• Journal of the Korea Society of Computer and Information
• /
• v.25 no.7
• /
• pp.27-37
• /
• 2020

In this paper, we propose the methodology based on data-driven approach using Natural Language Processing and Machine Learning for classifying requirements into functional requirements and non-functional requirements. Through the analysis of the results of the requirements classification, we have learned that the trained models derived from requirements classification with data-preprocessing and classification algorithm based on the characteristics and information of existing requirements that used term weights based on TF and IDF outperformed the results that used stemming and stop words to classify the requirements into functional and non-functional requirements. This observation also shows that the term weight calculated without removal of the stemming and stop words influenced the results positively. Furthermore, we investigate an optimized method for the study of classifying software requirements into functional and non-functional requirements.

• The Korean Journal of Archival Studies
• /
• no.49
• /
• pp.247-275
• /
• 2016

The classification scheme of records indicates a table that intends to express organic relations between records by organizing records and enabling internal order. Although the principles of organic classification have remained in traditional records management environment, they have been changed to "function and business" in the modern times. Therefore, Korea introduced a business reference model (BRM) based on function and business from 2008 and subsequently implemented its operation. However, it has been pointed out that the roles of the classification scheme of records have not been played because the analysis of "Danwigwaje," which belongs to the lowest level of business reference models, is poor. According to this indication, the Gangbuk District of Seoul Special City established a functional classification scheme by executing a business process analysis of "Danwigwaje." First, the record manager carried out analyses on the principles of "Danwigwaje," small function, and "Danwigwaje." Then, the functional classification scheme of "Danwigwaje" was modified by looking into the opinion inquiry process of the treatment department and performing a test operation. Through the case of the Gangbuk District in Seoul Special City, analytical procedures and methods of "Danwigwaje," as well as implications according to the establishment of a functional classification scheme of basic local governments, were arranged in a written format.

• The Journal of the Korea Contents Association
• /
• v.9 no.7
• /
• pp.248-256
• /
• 2009

The purpose of this study was to investigate the relationship among functional evaluation systems, the Manual Ability Classification System (MACS), the Gross Motor Function Classification System (GMFCS), and the functional status (WeeFIM) in children with spastic cerebral palsy and to provide the foundation data about MACS for evaluation system of hand function in children with spastic cerebral palsy. For this, sixty children with spastic cerebral palsy were employed in this study. The sixty children were evaluated by using the MACS for their hand function and by using the GMFCS for their motor function. The functional status were assessed by using the Functional Independence Measure of Children (WeeFIM). There were a significant correlation between the MACS and the GMFCS (r =.659, p <.05). The good correlation between the MACS and WeeFIM was found (r = -.576, p <.05). The functional status according to the hand function level evaluated by using the MACS were different significantly (p <.05). The MACS in practice will provide usefulness for assessment of hand function in children with spastic cerebral palsy.

• Journal of Information Science Theory and Practice
• /
• v.3 no.4
• /
• pp.62-77
• /
• 2015

This paper describes qualities of a library classification system that are commonly discussed in the LIS tradition and literature, and explains such a system’s three main functions, namely knowledge mapping, information retrieval, and shelf arrangement. In this vein, the paper states the functional requirements of bibliographic classifications, which broadly are subject collocation and facilitation of browsing the collection. It explains with details the components of a library classification system and their functions. The major components are schedules, notations, and index. It also states their distinguished features, such as generalia class, form divisions, book numbers, and devices for number synthesis which are not required in a knowledge classification. It illustrates with examples from the WebDewey good examples of added features of an online library classification system. It emphasizes that institutional backup and a revision machinery are essential for a classification to survive and remain relevant in the print and e-environment.

• Journal of the Korean Society of Systems Engineering
• /
• v.16 no.1
• /
• pp.51-57
• /
• 2020

The fighter pilot uses radar mounted on the fighter to obtain high-resolution SAR (Synthetic Aperture Radar) images for a specific area of distance, and then the pilot visually classifies targets within the image. However, the target configuration captured in the SAR image is relatively small in size, and distortion of that type occurs depending on the depression angle, making it difficult for pilot to classify the type of target. Also, being present with various types of clutters, there should be errors in target classification and pilots should be even worse if tasks such as navigation and situational awareness are carried out simultaneously. In this paper, the concept of operation and functional structure of radar system for fighter jets were presented to transfer the SAR image target classification task of fighter pilots to radar system, and the method of target classification with high accuracy was studied using the CNN ensemble model to archive higher classification accuracy than single CNN model.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.3
• /
• pp.186-199
• /
• 2013

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.2
• /
• pp.311-324
• /
• 2006

The Support Vector Machine(SVM) is very functional and efficient classification method to any other classification analysis method. However, its optimal extension to more than two classes is not obvious. In this paper several multi-category SVM methods are introduced and compared using simulation and real data sets. Also comparison with traditional multi-category classification and SVM based methods is performed.

• Journal of The Korean Association For Science Education
• /
• v.32 no.3
• /
• pp.524-531
• /
• 2012

Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI. Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed. Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.

• Journal of Energy Engineering
• /
• v.20 no.3
• /
• pp.200-208
• /
• 2011

KHNP (Korea Hydro & Nuclear Power Co.) defines and manages equipment of Nuclear Power Plants systematically with functional importance determination of each equipment for efficient maintenance and optimal preventive maintenance. But the existing functional importance determinations have some different results between the plants, systems and engineers due to gap of understanding of classification criteria because they have been done in terms of equipment level rather than function level. so that caused the repeated work. To make up for this problem improve methodology of functional importance determination using MR (Maintenance Rule) and do classification of equipment for new nuclear power plants based on function level. In addition, methodical documentation for basis of importance determination is done to help that system engineers can easily understand and use.

• Journal of Korean Library and Information Science Society
• /
• v.41 no.4
• /
• pp.151-170
• /
• 2010

There is a growing interest in user-centered classification or reader-interest classification, as questions have arisen from the meanings and the effects of traditional library classification. American public libraries have used fiction genre classification called bookstore model as an alternative to the traditional classification schemes. As a result, accessibility to the collection was promoted and library service for their users was improved. This study intends to make a comprehensive inquiry about the philosophical background and functional features of genre classification. To the end, literature survey and interviews or e-mails with librarians in American public libraries were conducted.