scholarly journals Evidence-Based Statistical Evaluation of Japanese L2-Learners’ Proficiency using Principal Component Analysis

2021 ◽  
Vol 102 ◽  
pp. 01005
Author(s):  
Masafumi Arai ◽  
Hajime Tsubaki ◽  
Yoshinori Sagisaka
Keyword(s):  
Principal Component Analysis ◽  
Prediction Method ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Global Scale ◽  
L2 Learners ◽  
Scale Scores ◽  
The Common ◽  
Conventional Methods

This paper aims at an automatic evaluation of second language (L2) learners’ proficiencies and tries to analyze English conversation data having 94 statistics and Global Scale scores of the Common European Framework of Reference (CEFR) given to each participant. The CEFR defines Range, Accuracy, Fluency, Interaction and Coherence as 5 subcategories, which constitute the CEFR Global Scale score. The statistics were classified into the CEFR’s 5 subcategories. We used the Principal Component Analysis (PCA), an unsupervised machine learning method, on each subcategory and obtained the participants’ principal component scores (PC scores) of the 5 subcategories for estimation parameters. We predicted the participants’ CEFR Global scores using the Multiple Regression Analysis (MRA). The proposed prediction method using the PC scores was compared with conventional methods with the 94 statistics. Based on the coefficients of determination (R2), the value of the proposed method (0.82) was nearly equivalent to one of values obtained by the conventional methods. Meanwhile, as for standard deviation, the proposed method showed the smallest value in the comparison. The results indicated usability of the PCA and PC scores calculated from the CEFR subcategory data for objective evaluation of L2 learners’ English proficiencies.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Tactile evaluation of down jacket fabric by the comprehensive handle evaluation system for fabrics and yarns

2020 ◽  
pp. 004051752097720
Author(s):  
Yuan Tian ◽  
Yi Sun ◽  
Zhaoqun Du ◽  
Dongming Zheng ◽  
Haochen Zou ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Principal Component Analysis ◽  
Evaluation System ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Quality Level ◽  
Fabric Handle ◽  
Good Agreement

Down jacket fabric is greatly important in determining the quality of a down jacket. In order to enrich the research on fabric handle, subjective and objective evaluations were made for down jacket fabrics that were less studied. The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) can be used to evaluate the tactile handle of the fabric by accurately and efficiently measuring the basic mechanical properties of the fabric. Therefore, the CHES-FY was used to link the objective evaluation with the subjective handle, so as to effectively estimate the total handle value of the down jacket fabric. Fifty-two kinds of down jacket fabrics were objectively tested through measuring 17 extracted parameters, and principal component analysis was adopted to establish the five main handle characteristics of fullness, softness, stiffness, smoothness, looseness and tightness to characterize basic style of the down jacket fabrics. The results showed that the subjective and objective results were in good agreement. These characteristics can be used as indicators to characterize fabric performance, and the principal component expression to characterize fabric handle can better predict the handle characteristics of down jacket fabrics. This also proves that the CHES-FY can quickly and accurately obtain the fabric handle value, and can also evaluate the fabric quality level.

scholarly journals Risk Assessment of Coal Mine Water Inrush Based On PCA-DBN

2021 ◽  
Author(s):  
Zhang ye ◽  
Tang Shoufeng ◽  
Shi Ke
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Coal Mine ◽  
Water Inrush ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Mine Safety ◽  
Training Time

Abstract To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on principal component analysis and deep confidence network optimization was proposed. Because deep belief network (DBN) is disadvantaged by a long training time when establishing a high-dimensional data classification model, the principal component analysis (PCA) method is used to reduce the dimensionality of many factors affecting the water inrush of the coal seam floor, thus reducing the number of variables of the research object, redundancy and the difficulty of feature extraction and shortening the training time of the model. Then, a DBN network was used to extract secondary features from the processed nonlinear data, and a more abstract high-level representation was formed by combining low-level features to find the expression of the nonlinear relationship between the characteristics of water inbursts. Finally, a prediction model was established to predict the water inrush in coal mines. The superiority of this method was verified by comparing the prediction of the actual working face with the actual situation in typical mining areas of North China.

scholarly journals New model for flavour quality evaluation of soy sauce

2013 ◽  
Vol 31 (No. 3) ◽  
pp. 292-305 ◽  
Author(s):  
J. Feng ◽  
X.-B. Zhan ◽  
Z.-Y. Zheng ◽  
D. Wang ◽  
L.-M. Zhang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Soy Sauce ◽  
Complex Information

The soy sauce samples established a model for its flavour quality evaluation. Initially, 39 types of flavour compounds, organic acids and free amino acids in six different types of soy sauce were identified and determined by HS-SPME GC/MS and HPLC. The model was developed based on the principal component analysis method for assessing and ranking of flavour quality of soy sauce. Using the principal component analysis which simplifies complex information, our correlative evaluation model was established, tested by comparing the traditional sensory evaluation method, providing a new methodology for objective evaluation of the flavour quality of soy sauce.  

scholarly journals HOW EFFECTIVE IS MICROFINANCE IN REACHING THE POOREST? EMPIRICAL EVIDENCE ON PROGRAMME OUTREACH IN RURAL PAKISTAN

2013 ◽  
Vol 14 (3) ◽  
pp. 467-480 ◽  
Author(s):  
Asad K. Ghalib
Keyword(s):  
Principal Component Analysis ◽  
Empirical Evidence ◽  
Principal Component ◽  
Household Survey ◽  
Component Analysis ◽  
Global Scale ◽  
Policy Implications ◽  
Microfinance Institutions ◽  
Rural Pakistan ◽  
The Poor

Microfinance has emerged on the global scale as a key strategy to reduce poverty and promote development. Most literature however, tends to concentrate on breadth as opposed to depth of programme outreach. This paper is based on a primary household survey of 1,132 respondents in the Punjab Province of Pakistan to assess which category of the poor is being served by microfinance institutions: are they the very poor, middle poor or less poor ones? In order to make comparisons, borrower (treatment) and non-borrower (control) households are ranked by poverty scores generated by employing Principal Component Analysis. The study reveals that the depth of poverty outreach is significantly lower than what has been claimed by lenders. The paper reflects on policy implications to enhance depth (as opposed to breadth) of outreach to address the needs of the ‘poorest of the poor’ in order to contribute meaningfully and effectively towards combating poverty.

scholarly journals Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Chengcai Leng ◽  
Jinjun Xiao ◽  
Min Li ◽  
Haipeng Zhang
Keyword(s):  
Principal Component Analysis ◽  
Image Registration ◽  
Medical Image ◽  
Principal Component ◽  
Component Analysis ◽  
Common Structure ◽  
The Common ◽  
Robust Adaptive ◽  
Adaptive Principal Component Analysis ◽  
Structure Patterns

This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.

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