• 한국도서관정보학회지
• /
• 제11권
• /
• pp.175-209
• /
• 1984

The purpose of this study is to build a general principle for the automatic book-classification which can be put to use in library operation, and to present a methodology of the automatic classification for the library. Since the enumerative classification scheme which exist as manual systems cannot be a n.0, pplied to the automation of classification, the principles of Colon Classification by S.R. Ranganathan is brought in and studied. The result of the study can be summarized as follows: (1) Automatic book-classification can be performed by the principles of faceted classification. (2) This study presents a general and an a n.0, pplication principles for the automatic book-classification. (3) File design for the automatic book-classification of a general classification is different from that of special classification, (4) The methodology is to classify the literature by inputting the title into a terminal. In addition, the expected Value from the Automatic Book-classification is as follows: (1) The prompt and accurate process of classification is possible. (2) Though a book is classified in any library it can have the same classification number. (3) The user can retrieve the classification code of a book for which he or she wants to search through the dialogue with the computer. (4) Since the concept coordination method is employed, even the representing of a multi-subject concept is made simple. (5) By performing automatic book-classification, the automation of library operation can be completed.

• 한국비블리아학회지
• /
• 제27권3호
• /
• pp.25-50
• /
• 2016

기록관리학이 독자적인 학문영역으로 발전해가고 있다. 하지만 KDC를 비롯한 기존의 문헌분류표에서는 분류 항목 배열이 기록관리학의 특성을 제대로 반영하지 못하여 분류항목의 전개가 불합리한 부분이 있으며, 주제영역별 세목전개의 재배치가 필요하다. 따라서 이 연구에서는 기록관리학의 학문 특성에 따라 주제영역을 기록관리 일반, 기록관리 법 정책, 기록물의 수집 선별 평가, 기록물의 조직, 기록정보서비스, 기록물 관리 및 보존, 기록관 운영, 기록관리 기관 등 8개 영역으로 설정하고, 현대 주요 문헌분류표인 KDC, DDC, NDC, UDC, LCC의 분류체계를 분석한 후, "대한민국 국가서지"의 기록관리학 분야 유별 자료현황 및 주제어 분석 결과를 반영하여 KDC 기록관리학 분야의 분류체계 수정 전개 방안을 제시하였다. 기록관리학 관련 주제영역 8개 분야의 내용은 KDC 028로 통합할 수 있도록 변경하였다.

• 한국ITS학회 논문지
• /
• 제10권5호
• /
• pp.47-52
• /
• 2011

본 연구는 차량 제원이 유사한 2축 차량의 차종분류에 있어서 정확도를 높이고자 차량 외형의 높이 프로파일을 이용한 차종분류 방안을 제시했다. 차종별 교통량 자료 생성은 도로를 주행하는 차량을 대상으로 AVC장비에서 계측되는 차량 제원들인 축수, 축간거리, 차량길이, 오버행 등을 활용하여 12종 분류 체계에 의해서 분류되고 있다. 그러나 차량 축이 2개인 2축 차량(1~4종 차량)의 경우 승용차(1종)의 다양화, 대형화로 인하여 화물수송용 차량(3종, 4종)의 제원과 유사해짐에 따라 기존 차량분류인자(축수, 축간거리, 차량길이 등)에 의한 차종분류 시 분류 오류가 발생할 수 있다. 이에 본 연구는 이러한 분류상의 한계를 극복하고자 차량 외관의 높이 프로파일 값을 통하여 주행차량의 형태를 파악하고 이를 이용한 차종분류 방법을 제시하였다. 그리고 현장실험을 통하여 제안된 방법의 정확도를 검증하였다.

• 한국지하수토양환경학회지:지하수토양환경
• /
• 제15권6호
• /
• pp.64-71
• /
• 2010

National inventory of soil and groundwater contamination is an efficient decision-making tool to identify and manage existing or potential contaminated sources and contaminants. It has been used as basic data for establishing the scheme of regulations and remediation plans of soil and groundwater contamination in developed countries. This study examined classification of existing or potential sources of soil and groundwater contamination from various countries to suggest implications that required for development of classification of soil and groundwater contamination sources in Korea. Each country has provided a list of currently or potentially contaminating activities or landuses and identified some of the potential contaminants related to those contamination sources. Consideration of sources which had not been mentioned or regarded as contamination sources before was suggested for Korea situation. In addition, it is necessary to compile a list of existing data and information as much as possible to develop a detailed and practical list of various contamination sources.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권1호
• /
• pp.288-301
• /
• 2017

In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.

• 자원리싸이클링
• /
• 제27권1호
• /
• pp.84-91
• /
• 2018

본 연구에서는 레이저유도 플라즈마 분광법(Laser induced breakdown spectroscopy, LIBS) 기반의 금속 종류별 스펙트럼 데이터를 이용하여 연성정보(soft information)를 추정하고 최빈 클래스로 분류하는(most probable classification) 금속 분류 방법을 제안한다. 폐금속 자원과 같이 사전 정보가 없는 금속을 분류하는 경우 몇 가지 핵심 구성성분에 대한 정량 분석을 통해서 클래스를 추정하는 방법이 효율적이다. 이에 따라 부분 집합 기반의 부분최소제곱회귀법(Partial Least Square Regression, PLSR)을 이용하여 LIBS 검출 스펙트럼으로부터 각 성분의 농도를 독립적으로 신뢰성 있게 추정하고, 인증 표준물질(CRM) 등 알려진 모집합의 농도정보에 기반하여 최고 확률을 갖도록 분류하는 기술을 제안한다. 샘플 스펙트럼들의 다변량 분석을 통해서 여러 성분의 추정 농도를 다변량 정규 분포를 갖는 것으로 가정하고 통합(Joint) 추정 연성정보를 구할 수 있으며, 이를 활용한 최빈 확률 검출이나 추가적인 사전 정보의 결합 등을 통해서 분류 성능을 향상시킬 수 있다. 제안된 기술의 평가를 위해서 9가지 종류의 CRM 금속시료의 LIBS 스펙트럼 데이터를 사용하며, 부분 집합 기반의 PLSR 농도 추정 기술을 기반으로 단변량 혹은 다변량 정규 분포 연성 정보추정을 통해 미지 금속의 검출과 연성 정보의 검출 등을 테스트 하였다. 또한 방사형 차트(Radar chart)를 이용하여 추정된 농도와 획득한 연성정보를 효과적으로 시각화함으로써 기존 라이브러리에 포함된 부분 집합의 금속과 비교하여 해당 금속과의 유사성을 그래프를 통해 추정할 수 있다.

• 한국문헌정보학회지
• /
• 제48권4호
• /
• pp.5-23
• /
• 2014

This study reports the first step in the Classification-based Search and Knowledge Discovery (CSKD) project, which aims to combine information organization and retrieval approaches for building digital library applications. In this study, we explored the generation and application of a faceted vocabulary as a potential mechanism to enhance knowledge discovery. The faceted vocabulary construction process revealed some heuristics that can be refined in follow-up studies to further automate the creation of faceted classification structure, while our concept search application demonstrated the utility and potential of integrating classification-based approach with retrieval-based approach. Integration of text- and classification-based methods as outlined in this paper combines the strengths of two vastly different approaches to information discovery by constructing and utilizing a flexible information organization scheme from an existing classification structure.

• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2005년도 하계학술대회 논문집 Vol.6
• /
• pp.566-567
• /
• 2005

Until now variable pattern classification methods have been introduced. So, variable methods in PD source classification were applied. NN(neural network) the most used scheme as a PD(partial discharge) source classification. But in recent year another method were developed. These methods is present superior to NN in the field of image and signal process function of classification. In this paper, it is show classification result in PD source using three methods; that is, BP(back-propagation), ANFIS(adaptive neuro-fuzzy inference system), PCA-LDA(principle component analysis-linear discriminant analysis).

• 정보기술응용연구
• /
• 제1권
• /
• pp.173-211
• /
• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• 천문학회보
• /
• 제41권2호
• /
• pp.49.1-49.1
• /
• 2016

Asteroid taxonomy dates back to the mid-1970's and is based mostly on broadband photometric and spectroscopic observations in the visible wavelength. Different taxonomic classes have long been characterized by spectral slope shortward of 0.75 microns and the absorption band in 1 micron, the principal components. In this way, taxonomic classes are grouped and divided into four broad complexes; silicates (S), carbonaceous (C), featureless (X), Vestoids (V), and the end-members that do not fit well within the S, C, X and V complexes. The past decade witnessed an explosion of data due to the advent of large-scale asteroid surveys such as SDSS. The classification scheme has recently been expanded with the analysis of the SDSS 4th Moving Object Catalog (MOC 4) data. However, the boundaries of each complex and subclass are rather ambiguously defined by hand. Furthermore, there are only few studies on asteroid taxonomy using Johnson-Cousins filters, and those were conducted on a small number of objects, with significant uncertainties. In this paper, we present our preliminary results for a new taxonomic classification of asteroids using SMASS, Bus and DeMeo (2014) and the SDSS MOC 4 datasets. This classification scheme is simply represented by a triplet of photometric colors, either in SDSS or in Johnson-Cousins photometric systems.