检索结果-内蒙古大学图书馆

13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Wang, Jie Li, Zuoyong Xu, Haiping Cheng, Xuesong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou350001 China College of Mathematics and Data Science Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350319156

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With Applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

引用

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

来源：评论

学校读者我要写书评

暂无评论

Holistically Associated Transductive Zero-Shot Learning

引用

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2022年第2期14卷 437-447页

作者： Xu, Yangyang Xu, Xuemiao Han, Guoqiang He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Minist Educ Key Lab Big Data & Intelligent Robot & Guangdong Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Bldg Sci Prov Key Lab Computat Intelligence & Cyberspace I Guangzhou 510006 Peoples R China

With the explosive growth of visual media categories, zero-shot learning (ZSL) aims to transfer the knowledge obtained from the seen classes to the unseen classes for recognizing novel instances. However, there is a domain gap between the seen and the unseen classes, and simply matching the unseen instances using nearest neighbor searching in the embedding space cannot bridge this gap effectively. In this article, we propose a holistically associated model to overcome this obstacle. In particular, the proposed model is designed to combat two fundamental problems of ZSL: 1) the representation learning and 2) label assignment of the unseen classes. The first problem is addressed by proposing an affinity propagation network, which considers holistic pairwise connections of all classes for producing exemplar features of the unseen samples. We cope with the second issue by proposing a progressive clustering module. It iteratively refines unseen clusters so that holistic unseen instance features can be used for a reliable classwise label assignment. Thanks to the precise exemplar features and classwise label assignment, our model eliminates the domain gap effectively. We extensively evaluate the proposed model on five human action and image data sets, i.e., Olympics Sports, HMDB51, UCF101, Animals with Attributes 2, and SUN. The experimental results show that the proposed model outperforms state-of-the-art methods on these substantially different data sets.

关键词： Visualization Semantics Artificial neural networks Predictive models Training Pairwise error probability Loss measurement Affinity matrix class association instance association zero-shot learning (ZSL)

来源：评论

学校读者我要写书评

暂无评论

Structured Binary Neural Networks for Image Recognition

引用

INTERNATIONAL JOURNAL OF COMPUTER VISION 2022年第9期130卷 2081-2102页

作者： Zhuang, Bohan Shen, Chunhua Tan, Mingkui Chen, Peng Liu, Lingqiao Reid, Ian Monash Univ Fac Informat Technol Melbourne Vic Australia Zhejiang Univ Hangzhou Zhejiang Peoples R China South China Univ Technol Sch Software Engn Guangzhou Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Beijing Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA Australia

In this paper, we propose to train binarized convolutional neural networks (CNNs) that are of significant importance for deploying deep learning to mobile devices with limited power capacity and computing resources. Previous works on quantizing CNNs often seek to approximate the floating-point information of weights and/or activations using a set of discrete values. Such methods, termed value approximation here, typically are built on the same network architecture of the full-precision counterpart. Instead, we take a new "structured approximation" view for network quantization - it is possible and valuable to exploit flexible architecture transformation when learning low-bit networks, which can achieve even better performance than the original networks in some cases. In particular, we propose a "group decomposition" strategy, termed GroupNet, which divides a network into desired groups. Interestingly, with our GroupNet strategy, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. We also propose to learn effective connections among groups to improve the representation capability. To improve the model capacity, we propose to dynamically execute sparse binary branches conditioned on input features while preserving the computational cost. More importantly, the proposed GroupNet shows strong flexibility for a few vision tasks. For instance, we extend the GroupNet for accurate semantic segmentation by embedding the rich context into the binary structure. The proposed GroupNet also shows strong performance on object detection. Experiments on image classification, semantic segmentation, and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature. Moreover, the speedup and runtime memory cost evaluation comparing with related quantization strategies is analyzed on GPU platforms, which serves as a strong benchmark for further research.

关键词： Binary neural networks Quantization Image classification Semantic segmentation Object detection

来源：评论

学校读者我要写书评

暂无评论

TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering 38

TFGDA: Exploring Topology and Feature Alignment in Semi-supe...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dan, Jun Liu, Weiming Xie, Chunfeng Yu, Hua Dong, Shunjie Tan, Yanchao Zhejiang University China Queen Mary University of London United Kingdom Dalian University of Technology China Shanghai Jiao Tong University China Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China Fujian Key Laboratory of Network Computing and Intelligent Information Processing China

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods. © 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

A Dual-Notch Ultra-Wideband Filter with High Selectivity and Miniaturization Based on Open Stubs

A Dual-Notch Ultra-Wideband Filter with High Selectivity and...

引用

International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Ya He Mingming Gao CongYing Wang Yan He ZhenZhi Yang WenXin Yang College of Electronics and Information Engineering Liaoning Technicial University Huludao Liaoning Province China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

This manuscript introduces the design of an ultra-wideband (UWB) microstrip line filter implemented through a novel multimode resonator comprising open-circuited stubs. The multimode resonator has three open-circuited stubs, two short lines, and a high-impedance microstrip line. The filter demonstrates a 3dB bandwidth ranging from 3.72 to 11.23 GHz, centered at 7.48 GHz. Asymmetric coupling techniques and split-ring resonators on both sides of the multimode resonator introduce notches at 8.62-8.73 GHz and 9.21-9.64 GHz. The UWB filter designed in this study exhibits a low insertion loss of merely 0.51 dB. The notches demonstrate remarkable attenuation, achieving 29.90 dB and 24.31 dB, respectively, highlighting superior notch suppression performance. Additionally, the filter's compact structure fulfills miniaturization requirements, possessing a size of only 0.61*0.20 $(\lambda_{g}*\lambda_{g})$ .

关键词： Couplings Microstrip filters Microwave technology Microstrip resonators Resonator filters Millimeter wave technology Resonant frequency

来源：评论

学校读者我要写书评

暂无评论

Spatial-Aware GAN for Instance-Guided Cross-Spectral Face Hallucination 2nd

Spatial-Aware GAN for Instance-Guided Cross-Spectral Face Ha...

引用

2nd CAAI International Conference on Artificial Intelligence (CICAI)

作者： Xiao, Wenpeng Xu, Cheng Zhang, Huaidong Xu, Xuemiao South China Univ Technol Guangzhou Peoples R China State Key Lab Subtrop Bldg Sci Guangzhou Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou Peoples R China

ISBN: (纸本)9783031204968;9783031204975

An efficient strategy to solve the Heterogeneous Face Recognition (HFR) is to translate the probes to the same spectrum domain of the galleries using generative models. However, without or with only globally-pooled appearance representation from a reference, the low-quality generated images restrict the recognition accuracy. The intuition of our paper is the spatially-distributed appearance contains details beneficial to higher-quality image synthesis. Particularly, we propose a semantic spatial adaptive alignment module to solve the inevitable mis-alignment between the content from the near-infrared (NIR) image and the appearance from the visible (VIS) reference. In this way, arbitrary VIS reference can provide appearance with sufficient details to assist the NIR-to-VIS translation. Based on this, we propose an unsupervised spatial-aware instance-guided cross-spectral facial hallucination network (SICFH) for visual-pleasing and identity-preserved VIS image translation. Qualitative and quantitative experiments on three challenging NIR-VIS datasets demonstrate the synthesized VIS images address the HFR problem effectively and achieve state-of-the-art recognition accuracy.

关键词： Cross-spectral face synthesis Heterogeneous face recognition Generative adversarial network

来源：评论

学校读者我要写书评

暂无评论

3DFaceMAE: Pre-training of Masked Autoencoder Using Patch-Based Random Masking Reconstruction and Super-resolution for 3D Face Recognition 7th

3DFaceMAE: Pre-training of Masked Autoencoder Using Patch-Ba...

引用

7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Gao, Ziqi Li, Qiufu Shen, Linlin Yang, Junpeng Shenzhen Univ Comp Vis Inst Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Guangdong Peoples R China Shenzhen Inst Artificial Intelligence Robot Soc A Shenzhen 518129 Guangdong Peoples R China Shenzhen Univ Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen 518060 Guangdong Peoples R China

ISBN: (纸本)9789819787944;9789819787951

Compared to 2D face recognition, 3D face recognition exhibits stronger robustness against variations like pose and illumination. However, due to the limited training data, the accuracy of existing 3D face recognition methods is still unsatisfactory. In this paper, we introduce 3DFaceMAE, which is the first masked autoencoder (MAE) based 3D face recognition method using point clouds. Specifically, we first synthesize a large-scale 3D point cloud facial dataset and combine it with the small-scale real data. In the pre-training of 3DFaceMAE, we extract the key facial regions from the input 3D facial point cloud, using normal difference techniques, and reconstruct these key regions using patch-based random masking reconstruction and super-resolution. We finally fine-tune the encoder of 3DFaceMAE on the real 3D face point cloud data. In the experiments, we test 3DFaceMAE on three 3D face datasets, as high as 91.17% was achieved on the Lock3DFace dataset, which is the first reported result surpassing 90%. In addition, the experimental results indicate that 3DFaceMAE has strong cross-quality generalization performance. We also validate the effectiveness of different components of 3DFaceMAE through ablation study.

关键词： Face recognition 3D point cloud

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning Framework for Delivery Capacity Scheduling with Vehicle Constraints

Reinforcement Learning Framework for Delivery Capacity Sched...

引用

IEEE International Conference on e-Business Engineering (ICEBE)

作者： Zhongneng Ma Zhuorui Qin Shuxia Shi Qingyao Wu South China University of Technology Guangzhou China School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou Henll Electronics Equipment Co. Ltd Guangzhou China Peng Cheng Laboratory

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

Improving the risk prevention capability of grid operations is a crucial requirement for smart resilient grids, where efficient dispatch and scheduling of delivery capacity play a pivotal role in enhancing grid resilience. Therefore, it is necessary to develop targeted intelligent dispatch algorithms based on operational data. To address this issue, this paper proposes a framework with inherent constraints and utilizes reinforcement learning using recursive neural networks. A constraint module is designed to identify available vehicle combinations based on de-livery capacity, allocate goods considering vehicle number, load, and size constraints, and generate delivery routes sequentially. Additionally, a reinforcement learning model is introduced, using negative trip length as a reward signal, optimizing parameters of the recursive neural network through policy gradient methods to automatically improve strategies for selecting optimal deliv-ery routes with improved vehicle combinations. To validate the effectiveness of this algorithm, extensive experiments were conducted using both real-world application validation data and the TSPLIB public dataset. Within a limited computational budget, the proposed algorithm outperforms several mature methods in terms of effectiveness and real-time performance, achieving near-optimal results in Euclidean graphs with nearly 100 nodes. These results demonstrate that this approach effectively addresses the intelligent scheduling of delivery capacity in digital twin systems for power storage,

关键词： Processor scheduling Prevention and mitigation Neural networks Reinforcement learning Real-time systems Scheduling Digital twins Smart grids Resilience Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

RESEARCH ON THE REWARDS AND PUNISHMENTS OF URBAN SEWAGE TREATMENT UNDER THE UNCERTAINTY THEORY

引用

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第3期86卷 111-124页

作者： Ruixi, Yuan Lin, Zhang Li, Cun-Lin Masron, Tajul Ariffin Yang, Bao-Jun School of management North Minzu University Yinchuan750021 China Universiti Sains Malaysia Pinang 13500 Malaysia School of Mathematics and Information Science Guangzhou University Guangzhou510006 China School of Management The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China School of Management Universiti Sains Malaysia Pinang 13500 Malaysia School of Business Northinzu University Yinchuan750021 China

This paper fills a gap in urban sewage treatment decision-making by exploring reward and punishment mechanisms within the uncertainty theory framework. Addressing challenges in characterizing sewage treatment capacity due to incomplete information, the study integrates incentive and punishment mechanisms in a decision-making model. Results indicate that government rewards encourage companies to treat more sewage, but a nuanced relationship exists between rewards, treatment capacity, and expected returns. The study emphasizes the need for a balanced approach, suggesting a focus on technology enhancement over sole reliance on rewards. Examining a double-headed sewage market, the research establishes an uncertain Cournot game model, providing insights into sewage treatment dynamics under asymmetric information. In conclusion, the paper contributes to practical reward and punishment strategies in sewage treatment policies, offering theoretical support for effective decision-making in urban sewage management within a concise framework.. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Sewage treatment

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：