Search results for: Libo Sun

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Sun</title> <meta name="description" content="Search results for: Libo Sun"> <meta name="keywords" content="Libo Sun"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Libo Sun"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Libo Sun</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Bag of Local Features for Person Re-Identification on Large-Scale Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yixiu%20Liu">Yixiu Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunzhou%20Zhang"> Yunzhou Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianning%20Chi"> Jianning Chi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Chu"> Hao Chu</a>, <a href="https://publications.waset.org/abstracts/search?q=Rui%20Zheng"> Rui Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Libo%20Sun"> Libo Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Guanghao%20Chen"> Guanghao Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Fangtong%20Zhou"> Fangtong Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last few years, large-scale person re-identification has attracted a lot of attention from video surveillance since it has a potential application prospect in public safety management. However, it is still a challenging job considering the variation in human pose, the changing illumination conditions and the lack of paired samples. Although the accuracy has been significantly improved, the data dependence of the sample training is serious. To tackle this problem, a new strategy is proposed based on bag of visual words (BoVW) model of designing the feature representation which has been widely used in the field of image retrieval. The local features are extracted, and more discriminative feature representation is obtained by cross-view dictionary learning (CDL), then the assignment map is obtained through k-means clustering. Finally, the BoVW histograms are formed which encodes the images with the statistics of the feature classes in the assignment map. Experiments conducted on the CUHK03, Market1501 and MARS datasets show that the proposed method performs favorably against existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20visual%20words" title="bag of visual words">bag of visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-view%20dictionary%20learning" title=" cross-view dictionary learning"> cross-view dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20re-identification" title=" person re-identification"> person re-identification</a>, <a href="https://publications.waset.org/abstracts/search?q=reranking" title=" reranking"> reranking</a> </p> <a href="https://publications.waset.org/abstracts/85908/bag-of-local-features-for-person-re-identification-on-large-scale-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">195</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Ordinary Differentiation Equations (ODE) Reconstruction of High-Dimensional Genetic Networks through Game Theory with Application to Dissecting Tree Salt Tolerance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Libo%20Jiang">Libo Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Huan%20Li"> Huan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Rongling%20Wu"> Rongling Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ordinary differentiation equations (ODE) have proven to be powerful for reconstructing precise and informative gene regulatory networks (GRNs) from dynamic gene expression data. However, joint modeling and analysis of all genes, essential for the systematical characterization of genetic interactions, are challenging due to high dimensionality and a complex pattern of genetic regulation including activation, repression, and antitermination. Here, we address these challenges by unifying variable selection and game theory through ODE. Each gene within a GRN is co-expressed with its partner genes in a way like a game of multiple players, each of which tends to choose an optimal strategy to maximize its “fitness” across the whole network. Based on this unifying theory, we designed and conducted a real experiment to infer salt tolerance-related GRNs for Euphrates poplar, a hero tree that can grow in the saline desert. The pattern and magnitude of interactions between several hub genes within these GRNs were found to determine the capacity of Euphrates poplar to resist to saline stress. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20regulatory%20network" title="gene regulatory network">gene regulatory network</a>, <a href="https://publications.waset.org/abstracts/search?q=ordinary%20differential%20equation" title=" ordinary differential equation"> ordinary differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=game%20theory" title=" game theory"> game theory</a>, <a href="https://publications.waset.org/abstracts/search?q=LASSO" title=" LASSO"> LASSO</a>, <a href="https://publications.waset.org/abstracts/search?q=saline%20resistance" title=" saline resistance"> saline resistance</a> </p> <a href="https://publications.waset.org/abstracts/65286/ordinary-differentiation-equations-ode-reconstruction-of-high-dimensional-genetic-networks-through-game-theory-with-application-to-dissecting-tree-salt-tolerance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65286.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">639</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> SCNet: A Vehicle Color Classification Network Based on Spatial Cluster Loss and Channel Attention Mechanism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fei%20Gao">Fei Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinyang%20Dong"> Xinyang Dong</a>, <a href="https://publications.waset.org/abstracts/search?q=Yisu%20Ge"> Yisu Ge</a>, <a href="https://publications.waset.org/abstracts/search?q=Shufang%20Lu"> Shufang Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Libo%20Weng"> Libo Weng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20transportation" title=" intelligent transportation"> intelligent transportation</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20color%20recognition" title=" vehicle color recognition"> vehicle color recognition</a> </p> <a href="https://publications.waset.org/abstracts/132381/scnet-a-vehicle-color-classification-network-based-on-spatial-cluster-loss-and-channel-attention-mechanism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132381.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">183</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Finite Element Modeling of a Lower Limb Based on the East Asian Body Characteristics for Pedestrian Protection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xianping%20Du">Xianping Du</a>, <a href="https://publications.waset.org/abstracts/search?q=Runlu%20Miao"> Runlu Miao</a>, <a href="https://publications.waset.org/abstracts/search?q=Guanjun%20Zhang"> Guanjun Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Libo%20Cao"> Libo Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Zhu"> Feng Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Current vehicle safety standards and human body injury criteria were established based on the biomechanical response of Euro-American human body, without considering the difference in the body anthropometry and injury characteristics among different races, particularly the East Asian people with smaller body size. Absence of such race specific design considerations will negatively influence the protective performance of safety products for these populations, and weaken the accuracy of injury thresholds derived. To resolve these issues, in this study, we aim to develop a race specific finite element model to simulate the impact response of the lower extremity of a 50th percentile East Asian (Chinese) male. The model was built based on medical images for the leg of an average size Chinese male and slightly adjusted based on the statistical data. The model includes detailed anatomic features and is able to simulate the muscle active force. Thirteen biomechanical tests available in the literature were used to validate its biofidelity. Using the validated model, a pedestrian-car impact accident taking place in China was re-constructed computationally. The results show that the newly developed lower leg model has a good performance in predicting dynamic response and tibia fracture pattern. An additional comparison on the fracture tolerance of the East Asian and Euro-American lower limb suggests that the current injury criterion underestimates the degree of injury of East Asian human body. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lower%20limb" title="lower limb">lower limb</a>, <a href="https://publications.waset.org/abstracts/search?q=East%20Asian%20body%20characteristics" title=" East Asian body characteristics"> East Asian body characteristics</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20accident%20reconstruction" title=" traffic accident reconstruction"> traffic accident reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20analysis" title=" finite element analysis"> finite element analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=injury%20tolerance" title=" injury tolerance"> injury tolerance</a> </p> <a href="https://publications.waset.org/abstracts/67879/finite-element-modeling-of-a-lower-limb-based-on-the-east-asian-body-characteristics-for-pedestrian-protection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">288</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational 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Search results for: Libo Sun