检索结果-内蒙古大学图书馆

7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Chen, Penghui Li, Qishen Li, Qiufeng Wu, Zhongyu Nanchang Hangkong University School of Information Engineering Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Nanchang Hangkong University School of Software Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350339161

Remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Selection of Optimal Focusing Evaluation Function Based on Sparse Microscopic images

Selection of Optimal Focusing Evaluation Function Based on S...

引用

2022 International Conference on Electrical, Electronics and Information Engineering, EEIE 2022

作者： Lv, Meini Gan, Hui Liu, Xin Chen, Jia Yang, Qiuhui Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou China

The auto focusing process is affected by the amount of image content, resulting in the focus curve not meeting the characteristics of the ideal focus curve. In this paper, aiming at the problem that the traditional autofocus cannot find the focal plane successfully in the case of microscopic sparse content, the qualitative analysis and quantitative analysis of 12 traditional focus evaluation functions are carried out. The experimental results show that the F Tenengrad function is suitable for the optimal focus evaluation function of sparse microscopic images. © Published under licence by IOP Publishing Ltd.

关键词： Focusing

来源：评论

学校读者我要写书评

暂无评论

Bimodal Feature Propagation and Fusion for Real-time Semantic Segmentation on RGB-D images 7

Bimodal Feature Propagation and Fusion for Real-time Semanti...

引用

7th International Conference on Intelligent Computing and Signal processing, ICSP 2022

作者： He, Ying Xiao, Li Sun, Zhigang Wang, Zhuo Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9781665478571

Semantic Segmentation is the foundation of scene understanding and automatic driving tasks. One of the challenges of semantic segmentation is the reduction of feature resolution as the network goes deep. In this paper, the low-resolution features are integrated into high-resolution features progressively, enabling high-resolution prediction. Currently, incorporating the auxiliary depth information into a semantic segmentation framework has proven to be helpful to improve accuracy. However, additional process of depth data immensely increases computational complexity, resulting in limitation for practical application. Here, a real-time bimodal semantic segmentation network that effectively extracts and fuses complementary information is investigated. Our network extends a light-weight architecture which can seamlessly take advantage of various prevalent backbone networks. Our model extracts RGB and depth features from two branches in parallel and integrates multi-level features of two modalities through bimodal feature fusion blocks. To reduce computation complexity, we employ depth-wise separable convolution while keep performance intact. Meanwhile, an attention mechanism is exploited to recalibrate the fusion modality features adaptively. Refinement components are retained to refine multi-resolution feature maps. We conduct ablative study to demonstrate the availability of our bimodal fusion blocks. Comprehensive experiments validate that the proposed network outperforms the state-of-the-arts and achieves 18 FPS on the challenging RGB-D dataset, NYUDv2. © 2022 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Analysis of MFCS topology for high efficiency and long lifetime 43

Analysis of MFCS topology for high efficiency and long lifet...

引用

43rd Chinese Control Conference, CCC 2024

作者： Xiang, Hongfu Wu, Jiajian Wang, Zhen Wang, Jie Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China

ISBN: (纸本)9789887581581

Multi-stack fuel cell system(MFCS) are an important basis for large-scale application of solid oxide fuel cell(SOFC) technology, MFCS can provide higher system power and longer service life. As the number of stacks increases, different topologies can be formed between stacks, including parallel structures and series structures, and the physical characteristics of MFCS will also change. In this study, the degradation of the stacks is first considered, and the system model of series structure and parallel structure including the degradation of the stacks is built. The parallel structure proposed in this paper is the key to ensure the high efficiency and long lifetime of MFCS. Through the simulation of systems with different topologies, it is found that the power generation efficiency of the systems caused by the topologies varies greatly in different output power stages. The efficiency of the parallel structure in the low power stage is significantly better than that of the series structure, and the parallel structure is more conducive to the high efficiency of the system in the whole stage. Different topological structures also have a greater impact on the degradation of stacks. The degradation rates of stacks in parallel structures are close to each other, and the state of stacks is more consistent. The overall life of the system in parallel structures is also better than that in series structures. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

Adaptive Neural Finite-Time Deployment of Heterogeneous Multi-agent Systems via a Cross-Species Bionic PDE-ODE Approach

Artificial Intelligence Science and Engineering

引用

Artificial Intelligence Science and Engineering 2025年第1期1卷 52-63页

作者： Jingtao MAN Zhigang ZENG Jun LI School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China

For large-scale heterogeneous multi-agent systems (MASs) with characteristics of dense-sparse mixed distribution, this paper investigates the practical finite-time deployment problem by establishing a novel cross-species bionic analytical framework based on the partial differential equation-ordinary differential equation (PDE-ODE) approach. Specifically, by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents, this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model, and that of sparsely distributed agents as several ODEs equivalent to the predator population models. The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE, thereby forming a PDE-ODE-based tracking error model for the considered MASs. Furthermore, by integrating adaptive neural control scheme with the aforementioned biological models, a “Flexible Neural Network” endowed with adaptive and self-stabilized capabilities is constructed, which acts upon the considered MASs, enabling their practical finite-time deployment. Finally, effectiveness of the developed approach is illustrated through a numerical example.

关键词： Adaptation models Uncertainty Biological system modeling Neural networks Stochastic processes Mathematical models Numerical models Topology Multi-agent systems Diseases

来源：评论

学校读者我要写书评

暂无评论

LCIQA: A Lightweight Contrastive-Learning Framework for image Quality Assessment via Cross-Scale Consistency Minimization

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Feng, Chenxi Min, Xiongkuo Ye, Long Yang, Yinghao Communication University of China State Key Laboratory of Media Convergence and Communication Beijing100024 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China Ministry of Education and School of Data Science and Media Intelligence Beijing100024 China

Blind image quality assessment (BIQA), which functions without the need for a reference image, is a challenging yet essential task in various image processing systems and downstream vision applications, ranging from semantic recognition to image enhancement. Traditionally, numerous BIQA models have been developed using supervised learning methodologies, which rely heavily on the availability and quality of ground truth data. To improve the generalization capability and robustness of these models, recent studies have explored the application of contrastive learning, aiming to enhance the quality representation capacity of model backbones through a self-supervised approach. However, the training process for contrastive learning is computationally intensive, posing significant challenges in resource-constrained environments. To mitigate this issue, we propose a Lightweight Contrastive-learning-based IQA (LCIQA) framework, designed to be efficiently trained on a single GPU without relying on ground truth data. This framework maintains a fixed vision backbone and focuses on optimizing the parameters of subsequent IQA heads through contrastive learning. To accommodate a lightweight framework, we incorporate a quality task adapter to eliminate semantic biases introduced by the features extracted from the fixed-parameter backbone. A coarse-to-fine contrastive learning strategy is then employed to train the quality regression module. Extensive experiments demonstrate the superior performance of our model in terms of both accuracy and complexity. In addition, ablation studies validate the effectiveness of each component within the proposed framework. © 1991-2012 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Ferroelectric control of spin-channel polarity in two-dimensional Sc2CO2/VSi2P4 van der Waals multiferroic heterostructures and application to nonvolatile logic-in-memory devices

引用

Physical Review Applied 2025年第2期23卷 024041-024041页

作者： Zhi Yang Xin-Yu Fu Shen-Ao Qin Bing-Xin Liu Chuan-Kui Wang Zong-Liang Li Shandong Key Laboratory of Medical Physics and Image Processing & Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and Electronics Shandong Normal University Jinan 250358 China

The ferroelectric (FE) control of the polarity of spin channels is crucial for designing multifunctional and high-performance spintronic devices. Here, based on first-principles calculations, we propose an effective strategy to achieve full-electrical FE control of the spin-polarized direction of the VSi2P4 layer by stacking Sc2CO2/VSi2P4 heterobilayers van der Waals (vdW) multiferroic structures. Our calculations show that the magnetization direction reversal of the VSi2P4 layer can be modulated by FE control due to the polarization-field-induced band-structure shift and interfacial charge transfer. On this basis, we design a lateral multiferroic tunnel junction (MFTJ) device with the VSi2P4 monolayer acting as the tunneling barrier, which can realize perfect bipolar spin-polarized current and spin-rectification ratio up to 4.0 × 105. The underlying mechanism is attributed to the overlap of the spin-dependent density of states in the two electrodes manipulated by FE polarization. Moreover, using the left and right FE layers as two inputs and considering the inverse spin Hall effect as the output, nonvolatile full-electrical writing and reading of magnetization states as well as multifunction spin-logic operations (and, nor, and xnor) are realized. Besides, we further design another MFTJ with the Sc2CO2 monolayer acting as the tunneling barrier, which demonstrates the tunneling electroresistance (TER) ratio increases with the tunneling barrier length and a giant TER ratio of up to 4.6 × 1021% is achieved. Our findings provide viable strategies to realize nonvolatile high-performance storage and computing functions for spintronic devices based on the Sc2CO2/VSi2P4 vdW multiferroic heterostructures.

关键词： Van der Waals forces

来源：评论

学校读者我要写书评

暂无评论

A Memristive Spiking Neural Network Circuit for Bio-inspired Navigation Based on Spatial Cognitive Mechanisms

引用

IEEE Transactions on Biomedical Circuits and Systems 2024年第3期19卷 686-698页

作者： Chen, Zhanfei Wang, Xiaoping Wang, Zilu Yang, Chao Huang, Tingwen Lai, Jingang Zeng, Zhigang School of Mechanical Engineering and Automation Shenzhen518055 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation China Hubei Key Laboratory of Brain-inspired Intelligent Systems Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Shenzhen University of Advanced Technology School of Computer Science and Control Engineering Shenzhen518055 China

Cognitive navigation,a high-level and crucial function for organisms' survival in nature,enables autonomous exploration and navigation within the environment. However,most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation,capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking;the action cell module determines the direction of subsequent movement;and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application,the entire SNN is quantized and deployed on a real memristive hardware platform,achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios. © 2007-2012 IEEE.

关键词： Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Small Object Detection via a Dense Connection and Feature Enhancement Network 9

Small Object Detection via a Dense Connection and Feature En...

引用

9th International Conference on Computer and communications, ICCC 2023

作者： Qiang, Baohua Chen, Lirui Guo, Shuiping Liu, Lingzhi Zhang, Shihao Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China The 7th Research Institute of China Electrics Technology Group Corporation Guangzhou China

ISBN: (纸本)9798350317251

When detecting small objects in complex environment, the features of small objects will become blurred or even lost as the number of network layers increases. To address this problem, we constructed a Dense Connection and Feature Extraction Network, termed DCFEN. First, we designed a dense-connected multi-scale feature enhancement framework, which can effectively extract and fuse multi-scale features. Second, we constructed a dense-connected subnet using dense connections, which enhances the propagation of features and the utilization of shallow feature information, improving the detection performance of small objects. Finally, extensive experimental results demonstrated the detection precision and superiority of our method. © 2023 IEEE.

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

Multi-scale convolutional neural networks for brain disease diagnosis

Multi-scale convolutional neural networks for brain disease ...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Zhongyi Gao, Libin Tong, Yinsheng Lu, Xingjin Li, Rui Xiao, Lei Li, Zuoyong Wenzhou University College of Computer Science and Aritificial Intelligence Wenzhou325035 China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9798350337488

The brain is the most sophisticated and complex organ in the human body. Nowadays, diagnosing complex and diverse brain diseases is a hot topic. Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), and others are common brain diseases. With the increased usage of Artificial Intelligence (AI) in medical image analysis, the endeavor to make AI comprehend brain images for assisting doctors in making objective diagnoses of brain diseases has gained considerable attention. Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for diagnosing and analyzing brain ***, the Pearson correlation coefficient (PCC) method constructs a dynamic functional connectivity (dFC) network using a fixed window size. However, this method has limitations as it is challenging to extract potential high-level features from the dFC and determine local feature relevance. This paper presents a method for constructing dFC based on multi-scale sliding windows and proposes Multi-scale Convolutional Neural Networks (MsCNN) for learning and analyzing dFC at various scales. Finally, a deep fusion of features learned at different scales is employed for the diagnostic classification of brain *** to the rs-fMRI dataset from ADNI, the classification accuracy was 84.0% for eMCI/lMCI, 84.4% for lMCI/AD, and 58.4% for NC/eMCI/lMCI/AD. Applied to rs-fMRI data from ABIDE, the accuracy was 74.7% for ASD/NC. The proposed method exhibits robust classification performance and introduces a new approach to diagnosing other brain diseases. © 2023 IEEE.

关键词： Brain diseases Classification Diagnostic Multi-scale convolutional neural networks (MsCNN) Resting-state functional magnetic resonance imaging (rs-fMRI)

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：