• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.603-612
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• 2002

Area of software measurement in software engineering is active more than thirty years. There is a huge collection of researches but still no concrete software development effort and cost estimation model. If we want to measure the effort and cost of a software project, we need to estimate the size of the software. A number of software metrics are identified in the literature; the most frequently cited measures are LOC (line of code) and FPA (function point analysis). The FPA approach has features that overcome the major problems with using LOC as a measure of system size. This paper presents simple linear regression model that related software development effort to software size measured in FP. The model is derived from the plotting of the effort and FP relation. The experimental data are collected from 789 software development projects that were recently developed under the various development environments and development methods. Also, the model is compare with other regression analysis model. The presented model has the best estimation ability among the software effort estimation models.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.37-40
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• 2018

빅데이터 산업 부상과 함께 교육 데이터 분석 분야가 새롭게 주목받고 있다. 교육 현장에서 학습 데이터의 양과 종류는 꾸준히 증가하고 있고 이를 분석하기 위한 정보기술도 계속 발전하고 있다. 한편, 학교 교육은 사회적 성취와 밀접한 관련이 있어 사회이동의 중요한 수단이 되는 만큼 학교 교육으로부터 이탈할 위험이 있는 학생들을 조기에 발견하여 이탈을 방지하는 것은 매우 중요하다. 본 논문은 대학생의 중도탈락을 예방하기 위해 로지스틱 회귀분석과 다층 퍼셉트론 기법을 이용해 학습 데이터를 분석하여 예측 모델을 생성하고 해당 모델을 평가한다. 평가 결과, 다층 퍼셉트론 모델이 로지스틱 회귀분석 모델에 비해 정확도와 재현율은 우수하였지만 정밀도는 약간 저조하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.245-256
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• 2024

Since 2020, quantitative easing and easy money policies have been implemented for the purpose of economic stimulus. As a result, real estate prices have skyrocketed. In this study, the relationship between sales and rental prices by housing type during the period of soaring real estate prices in Busan was analyzed spatio-temporally. Based on the actual transaction price data, housing type, transaction type, and monthly data of district units were constructed. Among the spatio-temporal analysis models, the SpVAR, which is used to understand the temporal and spatial effects of variables, and the GSTAR, which is used to understand the effects of each region on those variables, were used. As a result, the sales price of apartment had positive effect on the sale price of apartment, row house, and detached house in the surrounding area, including the target area. On the other hand, it was confirmed that demand was converted to apartment rental due to an increase in apartment sales prices, and the sale price fell again over time. The spatio-temporal spillover effect of apartments was positive, but the positive effect of row house and detached house were concentrated in the original downtown area.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.147-158
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• 2009

Recently, the data model in stream data environment has massive, continuous, and infinity properties. However the stream data processing like query process or data analysis is conducted using a limited capacity of disk or memory. In these environment, the traditional frequent pattern discovery on transaction database can be performed because it is difficult to manage the information continuously whether a continuous stream data is the frequent item or not. In this paper, we propose the method which we are able to predict the frequent items using the regression model on continuous stream data environment. We can use as a prediction model on indefinite items by constructing the regression model on stream data. We will show that the proposed method is able to be efficiently used on stream data environment through a variety of experiments.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.1
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• pp.57-64
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• 2009

This study intends to evaluate of the characteristics of pavement deformation and develop the model for prediction model in the asphalt layer using a regression analysis. In test, there are two different asphalt binders and 5 different aggregate types. The air voids of hot mix asphalt are 6% and 10% for target value. Repeated triaxial compression test with 3 different confining pressures was used for test at 3 different test temperatures. It is going to verify the main parameters for permanent deformation of HMA and to develop the distress model. This paper is to figure out the factor affecting the pavement deformation, and then to develop model the pavement deformation for asphalt mixture. Also, the reliability of prediction model has been studied. The permanent deformation prediction model for asphalt mixtures with temperature, loading time, and air voids has been developed and the proposed permanent deformation prediction model has been validated by using the multiple regression approach which is called Statistical Package for the Social Sciences(SPSS).

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.9
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• pp.77-81
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• 2016

Logistic regression (LR) has been widely used for predicting the relationships among variables in various fields. We propose a new logistic regression model with a fine-grained weighting method, called value weighted logistic regression, by assigning different weights to each feature value. A gradient approach is utilized to obtain the optimal weights of feature values. We conduct experiments on several data sets and the experimental results show that the proposed method shows meaningful improvement in prediction accuracy.

• The Journal of Engineering Geology
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• v.14 no.2
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• pp.211-222
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• 2004

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The seven landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The seven factors consist of two topographic factors and five geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 90.74% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.6
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• pp.43-51
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• 2013

This study aims to examine the presence of non-stationary relationship between water quality and land use in watersheds. In investigating the relationships between land use and water quality, most previous studies adopted OLS method which is assumed stationarity. However, this approach is difficult to capture the local variation of the relationships. We used 146 sampling data and land cover data of Korean Ministry of Environment to build conventional regressions and GWR models for BOD, TN and TP. Regression model and GWR models of BOD, TN, TP were compared with $R^2$, AICc and Moran's I. The results of comparisons and descriptive statistics of GWR models strongly indicated the presence of Non-Stationarity between water quality and land use.

• Journal of KIISE:Software and Applications
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• v.41 no.8
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• pp.598-604
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• 2014

In this paper, we develop stepwise regression data envelopment model to select important variables. We formulate null hypothesis to understand the importance of each variable and use Kruskal-Wallis test for this purpose. If the Kruskal-Wallis test does reject the null hypothesis this will imply there is significant fluctuation in the efficiency score relative to base model. And therefore we have to further check the pair of variables that causes the fluctuation in order to determine its importance using Conover-Inman test. The proposed models helps understand the extent of misclassification decision making units as efficient/inefficient when variables are retained or discarded alongside provides useful managerial prescription to make improvement strategies.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.28-31
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• 2014

본 논문에서는 블랙박스 혹은 운전석에 장착된 카메라로부터 얻어진 차량 영상에 대한 영역별 수직 히스토그램 매칭 및 선형 회귀분석 모델(linear regression model)을 활용한 강건한 차량 운행 동영상의 안정화(video stabilization) 기법을 제안한다. 동영상 안정화 기법은 영상의 흔들림 보정뿐 아니라 동영상 내 강건한 특징점 추적 및 매칭을 위한 이전의 전처리 과정으로 적용된다. 일반적으로 촬영 과정에서 많은 떨림이 포함될 수 있는 야외 CCTV 영상이나 손으로 들고(hand-held) 촬영된 동영상에 대한 흔들림 보정 등에 적용되고 있으나 영상 내 특징점이 지속적으로 변하고 영상의 변화 정도가 매우 심한 차량 운행 동영상에서는 적용된 사례가 드물다. 본 연구에서는 일반적인 비디오 안정화 기술이 적용되기 어려운 차량 운행 동영상에 대하여 수직 투영 히스토그램 매칭 및 선형 회귀분석 모델 기반의 안정화 기법을 제안한다. 제안된 기법은 입력영상에 대한 영역별 수직 투영 히스토그램 매칭을 수행하고 선형 회귀모델을 통해 영상에 나타나는 수직 및 회전이동 변환을 선형 근사하여 시간 영역 상의 입력 영상에 대한 안정화를 달성한다. 제안 방법의 검증을 위해 블랙박스로 촬영된 실제 동영상에 동영상 안정화 기술을 적용하였으며, 운행 중 불규칙한 노면으로 인한 영상의 흔들림이 효과적으로 제거되는 것을 확인할 수 있었다.