检索结果-内蒙古大学图书馆

A Method for Improving CNN-Based Image Recognition Using DCGAN

computers, Materials & Continua 2018年第10期57卷 167-178页

作者： Wei Fang Feihong Zhang Victor S.Sheng Yewen Ding School of Computer&Software Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science&TechnologyNanjing210044China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing210023China Computer Science Department University of Central ArkansasConway AR72035USA

Image recognition has always been a hot research topic in the scientific community and *** emergence of convolutional neural networks(CNN)has made this technology turned into research focus on the field of computer vision,especially in image *** it makes the recognition result largely dependent on the number and quality of training ***,DCGAN has become a frontier method for generating images,sounds,and *** this paper,DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating *** combine DCGAN with CNN for the second *** DCGAN to generate samples and training in image recognition model,which based by *** method can enhance the classification model and effectively improve the accuracy of image *** the experiment,we used the radar profile as dataset for 4 categories and achieved satisfactory classification *** paper applies image recognition technology to the meteorological field.

关键词： DCGAN image recognition CNN samples

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Wireless Communication Signal Strength Prediction Method Based on the K-nearest Neighbor Algorithm 5th

Wireless Communication Signal Strength Prediction Method Bas...

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5th International Conference of Pioneer computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Chen, Zhao Xiong, Ning Wang, Yujue Ding, Yong Xiang, Hengkui Tang, Chenjun Liu, Lingang Zou, Xiuqing Luo, Decun Key Lab on Cognitive Radio and Information Processing School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811501173

Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes’ signal strength and evaluate the interference protection with high accuracy. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Spectrum analyzers

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Adaptive temporal segmentation for action recognition

Adaptive temporal segmentation for action recognition

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2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019

作者： Chen, Zhiyu Gu, Yangwei Deng, Chunhua Zhu, Ziqi School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Lab of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781728159287

Learning deep representations have been widely used in action recognition task. However, the features extracted by deep convolutional neural networks (CNNs) have many redundant information. This paper aims to discover the relevance between temporal features extracted by CNNs. Different fromTemporal Segment Networks (TSN) to randomly select video clips. Based on the matrix-based Rényi's α-entropy, we estimate the mutual information between temporal domain features. Through our experiments, we propose an adaptive temporal segmentation scheme to represent the entire videos. We also combine the features of RGB and optical flow frames extracted by 3D ConvNets to verify the complementary information between them. We show that the proposed approach achieves 94.4 and 72.8 percent accuracy, in the UCF- 101 and HMDB-51 datasets. © 2019 IEEE.

关键词： Deep neural networks

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Learning label-Specific Features and Class-Dependent labels for Multi-label Classification

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IEEE Transactions on Knowledge and Data Engineering 2016年第99期PP卷 3309-3323页

作者： Huang, Jun Li, Guorong Huang, Qingming Wu, Xindong School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101480 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Computer Science University of Vermont 33 Colchester Avenue BurlingtonVT05405 United States

Binary Relevance is a well-known framework for multi-label classification, which considers each class label as a binary classification problem. Many existing multi-label algorithms are constructed within this framework and utilize identical data representation in the discrimination of all the class labels. In multi-label classification, however, each class label might be determined by some specific characteristics of its own. In this paper, we seek to learn label-specific data representation for each class label, which is composed of label-specific features. Our proposed method LLSF can not only be utilized for multi-label classification directly, but also be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Inspired by the research works on modeling high-order label correlations, we further extend LLSF to learn class-Dependent labels in a sparse stacking way, denoted as LLSFDL. It incorporates both second-order and high-order label correlations. A comparative study with the state-of-the-art approaches manifests the effectiveness and efficiency of our proposed methods. © 2016 IEEE.

关键词： Classification (of information)

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Learning Dual-Domain Calibration and Distance-Driven Correlation Filter:A Probabilistic Perspective for UAV Tracking

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computers, Materials & Continua 2023年第12期77卷 3741-3764页

作者： Taiyu Yan Yuxin Cao Guoxia Xu Xiaoran Zhao Hu Zhu Lizhen Deng Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjingChina School of Computer Science Qufu Normal UniversityQufuChina National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjingChina

Unmanned Aerial Vehicle(UAV)tracking has been possible because of the growth of intelligent information technology in smart cities,making it simple to gather data at any time by dynamically monitoring events,people,the environment,and other aspects in the *** traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking *** these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization *** light of the aforementioned issues,this work suggests a dual-domain Jensen-Shannon divergence correlation filter(DJSCF)model address the probability-based distance measuring issue in the event of filter *** two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and *** new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response *** model is roughly transformed into a linear equality constraint issue in the iterative solution,which is then solved by the alternate direction multiplier method(ADMM).The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.

关键词： Dual-domain weighting distance measure correlation filter ADMM

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Vertex-disjoint paths in the generalized hypercube under 1-restricted connectivity 21

Vertex-disjoint paths in the generalized hypercube under 1-r...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Wang, Guijuan Lin, Cheng-Kuan Fan, Jianxi Cheng, Baolei Liu, Zhao School of Computer Science and Technology Soochow University Suzhou215006 China College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing210003 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

ISBN: (纸本)9781728120584

The generalized hypercube is an excellent interconnection network since it includes many interconnection topologies and it can be used to construct many data center networks. Considering the probability that, in general, all neighbors of one vertex becoming faulty at the same time is extremely low, we assume that each vertex has at least one fault-free neighbor. An r-dimensional generalized hypercube is denoted by G(m-r, m-r-1,⋅, m-1). In this paper, we proposed an efficient algorithm which can construct at least κ^1(G) disjoint paths based on any two adjacent vertices in G(m-r, m-r-1, m-1) in O(rm) time where κ^1(G) is the 1-restricted connectivity of G(m-r, m-r-1, m-1). The maximum length of these disjoint paths is bounded by 7. © 2019 IEEE.

关键词： Fault tolerance

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Effective camera motion analysis approach

Effective camera motion analysis approach

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2010 International Conference on Networking, Sensing and Control, ICNSC 2010

作者： Ma, Shugao Wang, Weiqiang Graduate University of Chinese Academy of Sciences Beijing China Key Lab. of Intelligent Information Processing Institute of Computer Technology Chinese Academy of Sciences China

ISBN: (纸本)9781424464531

Camera motion analysis (CMA) is very useful for many video content analysis tasks, but few works competently handle the videos with significant camera or object motion. In this paper, we present an effective CMA approach for such challenging cases. The effectiveness of our approach comes from two aspects: (1) reliably matching keypoints on consecutively sampled frames, where both the appearance similarity and motion smoothness of keypoints are considered;(2) effectively distinguishing background keypoints from foreground ones by a novel and advanced voting process, where the voting weights of keypoints are dynamically adjusted to guarantee that the influence of foreground motion can be largely reduced in CMA. Thus, a parametric camera motion model can be naturally derived by the accurate estimation of the motion of background keypoints. The experimental results on the TRECV2005 dataset and 500 shots from seven classic action movies demonstrate the effectiveness of our approach. ©2010 IEEE.

关键词： Motion analysis

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A bigtree index for resource space model

A bigtree index for resource space model

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6th International Conference on Semantics, Knowledge and Grid, SKG 2010

作者： Zeng, Zhongyi Zhuge, Hai Department of Computer Science and Technology University of Science and Technology of China Hefei China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Big tree is a hierarchical data model for efficiently managing large scale web resources. This paper presents a new type of hierarchical data model, which is inspired by resource space model. Also it could be thought ... 详细信息

ISBN: (纸本)9780769541891

关键词： Trees (mathematics)

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Predicting helix boundaries of α-helix transmembrane protein with feedback conditional random fields 11th

Predicting helix boundaries of α-helix transmembrane protei...

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11th International Conference on Intelligent Computing, ICIC 2015

作者： Wang, Kun Wu, Hongjie Lu, Weizhong Fu, Baochuan Lü, Qiang Huang, Xu School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China School of Computer Science and Technology Soochow University Suzhou215006 China Jiangsu Provincial Key Lab for Information Processing Technologies Suzhou215006 China School of Information Engineering Huzhou University Huzhou313000 China

ISBN: (纸本)9783319221793

Transmembrane proteins play an important role in cellular energy production, signal transmission, metabolism. Existing machine learning methods are difficult to model the global correlation of the membrane protein sequence, and they also can not improve the quality of the model from sophisticated sequence features. To address these problems, in this paper we proposed a novel method by a feedback conditional random fields (FCRF) to predict helix boundaries of α-helix transmembrane protein. A feedback mechanism was introduced into multi-level conditional random fields. The results of lower level model were used to calculate new feedback features to enhance the ability of basic conditional random fields. One wide-used dataset DB1 was used to validate the performance of the method. The method achieved 95% on helix location accuracy. Compared with the other predictors, FCRF ranks first on the accuracy of helix location. © Springer International Publishing Switzerland 2015.

关键词： Learning systems

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DO-FAM: Disentangled Non-Linear Latent Navigation For Facial Attribute Manipulation 48

DO-FAM: Disentangled Non-Linear Latent Navigation For Facial...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Yuan, Yifan Ma, Siteng Shan, Hongming Zhang, Junping Shanghai Key Lab of Intelligent Information Processing School of Computer Science China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9781728163277

Facial attribute manipulation (FAM) aims to edit the semantic attributes of facial images according to the user's requirements. Unfortunately, the majority of existing FAM methods struggle in meeting at least one of the two requirements: high reconstruction quality and high irrelevance preservation. To alleviate these two limitations, we propose a novel Disentangled nOn-linear latent navigation framework for FAM, termed DO-FAM. To promote the reconstruction quality, we leverage hypernetworks to fine-tune a pre-trained StyleGAN2 generator. To decouple entangled attributes, we propose a novel Disentangled nOn-Linear Latent transformation module, named DOLL, which consists of three components: (1) a decomposer to factorize input latent codes into two parts: attribute-related and attribute-unrelated;(2) a non-linear Latent Transformation Network (LTNet) to navigate the attribute-related latent codes to the target one with respect to the designed attribute(s);and (3) a latent classifier tasked with predicting latent codes' attributes to guide the latent code navigation. Extensive experimental results on a widely-used benchmark facial editing dataset, CelebA-HQ, demonstrate the superiority of our method over state-of-the-art methods. © 2023 IEEE.

关键词： disentangle learning Facial attribute manipulation Hypernetwork style transfer StyleGAN

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