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Search Results: 1 - 10 of 78292 matches for " Huilin Chen "
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The Moderating Effects of Item Order Arranged by Difficulty on the Relationship between Test Anxiety and Test Performance  [PDF]
Huilin Chen
Creative Education (CE) , 2012, DOI: 10.4236/ce.2012.33052
Abstract: Taking cultural knowledge tests as the case study, this research carries out a series of empirical investigations to verify the moderating effects of item order arranged by difficulty on the relationship between test anxiety and test performance. Groups classified according to test anxiety take tests with two major types of item order: item order arranged according to item bank calibrated item difficulty and item order adjusted according to individual examinee’s perceived item difficulty. The means of those test results are compared between groups to see whether the differences are significant. The investigations obtain the following findings: the higher the test taker’s level of test anxiety, the higher significance of the moderating effects and vice versa; item order adjusted according to individual examinee’s perceived item difficulty may have a more significant moderating effect than item order arranged according to item bank calibrated item difficulty has.
Impact of Parent’s Socioeconomic Status on Perceived Parental Pressure and Test Anxiety among Chinese High School Students
Huilin Chen
International Journal of Psychological Studies , 2012, DOI: 10.5539/ijps.v4n2p235
Abstract: This study carries out empirical researches among Mainland Chinese high school students to explore the impact of parent’s socioeconomic status on perceived parental pressure and test anxiety. The discoveries of the study include: perceived parental pressure has significant impact on test anxiety; parents’ occupations, parents’ income and mother’s education have significant impact on perceived parental pressure; parents’ occupations, parents’ income and mother’s education have significant impact on test anxiety. There are sufficient evidences to support the notion that the ethic stressing family glory and material success can be a major source of perceived parental pressure and test anxiety in China. Another finding of the study is that there may exist a mediation relationship among parent’s socioeconomic status, perceived parental pressure, and test anxiety. By controlling perceived parental pressure, the mediator variable, the impact of parent’s socioeconomic status on test anxiety can be greatly reduced.
Image Classification Based on the Fusion of Complementary Features
Image Classification Based on the Fusion of Complementary Features

Huilin Gao,Wenjie Chen
- , 2017, DOI: 10.15918/j.jbit1004-0579.201726.0208
Abstract: Image classification based on bag-of-words (BOW) has a broad application prospect in pattern recognition field but the shortcomings such as single feature and low classification accuracy are apparent. To deal with this problem, this paper proposes to combine two ingredients:(i) Three features with functions of mutual complementation are adopted to describe the images, including pyramid histogram of words (PHOW), pyramid histogram of color (PHOC) and pyramid histogram of orientated gradients (PHOG). (ii) An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the decision level fusion of multiple features are employed. Experiments are carried out on the Caltech 101 database, which confirms the validity of the proposed approach. The experimental results show that the classification accuracy rate of the proposed method is improved by 7%-14% higher than that of the traditional BOW methods. With full utilization of global, local and spatial information, the algorithm is much more complete and flexible to describe the feature information of the image through the multi-feature fusion and the pyramid structure composed by image spatial multi-resolution decomposition. Significant improvements to the classification accuracy are achieved as the result.
Image classification based on bag-of-words (BOW) has a broad application prospect in pattern recognition field but the shortcomings such as single feature and low classification accuracy are apparent. To deal with this problem, this paper proposes to combine two ingredients:(i) Three features with functions of mutual complementation are adopted to describe the images, including pyramid histogram of words (PHOW), pyramid histogram of color (PHOC) and pyramid histogram of orientated gradients (PHOG). (ii) An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the decision level fusion of multiple features are employed. Experiments are carried out on the Caltech 101 database, which confirms the validity of the proposed approach. The experimental results show that the classification accuracy rate of the proposed method is improved by 7%-14% higher than that of the traditional BOW methods. With full utilization of global, local and spatial information, the algorithm is much more complete and flexible to describe the feature information of the image through the multi-feature fusion and the pyramid structure composed by image spatial multi-resolution decomposition. Significant improvements to the classification accuracy are achieved as the result.
Kernel-based distance metric learning for microarray data classification
Huilin Xiong, Xue-wen Chen
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-299
Abstract: In this paper, we present a modified K-nearest-neighbor (KNN) scheme, which is based on learning an adaptive distance metric in the data space, for cancer classification using microarray data. The distance metric, derived from the procedure of a data-dependent kernel optimization, can substantially increase the class separability of the data and, consequently, lead to a significant improvement in the performance of the KNN classifier. Intensive experiments show that the performance of the proposed kernel-based KNN scheme is competitive to those of some sophisticated classifiers such as support vector machines (SVMs) and the uncorrelated linear discriminant analysis (ULDA) in classifying the gene expression data.A novel distance metric is developed and incorporated into the KNN scheme for cancer classification. This metric can substantially increase the class separability of the data in the feature space and, hence, lead to a significant improvement in the performance of the KNN classifier.DNA microarray technology is designed to measure the expression levels of tens of thousands of genes simultaneously. As an important application of this novel technology, the gene expression data are used to determine and predict the state of tissue samples, which has shown to be very helpful in clinical oncology. The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. In combination with pattern classification techniques, gene expression data can provide more reliable means to diagnose and predict various types of cancers than the traditional clinical methods.Compared with traditional pattern classifications, gene expression-based data classification is typically characterized by high dimensionality and small sample size, which make the task quite challenging. In the literature, a number of methods have been applied or developed to classify microarray data [1-6]. These methods include K-near
Image classification based on support vector machine and the fusion of complementary features
Huilin Gao,Wenjie Chen,Lihua Dou
Computer Science , 2015,
Abstract: Image Classification based on BOW (Bag-of-words) has broad application prospect in pattern recognition field but the shortcomings are existed because of single feature and low classification accuracy. To this end we combine three ingredients: (i) Three features with functions of mutual complementation are adopted to describe the images, including PHOW (Pyramid Histogram of Words), PHOC (Pyramid Histogram of Color) and PHOG (Pyramid Histogram of Orientated Gradients). (ii) The improvement of traditional BOW model is presented by using dense sample and an improved K-means clustering method for constructing the visual dictionary. (iii) An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the fusion of multiple features is adopted. Experiments carried out on Caltech 101 database confirm the validity of the proposed approach. From the experimental results can be seen that the classification accuracy rate of the proposed method is improved by 7%-17% higher than that of the traditional BOW methods. This algorithm makes full use of global, local and spatial information and has significant improvements to the classification accuracy.
Optical System Design of Inter-Spacecraft Laser Interferometry Telescope  [PDF]
Shengnan Chen, Huilin Jiang, Chunyan Wang, Zhe Chen
Optics and Photonics Journal (OPJ) , 2019, DOI: 10.4236/opj.2019.98B004
Abstract:
The fundamental measurement of space gravitational wave detection is to monitor the relative motion between pairs of freely falling test masses using heterodyne laser interferometry to a precision of 10 pm. The masses under test are millions of kilometers apart. The inter-spacecraft laser interferometry telescope deliver laser efficiently from one spacecraft to another. It is an important component of the gravitational wave detection observatory. It needs to meet the requirements of large compression ratio, high image quality and extraordinary stray light suppression ability. Based on the primary aberration theory, the method of the large compression ratio off-axis four-mirror optical system design is explored. After optimization, the system has an entrance pupil of 200 mm, compression ratio of 40 times, scientific field of view (FOV) of ±8 μrad. To facilitate suppressing the stray light and delivering the laser beam to the back-end scientific interferometers, the intermediate images and the real exit pupils are spatially available. Over the full FOV, the maximum root mean square (RMS) wavefront error is less than 0.007λ, PV value is less than 0.03λ (λ = 1064 nm). The image quality is approached to the diffraction-limit. The TTL noise caused by the wavefront error of the telescope is analyzed. The TTL noise in the image space of 300 μrad range is less than 1 × 10-10 m whose slope is lower than 0.6 μm/rad, which is under the noise budget of the laser interferometer space antenna (LISA), satisfying the requirements of space gravitational wave detection.
Regional Variations in the Cellular, Biochemical, and Biomechanical Characteristics of Rabbit Annulus Fibrosus
Jun Li, Chen Liu, Qianping Guo, Huilin Yang, Bin Li
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0091799
Abstract: Tissue engineering of annulus fibrosus (AF), the essential load-bearing disc component, remains challenging due to the intrinsic heterogeneity of AF tissue. In order to provide a set of characterization data of AF tissue, which serve as the benchmark for constructing tissue engineered AF, we analyzed tissues and cells from various radial zones of AF, i.e., inner AF (iAF), middle AF (mAF), and outer AF (oAF), using a rabbit model. We found that a radial gradient in the cellular, biochemical, and biomechanical characteristics of rabbit AF existed. Specifically, the iAF cells (iAFCs) had the highest expression of collagen-II and aggrecan genes, while oAF cells (oAFCs) had the highest collagen-I gene expression. The contents of DNA, total collagen and collagen-I sequentially increased from iAF, mAF to oAF, while glycosaminoglycan (GAG) and collagen-II levels decreased. The cell traction forces of primary AFCs gradually decreased from iAFCs, mAFCs to oAFCs, being 336.6±155.3, 199.0±158.8, and 123.8±76.1 Pa, respectively. The storage moduli of iAF, mAF, and oAF were 0.032±0.002, 2.121±0.656, and 4.130±0.159 MPa, respectively. These measurements have established a set of reference data for functional evaluation of the efficacy of AF tissue engineering strategies using a convenient and cost-effective rabbit model, the findings of which may be further translated to human research.
Strong Law of Large Numbers of the Offspring Empirical Measure for Markov Chains Indexed by Homogeneous Tree
Huilin Huang
ISRN Applied Mathematics , 2012, DOI: 10.5402/2012/536530
Abstract:
Strong Law of Large Numbers for Hidden Markov Chains Indexed by Cayley Trees
Huilin Huang
ISRN Probability and Statistics , 2012, DOI: 10.5402/2012/768657
Abstract:
Some Limit Properties of the Harmonic Mean of Transition Probabilities for Markov Chains in Markovian Environments Indexed by Cayley's Trees
Huilin Huang
International Journal of Stochastic Analysis , 2013, DOI: 10.1155/2013/961571
Abstract: We prove some limit properties of the harmonic mean of a random transition probability for finite Markov chains indexed by a homogeneous tree in a nonhomogeneous Markovian environment with finite state space. In particular, we extend the method to study the tree-indexed processes in deterministic environments to the case of random enviroments. 1. Introduction A tree is a graph which is connected and doesn't contain any circuits. Given any two vertices , let be the unique path connecting and . Define the graph distance to be the number of edges contained in the path . Let be an infinite tree with root . The set of all vertices with distance from the root is called the th generation of , which is denoted by . We denote by the union of the first generations of . For each vertex , there is a unique path from to and for the number of edges on this path. We denote the first predecessor of by . The degree of a vertex is defined to be the number of neighbors of it. If every vertex of the tree has degree , we say it is Cayley’s tree, which is denoted by . Thus, the root vertex has neighbors in the first generation and every other vertex has neighbors in the next generation. For any two vertices and of tree , write if is on the unique path from the root to . We denote by the farthest vertex from satisfying and . We use the notation and denote by the number of vertices of . In the following, we always let denote the Cayley tree . A tree-indexed Markov chain is the particular case of a Markov random field on a tree. Kemeny et al. [1] and Spitzer [2] introduced two special finite tree-indexed Markov chains with finite transition matrix which is assumed to be positive and reversible to its stationary distribution, and these tree-indexed Markov chains ensure that the cylinder probabilities are independent of the direction we travel along a path. In this paper, we omit such assumption and adopt another version of the definition of tree-indexed Markov chains which is put forward by Benjamini and Peres [3]. Yang and Ye[4] extended it to the case of nonhomogeneous Markov chains indexed by infinite Cayley’s tree and we restate it here as follows. Definition 1 (T-indexed nonhomogeneous Markov chains (see [4])). Let be an infinite Cayley tree, a finite state space, and a stochastic process defined on probability space , which takes values in the finite set . Let be a distribution on and a transition probability matrix on . If, for any vertex , then will be called -valued nonhomogeneous Markov chains indexed by infinite Cayley’s tree with initial distribution (1) and
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