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Novel 1-$\varphi$ High-Voltage Boosting Transformerless Inverter Topology with Optimal Power Components and Negligible Leakage Currents

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IEEE Transactions on Industry Applications 2023年第5期59卷 1-14页

作者： Chamarthi P.K. Muduli U.R. Moursi M.S.E. Al-Durra A. Khadkikar V. Hosani K.A. EL-Fouly T.H.M. Real-Time Control and Optimization Laboratory (RT-COOL) in Holcombe Department of Electrical and Computer Engineering Clemson University USA APEC Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

Inverter topologies for integrating a rooftop photovoltaic (PV) unit into a microgrid are becoming increasingly complex. This paper proposes a high-voltage boosting transformerless inverter (HVBTI) topology for enhancing such applications. The coupled inductor-based high voltage gain feature of the HVBTI configuration allows power to be delivered into the grid from a lower voltage PV source without using higher duties. In addition, HVBTI suppresses leakage current as the common connection shared between the

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terminal of the PV source and the grid neutral point. In comparison to existing topologies, the HVBTI topology uses a compact pulse width modulation strategy to control only six controllable switching devices. Again, using lower-rated switching devices is more cost-effective while increasing reliability and efficiency. In a laboratory prototype of a 1 kVA grid integrated system, the proposed HVBTI configuration is validated, and the maximum efficiency of the HVBTI is estimated at approximately 95%. Author

关键词： Boosting Inverter Inductors Inverters Leakage currents Low Switch Count Microgrid Rooftop Photovoltaics Switches Topology Transformers Voltage Voltage Gain

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Color-conversion displays:current status and future outlook

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Light(science & Applications) 2024年第12期13卷 2941-2961页

作者： Guijun Li Man-Chun Tseng Yu Chen Fion Sze-Yan Yeung Hangyu He Yuechu Cheng Junhu Cai Enguo Chen Hoi-Sing Kwok Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina National and Local United Engineering Laboratory of Flat Panel Display Technology College of Physics and Information EngineeringFuzhou UniversityCollege of Physics and Information Engineering 350108FuzhouChina Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou 350108China

The growing focus on enhancing color quality in liquid crystal displays(LCDs)and organic light-emitting diodes(OLEDs)has spurred significant advancements in color-conversion ***,color conversion is also important for the development and commercialization of *** article provides a comprehensive review of different types of color conversion methods as well as different types of color conversion *** summarize the current status of patterning process,and discuss key strategies to enhance display ***,we speculate on the future prospects and roles that color conversion will play in ultra-high-definition micro-and projection displays.

关键词： materials projection outlook

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Densely Convolutional BU-NET Framework for Breast Multi-Organ Cancer Nuclei Segmentation through Histopathological Slides and Classification Using Optimized Features

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computer Modeling in engineering & sciences 2024年第12期141卷 2375-2397页

作者： Amjad Rehman Muhammad Mujahid Robertas Damasevicius Faten S.Alamri Tanzila Saba Artificial Intelligence&Data Analytics Lab College of Computer&Information Sciences(CCIS)Prince Sultan UniversityRiyadh11586Saudi Arabia Centre of Real Time Computer Systems Kaunas University of TechnologyKaunasLT-51386Lithuania Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

This study aims to develop a computational pathology approach that can properly detect and distinguish histology *** is crucial for histopathological image analysis,as it involves segmenting cell ***,challenges exist,such as determining the boundary region of normal and deformed nuclei and identifying small,irregular nuclei *** learning approaches are currently dominant in digital pathology for nucleus recognition and classification,but their complex features limit their practical use in clinical *** existing studies have limited accuracy,significant processing costs,and a lack of resilience and generalizability across diverse *** proposed the densely convolutional Breast U-shaped Network(BU-NET)framework to overcome the mentioned *** study employs BU-NET’s spatial and channel attention methods to enhance segmentation *** inclusion of residual blocks and skip connections in the BU-NEt architecture enhances the process of extracting features and reconstructing the *** enhances the robustness of training and convergence processes by reducing the occurrence of vanishing *** primary objective of BU-NEt is to enhance the model’s capacity to acquire and analyze more intricate features,all the while preserving an efficient working *** BU-NET experiments demonstrate that the framework achieved 88.7%average accuracy,88.8%F1 score for Multi-Organ Nuclei Segmentation Challenge(MoNuSeg),and 91.2%average accuracy,91.8%average F1 for the triple-negative breast cancer(TNBC)*** framework also achieved 93.92 Area under the ROC Curve(AUC)for *** results demonstrated that the technology surpasses existing techniques in terms of accuracy and effectiveness in ***,it showcases the ability to withstand and recover from different tissue types and diseases,indicating possible uses in medical *** research evaluated the efficacy of the proposed method

关键词： Breast cancer histopathology BU-NET deep learning

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Decision making framework for heterogeneous QoS information: an application to cloud service selection

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2915-2934页

作者： Tiwari, Rohit Kumar Kumar, Rakesh Baranwal, Gaurav Buyya, Rajkumar Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur273010 India Department of Computer Science BHU Varanasi India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

In recent times, appropriate decision-making in challenging and critical situations has been very well supported by multicriteria decision-making (MCDM) methods. The technique for order of preference by similarity to ideal solution (TOPSIS) is the most widely used MCDM method for solving decision problems. However, it restricts decision-makers to use only one type of Quality of Service (QoS) information, and it suffers from the rank reversal problem. Restriction to only one type of QoS makes the decision problems more challenging, as it restricts the decision-makers freedom. Further, the rank reversal problem makes the decision result unreliable. To address these issues of TOPSIS, we have proposed a reliable rank reversal robust modular TOPSIS (RMo-TOPSIS). RMo-TOPSIS allows crisp, interval, fuzzy, intuitionistic and neutrosophic fuzzy QoS metrics. It does not suffer from the rank reversal problem. Cloud computing provides computing services on-demand basis without involving maintenance by its users. The availability of many cloud service providers and their services makes cloud service selection a challenging problem. To validate RMo-TOPSIS, we select the cloud service selection consisting of different types of QoS metrics as an application. Experiments on cloud service selection show consistency and accuracy in results obtained by RMo-TOPSIS and its robustness against rank reversal. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision making

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Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： Graph neural networks federated learning collaborative learning few-shot learning graph classification

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Wind farm combination forecasting model based on dynamic graph attention 8

Wind farm combination forecasting model based on dynamic gra...

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8th International Conference on Electronic Technology and information science, ICETIS 2023

作者： Liao, Xuechao Cheng, Yiqun School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Hubei Wuhan430065 China

ISBN: (纸本)9781510666535

In recent years, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance. © 2023 SPIE.

关键词： Convolution

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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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Simultaneous segmentation and classification of colon cancer polyp images using a dual branch multi-task learning network

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Mathematical Biosciences and engineering 2024年第2期21卷 2024-2049页

作者： Li, Chenqian Liu, Jun Tang, Jinshan School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China Department of Health Administration and Policy College of Public Health George Mason University FairfaxVA22030 United States

Accurate classification and segmentation of polyps are two important tasks in the diagnosis and treatment of colorectal cancers. Existing models perform segmentation and classification separately and do not fully make use of the correlation between the two tasks. Furthermore, polyps exhibit random regions and varying shapes and sizes, and they often share similar boundaries and backgrounds. However, existing models fail to consider these factors and thus are not robust because of their inherent limitations. To address these issues, we developed a multi-task network that performs both segmentation and classification simultaneously and can cope with the aforementioned factors effectively. Our proposed network possesses a dual-branch structure, comprising a transformer branch and a convolutional neural network (CNN) branch. This approach enhances local details within the global representation, improving both local feature awareness and global contextual understanding, thus contributing to the improved preservation of polyp-related information. Additionally, we have designed a feature interaction module (FIM) aimed at bridging the semantic gap between the two branches and facilitating the integration of diverse semantic information from both branches. This integration enables the full capture of global context information and local details related to polyps. To prevent the loss of edge detail information crucial for polyp identification, we have introduced a reverse attention boundary enhancement (RABE) module to gradually enhance edge structures and detailed information within polyp regions. Finally, we conducted extensive experiments on five publicly available datasets to evaluate the performance of our method in both polyp segmentation and classification tasks. The experimental results confirm that our proposed method outperforms other state-of-the-art methods. ©2024 the Author(s), licensee AIMS Press.

关键词： Classification (of information)

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Multi-Scale time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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A Study on Semi-Supervised Semantic Segmentation of Ship Flame Images 29

A Study on Semi-Supervised Semantic Segmentation of Ship Fla...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Wan, Junwei Chen, Yaojie Zheng, Xuecong Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350330717

In addressing the challenges of limited semantic labeling data and imprecise segmentation regions prevalent in the semantic segmentation of ship flame images, this study introduces a semi-supervised semantic segmentation method tailored for ship flame images. This approach is grounded in the principles of pairwise similarity with strong-weak perturbations. Adopting a pseudo-label self-learning strategy, the method employs weak-strong perturbation pairwise similarity, ensuring the propagation of high-confidence predictions during label generation, and thereby enhancing performance. Additionally, to tackle the inherent difficulties presented by smoke enveloping and obscuring flame regions in ship images, we have conceived a Vertical Continuity Feature Enhancement module. This module empowers the model to discern more explicit inter-class boundaries. Empirical evaluations on our proprietary dataset substantiate the superiority of our method, registering an improvement of 4.12 percentage points in mIoU over other semi-supervised approaches. Such results attest to the efficacy of the proposed pairwise similarity approach and the Vertical Continuity Feature Enhancement module in flame semantic segmentation. © 2023 IEEE.

关键词： Feature enhancemen Pairwise similarity Semantic segmentation Semi-Supervised learning

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