• The Korean Journal of Archival Studies
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• no.53
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• pp.201-228
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• 2017

Recently, public organizations have been improving the classification in order to introduce records management reference table in accordance with the act on the management of public archives. However, there is no system to mount the revised records management reference table, and there is a problem in usability. The purpose of this study is to design a system function to manage the classification which is the foundation of records management for public organizations. In order to design the function, interviews with the records management specialists of the five public organizations were conducted. Based on this, we have designed a multi-classification system registration function, a hierarchical structure setting function of records, and a record management standard management function.

• Journal of Korean Society of Archives and Records Management
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• v.5 no.2
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• pp.73-101
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• 2005

This study aims to identify the developing process of classification shemes for NGO records. And it chooses the KFEM(Korea Federation for Environmental Movenment) for case study, which is a representative NGO of Korea. This study proposes the classification principles in the form that the function classification and subject classification are combined. The development model of function classification schemes on the KFEM records is based on the Australian Standard Work Process Analysis for Recordkeeping(AS 5090) and the DIRKS (Designing and Implementing Recordkeeping Systems) methodology. Literature review, interviews, work process analysis, and questionnaire surveys have been employed as research methodology.

• Biomolecules & Therapeutics
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• v.17 no.4
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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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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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.

• Proceedings of the KSRS Conference
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• v.2
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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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• v.10 no.1
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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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• v.28 no.6
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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.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.547-560
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• 2008

It is well known that spectral density function determines auto-covariance function of stationary time-series data and smoothed periodogram is a consistent estimator of spectral density function. Recently, Kim and Park (2007) showed that smoothed- periodogram based distances performs very well for the classification. In this paper, we introduce classification methods with smoothed periodogram and apply the approaches to the monthly precipitation measurements obtained from January, 1987 through December, 2007 at 22 locations in South Korea.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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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%.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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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.