• 응용통계연구
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• 제9권1호
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• pp.153-164
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• 1996

분포함수의 모수가 순서제약조건을 갖는 경우에 깁스샘플러(Gibbs sampler)를 이용한 모수 추정에 관해 논의하였다. 순서화 모수를 갖는 지수분포족 및 이항분포모형을 고려하고 완전조건부 분포를 유도하였으며 순서제약 조건을 만족하는 표본추출을 위해 일 대 일 대응 추출 알고리즘을 적용하였다. 동위회귀 최우추정량 및 동위베이지안 추정량과 그 결과를 비교하였다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1991년도 가을 학술발표회 논문집
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• pp.87-92
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• 1991

It was achieved to formulate numerically the stress-strain relationship of concrete, which is a fundamemtal factor for the Elasto-Plastic analysis of concrete structures, for normal concrete by using random statistics. As a result of experiment, in the shape of stress-strain curves of normal concrete it has approach linear from first loading to peak point, and after that point deformation increased radically and specimens were brokendown abruptly. From the multiple linear regression, and obtained the exponential equaion for stress-strain relationship of concrete as follows: $\sigma$/$\sigma$max=e(1-$\varepsilon$/$\varepsilon$max)$\varepsilon$/$\varepsilon$max

• Journal of Electrical Engineering and Technology
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• 제2권1호
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• pp.35-41
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• 2007

This paper presents the results of investigating harmonic levels on medium voltage motors at loading conditions in air separation plant. The essential results of the measurements of the medium voltage motor harmonics are summarized in the values for the total harmonic distortion (THD). Motors loading case is used to assess the current and voltage harmonic distortions. Proper system analysis is important when adding a new motor starting and controlling the equipment. With the result of the paper it is possible to suggest the most appropriate starting and control method. Two medium voltage motors of air separation unit measurement results and simulations are summarized. Both current and voltage harmonic distortions are fitted by using a linear and exponential regression model. The prediction of THD values can be used for this kind of process for future planning by utilities.

• 응용통계연구
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• 제25권6호
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• pp.1037-1047
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• 2012

The generalized linear mixed model(GLMM) is widely used in fitting categorical responses of clustered data. In the numerical approximation of likelihood function the normality is assumed for the random effects distribution; subsequently, the commercial statistical packages also routinely fit GLMM under this normality assumption. We may also encounter departures from the distributional assumption on the response variable. It would be interesting to investigate the impact on the estimates of parameters under misspecification of distributions; however, there has been limited researche on these topics. We study the sensitivity or robustness of the maximum likelihood estimators(MLEs) of GLMM for counts data when the true underlying distribution is normal, gamma, exponential, and a mixture of two normal distributions. We also consider the effects on the MLEs when we fit Poisson-normal GLMM whereas the outcomes are generated from the negative binomial distribution with overdispersion. Through a small scale Monte Carlo study we check the empirical coverage probabilities of parameters and biases of MLEs of GLMM.

• 한국의류학회지
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• 제46권5호
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• pp.827-835
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• 2022

Clothing pressure is considered the essential factor affecting the comfort of clothing, so it is crucial that it is measured precisely. The purpose of this study is to construct a prototype using the Adafruit Flora as the Arduino system, which can be used as a wearable framework for easy, low-cost, and precise clothing pressure measurement. The study also aims to determine how best to conduct the procedure of sensor calibration. To optimize the accuracy of the sensors, the calibration procedure was implemented using mathematical methods that combined polynomial and exponential regression in a hybrid approach. The prototype can easily measure clothing pressure even during active movements, as seen in the detection of stable signals. In addition, since the system was specifically proposed as a wearable patch that can be easily attached and removed as necessary, it can also be used to standardize the value of clothing pressure in each movement.

• 한국정밀공학회지
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• 제11권6호
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• pp.136-142
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• 1994

This study presents an algorithm and related techniques which could satisfy the important properties of check weighers and conveyor scales. The algorithm of Recursive Least Squares Regression is described for te weighing system simulated as a dynamic model of the second order. Using the model and the algorithm, model parameters and then the mass being weighed can be determined from the step input. The performance of the algorithm is illustrated in digital simulation. Discussions are extended to the development of fast converging algorithm. It turns out that the algorithm shows several desirable features suitable for microcomputer assisted real-time signal processing, which are high precision and stability in noisy environment.

• East Asian Economic Review
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• 제28권1호
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• pp.69-94
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• 2024

The Covid-19 pandemic has caused unprecedented disruptions across industries worldwide. This paper aims to analyze the economic impact of the pandemic on the sales performance of basic commercial areas in Seoul, Korea. Using a regression analysis with credit card transaction data, the study underscores the critical nature of determining the reference point for comparison. Firstly, in comparison to the revenue in the same quarter before the onset of the pandemic, a significant decrease in revenue was observed across most categories during the pandemic periods. Secondly, when compared to the counterfactual revenue in the same period, extrapolated by an exponential smoothing forecasting, the overall revenue decrease during the periods was less pronounced, except in a few categories. Interestingly, certain categories appeared to witness marginal increases in sales after the pandemic. The paper discusses some policy implications of these findings.

• 대한원격탐사학회지
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• 제33권4호
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• pp.401-410
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• 2017

Estimation of snow depth using optical image is conducted by using correlation with Snow Cover Fraction (SCF). Various algorithms have been proposed for the estimation of snow cover fraction based on Normalized Difference Snow Index (NDSI). In this study we tested linear, quadratic, and exponential equations for the generation of snow cover fraction maps using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite in order to evaluate their applicability to the complex terrain of South Korea and to search for improvements to the estimation of snow depth on this landscape. The results were validated by comparison with in-situ snowfall data from weather stations, with Root Mean Square Error (RMSE) calculated as 3.43, 2.37, and 3.99 cm for the linear, quadratic, and exponential approaches, respectively. Although quadratic results showed the best RMSE, this was due to the limitations of the data used in the study; there are few number of in-situ data recorded on the station at the time of image acquisition and even the data is mostly recorded on low snowfall. So, we conclude that linear-based algorithms are better suited for use in South Korea. However, in the case of using the linear equation, the SCF with a negative value can be calculated, so it should be corrected. Since the coefficients of the equation are not optimized for this area, further regression analysis is needed. In addition, if more variables such as Normalized Difference Vegetation Index (NDVI), land cover, etc. are considered, it could be possible that estimation of national-scale snow depth with higher accuracy.

• 산업경영시스템학회지
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• 제40권1호
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• pp.87-94
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• 2017

Many industrial accidents have occurred continuously in the manufacturing industries, construction industries, and service industries of Korea. Fatal accidents have occurred most frequently in the construction industries of Korea. Especially, the trend analysis of the accident rate and fatal accident rate is very important in order to prevent industrial accidents in the construction industries systematically. This paper considers forecasting of the accident rate and fatal accident rate with static and dynamic time series analysis methods in the construction industries. Therefore, this paper describes the optimal accident rate and fatal accident rate by minimization of the sum of square errors (SSE) among regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM), and kalman filtering model (KFM) with existing accident data in construction industries. In this paper, microsoft foundation class (MFC) soft of Visual Studio 2008 was used to predict the accident rate and fatal accident rate. Zero Accident Program developed in this paper is defined as the predicted accident rate and fatal accident rate, the zero accident target time, and the zero accident time based on the achievement probability calculated rationally and practically. The minimum value for minimizing SSE in the construction industries was found in 0.1666 and 1.4579 in the accident rate and fatal accident rate, respectively. Accordingly, RAM and ARIMA model are ideally applied in the accident rate and fatal accident rate, respectively. Finally, the trend analysis of this paper provides decisive information in order to prevent industrial accidents in construction industries very systematically.

• 산업경영시스템학회지
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• 제39권1호
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.