The device act as an alternative, cheaper and safer device which can be employed by all building owners in the future. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Air quality

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通用最优轨迹规划——基于最小作用量原理实现自动驾驶（英文）

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engineering 2024年第2期 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua University Department of Cognitive Robotics Faculty of Mechanical Maritime and Materials Engineering (3mE) Delft University of Technology Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Xi'an Institute of High-Technology

This study presents a general optimal trajectory planning(GOTP) framework for autonomous vehicles(AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers' behavior and summarizing their manipulation characteristics of "seeking benefits and avoiding losses." Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework's feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multisource interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers' manipulation.

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From sensing to energy savings: A comprehensive survey on integrating emerging technologies for energy efficiency in WBANs

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Digital Communications and Networks 2024年

作者： Shumaila Javaid Hamza Fahim Sherali Zeadally Bin He Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai 201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai 201210 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai 201210 China Department of Computer Science Faculty of Computer Sciences Ilma University Karachi 75190 Pakistan College of Communication and Information University of Kentucky Lexington KY USA Academy of Computer Science & Software Engineering University of Johannesburg Johannesburg South Africa

Energy is essential for human existence, and its high consumption is a growing concern in today's technology-driven society. Global initiatives aim to reduce energy consumption and pollution by developing and deploying energy-efficient sensing technologies for long-term monitoring, control, automation, security, and interactions. Wireless Body Area Networks (WBANs) benefit a lot from the continuous monitoring capabilities of these sensing devices, which include medical sensors worn on or implanted in the human body for healthcare monitoring. Despite significant advancements, achieving energy efficiency in WBANs remains a significant challenge. A deep understanding of the WBAN architecture is essential to identify the causes of its energy inefficiency and develop novel energy-efficient solutions. We investigate energy efficiency issues specific to WBANs. We discuss the transformative impact that artificial intelligence and Machine Learning (ML) can have on achieving the energy efficiency of WBANs. Additionally, we explore the potential of emerging technologies such as quantum computing, nano-technology, biocompatible energy harvesting, and Simultaneous Wireless Information and Power Transfer (SWIPT) in enabling energy efficiency in WBANs. We focus on WBANs' architecture, hardware, and software components to identify key factors responsible for energy consumption in the WBAN environment. Based on our comprehensive review, we introduce an innovative, energy-efficient three-tier architecture for WBANs that employs ML and edge computing to overcome the limitations inherent in existing energy-efficient solutions. Finally, we summarize the lessons learned and highlight future research directions that will enable the development of energy-efficient solutions for WBANs.

关键词： Artificial intelligence Energy Healthcare Sensors Wireless body area networks

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Spatio-temporal graph neural network based child action recognition using data-efficient methods: A systematic analysis

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computer Vision and Image Understanding 2025年

作者： Sanka Mohottala Asiri Gawesha Dhrashana Karshutrirathna Pradeepa Samarasinghe Charith Abhayaratne Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Science Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka School of Electrical and Electronic Engineering The University of Sheffield United Kingdom

This paper presents implementations on child activity recognition (CAR) using spatial–temporal graph neural network (ST-GNN)-based deep learning models with the skeleton modality. Prior implementations in this domain have predominantly utilized CNN, LSTM, and other methods, despite the superior performance potential of graph neural networks. To the best of our knowledge, this study is the first to use an ST-GNN model for child activity recognition employing both in-the-lab, in-the-wild, and in-the-deployment skeleton data. To overcome the challenges posed by small publicly available child action datasets, transfer learning methods such as feature extraction and fine-tuning were applied to enhance model performance. As a principal contribution, we developed an ST-GNN-based skeleton modality model that, despite using a relatively small child action dataset, achieved superior performance (94.81%) compared to implementations trained on a significantly larger (x10) adult action dataset (90.6%) for a similar subset of actions. With ST-GCN-based feature extraction and fine-tuning methods, accuracy improved by 10%–40% compared to vanilla implementations, achieving a maximum accuracy of 94.81%. Additionally, implementations with other ST-GNN models demonstrated further accuracy improvements of 15%–45% over the ST-GCN baseline. The results on activity datasets empirically demonstrate that class diversity, dataset size, and careful selection of pre-training datasets significantly enhance accuracy. In-the-wild and in-the-deployment implementations confirm the real-world applicability of above approaches, with the ST-GNN model achieving 11 FPS on streaming data. Finally, preliminary evidence on the impact of graph expressivity and graph rewiring on accuracy of small dataset-based models is provided, outlining potential directions for future research.

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer’s Disease

arXiv

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arXiv 2023年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China The Department of Systems and Enterprises Stevens Institute of Technology United States School of Science and Technology Hong Kong Metropolitan University Hong Kong College of Engineering and Computer Science VinUni-Illinois Smart Health Center VinUniversity Viet Nam Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Game Design Faculty of Arts Uppsala University Sweden The School of Software Tiangong University Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer’s disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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An Integrated Model for Evaluation of Big Data Interactive Systems Under Uncertainty

Research Square

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Research Square 2021年

作者： Asemi, Adeleh Asemi, Asefeh Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Doctoral School of Economic Business & Informatics Corvinus University of Budapest Fovam ter 8. Budapest1093 Hungary

The study aimed to propose a judgment-based evaluation model for usability evaluating of big data interactive system s. Human judgment is associated with uncertainty and gray information. We used the fuzzy technique for integration, summarization, and distance calculation of quality value judgment. The proposed model is an integrated fuzzy Multi Factors Evaluation (MFE) model based on experts’ judgments in HCI, ISPD, and AMLMs. We provided a Fuzzy Inference System (FIS) for scoring usability evaluation metrics in different big data interactive system s. A multi-model big data interactive system is implemented for experimental testing of the model. The achieved results from the proposed model and experimental tests are compared using statistical correlation tests. The results show the ability of the proposed model for usability evaluation of big data interactive systems without the need for conducting empirical tests. It is concluded that applying a dataset in a neuro-FIS and training system cause to produce more than a hundred effective rules. The findings indicate that the proposed model can be applied for big data interactive system evaluation, informative evaluation, and complex empirical tests. Future studies may improve the FIS with the integration of artificial neural networks. © 2021, CC BY.

关键词： Fuzzy systems

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