检索结果-内蒙古大学图书馆

BEVGGC:Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images

computer Modeling in Engineering & Sciences 2021年第11期129卷 729-753页

作者： Junding Sun Xiang Li Chaosheng Tang Shixin Chen College of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China Nursing Department The Fourth People’s Hospital of Huai’anHuai’an223001China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou215000China

Purpose:As to January 11,2021,coronavirus disease(COVID-19)has caused more than 2 million deaths *** diagnostic methods of COVID-19 are:(i)nucleic acid *** method requires high requirements on the sample testing *** collecting samples,staff are in a susceptible environment,which increases the risk of infection.(ii)chest computed *** cost of it is high and some radiation in the scan process.(iii)chest X-ray *** has the advantages of fast imaging,higher spatial recognition than chest computed ***,our team chose the chest X-ray images as the experimental dataset in this ***:We proposed a novel framework—BEVGG and three methods(BEVGGC-I,BEVGGC-II,and BEVGGC-III)to diagnose COVID-19 via chest X-ray ***,we used biogeography-based optimization to optimize the values of hyperparameters of the convolutional neural ***:The experimental results show that the OA of our proposed three methods are 97.65%±0.65%,94.49%±0.22%and 94.81%±0.52%.BEVGGC-I has the best performance of all ***:The OA of BEVGGC-I is 9.59%±1.04%higher than that of state-of-the-art methods.

关键词： Biogeography-based optimization convolutional neural networks depthwise separable convolution dilated

来源：评论

学校读者我要写书评

暂无评论

An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations 38

An Expectation-Maximization Algorithm for Training Clean Dif...

引用

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Bai, Weimin Wang, Yifei Chen, Wenzheng Sun, He Academy for Advanced Interdisciplinary Studies Peking University China College of Future Technology Peking University China National Biomedical Imaging Center Peking University China Yuanpei College Peking University China Wangxuan Institue of Computer Technology Peking University China State Key Laboratory of Multimedia Information Processing Peking University Beijing China

Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, clean datasets for training limits their practical use where clean data is scarce. In this paper, we propose EMDiffusion, an expectation-maximization (EM) approach to train diffusion models from corrupted observations. Our method alternates between reconstructing clean images from corrupted data using a known diffusion model (E-step) and refining diffusion model weights based on these reconstructions (M-step). This iterative process leads the learned diffusion model to gradually converge to a local optimum, that is, to approximate the true clean data distribution. We validate our method through extensive experiments on diverse computational imaging tasks, including random inpainting, denoising, and deblurring, achieving new state-of-the-art performance. The code is available at https://***/ai4imaging/EMDiffusoin. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evidential Open Set Recognition for Imbalanced Medical Images via Multi-level Data Augmentation

Evidential Open Set Recognition for Imbalanced Medical Image...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rui-Wei Zhao Yiqian Xu Ying Zhang Rui Feng Academy for Engineering and Technology Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University College of Information Engineering Shanghai Maritime University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Due to the exstence of common and rare diseases, the complex clinical scenario often poses the challenging class imbalanced open set recognition problem. Unfortunately, most existing approaches are ill-suited for such situations with limited and imbalanced data during training and the possibility of encountering unseen classes during test. In this work, we propose a novel Multi-level mixup-based Evidential Open Set Recognition (ME-OSR) approach to more explicitly and effectively address the open set recognition for class-imbalanced medical images. Briefly, we first extract disentangled discriminative and background image features. Then, based on the original images and extracted features, we propose to sample and conduct Multi-level Open Mixup (MOM) for a more balanced open set data augmentation. It includes extensive intra- and interclass mixup operations in both image and feature spaces, which can augment rare classes with different feature combinations and generate potential pseudo-unknown class examples in the open set to boost the model training. Based on the augmented data and their extracted discriminative features, we propose a Regularized Evidential Deep Learning (REDL) classifier to work with the augmented data to achieve open set recognition with prediction uncertainty estimation and unknown example rejection. Through comparative experiments and ablation studies on several representative medical datasets, we showed that our proposed method outperforms other state-of-the-arts on four popular medical OSR datasets.

关键词： Training Deep learning Image recognition Uncertainty Image analysis Feature extraction Data augmentation Data mining Method of moments Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Knowledge Concept Recommendation Model for MOOCs with Local Sub-graph Embedding

Knowledge Concept Recommendation Model for MOOCs with Local ...

引用

2022 International Conference on Automation, Robotics and computer Engineering, ICARCE 2022

作者： Ju, Chengcheng Zhu, Yi Wang, Chenyu Jiangsu Normal University Department of Computer Science and Technology Xuzhou China Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Key Laboratory of Safety-Critical Software Nanjing China

ISBN: (纸本)9781665475488

Massive open online courses (MOOCs) play a crucial role in modern education by providing users with largescale open online learning platforms. Substantial research has been conducted to reduce user learning blindness and improve user experience, particularly in personalized course recommendations based on graph neural networks. However, these efforts have focused primarily on fixed or homogeneous graphs, are vulnerable to data sparsity problems, and are difficult to scale. This study proposes to overcome this limitation using graph convolution on local subgraphs combined with an extended matrix factorization model. First, the proposed method decomposes the heterogeneous graph into multiple meta-path-based subgraphs and combines random wandering sampling methods to capture complex semantic relationships between entities while sampling nodes' influence-rich neighborhoods. Next, the attention mechanism adaptively fuses the contextual information of different subgraphs for a more comprehensive construction of user preferences. Experiments on publicly available MOOCs datasets reveal that the proposed model outperforms other benchmark models and is highly scalable while alleviating the data sparsity problem. © 2022 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A Harmonic Current Self-Compensation Control Strategy of DC Active Power Filter

引用

IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Li, Gaoxiang Wu, Huanchao Chen, Jining Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical En-gineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Guangxi Uni-versity Nanning530004 China

Due to the diversity of nonlinear loads, the harmonic frequency of the DC microgrid is unpredictable, which poses a challenge to traditional DC active power filters (DC-APF). Firstly, the harmonic compensation mechanism and characteristics of the DC-APF with classical control strategy are analyzed. For the classical DC-APF, the voltage control and harmonic extraction are independent of each other, which may lead to their conflict and reduce the harmonic compensation performance. To address this issue, a harmonic current self-compensation control strategy of DC-APF is proposed, which uses a first-order inertia controller in the voltage loop and can simultaneously achieve voltage control and harmonic extraction. Then, the mechanism, characteristics, and parametric design method of the proposed harmonic current self-compensation control are introduced in detail. Compared with the classical DC-APF, the proposed DC-APF does not require any harmonic extraction filter and has better harmonic current compensation performance. Finally, the proposed control strategy and theoretical analysis are verified by simulation and experiment. © 2025 IEEE. All rights reserved.

关键词： Active filters

来源：评论

学校读者我要写书评

暂无评论

Insulator Defect Detection Based on YOLOv4-tiny with Improved Feature Fusion 12

Insulator Defect Detection Based on YOLOv4-tiny with Improve...

引用

12th International Conference on information technology in Medicine and Education, ITME 2022

作者： Zhong, Yuning Hu, Rong Li, Zuoyong Cai, Yuanzheng Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Insulator exposed to a wild environment for long-term running usually suffers from the damage of natural calamities that makes them become one of the most fragile components in the whole power system. In this case, even a slight fault will seriously affect the power supply safety of the power grid. Therefore, it is critical to find and identify the faulty insulator efficiently and accurately. However, an insulator is quite a small component in comparison with a huge power system which makes it hard to visible with naked eyes. This characteristic leads to poor recognition accuracy for existing detection methods. In order to solve the above problems, we propose an insulator defect detection algorithm based on an improved YOLOv4-tiny model. It differs from existing insulator detection models, our algorithm applies the effective channel attention network to the feature extraction network of YOLOv4-tiny, which significantly enhances the feature extraction ability of the backbone network. These two-way features of the pyramid can be fully fused in the two-way feature fusion stages seamlessly. Experimental results show that compared with, our proposed algorithm outperforms Faster RCNN and YOLOv3 in average classification accuracy, missed detection, and false detection. © 2022 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Caregiving in Digital Healthcare Setting: Impacts of Data-driven Technologies to Caregivers in Practice 25

Caregiving in Digital Healthcare Setting: Impacts of Data-dr...

引用

25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Bin Zhao, Jin Sun, Yuling Fudan University School of Computer Science Shanghai China Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China East China Normal University School of Computer Science and Technology Shanghai China

ISBN: (纸本)9781665405270

Using data-driven technologies to support healthcare has been largely celebrated as an effective means. Yet, most existing data-driven technologies are designed with the purposes of improving productivity, efficiency, and effectiveness etc., i.e. care administer-centric design. The needs and perceptions of caregiver, the actual users of most technologies, are largely ignored. In this paper, we examine the impacts of data-driven technologies from the perspective of caregivers. Through a questionnaire study with 191 caregivers in Shanghai China, we quantify the impacts of data-driven technologies to caregivers' work and experiences. Our results show that while the embedded data-driven technologies provide significant benefits to caregivers' work, these benefits are often limited by the complex, situated and fragmented nature of healthcare. We analyze the results and propose our suggestions to the further design. © 2022 IEEE.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

Space-time Constraint Resources Modeling and Safety Verification Method for Automated Vehicles 9

Space-time Constraint Resources Modeling and Safety Verifica...

引用

9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Zhu, Yi Chen, Xiaoying Zhao, Yu School of Computer Science and Technology Jiangsu Normal University XuZhou China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Safety-Critical Software Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Automated vehicle combines physics and computation on the basis of environment perception. It can realize intelligent interaction with the environment. Automated vehicle is a typical CPS. However, the continuous changes of driving physical space bring certain challenges to the safety of CPS resources. Therefore, how to solve this kind of CPS resource safety problems caused by space and time changes becomes the key. We propose a space-time constraint resource modeling and safety verification method for automated vehicle to solve this problem. Firstly, the physical topology model is proposed to model the physical topology space of CPS, which is able to describe the topology space. Secondly, the Resource-Space Time Communicating Sequential Process (RS- TCSP) is proposed by extending the resource vector on the basis of Time Communicating Sequential Process(TCSP) to describe the resources in CPS topology. Thirdly, the physical topology model and RS- TCSP are mapped to bigraphs and bigraphs reactive system, respectively. The safety of CPS resources is verified by BigMC, the verification tool of bigraphs, and the counterexample path is modified. Finally, a driving scene is given to verify the effectiveness of the proposes method. © 2022 IEEE.

关键词： Formal verification

来源：评论

学校读者我要写书评

暂无评论

Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

引用

IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

来源：评论

学校读者我要写书评

暂无评论

A Model-Agnostic Interpretability Approach Based on Enhanced Hierarchical Clustering

A Model-Agnostic Interpretability Approach Based on Enhanced...

引用

IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Xi Ding Yi Zhu Qiao Yu Chongyang Huang School of Computer Science and Technology Jiangsu Normal University Xuzhou Jiangsu China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Nanjing Jiangsu China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

The Local Interpretable Model-Agnostic Explanations (LIME) is a relatively novel technique for explaining black-box models. LIME generates perturbed instances of the model based on a random instance, and then approximates the original model with a linear model derived from the weighted perturbed instances, thereby providing an explanation for individual predictions. While LIME effectively addresses the opacity of black-box models, the randomness in instance generation often leads to unstable and inaccurate predictions. For a given prediction, different explanations may be generated, posing critical issues, particularly in security-sensitive domains. In response to these challenges, we propose a stable version of LIME, named DDLIME(Density and Distance-based LIME). DDLIME leverages improved hierarchical clustering and KNN algorithms to select instances that are most similar to the data, thus reducing the impact of perturbations. Additionally, the enhanced hierarchical clustering, incorporating density and distance metrics, enables DDLIME to address the limitations of traditional hierarchical clustering in clustering non-spherical clusters. Consequently, DDLIME can effectively capture complex structures and patterns within the data. The experimental results on three binary public datasets from the UCI repository demonstrate that DDLIME provides more stable explanations compared to LIME. Furthermore, compared to both baseline models, DDLIME exhibits superior performance across various evaluation metrics.

关键词： Measurement Accuracy Perturbation methods Clustering algorithms Closed box Predictive models Nearest neighbor methods Prediction algorithms Stability analysis Software reliability

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：