检索结果-内蒙古大学图书馆

Mountain landslide monitoring based on wireless sensor network

Information Technology Journal 2013年第15期12卷 3357-3362页

作者： Li, Xiaoling Yuan, Jimin School of Computer Science and Technology Chengdu University 610106 Chengdu China University Key Laboratory of Pattern Recognition Intelligent Information Processing 610106 Chengdu China Department Computer Engineering Chengdu Aeronautic Vocational and Technical College Chengdu 610000 China

The study provides a design scheme for the mountain landslide monitoring System based on wireless sensor networks. Different sensors are mainly used to collect data about the water depth in the soil and the sloping angel of the hillside in real-time. After data analysis and processing by monitoring center, the warning information is provided for related departments to take effective measures rapidly to protect people's lives and properties. © 2013 Asian Network for Scientific Information.

关键词： Landforms

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Oracle character recognition by nearest neighbor classification with deep metric learning 15

Oracle character recognition by nearest neighbor classificat...

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15th IAPR International Conference on Document Analysis and recognition, ICDAR 2019

作者： Zhang, Yi-Kang Zhang, Heng Liu, Yong-Ge Yang, Qing Liu, Cheng-Lin National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Center for Excellence of Brain Science and Intelligence Technology Beijing China School of Computer & Information Engineering Anyang Normal University Henan China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Henan China

ISBN: (纸本)9781728128610

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU. © 2019 IEEE.

关键词： Character recognition

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Automatic needle segmentation using 3D Gray-level Hough Transform in 3D ultrasound images

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Journal of Computational Information Systems 2008年第5期4卷 2021-2026页

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming Image Processing and Intelligence Control Key Lab. of Education Ministry of China Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Department of Biomedical Engineering School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

This paper describes a novel 3D needle segmentation algorithm for 3DUS data. The algorithm includes the 3D Gray-level Hough Transform (3DGHT), which is based on the representation (ψ, θ, ρ, α) of straight lines in 3D space and Local Dimensionality Estimator (GST). Different from the traditional Hough transformation the process of binarization is not needed, because the proposed algorithm adopts the original gray level image directly. We tested it with the synthetic data and water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1° and position deviation less than 1 mm. © 2008 Binary Information Press.

关键词： Hough transforms

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pNovo+: De Novo Peptide Sequencing and Assembly Using Complementary HCD and ETD Tandem Mass Spectra

pNovo+: De Novo Peptide Sequencing and Assembly Using Comple...

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第八届中国蛋白质组学大会

作者： Hao Chi Haifeng Chen Kun He Long Wu Bing Yang Rui-Xiang Sun Jianyun Liu Wen-Feng Zeng Chun-Qing Song Si-Min He Meng-Qiu Dong Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China National Institute of Biological SciencesBeijingBeijing 102206China Laboratory of Intelligent Recognition and Image ProcessingBeijing Key Laboratory of Digital MediaBeihang UniversityBeijing100191China

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Cross-media analysis and reasoning: advances and directions

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Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 44-57页

作者： Yu-xin PENG Wen-wu ZHU Yao ZHAO Chang-sheng XU Qing-ming HUANG Han-qing LU Qing-hua ZHENG Tie-jun HUANG Wen GAO Institute of Computer Science and Technology Peking University Department of Computer Science and Technology Tsinghua University Institute of Information Science Beijing Jiaotong University National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences Department of Computer Science and Technology Xi'an Jiaotong University School of Electronics Engineering and Computer Science Peking University

Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances, challenges, and future directions for the field. To address these issues, we provide an overview as follows：（1） theory and model for cross-media uniform representation; （2） cross-media correlation understanding and deep mining; （3） cross-media knowledge graph construction and learning methodologies; （4） cross-media knowledge evolution and reasoning; （5） cross-media description and generation; （6） cross-media intelligent engines; and （7） cross-media intelligent applications. By presenting approaches, advances, and future directions in cross-media analysis and reasoning, our goal is not only to draw more attention to the state-of-the-art advances in the field, but also to provide technical insights by discussing the challenges and research directions in these areas.

关键词： Cross-media analysis Cross-media reasoning Cross-media applications

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Learning data-adaptive nonparametric kernels

arXiv

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arXiv 2018年

作者： Liu, Fanghui Huang, Xiaolin Gong, Chen Yang, Jie Li, Li Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Automation Tsinghua University

Kernel methods have been extensively used in a variety of machine learning tasks such as classification, clustering, and dimensionality reduction. For complicated practical tasks, the traditional kernels, e.g., Gaussian kernel and sigmoid kernel, or their combinations are often not sufficiently flexible to fit the data. In this paper, we present a Data-Adaptive Nonparametric Kernel (DANK) learning framework in a data-driven manner. To be specific, in model formulation, we impose an adaptive matrix on the kernel/Gram matrix in an entry-wise strategy. Since we do not specify the formulation of the adaptive matrix, each entry in the adaptive matrix can be directly and flexibly learned from the data. Therefore, the solution space of the learned kernel is largely expanded, which makes our DANK model flexible to capture the data with different local statistical properties. Specifically, the proposed kernel learning framework can be seamlessly embedded to support vector machines (SVM) and support vector regression (SVR), which has the capability of enlarging the margin between classes and reducing the model generalization error. Theoretically, we demonstrate that the objective function of our DANK model embedded in SVM/SVR is gradient-Lipschitz continuous. Thereby, the training process for kernel and parameter learning in SVM/SVR can be efficiently optimized in a unified framework. Further, to address the scalability issue in nonparametric kernel learning framework, we decompose the entire optimization problem in DANK into several smaller easy-to-solve problems, so that our DANK model can be efficiently approximated by this partition. The effectiveness of this approximation is demonstrated by both empirical studies and theoretical guarantees. Experimentally, the proposed DANK model embedded in SVM/SVR achieves encouraging performance on various classification and regression benchmark datasets when compared with other representative kernel learning based algorithms. Copyrig

关键词： Matrix algebra

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The first nontrivial three color upper domination Ramsey number is 13

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Journal of Combinatorial Mathematics and Combinatorial Computing 2012年 83卷 161-165页

作者： Su, Changming Lang, Fangnian Shao, Zehui School of Information Science and Technology Chengdu University Chengdu 610106 China University Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province China School of Electronic Engineering University of Electronic Science and Technology of China Chengdu 610054 China

The upper domination Ramsey number u(3, 3, 3) is the smallest integer n such that every 3-coloring of the edges of complete graph Kn contains a monochromatic graph G with T(G) ≥ 3, where T(G) is the maximum order over all the minimal dominating sets of the complement of G. In this note, with the help of computers, we determine that U(3, 3, 3) = 13, which improves the results that 13 ≤ U(3, 3, 3) ≤ 14 provided by Michael A. Henning and Ortrud R. Oellermann.

关键词： Graph theory

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The Intelligent Preparation of Scene Matching Guidance Reference Map Based on Prior Knowledge

The Intelligent Preparation of Scene Matching Guidance Refer...

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Pan Chen Yaozong Zhang Jinmeng Wu Xingxun Li School of Electrical Information Wuhan Institute of Technology Wuhan China Hubei Engineering Research Center of Video Image and HD Projection Wuhan China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

Aiming at the problem that the automatic processing level of reference map preparation in image matching guidance is not high enough, our work proposes a novel reference map preparation based on the prior knowledge such as infrared imaging conditions and trajectory information. In the image preprocessing stage, the deep convolution network is used to extract the regions of interest automatically and meanwhile, combined with the analysis of connected region, the ROI extraction results can be optimized; Then, the natural scene is classified by texture of material, and the periodic temperature change of the feature is automatically mapped to the gray level, which conforms to the actual imaging characteristics and reduces the artificial gray level error. Next, combined with trajectory information and perspective transformation, multi-scale and multi-view scene images can be automatically obtained, which can avoid the situation that the manually selected target or landmark is not obvious or does not exist in the actual imaging. With comprehensive analysis from three aspects of reliability, stability and uniqueness, the best matching area is automatically determined, which improves the accuracy of matching algorithm and the time performance of reference map preparation system.

关键词： Temperature Convolution Target recognition Real-time systems Surface treatment Knowledge engineering Atmospheric modeling

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Learning with asymmetric kernels: Least squares and feature interpretation

arXiv

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arXiv 2022年

作者： He, Mingzhen He, Fan Shi, Lei Huang, Xiaolin Suykens, Johan A.K. Institute Of Image Processing And Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory For Contemporary Applied Mathematics Fudan University Shanghai200433 China School Of Mathematical Sciences Fudan University Shanghai200433 China KU Leuven LeuvenB-3001 Belgium

Asymmetric kernels naturally exist in real life, e.g., for conditional probability and directed graphs. However, most of the existing kernel-based learning methods require kernels to be symmetric, which prevents the use of asymmetric kernels. This paper addresses the asymmetric kernel-based learning in the framework of the least squares support vector machine named AsK-LS, resulting in the first classification method that can utilize asymmetric kernels directly. We will show that AsK-LS can learn with asymmetric features, namely source and target features, while the kernel trick remains applicable, i.e., the source and target features exist but are not necessarily known. Besides, the computational burden of AsK-LS is as cheap as dealing with symmetric kernels. Experimental results on the Corel database, directed graphs, and the UCI database will show that, in the case asymmetric information is crucial, the proposed AsK-LS can learn with asymmetric kernels and performs much better than the existing kernel methods that have to do symmetrization to accommodate asymmetric kernels. © 2022, CC BY.

关键词： Directed graphs

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Multi-level graph convolutional network with automatic graph learning for hyperspectral image classification

arXiv

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arXiv 2020年

作者： Wan, Sheng Gong, Chen Pan, Shirui Yang, Jie Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image classification as two separate tasks, which often results in suboptimal performance. Another defect of these methods is that they mainly focus on modeling the local pairwise importance between graph nodes while lack the capability to capture the global contextual information of HSI. In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph information at both local and global levels. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions, which helps encode the spatial context to form the graph information at local level. Moreover, we utilize multiple pathways for local-level graph convolution, in order to leverage the merits from the diverse spatial context of HSI and to enhance the expressive power of the generated representations. To reconstruct the global contextual relations, our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level. Then inference can be performed along the reconstructed graph edges connecting faraway regions. Finally, the multi-level information is adaptively fused to generate the network output. In this means, the graph learning and image classification can be integrated into a unified framework and benefit each other. Extensive experiments have been conducted on three real-world hyperspectral datasets, which are shown to outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： image classification

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