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Tourism Competitiveness Evaluation and Spatio-temporal Characteristics of Chinese Border Counties

Chinese Geographical Science 2016年第6期26卷 817-828页

作者： shi yuqing ZHONG Linsheng CHEN Tian YU Hu Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Under the current government strategy of building a Silk Road economic belt, tourism in Chinese border counties has becoming increasingly popular. Studying tourism competitiveness in Chinese border counties is of sizable theoretical and practical importance, as there are several notable factors involved. In this study, we constructed a tourism competitiveness evaluation model based on eight factors: natural environment, tourism resource, location and transportation, social environment, tourism service facility, border port, tourism industrial cluster and tourism market. We then analyzed the spatial characteristics of tourism competitiveness in border counties and identified five types of border counties: resource advantage type(RA), border-port advantage type(PA), location advantage type(LA), agglomeration advantage type(AA), and relative balance type(RB), and examined the correlation between tourism market competitiveness and interior competitiveness factors in the counties from 2006 to 2011. Results showed that tourism resource, location and transportation, and tourism service facility are the most important competition factors for RA border counties during the study period. Competition factors in PA counties transferred from tourism resource, social environment and tourism service facility to border port and tourism industrial cluster; competition factors in LA counties transferred from natural environment and tourism resource to tourism service facility and tourism industrial cluster and border port. Competition factors in AA counties transferred from tourism service facility to tourism resource. Tourism industrial cluster, tourism service facility and tourism resource proved to be important competition factors in RB counties. The findings of this study can be used to target tourism strategies according to different county types.

关键词： border counties tourism competiveness spatio-temporal characteristics type classification

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Wearable sweat biosensors on textiles for health monitoring

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Journal of Semiconductors 2023年第2期44卷 11-24页

作者： yuqing shi Ziyu Zhang Qiyao Huang Yuanjing Lin Zijian Zheng School of Microelectronics Southern University of Science and TechnologyShenzhen 518055China School of Fashion&Textiles The Hong Kong Polytechnic UniversityHong Kong SARChina Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic UniversityHong Kong SARChina Research Institute for Intelligent Wearable Systems(RI-Wear) The Hong Kong Polytechnic UniversityHong Kong SARChina

With the rapid technological innovation in materials engineering and device integration,a wide variety of textilebased wearable biosensors have emerged as promising platforms for personalized healthcare,exercise monitoring,and *** paper reviews the recent progress in sweat biosensors and sensing systems integrated into textiles for wearable body status *** mechanisms of biosensors that are commonly adopted for biomarkers analysis are first *** classification,fabrication methods,and applications of textile conductors in different configurations and dimensions are then ***,innovative strategies to achieve efficient sweat collection with textile-based sensing patches are presented,followed by an in-depth discussion on nanoengineering and system integration approaches for the enhancement of sensing ***,the challenges of textile-based sweat sensing devices associated with the device reusability,washability,stability,and fabrication reproducibility are discussed from the perspective of their practical applications in wearable healthcare.

关键词： biosensor textile-based electronics wearable device sweat analysis health monitoring

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Manifold Regularized Robust Unsupervised Feature Selection for Image Clustering 36

Manifold Regularized Robust Unsupervised Feature Selection f...

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第36届中国控制会议

作者： yuqing shi shiqiang Du School of Electrical Engineering Northwest Minzu University School of Mathematics and Computer Science Northwest Minzu University

ISBN: (纸本)9781538629185

Dimensionality reduction is a challenging task for high dimensional data processing in machine learning and data *** an effective dimension reduction technique,unsupervised feature selection aims at finding a subset of features to retain the most relevant *** this paper,we propose a novel unsupervised feature selection method,called Manifold Regularized Robust Unsupervised Feature Selection(MRUFS) for image *** performs robust discriminative feature selection and robust clustering simultaneously under l

关键词： unsupervised feature selection manifold regularization matrix factorization dimensionality reduction

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An Unsupervised Kernel Optimization in Dimensional Reduction

An Unsupervised Kernel Optimization in Dimensional Reduction

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第25届中国控制与决策会议

作者： yuqing shi shiqiang Du Weilan Wang School of Electrical Engineering Northwest University for Nationalities School of Mathematics and Computer Science Northwest University for Nationalities

ISBN: (纸本)9781467355322

Subspace analysis is an effective dimensional reduction approach for face recognition. Finding a suitable low dimensional subspace is a key step of subspace analysis, for it has a direct effect on recognition performance. In this paper, we propose a new subspace analysis method called center kernel unsupervised discriminant projection (CKUDP). The kernel trick is adopted to allow the efficient computation of unsupervised discriminant projection in high-dimensional feature space. Moreover, a center solution for obtaining the optimal feature vectors in feature space is presented which can preserve the most discriminative information. Experiments results on the ORL database and Yale database demonstrate the utility of the proposed approach.

关键词： kernel method manifold learning central kernel unsupervised discriminant analysis

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Sparse Dual Regularized Concept Factorization for Image Representation

Sparse Dual Regularized Concept Factorization for Image Repr...

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第25届中国控制与决策会议

作者： shiqiang Du yuqing shi Weilan Wang School of Mathematics and Computer Science Northwest University for Nationalities School of Electrical Engineering Northwest University for Nationalities

ISBN: (纸本)9781467355339

Low-rank matrix factorization is one of the most useful tools in image representation and computer vision. Among of its techniques, Concept Factorization （CF） is a new matrix decomposition technique for data representation. A modified CF algorithm called Sparse Dual Regularized Concept Factorization （SDRCF） is proposed for addressing the limitations of CF and Local Consistent Concept Factorization （LCCF）, which did not consider the geometric structure or the label information of the data. SDRCF simultaneously preserves the intrinsic geometry of the data and the feature as regularized term, and preserve the sparse reconstructive relationship of the data. We also present SDRCF as an extension of CF and LCCF. Compared with Non-Negative Matrix Factorization （NMF）, Graph NMF （GNMF）, CF and LCCF, experiment results on ORL face database and Coil20 image database have shown that the proposed method achieves better clustering results.

关键词： Image Clustering sparse representation Graph Regularized Concept Factorization (CF)

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Disorders of organic acid metabolism and epilepsy

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Acta Epileptologica 2024年第3期7卷 165-177页

作者： yuqing shi Zihan Wei Yan Feng Yajing Gan Guoyan Li Yanchun Deng Xi’an Medical University Xi’an710021China Department of Neurology Xijing HospitalFourth Military Medical University127 West Changle RoadXi’an710032China Xijing Institute of Epileptic Encephalopathy Xi’anShaanxi710065China

Epilepsy can be caused by a variety of causes,such as inborn errors of metabolism,organic acid disorders are the most significant type of metabolic disorders that cause *** clinical manifestations of these diseases are generally nonspecific,and the types of seizures are *** for multisystem clinical symptoms and identifying the underlying etiology are crucial for early treatment of epileptic *** article provides a comprehensive summary of the pathogenesis,clinical features,diagnosis and treatment of epilepsy associated with organic acid metabolism ***,relevant literature has also been reviewed to assist clinicians in the diagnosis of cases characterized by the coexistence of multisystemic symptoms and epileptic manifestations.

关键词： Organic acids Epilepsy Gene genetic variants Diagnosis,Treatment

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筒形件拉深孔成形工艺数值模拟分析

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农业机械学报 2008年第12期39卷 191-195页

作者：施于庆楼易浙江科技学院机械与汽车工程学院

提出了一种能提高板料成形性能的拉深孔成形工艺,该工艺是通过减小拉深时凹模圆角处对板料的压应力,实现对板料成形性能的优化。以带凸缘的圆筒形件为研究对象,对采用该工艺进行拉深有限元模拟,从拉深后拉深件的危险断面处厚度减薄率和... 详细信息

提出了一种能提高板料成形性能的拉深孔成形工艺,该工艺是通过减小拉深时凹模圆角处对板料的压应力,实现对板料成形性能的优化。以带凸缘的圆筒形件为研究对象,对采用该工艺进行拉深有限元模拟,从拉深后拉深件的危险断面处厚度减薄率和成形极限图方面得到了很好验证,同时分析了该工艺的成形机理和力学特征,并阐明了该工艺是提高板料成形性能的技术关键。

关键词：筒形件凹模圆角拉深孔模拟

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Constrained Graph Concept Factorization for Image Clustering

Constrained Graph Concept Factorization for Image Clustering

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第26届中国控制与决策会议

作者： yuqing shi shiqiang Du Weilan Wang School of Electrical Engineering Northwest University for Nationalities School of Mathematics and Computer Science Northwest University for Nationalities

ISBN: (纸本)9781479937097

Matrix factorization techniques have been frequently applied in data representation and pattern recognition. One of them is Concept Factorization（CF）, which is a new matrix decomposition technique for data representation. In this paper, we propose a novel semi-supervised matrix factorization algorithm, called Constrained Graph Concept Factorization（CGCF）, which incorporates the label information as additional constraints. Specifically, CGCF preserves the intrinsic geometry of data as regularized term and use the label information as semi-supervised learning, it makes nearby samples with the same class-label are more compact, and nearby classes are separated. An efficient multiplicative updating procedure was produced along with its theoretic justification of the algorithmic convergence. Compared with NMF, GNMF, CF, LCCF and Kmeans, experiment results on ORL and YALE face databases have shown that the proposed method achieves better clustering results.

关键词： Data Representation Concept Factorization(CF) Semi-supervised Learning Clustering

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L3/2 Sparsity Constrained Graph Non-negative Matrix Factorization for Image Representation

L3/2 Sparsity Constrained Graph Non-negative Matrix Factoriz...

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第26届中国控制与决策会议

作者： shiqiang Du yuqing shi Weilan Wang School of Mathematics and Computer Science Northwest University for Nationalities School of Electrical Engineering Northwest University for Nationalities

ISBN: (纸本)9781479937097

For enhancing the cluster accuracy, this paper presents a novel algorithm called L3 2 Sparsity Constrained Graph Non-negative Matrix Factorization(FGNMF), which based on the convex and smooth L3 2 norm. When original data is factorized in lower dimensional space using NMF, FGNMF preserves the local structure and intrinsic geometry of data, using the convex and smooth L3 2 norm as sparse constrains for the low dimensional feature. An efficient multiplicative updating procedure was produced, the relation with gradient descent method showed that the updating rules are special case of its. Compared with NMF and its improved algorithms based on sparse representation, experiment results on USPS handwrite database and COIL20 image database have shown that the proposed method achieves better clustering results.

关键词： Image Representation Non-negative Matrix Factorization(NMF) Sparse constrained Clustering

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An Dual-Branch Network Based on Wavelet Decomposition for Image Inpainting

An Dual-Branch Network Based on Wavelet Decomposition for Im...

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第43届中国控制会议

作者： yuqing shi Jiacheng Li Yang Zhang shiqiang Du College of Electronic Engineering Northwest Minzu University Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Chinese National Information Technology Research Institute Northwest Minzu University College of Mathematics and Computer Science Northwest Minzu University

In this paper, we propose the Wavelet-Guided Dual-Branch Network(WGDN), an innovative deep learning framework designed for image inpainting. WGDN uniquely leverages wavelet decomposition to address the challenge of reconstructing large missing regions by analyzing both low-frequency and high-frequency subbands of the image. The structure branch focuses on the low-frequency subband to restore overall structure, while the texture branch reconstructs intricate details using three high-frequency subbands. Key enhancements include the integration of an Efficient Transformer Block(ETB) in the structure branch for global information capture and a Residual in Residual Dense Block(RRDB) in the texture branch for detailed texture recovery. Additionally, we employ the CIEDE2000 color difference formula to ensure color consistency in the restored *** extensive testing demonstrates that WGDN outperforms existing methods in both qualitative and quantitative measures,establishing new benchmarks for the field of image inpainting.

关键词： Image Inpainting Wavelet Transform Attention Mechanism Image Processing

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