• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.65-76
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• 2015

The Clean Development Mechanism (CDM) is the multi-lateral 'cap and trade' system endorsed by the Kyoto Protocol. CDM allows developed (Annex I) countries to buy CER credits from New and Renewable (NE) projects of non-Annex countries, to meet their carbon reduction requirements. This in effect subsidizes and promotes NE projects in developing countries, ultimately reducing global greenhouse gases (GHG). To be registered as a CDM project, the project must prove 'additionality,' which depends on numerous factors including the adopted technology, baseline methodology, emission reductions, and the project's internal rate of return. This makes it difficult to determine ex ante a project's acceptance as a CDM approved project, and entails sunk costs and even project cancellation to its project stakeholders. Focusing on hydro power projects and employing UNFCCC public data, this research developed a prediction model using logistic regression and CART to determine the likelihood of approval as a CDM project. The AUC for the logistic regression and CART model was 0.7674 and 0.7231 respectively, which proves the model's prediction accuracy. More importantly, results indicate that the emission reduction amount, MW per hour, investment/Emission as crucial variables, whereas the baseline methodology and technology types were insignificant. This demonstrates that at least for hydro power projects, the specific technology is not as important as the amount of emission reductions and relatively small scale projects and investment to carbon reduction ratios.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.15-32
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• 2016

Online news on the Internet, as published open data, contain facts or opinions about a specific affair and hence influences considerably on the decisions of the general publics who are interested in a particular issue. Therefore, we can predict the people's choices related with the issue by analyzing a large number of related internet news. This study aims to propose a text analysis methodto predict the outcomes of events that take place in a specific place. We used topics of the news articles because the topics contains more essential text than the news articles. Moreover, when it comes to mobile environment, people tend to rely more on the news topics before clicking into the news articles. We collected the titles of news articles and divided them into the learning and evaluation data set. Morphemes are extracted and their polarity values are identified with the learning data. Then we analyzed the sensitivity of the entire articles. As a result, the prediction success rate was 70.6% and it showed a clear difference with other analytical methods to compare. Derived prediction information will be helpful in determining the expected demand of goods when preparing the event.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• 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 the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.225-230
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• 2017

To generate mechanical movements in one-shot devices such as missiles and space launch vehicles, pyrotechnic mechanical device(PMD) such as pin pullers using pyrotechnic charge has been widely used. Reliability prediction of pin pullers is crucial to successfully execute target missions for the one-shot devices. Because the pin pullers require destructive tests to evaluate their reliability, one would need about 3,000 samples of success to guarantee a reliability of 99.9 % with a confidence level of 95 %. This paper suggests the application of a probit model using the charge amount as a functional parameter for estimation of functional reliability of pin puller. To guarantee target reliability, we propose estimation methods of the lower bound of functional reliability by applying the probit model. Given lower bound of functional reliability, we quantitatively show that the optimum amount of charge increases as the number of samples decreases. Along with a variety of simulations the validity of our new model via real test results is confirmed.

• The Journal of the Korean dental association
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• v.57 no.12
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• pp.736-746
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• 2019

Supportive periodontal therapy(SPT) is essential for the long-term success of periodontal treatment. A patient's compliance with SPT is one of the most important factors affecting periodontal status. There are few studies quantifying compliance with SPT. The aim of this study is to quantify patient's compliance using new method and evaluate tooth loss depending on patient's supportive periodontal treatment compliance index(SPTCI) with SPT. This study included subjects diagnosed with generalized moderate to severe chronic periodontitis, who had completed active periodontal treatment and had SPT over 5 years in Wonkwang university dental hospital. Chart review and radiography analysis were performed. To quantify compliance, SPTCI representing average of gap between recommended schedules and actual visits has been used and evaluated with tooth loss. Mean period of SPT was 8.9 years and mean SPTCI was about 120. In statical analysis, patients who have higher SPTCI with SPT are more likely to have higher rate of tooth loss. Under SPTCI of 120, there were no significant co-relation between SPTCI and tooth loss. Patients diagnosed with moderate chronic periodontitis have significant co-relation between SPTCI and tooth loss, whereas patients diagnosed with severe chronic periodontitis have no co-relation. SPTCI, new method of quantifying compliance in this study, affected to tooth loss. This study suggests that using SPTCI could be helpful for prediction of tooth loss and be used to determine the interval of visit.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.4
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• pp.397-402
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• 2013

In this study, numerical hull form development of a platform supply vessel, a full scale with the overall length of 26.75m, was performed to predict a bare-hull resistance and a large scale of model tests with a 1/10 scaled model were conducted to verify the success of numerical results. Numerical analysis on heave and pitch motion as a function of encounter frequency and ship's speed for the prediction of seakeeping characteristics are also presented. The experiment results of resistance agreed well with numerical analysis. As a result in the motion response characteristics, the heave RAO indicates high values with the range of encounter frequency 1.8~2.0. The Pitch RAO indicates high motion response characteristics at Beaufort scale No. 3 and 4 in rough seas.

• Korean Journal of Human Ecology
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• v.18 no.3
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• pp.719-727
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• 2009

The ultimate success of commercial applications of body scan data in the apparel industry will be consumers' substantial applications such as automated custom fit, size prediction, virtual try-on, personal shopper services (Loker, S. et al., 2004). In this study, we surveyed fifty consumers and forty-seven apparel industry workers about their recognition and interest in 3D body scanning and virtual try-on. The results are as follows: 55% of the apparel industry workers has recognized 3D body scanning as a convenient technology, but do not know how to use it. To the questions regarding virtual try-on, 53% of the workers give positive answers. The consumers have a more positive view on virtual try-on than the workers do. The workers predict that the application of 3D body scan technology to the apparel industry could offer customers helpful information in their clothing selection by using virtual images of various size and style, and increase mass production of MTM(Made-To-Measure). The answers from the male consumers in their twenties indicate that virtual try-on is useful by 88% on offline shopping and by 100% on online shopping. 53% of the workers and 68% of the consumers gave answers that just by virtual try-on they could judge the quality of the apparel products and purchase them. Absolutely 3D virtual try-on is an effective tool for online shoppers. 85% of the workers anticipate applications of the 3D body scanning also in 'body measurement', 'custom pattern development' as well as 'virtual try-on' in the near future. With the positive reactions and the stimulating interests in virtual try-on, the conditions of contemporary world encourage more active researches and wide usages of the technology in apparel industry.

• 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.