• 기록학연구
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• 제53호
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• pp.201-228
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• 2017

최근 정부산하공공기관은 "공공기록물 관리에 관한 법률" 시행령에 따라 기록관리기준표를 도입하기 위해 분류체계를 정비하고 있다. 하지만 정비한 기록관리기준표를 탑재할 시스템이 부재하여 활용성에 문제가 있다. 본 연구는 고유의 미션을 가진 정부산하공공기관을 대상으로 기록관리의 토대가 되는 분류체계를 관리 할 수 있는 분류체계관리시스템 기능 설계를 목적으로 수행되었다. 기능 설계를 위해 5개 정부산하공공기관 기록물전문 요원과의 심층면담을 수행하였고, 이를 토대로 다양한 분류체계 등록 기능, 집합체 계층 구조 설정 기능, 기록관리기준 관리 기능을 설계하였다.

• 한국기록관리학회지
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• 제5권2호
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• pp.73-101
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• 2005

본 연구는 시민단체 기록의 분류방안을 마련해 보는 데에 연구의 목적을 두고, 환경운동연합을 사례로 환경연합기록의 분류체계 및 처리일정표 개발 과정을 제시해 보았다. 환경연합 기록의 분류원칙으로 기능분류에 주제분류를 결합한 형태의 분류원칙을 적용하였으며, 기능분류체계 개발을 위해 기록관리 업무분석 표준인 AS 5090와 DIRKS 방법론을 활용하였다. 연구 방법으로는 문헌연구, 자료조사, 인터뷰, 업무분석, 설문조사 등을 활용하였다.

• Biomolecules & Therapeutics
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• 제17권4호
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• pp.335-347
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• 2009

This review covers current developments in molecular-based studies of the structure and function of carboxylesterases. To allay the confusion of the classic classification of carboxylesterase isozymes, we have proposed a novel nomenclature and classification of mammalian carboxylesterases on the basis of molecular properties. In addition, mechanisms of regulation of gene expression of carboxylesterases by xenobiotics, and involvement of carboxylesterase in drug metabolism are also described.

• 전자공학회논문지B
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• 제29B권7호
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• pp.70-78
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• 1992

In this paper, an approach for 3-D object segmentation and classification, which is based on eigen-values of polynomial function as their surface features, using neural network is proposed. The range images of 3-D objects are classified into surface primitives which are homogeneous in their intrinsic eigenvalue properties. The misclassified regions due to noise effect are merged into correct regions satisfying homogeneous constraints of Hopfield neural network. The proposed method has advantage of processing both segmentation and classification simultaneously.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.812-815
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• 2006

Proteins are essential agents for controlling, effecting and modulating cellular functions, and proteins with similar sequences have diverged from a common ancestral gene, and have similar structures and functions. Function prediction of unknown proteins remains one of the most challenging problems in bioinformatics. Recently, various computational approaches have been developed for identification of short sequences that are conserved within a family of closely related protein sequence. Protein function is often correlated with highly conserved motifs. Motif is the smallest unit of protein structure and function, and intends to make core part among protein structural and functional components. Therefore, prediction methods using data mining or machine learning have been developed. In this paper, we describe an approach for protein function prediction of motif-based models using data mining. Our work consists of three phrases. We make training and test data set and construct classifier using a training set. Also, through experiments, we evaluate our classifier with other classifiers in point of the accuracy of resulting classification.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.59-64
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• 2010

To improve system performance, we apply the concept of membership function to Variance Considered Machines (VCMs) which is a modified algorithm of Support Vector Machines (SVMs) proposed in our previous studies. Many classification algorithms separate nonlinear data well. However, existing algorithms have ignored the fact that probabilities of error are very high in the data-mixed area. Therefore, we make our algorithm ignore data which has high error probabilities and consider data importantly which has low error probabilities to generate system output according to the probabilities of error. To get membership function, we calculate sigmoid function from the dataset by considering means and variances. After computation, this membership function is applied to the VCMs.

• ETRI Journal
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• 제28권6호
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• pp.777-786
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• 2006

Since the introduction of competition in the telecommunication market due to the growth of the interconnection between heterogeneous networks, particularly fixed and mobile networks, the interconnection charge based on traffic-sensitive (TS) and non-traffic-sensitive (NTS) costs has become more important. Although there have been many studies of the public switched telephone network (PSTN), previous studies of TS and NTS costs in mobile networks are very few. In this paper, as a pilot study, we propose three criteria and a procedure for the classification of TS and NTS costs based on mobile systems. The three criteria are the following: function type, investment requirement, and main exhaust driver. Moreover, for a CDMA mobile system, strongly TS, strongly NTS, and mixed components are classified by the proposed criteria and procedure. The proposed criteria, procedure, and classification can provide a systematic and useful guideline to decide the scope of mobile facilities and to determine the terminating cost on mobile networks from fixed networks.

• 응용통계연구
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• 제21권3호
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• pp.547-560
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• 2008

스펙트럼 밀도함수(spectral density function)는 시계열 자료가 정상성(stationarity)을 만족하는 경우에 주파수 영역(frrqllrnFr domain)에서 시계열 자료의 자기공분산함수(auto-covariance function)을 결정짓는 함수이고, 평활된 주기도(smoothed periodogram)는 스펙트럼 밀도함수의 일치 추정량(consistent estimator)이 됨이 잘 알려져 있다. 본 연구에서는 시계열 자료를 평활된 주기도를 이용하여 군집화하는 방법을 소개한다. 최근 김희영과 박만식 (2007)의 연구에 의하면 이 거리는 정상시계열들을 효율적으로 분류하고 있음을 알 수 있다. 본 연구는 시계열 자료를 분류하는데 사용된 기존의 거리들을 간략히 소개하고, 우리나라 22개 지역에서 1987년 1월부터 2007년 12월까지 측정한 월별 강수량 자료를 대상으로 평활된 주기도 거리를 이용하여 지역을 군집화한다.

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.137-146
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• 2023

The dropout of university freshmen is a very important issue in the financial problems of universities. Moreover, the dropout rate is one of the important indicators among the external evaluation items of universities. Therefore, universities need to predict dropout students in advance and apply various dropout prevention programs targeting them. This paper proposes a method to predict such dropout students in advance. This paper is about a method for predicting dropout students. It proposes a method to select dropouts by applying logistic regression using a shift sigmoid classification function using only quantitative data from the first semester of the first year, which most universities have. It is based on logistic regression and can select the number of prediction subjects and prediction accuracy by using the shift sigmoid function as an classification function. As a result of the experiment, when the proposed algorithm was applied, the number of predicted dropout subjects varied from 100% to 20% compared to the actual number of dropout subjects, and it was found to have a prediction accuracy of 75% to 98%.

• 제어로봇시스템학회논문지
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• 제11권12호
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• pp.991-995
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• 2005

Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as health, environment and habitat monitoring, military, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modern teaming techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes haying the capability of simple processing and wireless communication. The proposed system is able to perform classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to hand gesture recognition system.