• Korean Journal of Remote Sensing
• /
• v.24 no.5
• /
• pp.437-444
• /
• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have tried to enhance classification accuracy. Previous studies have shown that the classification technique based on wavelet transform is more effective than traditional techniques based on original pixel values, especially in complicated imagery. Various basis functions such as Haar, daubechies, coiflets and symlets are mainly used in 20 image processing based on wavelet transform. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we first computed the wavelet coefficients of satellite image using ten different basis functions, and then classified images. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis functions. The energy parameters of wavelet detail bands and overall accuracy are clearly correlated. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.4
• /
• pp.749-755
• /
• 2009

Support vector classification (SVC) provides more complete description of the lin-ear and nonlinear relationships between input vectors and classifiers. In this paper. we propose the sparse kernel classifier to solve the optimization problem of classification with a modified hinge loss function and absolute loss function, which provides the efficient computation and the sparsity. We also introduce the generalized cross validation function to select the hyper-parameters which affects the classification performance of the proposed method. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• Physical Therapy Korea
• /
• v.21 no.3
• /
• pp.55-62
• /
• 2014

This study investigated the relationship between function classification systems and the Pediatric Evaluation of Disability Inventory (PEDI) functional skills in children with cerebral palsy (CP). Two hundred and eleven children with CP participated in this study. The Korean-Gross Motor Function Classification System (K-GMFCS), Korean-Manual Ability Classification System (K-MACS), Korean-Communication Function Classification System (K-CFCS), and self-care, mobility, and social function domains of the Korean-Pediatric Evaluation of Disability Inventory (K-PEDI) functional skills were measured by physical therapists or occupational therapists. All of the function classification systems were significantly correlated with PEDI functional skills ($r_s$=-.549 to -.826) (p<.05). Especially, K-GMFCS, K-MACS, and K-CFCS were correlated significantly with mobility, self-care, and social function, respectively. Using stepwise multiple regression analysis, we established that K-GMFCS, K-MACS, and K-CFCS were predictors of self-care skills (74.3%) and mobility skills (79.5%) of the K-PEDI (p<.05). In addition, K-CFCS and K-MACS were predictors of social function (65.9%) of the K-PEDI (p<.05). The information gathered in this study using the levels measured in the function classification systems may be useful to clinicians for estimating the PEDI functional skills in children with CP.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.169-172
• /
• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have been tried to enhance classification accuracy. Previous studies show that the classification technique based on wavelet transform is more effective than that of traditional techniques based on original pixel values, especially in complicated imagery. Various wavelets can be used in wavelet transform. Wavelets are used as basis functions in representing other functions, like sinusoidal function in Fourier analysis. In these days, some basis functions such as Haar, Daubechies, Coiflets and Symlets are mainly used in 2D image processing. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we computed the wavelet coefficients of satellite image using 10 different basis functions, and then classified test image. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis function. The energy parameter of signal is the sum of the squares of wavelet coefficients. The energy parameter is calculated by sub-bands after the wavelet decomposition and the energy parameter of each sub-band can be a favorable feature of texture. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• ETRI Journal
• /
• v.37 no.4
• /
• pp.813-823
• /
• 2015

Because protein is a primary element responsible for biological or biochemical roles in living bodies, protein function is the core and basis information for biomedical studies. However, recent advances in bio technologies have created an explosive increase in the amount of published literature; therefore, biomedical researchers have a hard time finding needed protein function information. In this paper, a classification system for biomedical literature providing protein function evidence is proposed. Note that, despite our best efforts, we have been unable to find previous studies on the proposed issue. To classify papers based on protein function evidence, we should consider whether the main claim of a paper is to assert a protein function. We, therefore, propose two novel features - protein and assertion. Our experimental results show a classification performance with 71.89% precision, 90.0% recall, and a 79.94% F-measure. In addition, to verify the usefulness of the proposed classification system, two case study applications are investigated - information retrieval for protein function and automatic summarization for protein function text. It is shown that the proposed classification system can be successfully applied to these applications.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.1195-1200
• /
• 2009

Two important concepts in VE are "function" and "cost." Cost can be expressed quantitatively. Unlike cost, the function can only be expressed qualitatively. Thus, to accurately evaluate the performance in VE analysis, it is required that the functional aspect should be considered a qualitative one. This study suggests a procedure of function oriented evaluation which can evaluate function enhancement of a VE proposal more logically and objectively. To conduct this study, problems were induced via case analysis, and solutions were found. In addition, the existing simple evaluation procedures were corrected, and a function enhancement evaluation procedure via function classification was suggested. For function classification, the use of the concepts, which were "intended function" and "additionally obtained function," was suggested. Function oriented evaluation procedure to VE proposals which is suggested in this study is expected to be a great help in treating valuable functions through VE job plan.

• Smart Structures and Systems
• /
• v.25 no.2
• /
• pp.255-264
• /
• 2020

This paper deals with a defect classification technique that considers the structural characteristics of a refrigerant compressor. First, the pressure pulsation of the refrigerant flowing in the suction pipe of a normal compressor was measured at the same time as the acceleration of the shell surface, and then the transfer function between the two signals was estimated. Next, the frequency-weighted acceleration signals of the defect classification target compressors were generated using the estimated transfer function. The estimation of the variance of the transfer function is presented to formulate the frequency-weighted acceleration signals. The estimated frequency-weighted accelerations were applied to defect classification using frequency-domain features. Experiments were performed using commercial compressors to verify the technique. The results confirmed that it is possible to perform an effective defect classification of the refrigerant compressor by the shell surface acceleration of the compressor. The proposed method could make it possible to improve the total inspection performance for compressors in a mass-production line.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.3
• /
• pp.905-912
• /
• 2006

Support vector classification(SVC) provides more complete description of the linear and nonlinear relationships between input vectors and classifiers. In this paper we propose to solve the optimization problem of SVC with a modified hinge loss function, which enables to use an iterative reweighted least squares(IRWLS) procedure. We also introduce the approximate cross validation function to select the hyperparameters which affect the performance of SVC. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• The Korean Journal of Archival Studies
• /
• no.9
• /
• pp.41-73
• /
• 2004

This article provides the base in relation to the classification system of public records of Japan and Tiwan which the original order of the classification system of public records of Government-General of Chosun is reconstructed and the efficient classification system is prepared. The classification system of public records at the period of Meiji(明治) in Japan was classified two forms, one is function-based classification, the other is organization-based classification. Each ministry(省) was fundamentally based In function-based classification and organization-based classification, adopted them in changed forms as its condition and situation had been changed. Government-General of Tiwan adopted Japan's archival management system and put its classification system and life schedule In operation. The classification system of Government-General of Tiwan adopted function-based classification of the ministry of foreign affairs in Japan, changed its forms as the organization and business activity were transformed. As a result of arrangement and analysis of examples for the classification of public records of Government-General of Chosun from 1910' to the middle area of 1930', the classification of public records of Government-General of Chosun was constructed on level order; 'organization of ministry(部) or department(局)--business activity of ministry or department--low function of business activity of ministry or department'. But this classification system had two sides, flexible and unstable in that the classification system had exeptional parts and the breadth of items was changed greatly. The classification system of Government-General of Chosun, which had adopted organization-based classification of the ministry of home affairs in Japan, result in expanding the breadth of items and causing great change of items for the organization and business activity were vast and its change was very great.

• IE interfaces
• /
• v.24 no.2
• /
• pp.151-155
• /
• 2011

In this study, we propose a signomial classification method with ₀-regularization (₀-)which seeks a sparse signomial function by solving a mixed-integer program to minimize the weighted sum of the ₀-norm of the coefficient vector of the resulting function and the $L_1$-norm of loss caused by the function. $SC_0$ gives an explicit description of the resulting function with a small number of terms in the original input space, which can be used for prediction purposes as well as interpretation purposes. We present a practical implementation of $SC_0$ based on the mixed-integer programming and the column generation procedure previously proposed for the signomial classification method with $SL_1$-regularization. Computational study shows that $SC_0$ gives competitive performance compared to other widely used learning methods for classification.