检索结果-内蒙古大学图书馆

第30届中国控制与决策会议

作者： Ruiyao Liu Xiaoyong Bian Yuxia Sheng School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Information Science and Engineenring Wuhan University of Science and Technology

In this papers, classification of remote sensing image scene is investigated. A scene classification approach based on multi-feature fusion has been proposed. In the proposed approach, three types of features are extracted. Specifically, extended multi-attribute profile(EMAP)-based texture feature, saliency-based shape feature and color ones. The texture features are extracted by EMAP. Furthermore, the Hu invariant moments are extracted from the saliency map, where the saliency map is obtained by frequency-tuned saliency detection. Meanwhile, the color moments are extracted as the color features from the image scenes. As for EMAP-based features, dimension reduction via principal component analysis(PCA) is first performed and combined with other two types of features to form a compact feature representation. Finally, support vector machine(SVM) is employed to classify the remote sensing image scenes. The experiments on the two challenging image scene datasets are performed to show that the proposed method is simple, yet efficient to implement, comparing with the state-of-the-arts.

关键词： Scene classification morphological attribute profile saliency map feature extraction

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A Feature Matching Method For Simultaneous Localization And Mapping 2

A Feature Matching Method For Simultaneous Localization And ...

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2017 IEEE 2nd information technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Chen Xiangkui Jiang Min Zuo Liangyu Jiang jian College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System

ISBN: (纸本)9781509064151;9781509064144

Simultaneous Localization and Mapping is an important technology which help a mobile robot to determine its location and build the environment map. Recently, the RGBD sensor is widely used in the robot, research on RGBD-SLAM becomes a hot topic. In order to calculate the movement parameters of robot, feature matching is adopted to register the two adjacent RGBD images in the video stream. This paper proposed an improved feature matching method for RGBDSLAM. The experiment results show that, compared with the traditional SIFT feature matching methods for RGBD-SLAM,the performance of the proposed method is improved significantly.

关键词： Simultaneous Localization and Mapping Feature Matching SIFT FREAK

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Visual Tracking Algorithm Based on Color Name Histogram

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IOP Conference Series: Materials Science and Engineering 2020年第1期790卷

作者： Chenchen Yue Wangsheng Yu Zhiqiang Hou Sugang Ma College of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China Information and Navigation Institute of Air Force Engineering University Xi'an 710077 China

Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.

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SPESC: A Specification Language for Smart Contracts

SPESC: A Specification Language for Smart Contracts

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Xiao He Bohan Qin Yan Zhu Xing Chen Yi Liu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China National Computer Network Emergency Response Technical Team Coordination Center of China

ISBN: (纸本)9781538626672

The smart contract is an interdisciplinary concept that concerns business, finance, contract law and information technology. Designing and developing a smart contract may require the close cooperation of many experts coming from different fields. How to support such collaborative development is a challenging problem in blockchain-oriented software engineering. This paper proposes SPESC, a specification language for smart contracts, which can define the specification of a smart contract for the purpose of collaborative design. SPESC can specify a smart contract in a similar form to real-world contracts using a natural-language-like grammar, in which the obligations and rights of parties and the transaction rules of cryptocurrencies are clearly defined. The preliminary study results demonstrated that SPESC can be easily learned and understood by both IT and non-IT users and thus has greater potential to facilitate collaborative smart contract development.

关键词： Contracts Cryptography Contract law Syntactics Collaboration Software

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Survey on Multi-Objective Evolutionary Algorithms

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Journal of Physics: Conference Series 2019年第1期1288卷

作者： Wenlan Huang Yu Zhang Lan Li School of Computer and Information Engineering Harbin University of Commerce Harbin 150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin University of Commerce Harbin 150028 China

Multi-objective evolutionary algorithm (MOEA) is the main method to solve multi-objective optimization problem (MOP), which has become one of the hottest research areas of evolutionary computation. This paper surveys the development of MOEA and its research status, classifies it into four categories, analyzes the advantages and disadvantages of these algorithms, and summarizes the main application fields of MOEA. Finally several viewpoints for the future research of MOEA are presented.

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X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data

arXiv

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arXiv 2020年

作者： Hong, Danfeng Yokoya, Naoto Xia, Gui-Song Chanussot, Jocelyn Zhu, Xiao Xiang Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Graduate School of Frontier Sciences University of Tokyo Chiba277-8561 Japan Geoinformatics Unit RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan School of Computer Science Wuhan University Wuhan430072 China Institute of Artificial Intelligence Wuhan University Wuhan430072 China State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Univ. Grenoble Alpes INRIA CNRS Grenoble INP LJK Grenoble38000 France Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A Detection Method of Unbearable Bit Error Rate for a 2.4kbps MELP Vocoder 9

A Detection Method of Unbearable Bit Error Rate for a 2.4kbp...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Ye Li Qiuyun Hao Peng Zhang Yanhong Fan Jingsai Jiang Xiaofeng Ma Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University) Ministry of Education

ISBN: (纸本)9781509038237;9781509038220

In harsh channel conditions, the quality of the synthetic speech at low bit rate would be affected severely. In order to improve the robustness of the vocoder and make it more resilient to errors in random channel, unequal error protection（UEP） channel coding is usually adopted. However, when the errors cannot be corrected in some cases, UEP channel coding will not improve the quality of the synthetic speech. More seriously, the quality of the synthetic speech may deteriorate rapidly. In order to improve the user’s feeling in such case, a detection method of unbearable bit error rate（BER） is proposed in the paper. This method can increase the error resilience capability of the vocoder in random channel. When the error bits cannot be corrected, the vocoder will output comfortable noise to improve the user’s feeling. This method is used for a 2.4 kbps vocoder based on MELP, and the test results show that it works effectively.

关键词： component MELP vocoder UEP unbearable BER

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Multiple Kernel Learning for Representation-based Classification of Hyperspectral Images

Multiple Kernel Learning for Representation-based Classifica...

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第30届中国控制与决策会议

作者： Yu Qin Xiaoyong Bian Yuxia Sheng School of Computer Science Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Information Science and Engineenring Wuhan University of Science and Technology

In the paper we propose a multiple kernel learning framework for representation-based classification（MKLC） of hyperspectral images. Unlike the existing methods that often exploit the single feature extraction method or the single kernel method;moreover, the single feature representation and kernelized RC is biased and less stable due to the high coherence of the training samples. The proposed approach is different from traditional kernelized methods and characterized by multiple features and multiple kernel learning in a representation-based classification manner. Experimental results on several real HSI datasets demonstrate that the proposed method can achieve superior performance than the state-of-the-art classification methods.

关键词： Hyperspectral image (HSI) multiple kernels learning (MKL) multiple features representation-based classification

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Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field

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Journal of Harbin Institute of technology(New Series) 2018年第1期25卷 57-66页

作者： Maozu Guo Shuang Cheng Chunyu Wang Xiaoyan Liu Yang Liu School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China Beijing Key Laboratory for Research on Intelligent Processing Method of Building Big Data( Beijing University of Civil Engineering and Architecture) Beijing 100044China Institute of Materials China Academy of Engineering PhysicsMianyang 621700SichuanChina

MicroRNAs( miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However,in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data,the decision fusion method is used to prioritize the results of existing methods. After that,the performance of decision fusion method is validated. Furthermore,in consideration of the long range dependencies of successive expression values,Hidden Conditional Random Field model( HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs.

关键词： expression profiling hidden conditional random field miRNA-disease association network

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Reply to Qin et al

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Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2020年第8期71卷 2020页

作者： Linhui Ruan Qingrun Zeng Yuanjing Feng Kunlin Jin Qichuan Zhuge Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research Department of Neurosurgery The First Affiliated Hospital of Wenzhou Medical University Wenzhou China. Institute of Information Processing and Automation College of Information Engineering Zhejiang University of Technology Hangzhou China. Department of Pharmacology and Neuroscience University of North Texas Health Science Center Fort Worth Texas USA.

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