• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.1
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• pp.61-69
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• 2001

This paper presents a two stage game model which examines the effect of learning-by-doing and spillover. Increases in the firm’s cumulative experience lower its unit cost in future period. However, the firm’s rival also enjoys the experience via spillover. Unlike previous theoretical research model, a cost asymmetric market entry game model is developed between the incumbent firm and new entrant. Mathematical results show that the incumbent firm exploits the learning curve to gain future cost advantage, and that the diffusion of learning to the new entrant induces the incumbent firm to choose decreasing output strategically. As a main result, we show that the relative magnitude between the learning and spillover rate determines the market share ratio of competing firms.

• Environmental and Resource Economics Review
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• v.27 no.4
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• pp.667-694
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• 2018

We analyze the learning-by-doing effects of the allowance pricing system on the Korea's emission trading scheme. The price of allowance (Korean Allowance Unit) is influenced differently by internal market factors and economic conditions variables in the first (January 2015 to June 2016 ) and the second commitment year(January 2016 to June 2017). The prices and transaction volumes of complementary credits (KCU and KOC) as well as economic conditions variables (such as call rate, exchange rate, stock price) are statistically significant only for the second commitment year. Thus, the learning-by-doing effect makes the market participation decision on K-ETS market more efficient in the second commitment year, adopting the previous experience and knowledge in the K-ETS market. The factors estimated significantly in both commitment periods include the institutional binary variable for requiring the submission of the emissions verification reports issued both on February and March.

• New & Renewable Energy
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• v.5 no.3
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• pp.49-55
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• 2009

Human resource for the new and renewable energy technology is an important factor in the respect of the sustainable growth and energy security. In this paper, we focused on measuring the economic effect of human resource development on new and renewable energy development programs. The human capital accumulation model developed by Mincer (1974) was modified in terms of the rate of the researchers' investment in human capital. As a result of a empirical case study, the value of human capital was estimated by 102 million Korean won per year worth 18% of the project labor cost. In case of the assumption of 100% participation of researchers, the level of human capital accumulation increased to 914 million Korean won per year. These results imply that the new and renewable energy development programs has been successful, on the concept of learning by doing, in terms of providing the researchers with opportunities to accumulate human capital.

• IE interfaces
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• v.25 no.4
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• pp.441-449
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• 2012

Solar PV(photovoltaic) is paid great attention to as a possible renewable energy source to overcome recent global energy crisis. However to be a viable alternative energy source compared with fossil fuel, its market competitiveness should be attained. Grid parity is one of effective measure of market competitiveness of renewable energy. In this paper, we forecast the grid parity timing of solar PV energy in Korea using two factor learning curve model. Two factors considered in the present model are production capacity and technological improvement. As a result, it is forecasted that the grid parity will be achieved in 2019 in Korea.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.531-540
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• 2024

Recently, research and development on automatic diagnosis solutions in the medical imaging field using deep learning are actively underway. In this study, we sought to find a fast and accurate classification deep learning modeling for classification of pneumonia in chest images using Inception V3, a deep learning model based on a convolutional artificial neural network. For this reason, after applying the optimization algorithms AdaGrad, RMS Prop, and Adam to deep learning modeling, deep learning modeling was implemented by selectively applying learning rates of 0.01 and 0.001, and then the performance of chest X-ray image pneumonia classification was compared and evaluated. As a result of the study, in verification modeling that can evaluate the performance of the classification model and the learning state of the artificial neural network, it was found that the performance of deep learning modeling for classification of the presence or absence of pneumonia in chest X-ray images was the best when applying Adam as the optimization algorithm with a learning rate of 0.001. I was able to. And in the case of Adam, which is mainly applied as an optimization algorithm when designing deep learning modeling, it showed excellent performance and excellent metric results when selectively applying learning rates of 0.01 and 0.001. In the metric evaluation of test modeling, AdaGrad, which applied a learning rate of 0.1, showed the best results. Based on these results, when designing deep learning modeling for binary-based medical image classification, in order to expect quick and accurate performance, a learning rate of 0.01 is preferentially applied when applying Adam as an optimization algorithm, and a learning rate of 0.01 is preferentially applied when applying AdaGrad. I recommend doing this. In addition, it is expected that the results of this study will be presented as basic data during similar research in the future, and it is expected to be used as useful data in the health and bio industries for the purpose of automatic diagnosis of medical images using deep learning.

• English Language & Literature Teaching
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• v.12 no.2
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• pp.87-105
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• 2006

Style is considered a pivotal construct in sociolinguistic variation studies. While previous studies have examined style in traditional forms of language such as speech, very little research has examined new and emerging styles such as computer-mediated discourse. Thus, the present study attempts to investigate style in the online communication mode of chat. In so doing, the study compares text-based online chat with speech and writing. Online chat has been previously described as a hybrid form of language that is close to speech. Here, the exact nature of online chat is elucidated by focusing on contraction use. Differential acquisition of stylistic variation is also examined according to English learning background. The empirical component consists of data from Korean speakers of English. Data is taken from a written summary, an oral interview, and a text-based online chat session. A multivariate analysis was conducted. Results indicate that online chat is indeed a hybrid form that is difficult to delineate from speech and writing. Text-based online chat shows a somewhat similar rate of contraction to speech, which confirms its hybridity.. Lastly, some implications of the study are given in terms of the learning and acquisition of style in general and in online contextual modes.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.83-88
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• 2017

The personal activity information is expanding as a way to utilize wearable devices that are emerging as next generation smart devices. This paper develops an application for collecting heartbeat rate and location information of a user using SmartWatch, which is a smartphone and wearable device, and analyzing it through machine learning to infer user's emotion information. By using smart phone and smart watch, developed application can collect biometric data and location information by simply executing application and doing everyday life. In addition, adding the location information to the hearbit rate data, it proves higher utilization than existing ones.

• Journal of the Korean Institute of Educational Facilities
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• v.24 no.5
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• pp.11-18
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• 2017

An university dorm has significant implications in terms of providing residential, living, and learning spaces. With its supportive function, a dorm enables each university to provide higher level of education. The operation & maintenance(O&M) condition of the dorm has a decisive effect on the students' satisfaction. Accordingly, high levels of O&M services should be performed for students. However, Korean dorms are being operated and maintained by their own O&M guidelines without the consideration of spatial characteristics of dorm facilities and the comprehensive and systematic understanding on effective O&M processes. Given the fact that dorm facility can be a crucial factor in determining the entire quality of university and its O&M condition is closely related to the satisfaction of students, it is imperative that we need to pay more attention to the O&M condition and services. Therefore, the main objective of this research is to improve dorm students' satisfaction levels by applying different O&M method, preventive maintenance rather than reactive maintenance which has been performed so far. In ordering for doing it, 'Facility Management(FM) Standard' from KS, 'Facility Performance Indicator(FPI)' from APPA: Leadership in educational facilities and 'Building O&M Inspection Manual' from Korean Ministry of Land, Infrastructure and Transport were analyzed to come up with 15 significant O&M factors. After extracting O&M factors, the survey was conducted to determine importance rate and performance rate of each O&M factor. Using the Important-Performance Analysis(IPA), the priority of 15 O&M factors was established. The result of this research will be helpful for the efficient dorm facility O&M services and for facility managers to appropriately allocate the limited resources and human power.

• Journal of Korean Library and Information Science Society
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• v.40 no.1
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• pp.433-449
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• 2009

The purpose of this study is to find out the factors which have interrupted Library Assisted Instruction in various schools' libraries and to analyze the factors by the tools and to propose the solutions to the obstacles for the desired Library Assisted Instruction. For doing this, some experimental processes had been made: firstly, to make the analysis model based on HPT for the improvement of the real LAI, secondly, to apply the model to LAI, to draw out the problems and to analyze them, and to propose the solutions. As the results of this study, the 5 solutions are presented; 1) LAIs should have the obvious and concrete teaching&learning objectives. 2) The supporting systems for LAI like the web community ought to be prepared. 3) External motivation systems, that is, teachers and teacher-librarians' performance assessment with LAI participation rate per year, should be developed. 4) The various, practical LAI programs should be developed. 5) The obscure refusal of teachers & teacher-librarians to LAI should be disappeared and the directors of schools should be confident of the LAI's educational, positive effects.