• The Korean Journal of Applied Statistics
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• v.2 no.2
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• pp.91-101
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• 1989

통계 전문가 시스템(Statistical Expert System, SES) 또는 Knowledge-Based Statistical Consulation System은 "통계적 방법에 의한 연구 과정(계획,자료수집,분석,결과해석 등)에서 발생하는 문제를 효과적,효율적으로 해결하기 위해 통계 전문가 또는 통계 상담자의 경험과 지식을 내장하여 이를 처리할 수 있도록 설계된 전산 시스템"으로 정의할 수 있을 것이다.SES를 구축함으로써 기대되는 효과는 1.초보사용자의 통계기법 오용을 방지하고, 2.기존 통계 software가 지니지 못한 탐색 및 설명기능, 결과해석기능 등을 제공하며, 3.사용자(초보자 및 전문가)에게 교육 및 통계전략에 대한 실험 환경을 제공하고, 4.전문가로 하여금 일상적이고 기계적인 업무로부터 벗어나 좀 더 기본적인 문제 해결에 전념할 수 있도록 보조적인 역할을 수행하는 것 등이다.행하는 것 등이다.

• Journal of Korea Water Resources Association
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• v.32 no.5
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• pp.515-524
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• 1999

A methodology to resolve a TOPMODEL problem has been suggested, which is associated with the spatial distribution of soil moisture behaviour in a runoff mechanism. A procedure to integrate the spatial information of saturation deficit in the TOPMODEL reflects the connectivity of saturated area in a watershed. The developed algorithm includes an improved basis in tracing the runoff path without increasing the number of parameters. The performance of the developed algorithm has been tested to an upland subwatershed, namely Dongok, which is the IHP watershed located at Wichon, Korea. Comparing with the original statistical version of the TOPMODEL, it has been found that the suggested algorithm can relax an overestimation of peak rate in the runoff simulation.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.516-524
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• 2004

In this paper, the initial value problem in clustering using K-means or Fuzzy-c-means is considered to reduce the number of iterations. Conventionally the initial values in clustering using K-means or Fuzzy-c-means are chosen randomly, which sometimes brings the results that the process of clustering converges to undesired center points. The choice of intial value has been one of the well-known subjects to be solved. The system of clustering using K-means or Fuzzy-c-means is sensitive to the choice of intial values. As an approach to the problem, the uniform partitioning method is employed to extract the optimal initial point for each clustering of data. Experimental results are presented to demonstrate the superiority of the proposed method, which reduces the number of iterations for the central points of clustering groups.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.718-729
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• 2006

There has been increasing interest in automatic term recognition (ATR), which recognizes technical terms for given domain specific texts. ATR is composed of 'term extraction', which extracts candidates of technical terms and 'term selection' which decides whether terms in a term list derived from 'term extraction' are technical terms or not. 'term selection' is a process to rank a term list depending on features of technical term and to find the boundary between technical term and general term. The previous works just use statistical features of terms for 'term selection'. However, there are limitations on effectively selecting technical terms among a term list using the statistical feature. The objective of this paper is to find effective features for 'term selection' by considering various aspects of technical terms. In order to solve the ranking problem, we derive various features of technical terms and combine the features using machine-learning algorithms. For solving the boundary finding problem, we define it as a binary classification problem which classifies a term in a term list into technical term and general term. Experiments show that our method records 78-86% precision and 87%-90% recall in boundary finding, and 89%-92% 11-point precision in ranking. Moreover, our method shows higher performance than the previous work's about 26% in maximum.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.116-120
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• 1999

현재 S회사의 생산공정은 전 과정에 걸쳐 통합된 정보처리 체계가 없고, 생산계획 및 통제 과정에서의 복잡한 계산 및 중요한 의사결정이 담당자의 경험적 지식에 의존하고 있다. 이러한 상황의 한계로, 현재의 생산공정계획 및 생산실행계획은 시간적 제약이 크고, 유연적인 수정이 어려울 뿐 아니라, 통계적 분석의 자료체계가 부실하다. 그러나, S회사는 보다 개선된 공정관리를 위해 새로운 생산계획전략의 필요성을 느끼고 있다. 본 논문에서는 이러한 문제를 해결하기 위한 하나의 접근방법으로써 DEVS 형식론을 바탕으로 공정을 효과적으로 모델링 및 시뮬레이션화 하였다. 생산계획의 핵심부분인 전문담당자의 경험적 지식을 체계적 규칙으로 정리하여 모델에 반영하였고 이를 통해 습득된 시뮬레이션 결과를 분석하여 생산계획 전략의 신뢰할만한 평가기준을 마련하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.387-388
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• 2022

본 연구에서는 비 정보과 교사들의 효과적인 SW 교육 교수효능감 함양을 위하여 팀 프로젝트 기반 융합 교육 프로그램을 개발하였다. 개발된 교육 내용은 융합교육 및 팀 프로젝트에 대한 이해를 바탕으로 수학, 과학, 정보 등이 융합된 다양한 프로젝트를 실습한 뒤, 직접 문제 해결을 위한 프로젝트의 설계 및 개발과 발표, 동료 평가 및 피드백의 과정으로 구성되어 있다. 이는 10주간 비 정보과 교사들에게 처치되었고, 사전-사후 t-검정 결과, 통계적으로 유의한 향상을 나타내었다. 하지만 본 연구의 실험은 단일집단을 대상으로 하였기 때문에 추후 통제집단과의 비교를 통하여 향상에 대한 통계적 비교가 필요로 된다.

• Journal of Broadcast Engineering
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• v.1 no.2
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• pp.165-175
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• 1996

In this paper, the image segmentation method based on the hierarchical pyramid image structure of reduced resolution versions of the image for solving the problems in the conventional methods is presented. This method is described the object detection and delineation by statistical approach. In the object detection method, IFSVR( Inverse-father-son variance ratio) method and FSVR(father-son variance ratio ) method are proposed for solving clustering validity problem occurred In the hierarchical pyramid image structure. An optimal object pixel Is detected at some level by this method. In the object delineation method, the iterative algorithm by top-down traversing method is proposed for moving the optimal object pixel to levels of higher resolution. Using the computer simulation, the results by the proposed statistical methods and object traversing method are investigated for the binary Image and the real image. At the results of computer simulation, the proposed methods of image segmentation based on the hierarchical pyramid Image structure seem to have useful properties and deserve consideration as a possible alternative to existing methods of image segmentation. The computation for the proposed method is required 0(log n) for n${\times}$n input image.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.425-440
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• 2012

As the scale of the internet grows, the amount of subjective data increases. Thus, A need to classify automatically subjective data arises. Sentiment classification is a classification of subjective data by various types of sentiments. The sentiment classification researches have been studied focused on NLP(Natural Language Processing) and sentiment word dictionary. The former sentiment classification researches have two critical problems. First, the performance of morpheme analysis in NLP have fallen short of expectations. Second, it is not easy to choose sentiment words and determine how much a word has a sentiment. To solve these problems, this paper suggests a combination of using web-scale data and a statistical approach to sentiment classification. The proposed method of this paper is using statistics of words from web-scale data, rather than finding a meaning of a word. This approach differs from the former researches depended on NLP algorithms, it focuses on data. Hadoop and MapReduce will be used to handle web-scale data.

• Journal of Elementary Mathematics Education in Korea
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• v.23 no.1
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• pp.119-141
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• 2019

This study was carried out in order to draw some implications for teaching statistical graph in mathematics education with respect to practical statistics education for promoting students' statistical literacy. We analyze 133 graphs appearing in 99 elementary textbooks of other subjects (subjects except from mathematics) by subjects and types, and identify some cases of graphs addressed by other subjects. As a results, statistical graph was most addressed in social studies, and bar graphs, line graphs, tables, and circle graphs are most used in other subjects. Moreover, there are some issues related to contents-(1) the problem of curricular sequencing between mathematics and other subjects, (2) the level of addressing ratio graph, and (3) the use of wavy lines. In terms of forms, (1) the visual variation of graphical representations, (2) representation combining multiple graphs, and (2) graphs specialized for particular subjects are drawn as other issues. We suggest some implications to be considered when teaching the statistical graph in elementary mathematics classes.

• Journal of KIISE
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• v.41 no.11
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• pp.921-926
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• 2014

Face detection refers to the process extracting facial regions in an input image, which can improve speed and accuracy of recognition or authorization system, and has diverse applicability. Since conventional works have tried to detect faces based on the whole shape of faces, its detection performance can be degraded by occlusion made with accessories or parts of body. In this paper we propose a method combining local feature descriptors and probability modeling in order to detect partially occluded face effectively. In training stage, we represent an image as a set of local feature descriptors and estimate a statistical model for normal faces. When the test image is given, we find a region that is most similar to face using our face model constructed in training stage. According to experimental results with benchmark data set, we confirmed the effect of proposed method on detecting partially occluded face.