• 노동경제논집
• /
• 제30권3호
• /
• pp.43-75
• /
• 2007

본 연구는 한국노동패널을 사용하여 2002~2005년 3년 동안 직장이직률 추세와 이에 영향을 주는 여러 요인들 가운데 노동조합의 역할을 중심으로 실증분석하였다. 기초통계량에 있어서는 노동조합에 가입된 근로자의 직장 유지율이 노조에 가입되어 있지 않은 근로자보다 평균 28.3%포인트 높게 나타났으나, 직장이직에 영향을 미치는 개인의 관측되는 변수를 통제한 결과에서는 노동조합이 직장유지율을 11~13%포인트 증가시키는 것으로 추정되었다. 노동조합이 근로자의 고용안정에 미치는 효과를 세부적으로 살펴보기 위하여 Fairlie(2003)가 개발한 비선형분해 방법을 사용하여 분석하였다. 비선형분해(Non-linear decomposition) 방법을 사용하여 노조-비노조 간 직장이 직률의 차이를 근로자의 관측되는 변수에 의해 설명되는 부분과 설명되지 못하는 부분으로 나누어 살펴볼 때 설명되는 부분의 기여도가 67~74%로서 추정되고 설명되지 못하는 부분이 나머지 26~33%를 차지함을 알 수 있다. 이는 노동조합이 조합원들의 고용안정에 기여한 부분 외에도 노조에 가입된 근로자의 직장이직 성향이 노조에 가입되지 않거나 노조가 조직되어 있지 않은 사업장에 종사하는 근로자보다 평균적으로 낮음을 시사한다. 실증분석 결과는 노동조합의 고용안정 효과는 최대 7~9%포인트 경계 안에서 한정됨을 시사한다. 노동조합의 고용안정에 미치는 효과가 이처럼 작은 원인은 기업별 단체교섭구조, 외환위기 이후 기업경쟁의 심화, 기업규모별 노동시장 분절화 등 다양한 원인을 추론해 볼 수 있을 것이다.

• 한국환경보건학회지
• /
• 제49권1호
• /
• pp.37-47
• /
• 2023

Background: Paraben is a widely used substance with a preservative effect found in various materials such as food, medicine, personal care products, and cosmetics. Objectives: This study was conducted to identify the level of urinary paraben concentrations (i.e., methyl-, ethyl-, and propyl-) among Korean adults and to explore the factors related with the exposure levels. Methods: We analyzed the third period (2015~2017) of the Korean National Environmental Health Survey (KoNEHS). R statistical software (version 4.1.1) was used to estimate representative values for the whole population with weight variables to reflect sampling design. Whether urinary concentrations tended to increase as the level of paraben exposure-related characteristics increased was tested and P_trend was calculated using general linear models. Results: Urinary concentrations of all three parabens (i.e., methyl-, ethyl- and propyl-) were higher in women than in men (P_trend<0.0001, 0.008, and <0.0001), and the values of methylparaben and propylparaben tended to increase as the age of subjects increased (P_trend<0.0001, and <0.0001). Urinary concentrations of methylparaben and propylparaben were associated with intensity of exercise (P_trend<0.001, and 0.004), and that of propylparaben was higher in non-smokers (P_trend=0.01). In terms of paraben exposure-related variables, urinary concentrations of parabens (i.e., methyl-, ethyl- and propyl-) increased as the daily average frequency of teeth-brushing (P_trend<0.0001, 0.03 and 0.0001), the frequency of use of hair products (P_trend=0.005, 0.05 and 0.04), the frequency of use of makeup products (P_trend<0.001, 0.001 and <0.001), and the frequency of use of antibacterial products (P_trend=0.005, 0.02 and 0.02) increased. Conclusions: In our study, urinary concentrations of all three parabens are associated with gender, teethbrushing, hair products, make-up products, and antibacterial products. Methyl- and proyl-parabens were associated with age and intensity of exercise, and propyl-paraben was associated with smoking.

• 대한토목학회논문집
• /
• 제31권5B호
• /
• pp.393-403
• /
• 2011

본 연구에서는 현재까지 경험적인 방법으로 적용되어 왔던 G/R 비를 선형 회귀선, 원점을 통과하는 선형 회귀선 및 원점과 관측자료의 무게중심을 지나는 추세선 등으로 구분하여 이론적으로 검토하였다. 이러한 검토에는 독립변수로 어떤 강우자료를 선택하느냐에 따른 문제와, 무강우 자료의 고려여부에 따른 영향 검토가 포함되었다. 이렇게 검토된 내용은 2003년에 발생한 태풍 매미 사상에 적용하여 평가하였다. 마지막으로 본 연구에서 유도된 회귀선 및 추세선을 이용한 레이더 강우의 보정결과와 관측된 우량계 강우 사이의 RMSE를 비교함으로서 최적의 G/R 비를 선정하였다. 그 결과는 다음과 같다. 먼저, 독립변수로 레이더 강우를 사용하는 것 보다는 우량계 강우를 사용하는 경우에 레이더 강우의 보정결과가 우수한 것으로 나타났다. 둘째, 무강우 자료의 영향은 레이더 및 우량계 자료의 구조에 따라 다르게 나타나는 것으로 확인되었다. 마지막으로 유도된 선형 회귀선 및 추세선의 평가 결과, 홍수 유출해석을 주목적으로 한 경우에는 독립변수에 우량계 자료를 적용하여 유도된 원점을 통과하는 선형 회귀선의 기울기를 G/R 비로 사용하는 것이 무난할 것으로 나타났다. 이 경우 무강우 자료의 영향은 상대적으로 미미한 것으로 나타났다.

• Animal Bioscience
• /
• 제37권4호
• /
• pp.622-630
• /
• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• 로봇학회논문지
• /
• 제5권1호
• /
• pp.70-76
• /
• 2010

This paper addresses a design issue of "model complexity and performance trade-off" in the application of bandwidth extension (BWE) methods to the intra-frame predictivevector quantization problem of wideband speech. It discusses model-based linear and non-linear prediction methods and presents a comparative study of them in terms of prediction gain. Through experimentation, the general trend of saturation in performance (with the increase in model complexity) is observed. However, specifically, it is also observed that there is no significant difference between HMM and GMM-based BWE functions.

• 대한임베디드공학회논문지
• /
• 제5권3호
• /
• pp.111-117
• /
• 2010

The easily accessible handheld devices equipped with camera are widely available as common commodities. According to this trend, utilization of images is popular among common users for various purposes resulting in huge amount of images in local or network based storage systems. In this environment, identification of an image with a solid and effective manner is demanded in behalf of safe distribution and efficient management of images. The generated identifiers can be used as a file name in file systems or an index in image databases utilizing the uniqueness of the identifiers. In this paper, we propose a method that generates image identifiers using linear components in images. Some experiments of generation of identifiers are performed, and the results evaluate that the proposed method has feasible effectiveness.

• 대한전기학회논문지:전력기술부문A
• /
• 제48권2호
• /
• pp.168-175
• /
• 1999

In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.

• 한국시스템다이내믹스연구
• /
• 제9권1호
• /
• pp.125-141
• /
• 2008

It is very difficult to predict future demand/supply, price for resources with acceptable accuracy using regression analysis. We try to use system dynamics to forecast the demand/supply and price for nickel. Nickel is very expensive mineral resource used for stainless production or other industrial production like battery, alloy making. Recent nickel price trend showed non-linear pattern and we anticipated the system dynamic method will catch this non-linear pattern better than the regression analysis. Our model has been calibrated for the past 6 year quarterly data (2002-2007) and tested for next 5 year quarterly data(2008-2012). The results were acceptable and showed higher accuracy than the results obtained from the regression analysis. And we ran the simulations for scenarios made by possible future changes in demand or supply related variables. This simulations implied some meaningful price change patterns.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 하계학술대회 논문집 G
• /
• pp.2299-2301
• /
• 1998

In this paper, a modified RBF (Radial Basis Function) neural network structure is suggested for the prediction of time series with non-linear, non-stationary characteristics. Conventional RBF neural network predicting time series by using past outputs is for sensing the trajectory of the time series and for reacting when there exists strong relation between input and hidden neuron's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden neurons are modified to react to the increments of input variable and multiplied by increments(or decrements) of out puts for prediction. When the suggested structure is applied to prediction of Lorenz equation, and Rossler equation, improved performances are obtainable.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2022년도 추계학술대회
• /
• pp.170-171
• /
• 2022

A ship's maneuvering motion model is important in a computer simulation, especially under the trend of intelligent navigation. This model is usually constructed by the hydrodynamic parameters of the ship which are generated by the principles of hydrodynamics. Ship's motion model is a nonlinear function. By using this function, ships' motion elements can be calculated, then the ship's trajectory can be predicted. Deeping neural networks can construct any linear or non-linear equation theoretically if there have enough and sufficient training data. This study constructs some kinds of deep Networks and trains this network by real ship motion data, and chooses the best one of the networks, uses real data to train it, then uses it to predict the ship's trajectory, getting some conclusions and experiences.