• IE interfaces
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• v.23 no.3
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• pp.205-212
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• 2010

The learning curve model is a mathematical form which represents the relationship between the manufacturing experience and its effectiveness. The semiconductor manufacturing is widely known as an appropriate example for the learning effect due to its complicated manufacturing processes. In this paper, I propose a new compound learning curve model for semiconductor products in which the general learning curve model and the growth curve are composed. The dependent variable and the effective independent variables of the model were abstracted from the existing learning curve models and selected according to multiple regression processes. The simulation results using the historical DRAM data show that the proposed compound learning curve model is one of adequate models for describing learning effect of semiconductor products.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.77-84
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• 1992

This paper constructs Croup Technology process-based learning curve model adjusted to a Group Technology environment which accounts for shared learning that occurs when multiple products utilize some of the same process steps. Through this constructed model, the estimated times and productivity of labor calculated by the Group Technology process-based learning curve model are compared with those generated by employing product-based 1 earning curve model. For sensitivity analysis of the model, the impact of learning rate and the ordered production quantity on the ratio differences between Group Technology process-based learning curve model and product-based learning curve model are examined. These results indicate the critical importance of employing Group Technology process-based learning curve model when a process spans multiple products.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.4
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• pp.208-213
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• 2004

In this paper, a new method for the estimation of the effect of DSM program is proposed. By identifying the learning curve of high efficient induction motor, the effect of DSM program applied to that product can be estimated. The learning curve of normal induction motor, to which no DSM program is applied, is identified also. Both learning curves, which are different shapes, means different teaming ratio. It can be concluded that DSM program makes the learning curve of the product change the shape. It also can be concluded that DSM program has influence on the sale of the product to which it is applied.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.4
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• pp.208-208
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• 2004

In this paper, a new method for the estimation of the effect of DSM program is proposed. By identifying the learning curve of high efficient induction motor, the effect of DSM program applied to that product can be estimated. The learning curve of normal induction motor, to which no DSM program is applied, is identified also. Both learning curves, which are different shapes, means different teaming ratio. It can be concluded that DSM program makes the learning curve of the product change the shape. It also can be concluded that DSM program has influence on the sale of the product to which it is applied.

• Journal of the Ergonomics Society of Korea
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• v.27 no.2
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• pp.59-65
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• 2008

Product usability consists of many attributes such as learnability, efficiency, memorability, and so on. In particular, learnability is one of the most important attributes in product usability. Therefore, many people consider the primary criterion for a good user interface to be the degree to which it is easy to learn. Learnability represents the degree of how much can easily learn the usage of a product. It concerns the features of the interactive system that allow novice users to understand how to use it initially and then how to attain a maximal level of performance. In this study, we studied on the application of learning curve to evaluate product learnability. In order to validate the applicability, we carried out simple experiment using mobile phone. We got task completion times through the experiment and predicted the times using learning curve model. And then, we compared prediction times to task completion times. Finally, we identified that learning curve could apply to predict and compare product learnability.

• 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.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.1
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• pp.165-172
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• 2012

For practical education, many practices using various practical equipments have to be provided to students. In this study, the application of learning curve to represent student's learning process in a practical education using a equipment was studied. Learning curve model was originally developed in production management and based on human performance in human factors aspects. In this study, the application of learning curve model was studied on the eye tracking system, which is used to evaluate the usability of a product in design area. As a case study for its applicability, practical education for eye tracking system was provided to three students and then task completion times were measured for hardware system setup and gaze image recording. Learning curves were estimated for two tasks and then task completion times were predicted using the learning curves. Through ANOVA(analysis of variance) and correlation analysis, the applicability of learning curve to practical education was analysed. As the result, learning curve could be effectively applied to practical eduacation using equipment.

• Journal of Gastric Cancer
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• v.13 no.1
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• pp.26-33
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• 2013

Purpose: The aims are to: (i) display the multidimensional learning curve of totally laparoscopic distal gastrectomy, and (ii) verify the feasibility of totally laparoscopic distal gastrectomy after learning curve completion by comparing it with laparoscopy-assisted distal gastrectomy. Materials and Methods: From January 2005 to June 2012, 247 patients who underwent laparoscopy-assisted distal gastrectomy (n=136) and totally laparoscopic distal gastrectomy (n=111) for early gastric cancer were enrolled. Their clinicopathological characteristics and early surgical outcomes were analyzed. Analysis of the totally laparoscopic distal gastrectomy learning curve was conducted using the moving average method and the cumulative sum method on 180 patients who underwent totally laparoscopic distal gastrectomy. Results: Our study indicated that experience with 40 and 20 totally laparoscopic distal gastrectomy cases, is required in order to achieve optimum proficiency by two surgeons. There were no remarkable differences in the clinicopathological characteristics between laparoscopy-assisted distal gastrectomy and totally laparoscopic distal gastrectomy groups. The two groups were comparable in terms of open conversion, combined resection, morbidities, reoperation rate, hospital stay and time to first flatus (P>0.05). However, totally laparoscopic distal gastrectomy had a significantly shorter mean operation time than laparoscopy-assisted distal gastrectomy (P<0.01). We also found that intra-abdominal abscess and overall complication rates were significantly higher before the learning curve than after the learning curve (P<0.05). Conclusions: Experience with 20~40 cases of totally laparoscopic distal gastrectomy is required to complete the learning curve. The use of totally laparoscopic distal gastrectomy after learning curve completion is a feasible and timesaving method compared to laparoscopy-assisted distal gastrectomy.

• 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.