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NON-PARAMETRIC TOPIC MODEL FOR DISCOVERING geoGRAPHICAL TOPIC VARIATIONS

Journal of Electronics(China) 2014年第6期31卷 576-586页

作者： Qi Xiang Huang Yu Song Jun Huang Tinglei Wang Hongqi Fu Kun Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

This paper presents a non-parametric topic model that captures not only the latent topics in text collections, but also how the topics change over space. Unlike other recent work that relies on either Gaussian assumptions or discretization of locations, here topics are associated with a distance dependent Chinese Restaurant Process(ddC RP), and for each document, the observed words are influenced by the document's GPS-tag. Our model allows both unbound number and flexible distribution of the geographical variations of the topics' content. We develop a Gibbs sampler for the proposal, and compare it with existing models on a real data set basis.

关键词： Text mining Topic model geographical topics Bayesian non-parameter

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MobiNDN:A Mobility Support Architecture for NDN 33

MobiNDN:A Mobility Support Architecture for NDN

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第三十三届中国控制会议

作者： Zhou Zijian Tan Xiaobin Li Hebi Zhao Zhifan Ma Dianbo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Department of Automation University of Science and Technology of China

ISBN: (纸本)9781479947249

Named Data networking(NDN) has emerged as a new communication paradigm designed for efficient dissemination of data. However, mobility issues are not considered sufficiently. Consumer or producer mobility can incur request staleness issue, the loss of Interest and Data packets and communication delay. Through analysis, we consider how to forward the buffered data from old access point(AP) to new one and how to keep routing consistency during handoff stage are two key points to address mobility issue in NDN. To minimize the loss of Interest and Data,handoff delay during moving, we design a mobility support architecture(MobiNDN), which is a centralized system architecture and consists of initialization and three stages:registration,deletion and updating. We design three mobility scenarios and schemes and evaluate the performance of MobiNDN by comparing it against exiting NDN using extensive ndnSIM simulation. Our simulation results clearly show that MobiNDN architecture effectively decreases handoff delay and receives data packets during/after handoffs comparing with the exiting NDN.

关键词： Controller,Mobility Support,NDN

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Markov Decision Process Based Wireless Multicast Opportunistic Routing 33

Markov Decision Process Based Wireless Multicast Opportunist...

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第三十三届中国控制会议

作者： Ma Dianbo Tan Xiaobin Zhou Zijian Yu Shanjin Department of Automation University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781479947249

There are more and more pervasive applications of Wireless Multicast Network, such as the video conference, the voice transmission and the software updates etc. The traditional wireless multicast routing method is to construct a multicast tree,which may ignore many available links. This paper presents Markov Decision Process Based Multicast Opportunistic Routing Model. Whether each node of the whole network has received a packet stands for different states of a Markov chains. That is to say that Markov state transition is a routing process. This paper provides a new approach for the study of wireless multicast networks routing. In the reward function of the model, the consumption of Request and ACK among the nodes is abundantly considered. In accordance with each state of the network, the network chooses the optimal forwarding nodes which make the reward function of the model maximum. The experiment and the simulation results proves MDP is most effective routing scheme,and meets with the network system's requirement.

关键词： Markov Decision Process,Opportunistic Routing,Multicast,reward function

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Retrieval of Significant Wave Height Under Typhoon Conditions from Gaofen-3 SAR Imagery

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Journal of Ocean University of China 2022年第1期21卷 81-90页

作者： WANG Xiaochen HAN Bing ZHONG Lihua YUAN Xinzhe Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100190China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research InstituteChinese Academy of ScienceBeijing 100190China The National Satellite Ocean Applications Center Beijing 100081China

The objective of this paper is to propose an empirical method to inverse significant wave height(SWH)under typhoon conditions from collected dual-polarization Gaofen(GF)-3 synthetic aperture radar(SAR)*** typhoon scenes were cap-tured from narrow scan(NSC)and wide scan(WSC)images,and collocated with European Center for Medium-Range Weather Fore-casts reanalysis data of(ECMWF).To improve the quality of GF-3 SAR images,the recalibration over rainforest and de-scalloping were carried *** establish the empirical relationship between SAR-derived parameters and collocated SWH,the sensitivity analysis of typical parameters about the normalized radar cross section(Nrcs)and imagery variance(Cvar)were performed to both VV and VH polarized *** scenes from GF-3 SAR imagery under typhoon conditions were used for training the model by the multivari-ate least square regression,and one scene was used for preliminary *** was found that the joint retrieval model based on VV and VH polarized SAR imagery performed better than any single polarized *** results,verified by using ECMWF data,revealed the soundness of this approach,with a correlation of 0.95,bias of 0 m,RMSE of 0.44 and SI of 0.01 when VV polarization and VH polarization data were both used.

关键词： Gaofen-3 SAR typhoon significant wave height

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Analysis and Prediction of Vehicle Exhaust Emission Using ANN 36

Analysis and Prediction of Vehicle Exhaust Emission Using AN...

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第36届中国控制会议

作者： Zhenyi Xu Yu Kang Wenjun Lv Department of Automation and State Key Laboratory of Fire Science University of Science and Technology of China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper focused on the analysis of vehicle emission based on the Hefei remote sensing data during the last three *** we propose a three-layer artificial neural network model for predicting vehicle exhaust emission using remote sensing ***,we take adaptive-lasso algorithm to analyze the various factors from the emission data,and determine the principal ***,after doing principal components analysis and selecting algorithm and architecture,the Back-Propagation neural network model with 7-12-1 architecture was established as the optimal ***,we give the prediction results on the testing data-set and prove the potentiality and validity of the proposed method in the prediction of vehicle exhaust emission.

关键词： Vehicle Exhaust Emission Adaptive-Lasso Artificial Neural Network

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Stabilization of time-delay Markovian jump systems via probability rate synthesis and state feedback 19

Stabilization of time-delay Markovian jump systems via proba...

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19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Ma, Shan Xiong, Junlin CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9783902823625

This paper considers the stabilization problem for Markovian jump systems with time delays. Both the probability rate matrix and the state feedback control law are to be designed. A sufficient condition is established for such designs such that the resulting closed-loop Markovian jump system is stochastically stable. This condition is given in terms of a system of linear matrix inequalities with rank constraints, and can be solved using some existing algorithms. When the system has polytopic uncertainties, the robust stabilization problem is studied as well. Finally, a numerical example is given to show the validity of the proposed method. © IFAC.

关键词： Markov processes

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Unambiguous parameter estimation of radial velocity approach for airborne SAR-GMTI

Unambiguous parameter estimation of radial velocity approach...

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2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2011

作者： Xu, Ruipeng Zhang, Dandan Huang, Lijia Hu, Donghui Ding, Chibiao Key Laboratory of Geo-spatial Information Processing and Application System Technology Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9788993246179

The ambiguous Doppler centroid causes incorrect estimation result of the radial velocity. For moving targets with fast radial velocity, an unambiguous estimation approach of the radial velocity is introduced for the airborne SAR system in this paper. First, two channels carried different wavelength can be constructed by range multilook processing (RMLP). Second, the instantaneous frequency curve of the moving target is obtained by polynomial fitting for each channel, where the linear term denotes the baseband Doppler centroid. Considering the fact that the difference of the estimated baseband Doppler centroid in two channels is equal to the difference of the absolute Doppler centroid, the unambiguous radial velocity can be estimated using it. Third, the range walk and the Doppler shift which are produced by the fast radial velocity can be eliminated by compensating a phase in the range-frequency and azimuth-time domain. As a result, the moving target will be reimaged and relocated after azimuth compression. The proposed approach will be applied to both single- and multi-channel SAR system. The simulation results validate the effectiveness of the proposed method. © 2011 KIEES.

关键词： Parameter estimation

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Learning Disentangled Feature Representation for Hybrid-Distorted Image Restoration 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Li, Xin Jin, Xin Lin, Jianxin Liu, Sen Wu, Yaojun Yu, Tao Zhou, Wei Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (数字)9783030585266

ISBN: (纸本)9783030585259

Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions. Existing HD-IR approaches usually ignore the inherent interference among hybrid distortions which compromises the restoration performance. To decompose such interference, we introduce the concept of Disentangled Feature Learning to achieve the feature-level divide-and-conquer of hybrid distortions. Specifically, we propose the feature disentanglement module (FDM) to distribute feature representations of different distortions into different channels by revising gain-control-based normalization. We also propose a feature aggregation module (FAM) with channel-wise attention to adaptively filter out the distortion representations and aggregate useful content information from different channels for the construction of raw image. The effectiveness of the proposed scheme is verified by visualizing the correlation matrix of features and channel responses of different distortions. Extensive experimental results also prove superior performance of our approach compared with the latest HD-IR schemes. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction

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LIRA: Lifelong Image Restoration from Unknown Blended Distortions 16th

LIRA: Lifelong Image Restoration from Unknown Blended Distor...

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16th European Conference on Computer Vision, ECCV 2020

作者： Liu, Jianzhao Lin, Jianxin Li, Xin Zhou, Wei Liu, Sen Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9783030585228

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise the novel lifelong image restoration problem for blended distortions. We first design a base fork-join model in which multiple pre-trained expert models specializing in individual distortion removal task work cooperatively and adaptively to handle blended distortions. When the input is degraded by a new distortion, inspired by adult neurogenesis in human memory system, we develop a neural growing strategy where the previously trained model can incorporate a new expert branch and continually accumulate new knowledge without interfering with learned knowledge. Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction

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CNN-BASED DEPTH MAP PREDICTION FOR FAST BLOCK PARTITIONING IN HEVC INTRA CODING

CNN-BASED DEPTH MAP PREDICTION FOR FAST BLOCK PARTITIONING I...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Feng, Aolin Gao, Changsheng Li, Li Liu, Dong Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781665438643

High Efficiency Video Coding (HEVC) achieves significant improvement in compression efficiency by introducing quadtree-based block partition. However, in the HEVC reference software-HM, the optimal partition is found by a recursive rate-distortion optimization (RDO) process, which is computationally expensive and not friendly to hardware implementation. We propose a fast block partitioning algorithm using convolutional neural network (CNN) based depth map prediction for HEVC intra coding. We use the depth map to represent the block partition of a coding tree unit (CTU). Then, we design a CNN to predict the depth map for a CTU, and we construct a large-scale dataset to train the CNN. Through the depth map prediction, we obtain a block partitioning structure for the entire CTU, and then we could directly compress each coding unit, getting rid of the recursive RDO process for partitioning. Experimental results show that our proposed method reduces 65.55% encoding time of HM at the cost of 2.02% Bjøntegaard Delta rate (BD-rate) increase on the common test sequences. For 4K sequences, our method achieves 76.97% time saving with 2.89% BD-rate increase. © 2021 IEEE

关键词： Forecasting

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