• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.178-188
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• 2022

The introduction of virtual power plants is actively being discussed to solve the problem of grid acceptability caused by the spread of distributed renewable energy, which is the key to achieving carbon neutrality. However, a new business such as virtual power plants is difficult to secure economic feasibility at the initial stage of introduction because it is common that there is no compensation mechanism. Therefore, appropriate support including subsidy is required at the early stage. But, it is generally difficult to obtain the cost model to determine the subsidy level because of the lack of enough data for the new business model. In this study, a survey of domestic experts on the requirements, appropriate scale, and cost required for the introduction of virtual power plants is conducted. First, resource composition scenarios are designed from the survey results to consider the impact of the resource composition on the cost. Then, the cost estimation model is obtained using the individual cost estimation data for their resource compositions using logistic regression analysis. In the case study, appropriate initial subsidy levels are analyzed and compared for the virtual power plants on the scale of 20-500MW. The results show that mid-to-large resource composition cases show 29-51% lower cost than small-to-large resource composition cases.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• Journal of Bio-Environment Control
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• v.26 no.1
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• pp.13-18
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• 2017

This study was conducted to investigate optimal temperature of garlic and develop bulb weight model in harvest time. Day and night temperature in chambers was set to $11/7^{\circ}C$, $14/10^{\circ}C$, $17/12^{\circ}C$, $20/15^{\circ}C$, $23/18^{\circ}C$, $28/23^{\circ}C$(16/8h). Bulb fresh and dry weight was heaviest on $20/15^{\circ}C$. In $11/7^{\circ}C$ and $14/10^{\circ}C$, leaf number and total leaf area increased slowly. But in the harvest, leaf number and total leaf area were not significant, except $28/23^{\circ}C$. Models were developed with fresh bulb weight. As a result of analyzing the model, $18{\sim}20^{\circ}C$ certified optimal mean temperature. And the growing degree day base temperature estimated $7.1^{\circ}C$, upper temperature threshold estimated $31.7^{\circ}C$. To verify the model, mean temperature on temperature gradient tunnel applied to the growth rate model. Lineal function model, quadric model, and logistic distribution model showed 79.0~95.0%, 77.2~92.3% and 85.0~95.8% accuracy, respectively. Logistic distribution model has the highest accuracy and good for explaining moderate temperature, growing degree day base temperature and upper temperature threshold.

• Korean Security Journal
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• no.43
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• pp.205-230
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• 2015

This study aims to compare the differences of crucial factors that are associated with probation warning tickets between sex offenders and non-sex offenders in South Korea. Serious high-profile cases have occurred in recent years which resulted in public and political conners for successful sex offender management and monitoring strategy through community corrections. The official response has been to initiate a series of legislative probation and parole measures by using GPS electronic monitoring system, chemical castration, and sex offender registry and notification. In this context, the current study is designed to explore the major factors that could affect the failure of probation by comparing the differences between sex offenders and non-sex offenders in terms of their major factors which are related to the failure of probation. The failure of probation is measured by the number of warning tickets which would be issued when there is the violation of probation conditions. The data is obtained from Seoul Probation office from January, 29, 2014 to February, 28, 2014. The sample number of sex offenders is 144 and the number of non-sex offenders is 1,460. The data includes the information regarding the offenders who completed their probation order after they were assigned to Seoul Probation in 2013. Furthermore, this study uses the chi-square and logistic regression analysis by using SPSS statistical package program. The result demonstrated that only prior criminal history was statistically significant factor that was related to the number of warning tickets in the sex offender group when other variables were controlled($X^2=25.15$, p<0.05, Nagelkerke $R^2=0.23$)(b=0.19, SE=0.08, p<0.05). By contrast, there were various factors that were associated with the number of warning tickets in non-sex offender group. Specifically, the logistic regression analysis for the non-sex offenders showed that demographic variable(marital status and employment type), offender-victim relationships, alcohol addiction, violent behavior, prior criminal history, community service order, and attendance order were statistically significant factors that were associated with the odds of warning tickets. Further policy implication will be discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.42-51
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• 2020

This study was performed to support the 6th forest basic planning by Korea Forest Service as predicting the change of forestland area by the transition of land use type in the future over 35 years in South Korea. It is very important to analyze upcoming forestland area change for future forest planning because forestland plays a basic role to predict forest resources change for afforestation, production and management in the future. Therefore, the transitional interaction between land use types in future of South Korea was predicted in this study using econometrical models based on past trend data of land use type and related variables. The econometrical model based on maximum discounted profits theory for land use type determination was used to estimate total quantitative change by forestland, agricultural land and urban area at national scale using explanatory variables such as forestry value added, agricultural income and population during over 46 years. In result, it was analyzed that forestland area would decrease continuously at approximately 29,000 ha by 2027 while urban area increases in South Korea. However, it was predicted that the forestland area would be started to increase gradually at 170,000 ha by 2050 because urban area was reduced according to population decrement from 2032 in South Korea. We could find out that the increment of forestland would be attributed to social problems such as urban hollowing and localities extinction phenomenon by steep decrement of population from 2032. The decrement and increment of forestland by unbalanced population immigration to major cities and migration to localities might cause many social and economic problems against national sustainable development, so that future strategies and policies for forestland should be established considering such future change trends of land use type for balanced development and reasonable forestland use and conservation.

• Journal of fish pathology
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• v.26 no.2
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• pp.65-75
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• 2013

Population growth equation of scuticociliate Miamiensis avidus was obtained from the experimental results of in vitro culture condition to estimate the growth rate and carrying capacity from the growth equation. In addition, intraperitoneal infections into olive flounder Paralichthys olivaceus were carried out into 2 different conditions: different concentrations of M. avidus in same water temperature and same concentration of M. avidus in different water temperatures. Olive flounder mortality was threshold dependent with both the temperature and M. avidus density parameters. In this paper, we propose a mathematical model to study M. avidus growth in olive flounder based upon the interactions between parasite and host. The mathematical model was logistic growth differential equation (1.2). The parameters were found with Matlab program through the Levenberge-Marquardt method. In theorem, equilibrium values between the infected fish population and dead population could found. Our equilibrium points were a stable equilibrium and an unstable equilibrium. From the equation (1.6), it was possible to predict the amount of cumulative mortality of olive flounder along with the time after M. avidus infection.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.453-468
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• 2020

This study was conducted to investigate factors influencing physical activity in female nursing students based on the habit formation model. The participants were 207 female students at G nursing college and J nursing college located in J city. All data were collected from 31, August to 14, September in 2020 and analyzed by descriptive statistics, ANOVA and Scheffĕ test, Pearson's correlation coefficient, Univariate, and Multivariate multinomial logistic regression using SPSS/WIN 22.0 program. The average level of physical activity measured by the Korean version of IPAQ was 2506.31±2807.05 MET-min/week. According to the physical activity category classified by IPAQ, there were 59students(28.5%) in the high group, 98students(47.3%) in the moderate group, and 50students(24.2%) in the low group. Physical activity habit strength was the significant factor influencing physical activity in female nursing students. Therefore, this study suggests that it is necessary to develop the habit formation program and verify effectiveness for enhancing and maintaining the physical activity in female nursing students.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.45-55
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• 2022

The size of the cryptocurrency market is growing. For example, market capitalization of bitcoin exceeded 500 trillion won. Accordingly, many studies have been conducted to predict the price of cryptocurrency, and most of them have similar methodology of predicting stock prices. However, unlike stock price predictions, machine learning become best model in cryptocurrency price predictions, conceptually cryptocurrency has no passive income from ownership, and statistically, cryptocurrency has at least three times higher liquidity than stocks. Thats why we argue that a methodology different from stock price prediction should be applied to cryptocurrency price prediction studies. We propose Reverse Walk-forward Validation (RWFV), which modifies Walk-forward Validation (WFV). Unlike WFV, RWFV measures accuracy for Validation by pinning the Validation dataset directly in front of the Test dataset in time series, and gradually increasing the size of the Training dataset in front of it in time series. Train data were cut according to the size of the Train dataset with the highest accuracy among all measured Validation accuracy, and then combined with Validation data to measure the accuracy of the Test data. Logistic regression analysis and Support Vector Machine (SVM) were used as the analysis model, and various algorithms and parameters such as L1, L2, rbf, and poly were applied for the reliability of our proposed RWFV. As a result, it was confirmed that all analysis models showed improved accuracy compared to existing studies, and on average, the accuracy increased by 1.23%p. This is a significant improvement in accuracy, given that most of the accuracy of cryptocurrency price prediction remains between 50% and 60% through previous studies.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1075-1083
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• 2009

The scientific research method went through construction of hypothesis and collection of data by experiment or observation and abstracting the hypothesis based on the experience which uses the data. The statistical methodology plays an important role in this process. The method which acquires a data becomes an initial process of abstraction and a survey research using structured questionnaires is a basic tool. After the data is acquired, the high-class statistical techniques such as the regression analysis and the linear structural equation model are used to abstract a hypothesis. By the way, from time to time the concepts which have become abstractive do not help us to understand an actual phenomena, rather it is need to extract some knowledge from questions themselves. In this article, we review the well known statistical methods providing the ways of finding core questions which possibly answer a researcher wants to know. We deal with course evaluation data as an example and try to set up the strategy for improving course evaluation.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.291-302
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• 2020

Korean popular music industry, which is lead by "Idol group", has forsaken their fandom all over the world. Therefore, idol groups has become not only an artist but also the most influential people in the Korean economy. A global idol group with a strong fandom can earn more than a trillion-dollar by attracting their global fan's interest in Korea. In other words, it is considerably important to carry the idol to a successful conclusion. This study tries to expect whether the idols can be survived or not at a certain point after their debut by ANN, Decision Tree, Random Forest. We decide that certain point as the three-year and eight-year after their debut, because it is their break-even point year and the year after their average renewal of the contract. In addition, this study also explains which feature is the most important to their survival by feature importance and Logistic regression. In conclusion, features like the number of idol competitors, the number of debut members and the number of the genre are significant. These results shed light on the efficient management of K-Pop idol to improve industrial competitiveness.