• Atmosphere
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• v.18 no.4
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• pp.299-316
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• 2008

The intensive upper-air observation network was organized over southwestern region of the Korean Peninsula during the Korea Enhanced Observing Program in 2005 (KEOP-2005). In order to examine the effect of additional upper-air observation on the numerical weather forecasting, three Observing System Experiments (OSEs) using Korea Local Analysis and Prediction System (KLAPS) and Weather Research and Forecasting (WRF) model with KEOP-2005 data are conducted. Cold start case with KEOP-2005 data presents a remarkable predictability difference with only conventional observation data in the downstream and along the Changma front area. The sensitivity of the predictability tends to decrease under the stable atmosphere. Our results indicates that the effect of intensive observation plays a role in the forecasting of the sensitive area in the numerical model, especially under the unstable atmospheric conditions. When the intensive upper-air observation data (KEOP-2005 data) are included in the OSEs, the predictability of precipitation is partially improved. Especially, when KEOP-2005 data are assimilated at 6-hour interval, the predictability on the heavy rainfall showing higher Critical Success Index (CSI) is highly improved. Therefore it is found that KEOP-2005 data play an important role in improving the position and intensity of the simulated precipitation system.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.441-452
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• 2013

This study analyzed the relationship between key film and a box office record success factors based on movies released in the first quarter of 2013 in Korea. An over-fitting problem can happen if there are too many explanatory variables inserted to regression model; in addition, there is a risk that the estimator is instable when there is multi-collinearity among the explanatory variables. For this reason, optimal variable selection based on high explanatory variables in box-office performance is of importance. Among the numerous ways to select variables, LASSO estimation applied by a generalized linear model has the smallest prediction error that can efficiently and quickly find variables with the highest explanatory power to box-office performance in order.

• Journal of the Society of Naval Architects of Korea
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• v.47 no.2
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• pp.150-162
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• 2010

Assessment of hydrodynamic performance of a ship hull has been focused on a model ship rather than a full-scale ship. In order to design the propeller of a ship, model-scale wake is often extended to full-scale based upon an empirical method or designer's experience, since wake measurement data for a full-scale ship is very rare. Recently modern CFD tools made some success in reproducing wake field of a model ship, which implicates that there are some possibilities of the accurate prediction of full-scale wakes. In this paper firstly the evaluation of model-scale wake obtained by Fluent package was performed. It was found that CFD calculation with the Reynolds-stress model (RSM) provided much better agreement with wake measurement in the towing tank than with the realizable k-$\varepsilon$ model (RKE). In the next full-scale wake was calculated using the same package to find out the difference between model and full-scale wakes. Three hull forms of KLNG, KCS, KVLCC2 having measurement data open for the public, were chosen for the comparison of resistance, form factor, and propeller plane wake between model ships and full-scale ships.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.4
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• pp.957-970
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• 2016

This study diagnosed the status of marine environmental impact assessment(MEIA) for project near the habitat of marine protected seagrass species such as Zostera caespitosa, Zostera asiatica, Phyllospadix iwatensis. For the preparation of a marine environmental impact statement, different monitoring parameters are used without any specific guideline for the assessment of current status. And also, both tools and techniques for MEIA are needed to improve for implementing. The monitoring plans and parameters are not considered well with the accuracy of the environmental predictions and effectiveness of any applicable mitigation measures. This study suggested the reasonable standard of the MEIA for the conservation of the marine protected seagrass species which have the habitat located near affected area. The inshore seagrasses need to be monitored including shoot count based on the "No Net Loss of Seagrass" as part of the monitoring parameters to assess the status of marine environment of environmental impact statement. In a process of effect prediction, we suggested a concentration of 10 mg/L suspended solids which added by the new developmental project near seagrasses habitat, referring to study of overseas case. But a further study for an appropriate standard is necessary effectively. In a mitigating process, priority needs to be considered in order of avoidance, minimization, reduction, compensation. In a post-monitoring process, it is necessary to monitor the seagrass species abundance to identify the variation of b/a (before and after) project. And in a case of implementing transplantation, survival rate need to be included to determine a success of project.

• Journal of Korea Technology Innovation Society
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• v.9 no.4
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• pp.812-841
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• 2006

Previous studies have shown that the ex-ante financial ratios, mainly used by financial institutions for loan evaluation purpose, are related to the ex-post finn's performance of venture firm's. The main objective of this study is to examine whether non-financial variables such as 'technology', 'marketability', and 'other business indexes' have extra explanatory power in forecasting the ex-post firm's performance of small and medium size venture firm's in Korea. The implications and results of this study are expected to be useful in loan evaluation, investment decision and internal management decisions of venture firms. Among small and medium sized manufacturing firms funded in the year of 1999 through 2005, 416 firms are selected for our analysis. The relationship between evaluation indexes and firm's success/failure is investigated using binary logistic regression analysis and factor analysis with an aid of SPSS program. The summarized results are as follows. First, current evaluation model, used for loan evaluation purpose for small and medium size manufacturing firms show the same discriminatory power as previous prediction model. Second, among the tested additional variables, significant indices are 'technological capability of CEO', 'managerial capability of CEO', and 'business feasibility'. Third, while previous studies on evaluation structure had 3 factors, this study showed that valuation's structure has 6 factors.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.241-250
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• 2005

New product development (NPD) is defined as the transformation of a market opportunity and a set of assumptions about product technology into a product available for sale. Managers charged with project selection decisions in the NPD process, such as go/no-go choices and specific resource allocation decisions, are faced with a complicated problem. Therefore, the ability to develop new successful products has identifies as a major determinant in sustaining a firm's competitive advantage. The purpose of this study is to develop a new evaluation model for NPD project selection in the high -tech industry using support vector machines (SYM). The evaluation model is developed through two phases. In the first phase, binary (go/no-go) classification prediction model, i.e. SVM for high-tech NPD project selection is developed. In the second phase. using the predicted output value of SVM, feasibility grade is calculated for the final NPD project decision making. In this study, the feasibility grades are also divided as three level grades. We assume that the frequency of NPD project cases is symmetrically determined according to the feasibility grades and misclassification errors are partially minimized by the multiple grades. However, the horizon of grade level can be changed by firms' NPD strategy. Our proposed feasibility grade method is more reasonable in NPD decision problems by considering particularly risk factor of NPD in viewpoints of future NPD success probability. In our empirical study using Korean NPD cases, the SVM significantly outperformed ANN and logistic regression as benchmark models in hit ratio. And the feasibility grades generated from the predicted output value of SVM showed that they can offer a useful guideline for NPD project selection.

• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.105-118
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• 2017

The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

• Korean Journal of Labor Studies
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• v.24 no.2
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• pp.1-28
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• 2018

Now labour policy of Moon Jae-in Administration is very different from the labour reform politics of the past ages in its structural conditions. Especially the difference is in the fact that the new labour policy is originated from the Candlelight Revolution in 2016 which has resisted to the 20years-long neoliberal domination. This kind of change in the political situation made a optimistic prediction with regard to the possibility of successful labour reform. However the future is in many points so uncertain that we could not confirm the success of labour reform at all. The uncertainty always resides in the structural unbalance between labour movement power and capitalist state power bloc in Korea. In this sense strategical orientation and practices of the democratic labour movement(KCTU) are very critical to produce some positive outcomes.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

• Journal of The Geomorphological Association of Korea
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• v.24 no.1
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• pp.77-91
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• 2017

Many debris landforms in the mountains of Korea have formed in the periglacial environment during the last glacial stage when the generation of sediments was active. Because these landforms are generally located on steep slopes and mostly covered by vegetation, however, it is difficult to observe and access them through field investigation. A scientific method is required to reduce the survey range before performing field investigation and to save time and cost. For this purpose, the use of remote sensing and GIS technologies is essential. This study has extracted the potential area of debris landform formation using a fuzzy set model as a mathematical data integration method. The first step was to obtain information about the location of debris landforms and their related factors. This information was verified through field observation and then used to build a database. In the second step, we conducted the fuzzy set modeling to generate a map, which classified the study area based on the possibility of debris formation. We then applied a cross-validation technique in order to evaluate the map. For a quantitative analysis, the calculated potential rate of debris formation was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). The prediction accuracy of the model was found to be 83.1%. We posit that the model is accurate and reliable enough to contribute to efficient field investigation and debris landform management.