• Journal of Astronomy and Space Sciences
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• 제7권2호
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• pp.73-103
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• 1990

Morphological luminosity parameters$(\mu_e,\;r_e,\;\mu_0,\;\alpha^{-1})$ and D/B were estimated from the decomposition of surface brightness distributions of 28 extra galaxies. Decomposition was made using the standard non-linear least square fitting method and we used the seeing convolved model to get the central brightness of these galaxies. Masses and $M/L_B$ were calculated using rotational velocities of these galaxies from the fitting to the generalized Toomre's mass model.

• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.279-288
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• 1998

We propose a multiprocess dynamic Poisson model for the analysis of Poisson process with the covariates. The algorithm for the recursive estimation of the parameter vector modeling time-varying effects of covariates is suggested. Also the algorithm for forecasting of numbers of events at the next time point based on the information gathered until the current time is suggested.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.781-790
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• 1999

In this paper we consider estimation of cancer incidence rates for local areas. The raw estimates usually are based on small sample sizes and hence are usually unreliable. A hierarchical Bayes generalized linear model is used which connects the local areas thereby enabling one to 'borrow strength' Random effects with pairwise difference priors model the spatial structure in the data. The methods are applied to cancer incidence estimation for census tracts in a certain region of the state of New York.

• Asia pacific journal of information systems
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• 제34권1호
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• pp.209-225
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• 2024

This research aims to use machine learning technology in human resource management to predict employees' work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers' work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study's outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

• 제어로봇시스템학회논문지
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• 제3권3호
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• Educational Technology International
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• 제15권2호
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• Journal of Oral Medicine and Pain
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• 제46권2호
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• pp.21-32
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• 2021

Purpose: The purpose of the present study was to investigate the correlation between age and 34 radiomorphometric parameters on panoramic radiographs, and to provide generalized linear models (GLMs) as a non-invasive, inexpensive, and accurate method to the forensic judgement of living individual's age. Methods: The study included 417 digital panoramic radiographs of Korean individuals (178 males and 239 females, mean age: 32.57±17.81 years). Considering the skeletal differences between the sexes, GLMs were obtained separately according to sex, as well as across the total sample. For statistical analysis and to predict the accuracy of the new GLMs, root mean squared error (RMSE) and adjusted R-squared (R²) were calculated. Results: The adjusted R²-values of the developed GLMs in the total sample, and male and female groups were 0.623, 0.637, and 0.660, respectively (p<0.001), while the allowable RMSE values were 8.80, 8.42, and 8.53 years, respectively. In the GLM of the total sample, the most influential predictor of greater age was decreased pulp area in the #36 first molar (beta=-26.52; p<0.01), followed by the presence of periodontitis (beta=10.24; p<0.01). In males, the most influential factor was the presence of periodontitis (beta=9.20; p<0.05), followed by the number of full veneer crowns (beta=2.19; p<0.001). In females, the most influential predictor was the presence of periodontitis (beta=18.10; p<0.001), followed by the tooth area of the #16 first molar (beta=-11.57; p<0.001). Conclusions: We established acceptable GLM for each sex and found out the predictors necessary to age estimation which can be easily found in panoramic radiographs. Our study provides reference that parameters such as the area of tooth and pulp, the number of teeth treated, and the presence of periodontitis should be considered in estimating age.

• 한국환경복원기술학회지
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• 제19권6호
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• pp.19-30
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• 2016

The projection of climate-related range shift is critical information for conservation planning of Korean fir (Abies koreana E. H. Wilson). We first modeled the distribution of Korean fir under current climate condition using five single-model species distribution models (SDMs) and the pre-evaluation weighted ensemble method and then predicted the distributions under future climate conditions projected with HadGEM2-AO under four $CO_2$ emission scenarios, the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. We also investigated the predictive uncertainty stemming from five individual algorithms and four $CO_2$ emission scenarios for better interpretation of SDM projections. Five individual algorithms were Generalized linear model (GLM), Generalized additive model (GAM), Multivariate adaptive regression splines (MARS), Generalized boosted model (GBM) and Random forest (RF). The results showed high variations of model performances among individual SDMs and the wide range of diverging predictions of future distributions of Korean fir in response to RCPs. The ensemble model presented the highest predictive accuracy (TSS = 0.97, AUC = 0.99) and predicted that the climate habitat suitability of Korean fir would increase under climate changes. Accordingly, the fir distribution could expand under future climate conditions. Increasing precipitation may account for increases in the distribution of Korean fir. Increasing precipitation compensates the negative effects of increasing temperature. However, the future distribution of Korean fir is also affected by other ecological processes, such as interactions with co-existing species, adaptation and dispersal limitation, and other environmental factors, such as extreme weather events and land-use changes. Therefore, we need further ecological research and to develop mechanistic and process-based distribution models for improving the predictive accuracy.

• 한국지반공학회지:지반
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• 제13권5호
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• pp.35-44
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• 1997

도심지 지반굴착 해석을 위한 전용 유한요소 프로그램(EM)을 개발하였다. 기존 범용 프로그램과는 달리 사용자가 간단한 입력자료를 작성하고 전.후처리는 자동으로 도화 출력되므로 굴 착해석에 쉽게 사용될 수 있도록 하였다. 특히, 새로 개발된 GDHM재료모델 ((GDHM, General ized Decoupled Hyperbolic Model)은 8면체평면상에서의 응력경로에 따른 강도변화를 고려하였다. 개발된 EM프로그램은 대형 굴착토조모형실험 결과와 비교 검토함으로서 개발된 재료모델과 굴착전용프로그램의 신뢰성을 검증하여 비교적 정확도가 높은 결과를 얻었고, 향후 미비점을 보완, 개선하여야 한다.

• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.127-135
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• 2009

한국의 경제규모가 꾸준히 커감에 따라 가정, 건물, 공장 등에서 필요로 하는 전력량이 지속적으로 증가하고 있다. 전력공급의 안정화를 위해서는 최대전력량보다 전력공급능력이 높아야 한다. 월별 최대전력량을 잘 설명할 수 있는 통계모형을 찾기 위해 Winters 모형, 분해 시계열모형, ARMA 모형, 설명 변수를 통해 추세성분과 계절성분을 교정한 모형을 살펴보았다. 모형의 예측력 비교 기준으로 모형적합으로부터 구한 RMSE와 MAPE가 사용되었다. 여름철 최대전력량을 예측하기 위해 평균기온과 열대야 일수를 설명 변수로 갖는 시계열 모형이 가장 우수하였다. 아울러 외부요인을 갖는 극단분포 모형을 이용한 분석을 시도하였다.