检索结果-内蒙古大学图书馆

Intermittent Fault Diagnosis of Split-Star Networks and its Applications

IEEE Transactions on Parallel and Distributed Systems 2023年第4期34卷 1253-1264页

作者： Song, Jiankang Lin, Limei Huang, Yanze Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou Fujian350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou Fujian350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid increase of the number of processors in multiprocessor systems and the fast expansion of interconnection networks, the reliability of interconnection network is facing severe challenges, where the fast recognition of fault processors is crucial. In practice, most of the processor failures are intermittent faults. In this article, we first determine the intermittent fault diagnosability t I PMC(S n 2) tIPMC(Sn2) of n-dimensional split-star network Sn2 under the PMC model. In addition, we propose a fast intermittent fault probabilistic diagnosis algorithm FIFPDPMC to identify the nodes with intermittent fault in the n-dimensional split-star network Sn2 under the PMC model, and we calculated the time complexity of the algorithm FIFPDPMC. Then we implement the algorithm FIFPDPMC in the IoT-based wireless sensor network (IoTWSN) and a randomly generated network (RGN) under different number of nodes with intermittent fault, and we evaluate the performance and efficiency of the algorithm FIFPDPMC in terms of accuracy, precision, recall (TPR), F1, G-mean, FPR, TNR and FNR. Experimental results show that, as the number of stages of executing the algorithm FIFPDPMC increases, the number of nodes with intermittent fault being diagnosed by the algorithm FIFPDPMC increases, which implies that the algorithm FIFPDPMC has good performance and efficiency in both IoTWSN and RGN. © 1990-2012 IEEE.

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control

Cooperative Multi-Agent Deep Reinforcement Learning for Adap...

引用

2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Zhang, Ying Yue, Meng Montana State University Department of Electrical and Computer Engineering BozemanMT United States Brookhaven National Laboratory Interdisciplinary Science Department UptonNY United States

ISBN: (纸本)9798350313604

Under voltage load shedding (UVLS) for power grid emergency control builds the last defensive perimeter to prevent cascade outages and blackouts in case of contingencies. This letter proposes a novel cooperative multi-Agent deep reinforcement learning (MADRL)-based UVLS algorithm in an adaptive decentralized way. With well-designed input signals reflecting the voltage deviation, newly structured neural networks are developed as intelligent agents to obtain control actions and their probabilities to accommodate high uncertainties in volatile power system operations. Moreover, the interaction among the agents for coordinated control is implemented and refined by a state-of-The-Art attention mechanism, which helps agents concentratively learn effective interacted information. The proposed method realizes decentralized coordinated control, adapting to extremely high uncertainties. Case studies on an IEEE benchmark system indicate the superior performance of the proposed algorithm. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenhang Jin, Yujian Liang, Jiayi Yang, YuHan Nie, Shanling Hai, Yang East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Sydney Australia

ISBN: (纸本)9798350337488

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer. © 2023 IEEE.

关键词： 3D medical image attention CT Liver cancer Segmentation transformer U-Net

来源：评论

学校读者我要写书评

暂无评论

Single-shot volumetric fluorescence imaging with neural fields

引用

Advanced Photonics 2025年第2期7卷 92-103页

作者： Oumeng Zhang Haowen Zhou Brandon Y.Feng Elin M.Larsson Reinaldo E.Alcalde Siyuan Yin Catherine Deng Changhuei Yang California Institute of Technology Department of Electrical Engineering Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory California Institute of Technology Division of Biology and Biological Engineering California Institute of Technology Department of Medical Engineering

Single-shot volumetric fluorescence（SVF） imaging offers a significant advantage over traditional imaging methods that require scanning across multiple axial planes, as it can capture biological processes with high temporal resolution. The key challenges in SVF imaging include requiring sparsity constraints, eliminating depth ambiguity in the reconstruction, and maintaining high resolution across a large field of view. We introduce the QuadraPol point spread function（PSF） combined with neural fields, an approach for SVF imaging. This method utilizes a custom polarizer at the back focal plane and a polarization camera to detect fluorescence, effectively encoding the three-dimensional scene within a compact PSF without depth ambiguity. In addition, we propose a reconstruction algorithm based on the neural field technique that provides improved reconstruction quality compared with classical deconvolution methods. QuadraPol PSF, combined with neural fields, significantly reduces the acquisition time of a conventional fluorescence microscope by ～20 times and captures a 100-mm3cubic volume in one shot. We validate the effectiveness of both our hardware and algorithm through all-in-focus imaging of bacterial colonies on sand surfaces and visualization of plant root morphology. Our approach offers a powerful tool for advancing biological research and ecological studies.

关键词： Point spread functions Polarization Fluorescence imaging Reconstruction algorithms Biological imaging Imaging systems Fluorescence Polarizers Cameras Image restoration

来源：评论

学校读者我要写书评

暂无评论

Overvoltages and Critical Backflashover Current of a 150 kV Overhead Line: Influence of Lightning Current Waveform and Tower Grounding System Modeling 24

Overvoltages and Critical Backflashover Current of a 150 kV ...

引用

24th EEEIC International Conference on Environment and Electrical engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Patsalis, Diamantis G. Datsios, Zacharias G. Stracqualursi, Erika Araneo, Rodolfo Mikropoulos, Pantelis N. Tsovilis, Thomas E. School of Electrical & Computer Engineering Aristotle University of Thessaloniki High Voltage Laboratory Thessaloniki Greece University of Rome 'Sapienza' Department of Astronautical Electrical and Energy Engineering Rome Italy

ISBN: (纸本)9798350355185

This study investigates lightning current waveform effects on the fast-front overvoltages and critical currents that cause backflashover of the insulation of a 150 kV overhead line by means of ATP-EMTP simulations, considering frequency- and current-dependent effects of tower grounding systems. The CIGRE waveform is used to simulate negative first return-strokes. Several cases are investigated by employing front time, maximum steepness, and time to half value statistical distributions. Various approaches to model concentrated and extended ground electrode configurations are applied. The first one uses a resistor with a fixed resistance. The second approach considers the frequency-dependent (FD) response of the grounding system with fixed electrical properties of soil. The third approach the FD response with soil properties varying with frequency. The fourth is a soil ionization model. A hybrid method is used to yield the tower grounding systems FD responses. This method combines electromagnetic field analysis with circuit theory. The modeling approach of the grounding system influences more the critical backflashover current for shorter and steeper wavefronts, as well as longer wavetails. The impact of lightning current waveform parameters is quantified and discussed for the 150 kV line under investigation. © 2024 IEEE.

关键词： Overhead lines

来源：评论

学校读者我要写书评

暂无评论

SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation

arXiv

引用

arXiv 2024年

作者： Peng, Shi-Feng Sun, Guolei Li, Yong Wang, Hongsong Xie, Guo-Sen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Computer Vision Laboratory ETH Zurich Switzerland School of Computer Science and Engineering Southeast University Nanjing China

The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain disparity between the training and inference phases, which can exist in either the input data or the target classes. Previous models struggle to learn feature representations that generalize to various unknown domains from limited training domain samples. In contrast, the large-scale visual model SAM, pre-trained on tens of millions of images from various domains and classes, possesses excellent generalizability. In this work, we propose a SAM-aware graph prompt reasoning network (GPRN) that fully leverages SAM to guide CD-FSS feature representation learning and improve prediction accuracy. Specifically, we propose a SAM-aware prompt initialization module (SPI) to transform the masks generated by SAM into visual prompts enriched with high-level semantic information. Since SAM tends to divide an object into many sub-regions, this may lead to visual prompts representing the same semantic object having inconsistent or fragmented features. We further propose a graph prompt reasoning (GPR) module that constructs a graph among visual prompts to reason about their interrelationships and enable each visual prompt to aggregate information from similar prompts, thus achieving global semantic consistency. Subsequently, each visual prompt embeds its semantic information into the corresponding mask region to assist in feature representation learning. To refine the segmentation mask during testing, we also design a non-parameter adaptive point selection module (APS) to select representative point prompts from query predictions and feed them back to SAM to refine inaccurate segmentation results. Experiments on four standard CD-FSS datasets demonstrate that our method establishes new state-of-the-art results. Code: https://***/CVL-hub/GPRN. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

A Composite Decomposition Method for Large-Scale Global Optimization

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6304-6319页

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China East China University of Science and Technology Engineering Research Center of Process System Engineering Ministry of Education Shanghai200237 China Westlake University School of Engineering Hangzhou310030 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74048 United States

Cooperative coevolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of nonadditively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups nonseparable variables by recursively considering the interactions between each nonseparable variable and the formed nonseparable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a nonseparable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group nonseparable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG-series designs. © 2024 IEEE.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Modeling Statistical Delay, Error-Rate, and Joint-Delay/Error-Rate QoS-Exponents Over M-MIMO Mobile Wireless Networks Using FBC

Modeling Statistical Delay, Error-Rate, and Joint-Delay/Erro...

引用

2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Zhang, Xi Wang, Jingqing Poor, H. Vincent Texas A&m University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

ISBN: (纸本)9781665475549

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing massive multiple-input and multiple-output (m-MIMO) mobile wireless network architectures for massive ultra-reliable and low-latency communications (mURLLC). One of the major design issues raised by mURLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying m-MIMO based wireless fading channels. To efficiently accommodate statistical QoS for mURLLC traffic, it is crucial to model and investigate m-MIMO based wireless fading channels' stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, etc., in the finite blocklength regime. However, how to rigorously and efficiently characterize the stochastic dynamics of m-MIMO mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither fundamentally understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling techniques and frameworks for statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish system models using FBC. Second, we develop a set of new statistical delay and error-rate bounded QoS metrics including delay, error-rate, and joint-delay/error-rate QoS-exponents, and the corresponding ?-effective capacities. Finally, our simulations validate and evaluate our developed modeling schemes for statistical QoS to support 6G mURLLC. © 2023 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Performance Assessment of an Electrically Reconfigurable Photovoltaic Array in an Off-Grid System 6th

Performance Assessment of an Electrically Reconfigurable Pho...

引用

6th International on Clean Energy and Electrical Systems, CEES 2024

作者： Chan, Aaron Keith Y. Macabebe, Erees Queen B. Ateneo Energy Research Laboratory Department of Electronics School of Science and Engineering Computer and Communications Engineering Ateneo de Manila University Loyola Heights Quezon City1108 Philippines

ISBN: (纸本)9789819757749

Partial shading of photovoltaic (PV) modules reduces the output power of a PV system. Electrically reconfigurable arrays have been proposed to increase the power output of shaded PV systems. In this paper, an electrically reconfigurable PV array was built using the total cross-tied (TCT) topology. The PV array was then tested manually using different shading patterns. Furthermore, the PV array was deployed and allowed to autonomously reconfigure in an environment where shading due to clouds and foliage is expected. Results from the I-V and P-V curves of the PV array show that the power improvements expected of the array after reconfiguration were met. However, when connected to the balance of system components, improvements in the power output were not met. During pilot deployment, the speed of changing shading patterns also affected the outcome. Thus, when designing a PV system that incorporates a reconfiguration of PV modules, the following need to be considered: first, identify the potential causes of partial shading at the target location, which include the expected shading patterns and speed of changing patterns, and second, investigate the effects of the balance of system components, such as component efficiencies, speed of maximum power point tracking, and system type. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Off-grid Photovoltaic System Partial Shading Solar PV Array Reconfiguration

来源：评论

学校读者我要写书评

暂无评论

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

引用

computers, Materials & Continua 2023年第4期75卷 2119-2135页

作者： Jie Yin Yuxuan Shi Wen Deng Chang Yin Tiannan Wang Yuchen Song Tianyao Li Yicheng Li Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina

In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT devices exposes them to more serious security concerns. First, aconvolutional neural network intrusion detection system for IoT devices isproposed. After cleaning and preprocessing the NSL-KDD dataset, this paperuses feature engineering methods to select appropriate features. Then, basedon the combination of DCNN and machine learning, this paper designs acloud-based loss function, which adopts a regularization method to preventoverfitting. The model consists of one input layer, two convolutional layers,two pooling layers and three fully connected layers and one output ***, a framework that can fully consider the user’s privacy protection isproposed. The framework can only exchange model parameters or intermediateresults without exchanging local individuals or sample data. This paperfurther builds a global model based on virtual fusion data, so as to achievea balance between data privacy protection and data sharing computing. Theperformance indicators such as accuracy, precision, recall, F1 score, and AUCof the model are verified by simulation. The results show that the model ishelpful in solving the problem that the IoT intrusion detection system cannotachieve high precision and low cost at the same time.

关键词： Internet of things intrusion detection system convolutional neural network federated learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：