检索结果-内蒙古大学图书馆

3rd International Conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Wang, Zhongbo He, Miao Ding, Qinghai Luo, Haibo Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Space Star Technology Co Ltd. Beijing100086 China

ISBN: (纸本)9781510670945

Synthetic aperture radar (SAR) has a special ability to work in any type of inclement weather, and is a very suitable tool for Ocean surveillance. Scene classification is an essential pre-task of other computer vision tasks for ocean monitoring. It is of great importance to develop scene classification technology of SAR sea images. Due to the excellent feature representation abilities of neural networks, the deep learning-based methods are far superior to the traditional methods based on manual features in scene classification task performance. Many lightweight classification networks have been proposed to improve the inference speed of the networks. But in comparison with ordinary CNNs, the lightweight networks have slightly lower accuracy for scene classification tasks. So in this article, we proposed an improved lightweight Convolutional Neural Network for scene classification of SAR sea images. First, in order to meet the real-time performance, we choose MobileNetv1 as the original classification network in this paper. Then, to compensate for the lack of accuracy, we use 1D asymmetric convolution kernels to strengthen each layer of the depthwise convolutions in the network. Finally, after training time, we merge the linear calculations of each layer of the network to convert it into the original structure. The experimental results show that the modified model has obtained an accuracy improvement than the original one on the scene classification of sea SAR images without extra computation. © 2023 SPIE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Optical fiber surface plasmon resonance sensor using electroless-plated gold film for thrombin detection

引用

Analytical and Bioanalytical Chemistry 2024年第6期416卷 1469-1483页

作者： Huang, Yu Pu, Xiaoyin Qian, Husun Chuang, Chin-Jung Dong, Shanshan Wu, Jiangling Xue, Jianjiang Cheng, Wei Ding, Shijia Li, Shengqiang Chongqing Key Laboratory of Multi-scale Manufacturing Technology Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Chongqing School University of Chinese Academy of Sciences Chongqing400714 China Department of Laboratory Medicine The Affiliated Hospital of Guizhou Medical University Guizhou Guiyang550004 China Department of Opto-Electronic Engineering National Dong Hwa University Hualien97401 Taiwan Department of Clinical Laboratory Medical Sciences Research Center University-Town Hospital of Chongqing Medical University Chongqing401331 China The Center for Clinical Molecular Medical Detection The First Affiliated Hospital of Chongqing Medical University Chongqing400016 China College of Laboratory Medicine Chongqing Medical University Chongqing400016 China Clinical Laboratory First Teaching Hospital of Tianjin University of Traditional Chinese Medicine Tianjin300000 China National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion Tianjin300000 China

This paper describes the simple and label-free detection of thrombin using optical fiber surface plasmon resonance (SPR) sensors based on gold films prepared by the cost-effective method of electroless plating. The plating conditions for simultaneously obtaining gold film on cylindrical core and end surfaces of an optical fiber suitable for measurement were optimized. The fabricated sensor exhibited a linear refractive index sensitivity of 2150 nm/RIU and 7.136 (a.u.)/RIU in the refractive index of 1.3329–1.3605 interrogated by resonance wavelength and amplitude methods respectively and a single wavelength monitoring method was proposed to investigate the sensing performance of this sensor. Polyadenine diblock and thiolated thrombin aptamers were immobilized on gold nanoparticles and gold films respectively to implement a sandwich optical fiber assay for thrombin. The developed optical fiber SPR sensors were successfully used in the determination of thrombin down to 0.56 nM over a wide range from 2 to 100 nM and showed good selectivity for thrombin, which indicated their potential clinical applications for biomedical samples. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2024.

关键词： Surface plasmon resonance

来源：评论

学校读者我要写书评

暂无评论

VARIATION-NET: INTERPRETABLE VARIATION-INSPIRED DEEP NETWORK FOR PANSHARPENING

VARIATION-NET: INTERPRETABLE VARIATION-INSPIRED DEEP NETWORK...

引用

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Kun Zhang, Wei Tian, Xin Ma, Jiayi Zhou, Huabing Wang, Zhongyuan Electronic Information School Wuhan University 430072 China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology 430073 China School of Computer Science Wuhan University 430072 China

ISBN: (纸本)9781665438643

In this study, we propose Variation-net, an interpretable variation-inspired deep network for pansharpening, which aims to fuse panchromatic (PAN) and multispectral (MS) images for a high-resolution MS image. We first construct a novel variational pansharpening model with clear physical meanings. As the relationship between the PAN and MS images in the real situation is complex and nonlinear, we explore the similarity between PAN and MS images from the sparsity of nonlinear transforms in this variational pansharpening model. As a result, spatial details can be accurately transferred from PAN image to MS image. Furthermore, we build the Variation-net by unrolling the iterative shrinkage-thresholding algorithm to solve the proposed variational pansharpening model. Therefore, all modules in Variation-net have clear physical meanings and are easily observed, leading to good generalization capability. Meanwhile, nonlinear transforms and other parameters in the variational pansharpening model are learned end-to-end. The experiments demonstrate that Variation-net outperforms the state-of-the-art methods from the aspects of visual effect and objective quality analysis. © 2021 IEEE Computer Society. All rights reserved.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Clustering-Based State Grouping Reinforcement Learning for Switching Decision of Autonomous Vehicles

Hierarchical Clustering-Based State Grouping Reinforcement L...

引用

2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Ouyang, Wenjie Jiao, Yiwen Liu, Yang Li, Yang Hu, Manjiang Qin, Hongmao College of Mechanical and Vehicle Engineering Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical Mechanical and Computer Science Delft University of Technology Delft Netherlands College of Mechanical and Vehicle Engineering Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Changsha China Institute of Hunan University Wuxi Intelligent Control Research Wuxi China

ISBN: (纸本)9798350316308

Reinforcement learning (RL) has gained wide attention, but its implementation in autonomous vehicles is still limited by insufficient sample efficiency and heavy training costs. The training efficiency of RL agents is influenced by the dimension of the state space, which can be partitioned to reduce the complexity of sampling and computation. This study proposes a hierarchical clustering-based state grouping reinforcement learning (HCSG-RL) method for the switching decision of autonomous vehicles. First, we partition the base state space into groups and generate a hierarchical tree of state space groups. Then, we train multiple sub-agents for each node in the hierarchical tree. Finally, we add these trained-well sub-model into master policy. This method allows us to fully explore all state spaces and improve the training efficiency of individual agents, which handles the 'long-tail' issue and the curse of dimensionality issue. We conduct experiments in a simulation environment and results show that the proposed method has 16-72% reward improvement compared to the tree model in different road length. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Product Resilience Evaluation: A Bayesian Network Modeling Based Method

SSRN

引用

SSRN 2024年

作者： Zhou, Ruihan Guo, Xin Cai, Miao Gou, Honggang Zhao, Wu Shi, Jiancheng School of Mechanical Engineering Sichuan University Chengdu610065 China Innovation Method and Creative Design Key Laboratory of Sichuan Province Chengdu610065 China Sichuan Engineering Technology Research Center of Broadband Electronic Intelligent Manufacturing Chengdu610036 China

Future products will have a higher degree of intelligence, and more complex and changing use environments, so resilience has been introduced into the design and operation of products as a concept that helps them cope with high-impact shocks and the damage they cause. With the work on product resilience, there is a need to find methods that can objectively reflect product resilience. Existing methods that can be used to evaluate product resilience are usually based on performance curves, but it is difficult to reflect the multifactorial and stochastic character of product resilience process. Therefore, this paper firstly reviews the resilience related research, and analyzes the factors affecting product resilience and their interrelationships layer by layer to construct the index system of product resilience. Then, a Bayesian network model is established based on the results of the above analysis, and the corresponding calculation method is proposed. Finally, the proposed method is illustrated with a case study of a complex terrain drilling rig and its improvement program. After discussion, the proposed method can be applied to the quantitative evaluation of product resilience, and the possible design direction of resilient products can be suggested using this method. © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Weld Seam Identification and Calibration Method of Spiral Steel Pipe Grinding robot

Weld Seam Identification and Calibration Method of Spiral St...

引用

World Conference on Mechanical Engineering and intelligent manufacturing (WCMEIM)

作者： Ao Cao Yaguang Zhu Yangyang Liu Qiong Liu Xu He The Key Laboratory of Road Construction Technology and Equipment of MOE Chang'an University Xi'an China Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components Shanghai China

ISBN: (纸本)9781665473705

The identification and calibration of weld seam is the key to solve the problem of low efficiency and low quality in weld grinding of spiral welded steel pipe. For the identification of internal and external weld seams of spiral welded steel pipes, this paper proposes three weld seam identification methods, which are respectively based on the sum of squares of residuals (RSS), template matching and segmentation search; In addition, aiming at the calibration of the feature points of the weld, this paper proposes the farthest distance method and slope analysis method to calibrate the left and right inflection points of the weld. The experimental results show that the three weld seam identification methods can accurately identify the relative position of the laser profiler and the weld seam. The weld seam calibration method can enable the feature points to be accurately identified and calibrated, guaranteeing subsequent pipe end weld removal.

关键词： Spirals Welding Lasers Production Real-time systems Calibration manufacturing

来源：评论

学校读者我要写书评

暂无评论

Word length-aware text spotting: Enhancing detection and recognition in dense text image

arXiv

引用

arXiv 2023年

作者： Wang, Hao Zhou, Huabing Zhang, Yanduo Lu, Tao Ma, Jiayi Hubei University of Arts and Science Xiangyang China College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China Electronic Information School Wuhan University Wuhan China

Scene text spotting is essential in various computer vision applications, enabling extracting and interpreting textual information from images. However, existing methods often neglect the spatial semantics of word images, leading to suboptimal detection recall rates for long and short words within long-tailed word length distributions that exist prominently in dense scenes. In this paper, we present WordLenSpotter, a novel word length-aware spotter for scene text image detection and recognition, improving the spotting capabilities for long and short words, particularly in the tail data of dense text images. We first design an image encoder equipped with a dilated convolutional fusion module to integrate multiscale text image features effectively. Then, leveraging the Transformer framework, we synergistically optimize text detection and recognition accuracy after iteratively refining text region image features using the word length prior. Specially, we design a Spatial Length Predictor module (SLP) using character count prior tailored to different word lengths to constrain the regions of interest effectively. Furthermore, we introduce a specialized word Length-aware Segmentation (LenSeg) proposal head, enhancing the network’s capacity to capture the distinctive features of long and short terms within categories characterized by long-tailed distributions. Comprehensive experiments on public datasets and our dense text spotting dataset DSTD1500 demonstrate the superiority of our proposed methods, particularly in dense text image detection and recognition tasks involving long-tailed word length distributions encompassing a range of long and short words. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

A Dual-Branch intelligent Fault Diagnosis Method Based on Spatial Variable Scale Pooling for Inter-Class Incomplete Data

A Dual-Branch Intelligent Fault Diagnosis Method Based on Sp...

引用

International Conference on Reliability, Maintainability and Safety,ICRMS

作者： Fei Chen Binbin Xu Di Wu Zheng Yang Longbo He Xin Zhou Boquan Ma Xiaobing Li Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China School of Mechanical and Aerospace Engineering Jilin University Changchun China Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology Guangzhou China

ISBN: (数字)9798350329988

ISBN: (纸本)9798350329995

The intelligent fault diagnosis method based on convolutional neural network (CNN) has achieved significant success in the field of mechanical fault diagnosis. However, its application has been limited to complete inter-class data obtained from controlled laboratory settings. In practical working scenarios, inter-class data often lacks sufficient fault data compared with health data, which leads to an unsatisfactory CNN performance. This paper proposes a dual-branch intelligent fault diagnosis method based on spatial variable scale pooling aiming at the constraint of CNN in capturing local features only to enhance the applicability of intelligent fault diagnosis methods in real working conditions and improve the fault feature representation capability and diagnostic accuracy. First, this method constructs a dual-branch model that combines the VGG16 convolutional network and the self-attention Encoder from the Transformer architecture, enabling the fusion of local and global features and enhancing the fault representation learning capability of the model. Second, the pooling layers within the convolutional branch are improved by introducing spatial variable scale pooling for extracting more detailed fault features. Finally, a fusion module is designed to align the extracted features from the dual-branch model semantically. The superiority of the proposed model is demonstrated through the experiment.

关键词： Fault diagnosis Representation learning Accuracy Rotating machines Feature extraction Transformers Stability analysis Robustness Safety Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Trajectory tracking of 4-DOF assembly robot based on quantification factor and proportionality factor self-tuning fuzzy PID control

引用

International Journal of Wireless and Mobile Computing 2020年第4期18卷 361-370页

作者： Li, Cuiqiao Sun, Ying Li, Gongfa Jiang, Du Zhao, Haoyi Jiang, Guozhang Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China

When the manipulator performs the assembling task, the performance of the system is easily affected by external interference and parameter changes, resulting in slow tracking response speed and low tracking accuracy. In order to improve the stability and robustness of the controller, a self-tuning fuzzy PID controller with quantification factor and proportionality factor is designed based on the fuzzy PID control. The parameters of the PID controller are dynamically fine-tuned by the fuzzy controller 1, and the quantification factor and proportionality factor are adjusted online by the modifying fuzzy controller 2 to fine-tune the parameters of PID controller. The control effect and robustness of the improved fuzzy PID controller to the manipulator system are simulated and verified on the MATLAB/Simulink platform. The simulation results show that the proposed method significantly improves the transient response speed, tracking accuracy and follower characteristics of the system, and has good dynamic performance. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Manipulators

来源：评论

学校读者我要写书评

暂无评论

Bimanual gesture recognition based on convolution neural network

引用

International Journal of Wireless and Mobile Computing 2020年第4期18卷 311-319页

作者： Wu, Hao Li, Gongfa Sun, Ying Jiang, Guozhang Jiang, Du Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China

Gesture recognition is a key research field in the human-computer interaction. At present, most of researchers focus on one-handed gesture recognition, but do not pay much attention to bimanual (two hands) gesture recognition. This paper presents a deep learning-based solution to tackle the self-occlusion and self-similarity. To solve this problem, this paper uses Kinect to collect many colour and depth images of different gestures, and each gesture contains multiple sample individuals. Colour images and depth images are used to train the recognition model of bimanual gesture respectively, and then the colour image and depth image are fused, and the bimanual gesture recognition model is trained based on colour image and depth image fusion. Then, the bimanual recognition effects of the three models are compared. The experimental results show that, regardless of the single gesture precision or the mean average precision, the bimanual gesture recognition effect of the fused model is better than the gesture recognition models based on either colour image or depth image. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Deep 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：