检索结果-内蒙古大学图书馆

High-fidelity parametric amplification of Ince–Gaussian beams

Chinese Optics Letters 2022年第11期20卷 105-109页

作者： Ding Yan Zhiyuan Zhong Tong Qi Hongying Chen Wei Gao Heilongjiang Provincial Key Laboratory of Quantum Control School of Measurement and Communication EngineeringHarbin University of Science and TechnologyHarbin 150080China

Ince–Gaussian (IG) beams, as eigenfunctions of the paraxial wave equation in elliptical coordinates, are attracting increasing interest owing to their propagation-invariant and full-field properties. Optical amplification via parametric interactions can further expand their application areas, yet it is rarely studied. In this work, we report on a high-fidelity parametric amplifier for IG beams. The nonlinear transformation of the spatial spectra of the signal and associated influences on the beam profiles of the amplified signal, under different pump structures, were theoretically and experimentally investigated. By using a perfect flattop beam as the pump, we show that the transverse structure of IG signals is well maintained,and the distortion induced by radial-mode degeneration is overcome during amplification. This proof-of-principle demonstration paves the way for a mode-independent and distortion-free amplifier of arbitrary structured light and has great significance in relevant areas, such as quantum optics, tunable infrared-laser generation, and image amplification.

关键词： optical parametric amplification Ince–Gaussian beam high fidelity perfect flattop beam

来源：评论

学校读者我要写书评

暂无评论

Graph Metrics Based Brain Hypergraph Learning in Schizophrenia Patients

Graph Metrics Based Brain Hypergraph Learning in Schizophren...

引用

2022 Chinese Automation Congress, CAC 2022

作者： Wu, Ke Song, Xiaoying Chai, Li Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan China Zhejiang University College of Control Science and Engineering Zhejiang China

ISBN: (纸本)9781665465335

Hypergraphs have been widely used in the analysis of brain network because of its ability to capture high order interaction among multiple brain regions. Most of the recent work is based on sparse representation to construct hypergraph, in which vertices represent brain regions. This kind of approaches is at the subject-level and does not take account of topological properties. To overcome this limitation, we propose a new method to identify biomarkers helpful to the diagnosis of brain disease. Firstly, we construct a graph-based network for each subject and derive graph metrics. Then we construct a group-level hypergraph based on similarities of topological properties. Graph metrics are introduced to guide the construction of hypergraph. We obtain the weights of each hyperedges by hypergraph learning. We evaluate our framework on the COBRE dataset and achieve good classification accuracy by merely selecting a small amount of features. Moreover, we find that the hyperedges with larger weights and smaller weights show good discriminability. Our method extends the framework of hypergraph learning and reveals the differences caused by the disease at the group-level. © 2022 IEEE.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

Optimization scheme for rapid analysis of copper ore grade using EDXRF

Optimization scheme for rapid analysis of copper ore grade u...

引用

2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Li, Wen Lai, Wanchang Chen, Dongdong Ma, Yutian Yang, Bingsong Zhang, Diancheng College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China National Key Laboratory of Ni&Co Associated Minerals Resources Development and Comprehensive Utilization China Gansu Jinchuan Intelligent Measurement and Control Technology Co. Ltd. China

ISBN: (纸本)9781510675032

In order to optimize the method and technology for the rapid determination of copper ore grade using Energy Dispersive X-ray Fluorescence (EDXRF), improve the accuracy of EDXRF analysis, guide the development and utilization of copper ore resources, and enhance production efficiency, the optimal operating conditions for the EDXRF analyzer were determined using Monte Carlo (MC) simulation and physical experiments. The recommended settings were a tube voltage of 40 kV and a tube current of 10 μA. Under these optimal conditions, the copper (Cu) content in various types of copper ore samples was analyzed. The copper ore was categorized into geological samples (including raw ore samples and tailings samples), copper concentrates, and matte. The correction of matrix effects was carried out using a binary regression method. Experimental results showed that the average absolute error of analyzing geological samples by EDXRF was 0.035%, with an average relative error of 4.08%. The average absolute error for analyzing copper concentrates was 0.238%, with an average relative error of 1.01%. For matte analysis, the average absolute error was 0.421%, with an average relative error of 1.01%. The accuracy and precision of the method meet the production requirements of enterprises, and it holds promising prospects for widespread application. © 2024 SPIE.

关键词： Ores

来源：评论

学校读者我要写书评

暂无评论

Multi-branch low-light enhancement algorithm based on spatial transformation

引用

Multimedia Tools and Applications 2025年第18期84卷 19647-19667页

作者： Wang, Wenlu Sun, Ying Zou, Chunlong Tang, Dalai Fang, Zifan Tao, Bo Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot010051 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Hydroelectric Design & Maintenance China Three Gorges University Yichang443002 China

Many state-of-the-art low-light image enhancement techniques now suffer from issues like color distortion, detail blurring, and the halo effect, hindering their ability to produce visual effects. This paper presents a multi-branch low-light enhancement algorithm based on spatial transformation to significantly improve the visual impact of images under low-light settings, overcoming the limitations of existing approaches. The process involves dividing the initial low-light picture into three distinct layers. Bootstrap filtering and Contrast Limited Adaptive Histogram Equalization are used to process the main feature layer. Primary feature extraction is used to extract features for the compensation layer, which is then integrated with Contrast Limited Adaptive Histogram Equalization and MSRCR. The auxiliary layer utilizes spatial transformation to apply gamma correction to the V-channel and performs color space transformation and additional processing. Ultimately, these three layers are combined and refined to create the final improved image. Based on the experimental results, the proposed method outperforms the standard MSRCR algorithm by 52.47%, 71.58%, and 12.10%, respectively, on the assessment metrics AG, SD, and IE. LOE declines by 22.3%. The processed image presents a good visual quality, effectively enhancing the image brightness while retaining a large amount of information and improving the image details. This paper proposes an effective method to deal with the problem of low illumination image enhancement, which provides a strong reference for future research and application in related fields. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Cross-Domain Few-Shot Learning With Spectral-Spatial Split-Attention For Hyperspectral Image Classification

Cross-Domain Few-Shot Learning With Spectral-Spatial Split-A...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Peng Luo Qingyan Wang Junping Zhang Shouqiang Kang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Hyperspectral image classification (HSIC) is a pivotal technology in hyperspectral remote sensing, playing a widespread role in remote sensing applications. However, the limited number of labeled samples has always made hyperspectral image classification difficult. In response to this issue, researchers have delved into cross-domain classification studies. Moreover, significant progress on cross-domain HSIC has been made in recent years. Nevertheless, existing methods exhibit shortcomings, including inadequate exploitation of spectral and spatial information and a slow training speed, rendering them unsuitable for downstream application tasks. To address these challenges, this paper introduces a model of cross-domain few-shot learning with spectral-spatial split attention(S 3 A-CFSL). Channel attention and split attention are presented to emphasize effective spectral and spatial information for HSIC adaptively. Additionally, the ResNet variant, called ResNeSt, is employed to expedite the training speed of the model. Experimental results demonstrate notable enhancements in the proposed method's classification accuracy and model training speed across two public datasets.

关键词： Training Adaptation models Accuracy Feature extraction Rendering (computer graphics) Data mining Few shot learning

来源：评论

学校读者我要写书评

暂无评论

Acceleration of Formation control Based on Hessian Matrix of Artificial Potential Field 42

Acceleration of Formation Control Based on Hessian Matrix of...

引用

42nd Chinese control Conference, CCC 2023

作者： Han, Haoran Xi, Zhilong Lv, Maolong Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Traffic Control and Navigation College Air Force Engineering University Xian710051 China

ISBN: (纸本)9789887581543

The formation control of multi-agent system has become a hotspot in recent years. One of the most commonly used methods is to use artificial potential field (APF) to generate the attractive and repulsive force to organize the agents into a low-energy and stable formation. In this paper, we regard the APF as the difference in the desired position, and introduce the derivative of the gradient of the APF with respect to time, which is simplified as the Hessian matrix of the APF, into the formation control from the perspective of trajectory tracking. The energy-based Lyapunov function is selected to prove the stability and collision avoidance ability of the proposed control law. Moreover, we show that the maneuverability of the multi-agent system is enormously improved with the additional Hessian matrix term. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Change Detection Based on Simam Multi-Scale Joint Features

Hyperspectral Image Change Detection Based on Simam Multi-Sc...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xinling Li Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The remote sensing satellite system has steadily developed, and the availability of massive high-quality satellite remote sensing data has rapidly improved. The dynamic monitoring of land cover changes using hyperspectral data has received great attention. The existing change detection methods usually use the spatial correlation or spatial-spectral correlation of hyperspectral images, lacking an overall consideration of the three-dimensional temporal-spatial-spectral joint features, resulting in suboptimal change detection results. Given the considerations above, this paper proposes a SimAM multi-scale joint feature extraction network for hyperspectral image change detection. The proposed method first adopts SimAM multi-scale joint features network to consider the hyperspectral image as a whole, and extracts multi-scale temporal-spatial-spectral joint features from the network. Then, uses multi-scale feature weighted fusion module to weight different scale features after simple fusion, highlighting the change regions. Finally, adopts the batch-balance measurement module to measure the similarity of the bi-temporal fusion features and output the change detection result map. Experiments show that on two public datasets, the proposed method can achieve a good change detection effect.

关键词： Satellites Correlation Fuses Land surface Euclidean distance Feature extraction Monitoring

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Classification Method Based On Node Similarity Feature Fusion

Hyperspectral Image Classification Method Based On Node Simi...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiameng Wang Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

There is abundant spectral and spatial information in Hyperspectral images (HSI). However, there exists a limitation of not using spatial information sufficiently in HSI classification. Besides, there is the limitation of mononuclear in feature extraction, resulting in insufficient feature extraction and insufficient utilization of data information. In view of these problems, a node similarity semi-supervised classification method of multiscale feature is proposed to break the limitation of mononuclear and achieve full extraction of spatial information. First, to extract pixel-level features, a three-dimensional (3-D) multiscale convolutional neural network (CNN) is used. Second, based on node similarity superpixel graph U-Net (NSGUNet) is proposed to extract superpixel-level features. Finally, the above two features are weighted fusing, the fused features are classified by sparse graph regularization. Experiments on three datasets illustrate that the proposed method is effective.

关键词： Accuracy Geoscience and remote sensing Feature extraction Convolutional neural networks Data mining Hyperspectral imaging Image classification

来源：评论

学校读者我要写书评

暂无评论

Joint Classification Of Hyperspectral And LiDAR Data Based On Heterogeneous Attention Feature Fusion Network

Joint Classification Of Hyperspectral And LiDAR Data Based O...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhaoyang Wang Qingyan Wang Junping Zhang Xintao Liang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The fusion of hyperspectral image (HSI) and LiDAR data for classification has gained widespread attention. However, the current fusion methods still have limitations on the use of heterogeneous data information and the interaction of heterogeneous data features. Therefore, we propose a classification method based on heterogeneous attention feature fusion network (HAFF-Net). Firstly, The convolutional neural network is employed to capture local spatial features from multi-scale inputs of heterogeneous data. Secondly, the heterogeneous feature attention module is designed to deeply fuse the extracted local spatial features and promote the interaction of heterogeneous data. Next, a transformer encoder is utilized to extract global spectral features. Finally, the two extracted features are classified by decision fusion using different classifiers. The proposed approach exhibits effectiveness through experimental results on two datasets.

关键词： Laser radar Fuses Convolution Soft sensors Geoscience and remote sensing Feature extraction Transformers

来源：评论

学校读者我要写书评

暂无评论

Collision-Free Formation control of Multi-agent System with Low Oscillation 7th

Collision-Free Formation Control of Multi-agent System with ...

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Li, Jingyi Han, Haoran Lv, Maolong Sun, Chenyang Cheng, Jian Glassgow School University of Electronic Science and Technology of China Chengdu611731 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Traffic Control and Navigation College Air Force Engineering University Xian710051 China

ISBN: (纸本)9789819733279

In recent years, multi-agent systems are becoming more complex, scalable, adaptive, and integrating machine learning for enhanced capabilities. A popular approach for addressing this challenge involves the utilisation of artificial potential functions (APF). In this article, we address the problem of obstacle avoidance and achieving a predefined formation, particularly promoting smooth trajectory generation in multi-agent formation control. We employ the APF method to enhance the smoothness of the agents’ movement paths during these tasks. Collision damping terms are generated by differentiating the gradient of APF with respect to time. Incorporating generated damping terms in the APF control, we suppress irregular fluctuations during agents’ motion procedures. To ensure stability in the multi-agent system, we design an energy-based Lyapunov function and utilise it for proof. Simulation examples are presented to validate the theoretical results and demonstrate the advantages of the proposed control algorithm. Through these simulations, it becomes evident that the collision-free formation control algorithm enhances the smoothness of the agents’ trajectories. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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：