• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.445-459
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• 2014

As a certain job is repeatedly done by a worker, the outcome comparative to the effort to complete the job gets more remarkable. The outcome may be the time required and fraction defective. This phenomenon is referred to a learning-curve effect. We focus on the parametric modeling of the learning-curve effects on count data using a logistic cumulative distribution function and some probability mass functions such as a Poisson and negative binomial. We conduct various simulation scenarios to clarify the characteristics of the proposed model. We also consider a real application to compare the two discrete-type distribution functions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.5 no.6
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• pp.69-78
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• 1982

Traditional CVP (Cost-Volume-Profit) analysis employs linear cost and revenue functions within some specified time period and range of operations. Therefore CVP analysis is assumption of constant labor productivity. The use of linear cost functions implicity assumes, among other things, that firm's labor force is either a homogenous group or a collection homogenous subgroups in a constant mix, and that total production changes in a linear fashion through appropriate increase or decrease of seemingly interchangeable labor unit. But productivity rates in many firms are known to change with additional manufacturing experience in employee skill. Learning curve is intended to subsume the effects of all these resources of productivity. This learning phenomenon is quantifiable in the form of a learning curve, or manufacturing progress function. The purpose d this study is to show how alternative assumptions regarding a firm's labor force may be utilize by integrating conventional CVP analysis with learning curve theory, Explicit consideration of the effect of learning should substantially enrich CVP analysis and improve its use as a tool for planning and control of industry.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.433-450
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• 2012

As a worker performs a certain operation repeatedly, he tends to become familiar with the job and complete it in a very short time. That means that the efficiency is improved due to his accumulated knowledge, experience and skill in regards to the operation. Investing time in an output is reduced by repeating any operation. This phenomenon is referred to as the learning curve effect. A learning curve is a graphical representation of the changing rate of learning. According to previous literature, learning curve effects are determined by subjective pre-assigned factors. In this study, we propose a new statistical model to clarify the learning curve effect by means of a basic cumulative distribution function. This work mainly focuses on the statistical modeling of binary data. We employ the Newton-Raphson method for the estimation and Delta method for the construction of confidence intervals. We also perform a real data analysis.

• Korean Journal of Construction Engineering and Management
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• v.14 no.6
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• pp.38-48
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• 2013

Focusing on repetitive works of construction, many research have been conducted about application of the learning curve effect. However, it is still controversial, especially on the high-rise project, since the productivity improvement from the learning curve effects are hard to prove. In the previous research, applicability of the learning curve was mainly derived from the labor productivity data. Although the research were based on the real data, they merely concentrated on the simple conclusion that the labor productivity had improved or not, instead of the process interpretation. Therefore, the purpose of this research is to analyze the influence factors of the learning curve effect in high-rise projects and elucidate the offset factors of the effect. Based on these factors, a model for estimating the labor productivity containing the concept of process learning is suggested. Through our research, traditional learning curve theory could be compensated and re-established with having more appropriateness for high-rise projects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2031-2048
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• 2021

Not only can air pollution reduce the overall competitiveness of tourist destinations, but also changes tourists' travel decisions, thereby affecting the tourism flows. The study presents a machine learning method to analyze how the haze pollution puts spatial effect on tourism flows in China from 2001 to 2018, and reveals the regional differences in heterogeneity among eastern, central, and western China. Our investigation reveals three interesting observations. First, the Environmental Kuznets Curve of the impact of haze pollution on tourism flows is not significant. In the eastern and western regions, the interaction between haze pollution and domestic tourism flows as well as inbound tourism flows shows an inverted U-shaped curve respectively. Second, there is an significantly positive spillover effect of tourism flows in all of the eastern, central, and western regions. As to the intensity of spillover, domestic tourism flows is higher than that of the inbound tourism flows. Both of the above figures are greatest in the eastern. Third, the Chinese haze pollution mainly reduces the inbound tourism flows, and only imposes significantly negative direct effects on the domestic tourism flows in the central region. In the central and eastern regions, significantly negative direct effects and spillover effects are exerted on inbound tourism.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.11 no.17
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• pp.25-37
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• 1988

Joint inventory 방법을 다룬 기존의 연구는 생산비용이 일정하다는 조건만을 고려하였다. 본 논문은 기존의 연구에다 새로운 변수(learning curve ratio and learning retension)를 제조업자 측면에서 고려하여 보다 확장된 모델을 다룬다. Joint inventory 모델은 첫째 단일구매자와 둘째 학습곡선비율과 learning retention의 정도에 있어서 그 범위를 결합시키는데 이용되기 위해 개발되어 졌다. 구매자와 제조업자를 위한 로트 사이즈를 결정하기 위하여 증분비용접근방법 (Incremental Cost Approach, ICA)을 쓴다. 총결합비용은 기존모델보다 현저하게 적은데 그 이유는 학습과 learning retention 효과로 인한 제조업자의 생산비 절감과 재고유지 비용의 감소 때문이다. 학습과 learning retention이 현격한 경우, 총결합비용은 제조업자와 구매자의 개별적인 최적정책에서의 비용합(합)보다 적다. 소개된 모델의 효과를 보이기 위해 수치예제를 이용하였다.

• Archives of Plastic Surgery
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• v.47 no.3
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• pp.242-249
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• 2020

Background The Exoscope is a novel high-definition digital camera system. There is limited evidence signifying the use of exoscopic devices in microsurgery. This trial objectively assesses the effects of the use of the Exoscope as an alternative to the standard operating microscope (OM) on the performance of experts in a simulated microvascular anastomosis. Methods Modus V Exoscope and OM were used by expert microsurgeons to perform standardized tasks. Hand-motion analyzer measured the total pathlength (TP), total movements (TM), total time (TT), and quality of end-product anastomosis. A clinical margin of TT was performed to prove non-inferiority. An expert performed consecutive microvascular anastomoses to provide the exoscopic learning curve until reached plateau in TT. Results Ten micro sutures and 10 anastomoses were performed. Analysis demonstrated statistically significant differences in performing micro sutures for TP, TM, and TT. There was statistical significance in TM and TT, however, marginal non-significant difference in TP regarding microvascular anastomoses performance. The intimal suture line analysis demonstrated no statistically significant differences. Non-inferiority results based on clinical inferiority margin (Δ) of TT=10 minutes demonstrated an absolute difference of 0.07 minutes between OM and Exoscope cohorts. A 51%, 58%, and 46% improvement or reduction was achieved in TT, TM, TP, respectively, during the exoscopic microvascular anastomosis learning curve. Conclusions This study demonstrated that experts' Exoscope anastomoses appear non-inferior to the OM anastomoses. Exoscopic microvascular anastomosis was more time consuming but end-product (patency) in not clinically inferior. Experts' "warm-up" learning curve is steep but swift and may prove to reach clinical equality.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.179-186
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• 2008

In this paper we analyse the effects of Bayesian algorithm in teaming class imbalance problems and compare the performance evaluation methods. The teaming performance of the Bayesian algorithm is evaluated over the class imbalance problems generated by priori data distribution, imbalance data rate and discrimination complexity. The experimental results are calculated by the AUC(Area Under the Curve) values of both ROC(Receiver Operator Characteristic) and PR(Precision-Recall) evaluation measures and compared according to imbalance data rate and discrimination complexity. In comparison and analysis, the Bayesian algorithm suffers from the imbalance rate, as the same result in the reported researches, and the data overlapping caused by discrimination complexity is the another factor that hampers the learning performance. As the discrimination complexity and class imbalance rate of the problems increase, the learning performance of the AUC of a PR measure is much more variant than that of the AUC of a ROC measure. But the performances of both measures are similar with the low discrimination complexity and class imbalance rate of the problems. The experimental results show 4hat the AUC of a PR measure is more proper in evaluating the learning of class imbalance problem and furthermore gets the benefit in designing the optimal learning model considering a misclassification cost.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.507-514
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• 2018

This study utilized latent growth curve modeling to investigate the trajectories of adolescent life satisfaction changes in middle and high school students. The effects of self-esteem, career identity, school learning activity, gender, and household earnings on life satisfaction changes were examined. Data was obtained from the Korea Child Youth Panel Survey (KYCPS), a longitudinal study following students for 7 years. Year 3-6 data was utilized. Results found that the life satisfaction trajectory resulted as a quadratic model in which individual differences were significant. Second, school learning activity used as a time variant variable had a positive significant effect on life satisfaction each year. Third, gender and self-esteem as time invariant variables had significant effects on initial levels while self-esteem had effects on the slope and quadratic change. Further implications and research issues are discussed.