• Journal of Ecology and Environment
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• 제33권2호
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• pp.165-173
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• 2010

Soil respiration ($R_S$) is a critical component of the annual carbon balance of forests, but few studies thus far have attempted to evaluate empirical regression models in $R_S$. The principal objectives of this study were to evaluate the relationship between $R_S$ rates and soil temperature (ST) and soil water content (SWC) in soil from a cool-temperate deciduous broad-leaved forest, and to evaluate empirical regression models for the prediction of $R_S$ using ST and SWC. We have been measuring $R_S$, using an open-flow gas-exchange system with an infrared gas analyzer during the snowfree season from 1999 to 2001 at the Takayama Forest, Japan. To evaluate the empirical regression models used for the prediction of $R_S$, we compared a simple exponential regression (flux = $ae^{bt}$Eq. [1]) and two polynomial multiple-regression models (flux = $ae^{bt}{\times}({\theta}{\nu}-c){\times}(d-{\theta}{\nu})^f:$ Eq. [2] and flux = $ae^{bt}{\times}(1-(1-({\theta}{\nu}/c))^2)$: Eq. [3]) that included two variables (ST: t and SWC: ${\theta}{\nu}$) and that utilized hourly data for $R_S$. In general, daily mean $R_S$ rates were positively well-correlated with ST, but no significant correlations were observed with any significant frequency between the ST and $R_S$ rates on periods of a day based on the hourly $R_S$ data. Eq. (2) has many more site-specific parameters than Eq. (3) and resulted in some significant underestimation. The empirical regression, Eq. (3) was best explained by temporal variations, as it provided a more unbiased fit to the data compared to Eq. (2). The Eq. (3) (ST $\times$ SWC function) also increased the predictive ability as compared to Eq. (1) (only ST exponential function), increasing the $R^2$ from 0.71 to 0.78.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2003년도 총회 및 춘계학술발표회
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• pp.217-220
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• 2003

In the case of the remediation studies, push pull test is a more time and cost effective mettled than multi-well tracer test. It also gives Just as much or more information than the traditionally used methods. But the data analysis for the hydraulic parameters, there have been some defections such as underestimation of dispersivity, requirement for effective porosity, and calculation of recovery of center of mass to estimate linear velocity. In this research, Weibull distribution function is proposed to estimate the center of mass of breakthrough curve for Push pull test. The hydraulic parameter estimation using Weibull function showed more exact values of center of mass than those of exponential regression for field test data.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 춘계 학술발표회 논문집
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• pp.215-219
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• 2005

We consider penalized likelihood regression with exponential family responses. Parallel to recent development in Gaussian regression, the fast computation through asymptotically efficient low-dimensional approximations is explored, yielding algorithm that scales much better than the O($n^3$) algorithm for the exact solution. Also customizations of the direct cross-validation strategy for smoothing parameter selection in various distribution families are explored and evaluated.

• 정보처리학회논문지D
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• 제11D권1호
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• pp.163-172
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• 2004

객체지향 개발 방법론을 적용하는 소프트웨어 개발 프로젝트에서 개발 노력 추정 기법으로 사용사례점수(UCP, Use Case Point)에 대한 연구가 계속되고 있다. 기존의 연구는 기술적 요인과 환경적 요인을 적용한 AUCP(Adjusted Use Case Point)에 상수를 곱하여 개발 노력을 계산하는 선형모델을 제시하고 있으나, AUCP와 UUCP(Unadjusted Use Case Point)를 이용하여 개발노력을 추정하는 통계적인 모델은 제시되지 않고 있다. 소프트웨어 규모가 증가함에 따라 개발 기간이 기하급수적으로 증가하는 선형 회귀모델이 부적합하다는 사실과 UCP 계산과정에서 TCF(Technical Complexity Factor)와 EF(Environmental Factor)를 적용에 따른 FP(Function Point) 오차 발생 문제점을 확인하였다. 이 논문은 사용사례점수를 기반으로 하여 기존 연구의 문제점인 TCF와 EF를 고려하지 않고 직접 UUCP로부터 개발 노력을 추정한 수 있는 선형, 로그형, 다항식, 거듭제곱 및 지수함수 회귀모델의 성능을 평가한 결과, 가장 적합한 모델로 지수형태의 비선형 회귀모델을 도출하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• 산업경영시스템학회지
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• 제42권1호
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• pp.129-136
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• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

• Communications for Statistical Applications and Methods
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• 제31권4호
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• pp.377-391
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• 2024

Accurate household projections are essential for sectors such as housing supply and tax policy planning, given the rapid social changes like declining birthrates, an aging population, and a rise in single-person households that impact household size and type. Korea introduced its first register-based census in 2015, transitioning from five-year general survey-based approach to an annual administrative data-based census. This change in census allows for more frequent and effective capturing the rapid demographic shifts and trends. However, this change in census has caused challenges in future projection by the existing household projection model due to the rapid dynamics. This paper proposes a new household projection method, the N-point Modified Exponential Model (MEM), that accurately reflects register-based census data and mitigates the impact of rapid demographic changes, in three types: the Weighted N-point MEM, the Regression-based N-point MEM, and the Rolling Weighted N+point MEM. Using register-based census data from 2016 to 2020 to forecast household headship rates by age, household size, and household type to 2051, the N-point modified exponential model outperformed the existing model in both long- and short-term forecast accuracy, suggesting its suitability as a future household projection model for Korea.

• 물과 미래
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• 제10권1호
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• pp.79-94
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• 1977

본 연구는 Run-Length와 Run-Sum에 의한 강우량과 하천유량을 분석하여 그 특성을 구명하고, 이로부터 모의발생 모델(Simulation Model)을 설정하여 검토하는데 목적을 두고 있다. 분석에 있어서는 우리나라의 주요도시(서울, 대구 및 부산)의 일강우량과 주요하천(한강, 낙동강 및 금강)의 일류량 자료들을 사용하였다. 또한 해석에 있어서는 Run-Length와 Run-Sum에 대한 각각의 분포형 분석으로부터 Weibull 분포 및 1-변수지수 분포를 하고 있음을 알았으며, 이로부터 Monte Carlo 기법(Monte Carlo technique)을 기초로 하는 Weibull모델(Weibull model)과 1-변수지수 모델(One-Parameter Exponential Model)에 의해서 Run-Length와 Run-Sum을 모의발생 시켰다. 그 결과 기록치(Historical Data)에 근사한 모의발생 자료(Simulation Data)를 얻었다.

• 자원ㆍ환경경제연구
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• 제16권4호
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• pp.901-922
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• 2007

본 연구는 하계냉방수요가 기온관련변수의 변화에 대해 어떤 반응을 보이는가, 또 어떤 종류의 기온관련변수가 하계냉방수요에 대한 설명변수로 더 적절한가를 보기 위해 일반적인 선형모형은 물론 각기 다른 특성을 가지고 있는 지수모형과 파워모형, S곡선모형 등 비선형모형을 이용하여 2004년부터 2007년까지 최근 4년간 자료를 분석하였다. 실증분석결과 본 연구에서는 기온관련변수들 가운데 불쾌지수가 일최고기온에 비해 설명력이 우수하다는 사실과 함께 하계냉방전력수요가 전체 4개년도 중 2006년을 제외한 다른 모든 연도들에 대해 지수모형을 따라 기온관련변수의 변화에 대응하고 있는 사실을 규명하였다. 또 소득수준의 향상을 반영하는 비냉방전력수요의 꾸준한 증가와 함께 냉방전력수요도 기온관련변수에 매년 더욱 민감하게 반응하고 있는 사실도 발견하였다.

• 산업경영시스템학회지
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• 제38권2호
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• pp.40-46
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• 2015

The modular assembly system can make it possible for the variety of products to be assembled in a short lead time. In this system, necessary components are assembled to optional components tailor to customers' orders. Budget for inventory investments composed of inventory and purchasing costs are practically limited and the purchasing cost is often paid when an order is arrived. Service cost is assumed to be proportional to service level and it is included in budget constraint. We develop a heuristic procedure to find a good solution for a continuous review inventory system of the modular assembly system with a budget constraint. A regression analysis using a quadratic function based on the exponential function is applied to the cumulative density function of a normal distribution. With the regression result, an efficient heuristics is proposed by using an approximation for some complex functions that are composed of exponential functions only. A simple problem is introduced to illustrate the proposed heuristics.