• Journal of Korea Water Resources Association
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• v.34 no.4
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• pp.347-356
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• 2001

Even though the increase of greenhouse gases such as $CO_2$ is thought to be the main cause for global warming, its impact on global climate has not been revealed clearly in rather quantitative manners. However, researches using Genral Circulation Model(GCM) has shown that the accumulation of greenhouse gases increases the global mean temperature, which in turn impacts on the global water circulation pattern. A climate predictive capability is limited by lack of understanding of the different process governing the climate and hydrologic systems. The prediction of the complex responses of the fully coupled climate and hydrologic systems can be achieved only through development of models that adequately describe the relevant process at a wide range of spatial and temporal scales. These models must ultimately couple the atmospheres, oceans, and lad and will involve many submodels that properly represent the individual processes at work within the coupled components of systems. So far, there are no climate and related hydrologic models except local rainfall-runoff models in Korea. The purpose of this research is to predict the change of temperature in Korean Peninsula using regional scale model(IRSHAM96 model) and GCM data obtained from the increasing scenarios of $CO_2$ Korean Peninsula increased by $2.5^{\circ}C$ and the duration of Winter in $lxCO_2$ condition would be shorter the $2xCo_2$ condition due to global warming.

• Journal of Bio-Environment Control
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• v.29 no.3
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• pp.285-292
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• 2020

Because the inner environment of greenhouse has a direct impact on crop production, many studies have been performed to develop technologies for controlling the environment in the greenhouse. However, it is difficult to apply the technology developed to all greenhouses because those studies were conducted through empirical experiments in specific greenhouses. It takes a lot of time and cost to develop the models that can be applicable to all greenhouse in real situation. Therefore studies are underway to solve this problem using computer-based simulation techniques. In this study, a model was developed to predict the inner environment of glass greenhouse using CFD simulation method. The developed model was validated using primary and secondary heating experiment and daytime greenhouse inner temperature data. As a result of comparing the measured and predicted value, the mean temperature and uniformity were 2.62℃ and 2.92%p higher in the predicted value, respectively. R² was 0.9628, confirming that the measured and the predicted values showed similar tendency. In the future, the model needs to improve by applying the shape of the greenhouse and the position of the inner heat exchanger for efficient thermal energy management of the greenhouse.

• Food Science and Preservation
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• v.16 no.1
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• pp.1-7
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• 2009

Watermelons (Citrulus vulgaris Schrad) are usually sorted manually by weight, appearance, and acoustic impulse, so grading of maturity and internal quality is subject to inaccuracies. It was necessary to develop a nondestructive evaluation technique of internal watermelon quality to reduce human error. Thus, acoustic characteristics related to internal quality factors were analyzed. Among these factors, three (ripeness, presence of an internal cavity, and blood-colored flesh) were selected for evaluation. The number of peaks and the sum of peak amplitudes for watermelons with blood-colored flesh were lower than for normal fruits. The portable evaluation system has an impact mechanism, a microphone sensor, a signal processing board, an LCD panel, and a battery. A performance test was conducted in the field. The internal quality evaluation model showed 87% prediction accuracy. Validation was conducted on 72 samples. The accuracy of quality evaluation was 83%. The quality of samples was evaluated by an inspector using conventional methods (hitting the watermelon and listening to the sounds), and then compared with prototype results. The quality evaluation accuracy of the prototype was better than that of the inspector. This nondestructive quality evaluation system could be useful in the field, warehouse, and supermarket

• Korea Real Estate Review
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• v.28 no.1
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• pp.105-114
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• 2018

According to Statistics Korea, South Korea has entered the realm of the "aging society" with the rapid development of the country's population. Researchers anticipate that the extremely high (73%) ratio of real estate property to total assets for mid-age to aged households in South Korea that do not have a fixed income may cause serious problems in the future. For example, the real estate market in South Korea may be bombarded with properties listed for sale, causing the average property price to drop due to the abundant supply. Although this prediction may be reasonable, this concept has excluded the idea of pension (which is crucial as it can be considered a consistent and fixed income) due to the limited amount of available data thereon; as such, it is important to include this factor to improve the pertinent research. Thus, this research was conducted using the data from the $3^{rd}$ and $5^{th}$ Korea Retirement and Income Study. For the study results, it was found that variables such as net asset, gender, education, and number of family members have the same impact as that found in the previous studies. To extend from here, two new factors were introduced: the existence of pensions and the amount of pension received by a household. From there, it was found that the existence of a consistent and fixed income such as a pension has led to an increase in housing consumption, the area of interest of the authors.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.4
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• pp.231-242
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• 2018

This experiment was carried out to study the effects of different environmental conditions and production techniques on forage productivity of Italian ryegrass (IRG) in central and southern regions of Korea from 2016 to 2017. Average dry matter yield of 27 IRG cultivation regions was 6,940kg/ha. Forage productivity of IRG have positive correlation with cultivation techniques(p<0.01) but not correlated with cultivation environments. Forage productivity of IRG have positive correlation with seeding and field management techniques(p<0.01) but not correlated with fertilization techniques. This results suggests that practices of cultivation techniques are more important than cultivation environments for increasing the forage productivity of IRG. Therefore, yield prediction techniques of IRG in Korea have to be considered the practices of cultivation techniques along with soil and climate conditions.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.143-159
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• 2017

Due to the rapid development of IT in recent years, not only personal information but also the key technologies and information leakage that companies have are becoming important issues. For the enterprise, the core technology that the company possesses is a very important part for the survival of the enterprise and for the continuous competitive advantage. Recently, there have been many cases of technical infringement. Technology leaks not only cause tremendous financial losses such as falling stock prices for companies, but they also have a negative impact on corporate reputation and delays in corporate development. In the case of SMEs, where core technology is an important part of the enterprise, compared to large corporations, the preparation for technological leakage can be seen as an indispensable factor in the existence of the enterprise. As the necessity and importance of Information Security Management (ISM) is emerging, it is necessary to check and prepare for the threat of technology infringement early in the enterprise. Nevertheless, previous studies have shown that the majority of policy alternatives are represented by about 90%. As a research method, literature analysis accounted for 76% and empirical and statistical analysis accounted for a relatively low rate of 16%. For this reason, it is necessary to study the management model and prediction model to prevent leakage of technology to meet the characteristics of SMEs. In this study, before analyzing the empirical analysis, we divided the technical characteristics from the technology value perspective and the organizational factor from the technology control point based on many previous researches related to the factors affecting the technology leakage. A total of 12 related variables were selected for the two factors, and the analysis was performed with these variables. In this study, we use three - year data of "Small and Medium Enterprise Technical Statistics Survey" conducted by the Small and Medium Business Administration. Analysis data includes 30 industries based on KSIC-based 2-digit classification, and the number of companies affected by technology leakage is 415 over 3 years. Through this data, we conducted a randomized sampling in the same industry based on the KSIC in the same year, and compared with the companies (n = 415) and the unaffected firms (n = 415) 1:1 Corresponding samples were prepared and analyzed. In this research, we will conduct an empirical analysis to search for factors influencing technology leakage, and propose an early warning system through data mining. Specifically, in this study, based on the questionnaire survey of SMEs conducted by the Small and Medium Business Administration (SME), we classified the factors that affect the technology leakage of SMEs into two factors(Technology Characteristics, Organization Characteristics). And we propose a model that informs the possibility of technical infringement by using Support Vector Machine(SVM) which is one of the various techniques of data mining based on the proven factors through statistical analysis. Unlike previous studies, this study focused on the cases of various industries in many years, and it can be pointed out that the artificial intelligence model was developed through this study. In addition, since the factors are derived empirically according to the actual leakage of SME technology leakage, it will be possible to suggest to policy makers which companies should be managed from the viewpoint of technology protection. Finally, it is expected that the early warning model on the possibility of technology leakage proposed in this study will provide an opportunity to prevent technology Leakage from the viewpoint of enterprise and government in advance.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.7
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• pp.380-394
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• 2015

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2014. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of heat and mass transfer, cooling and heating, and air-conditioning, the flow inside building rooms, and smoke control on fire. Research issues dealing with duct and pipe were reduced, but flows inside building rooms, and smoke controls were newly added in thermal and fluid engineering research area. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results for thermal contact resistance measurement of metal interface, a fan coil with an oval-type heat exchanger, fouling characteristics of plate heat exchangers, effect of rib pitch in a two wall divergent channel, semi-empirical analysis in vertical mesoscale tubes, an integrated drying machine, microscale surface wrinkles, brazed plate heat exchangers, numerical analysis in printed circuit heat exchanger. In the area of pool boiling and condensing, non-uniform air flow, PCM applied thermal storage wall system, a new wavy cylindrical shape capsule, and HFC32/HFC152a mixtures on enhanced tubes, were actively studied. In the area of industrial heat exchangers, researches on solar water storage tank, effective design on the inserting part of refrigerator door gasket, impact of different boundary conditions in generating g-function, various construction of SCW type ground heat exchanger and a heat pump for closed cooling water heat recovery were performed. (3) In the field of refrigeration, various studies were carried out in the categories of refrigeration cycle, alternative refrigeration and modelling and controls including energy recoveries from industrial boilers and vehicles, improvement of dehumidification systems, novel defrost systems, fault diagnosis and optimum controls for heat pump systems. It is particularly notable that a substantial number of studies were dedicated for the development of air-conditioning and power recovery systems for electric vehicles in this year. (4) In building mechanical system research fields, seventeen studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, and renewable energies, piping in the buildings. Proposed designs, performance performance tests using numerical methods and experiments provide useful information and key data which can improve the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the evaluation of work noise in tunnel construction and the simulation and development of a light-shelf system. The subjects of building energy were worked on the energy saving of office building applied with window blind and phase change material(PCM), a method of existing building energy simulation using energy audit data, the estimation of thermal consumption unit of apartment building and its case studies, dynamic window performance, a writing method of energy consumption report and energy estimation of apartment building using district heating system. The remained studies were related to the improvement of architectural engineering education system for plant engineering industry, estimating cooling and heating degree days for variable base temperature, a prediction method of underground temperature, the comfort control algorithm of car air conditioner, the smoke control performance evaluation of high-rise building, evaluation of thermal energy systems of bio safety laboratory and a development of measuring device of solar heat gain coefficient of fenestration system.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.536-555
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• 2021

Sea-surface wind is an important variable in ocean-atmosphere interactions, leading to the changes in ocean surface currents and circulation, mixed layers, and heat flux. With the development of satellite technology, sea-surface winds data retrieved from scatterometer observation data have been used for various purposes. In a complex marine environment such as the Korean Peninsula coast, scatterometer-observed sea-surface wind is an important factor for analyzing ocean and atmospheric phenomena. Therefore, the validation results of wind accuracy can be used for diverse applications. In this study, the sea-surface winds derived from ASCAT (Advanced SCATterometer) mounted on MetOp-A/B (METeorological Operational Satellite-A/B) were validated compared to in-situ wind measurements at 16 marine buoy stations around the Korean Peninsula from January to December 2020. The buoy winds measured at a height of 4-5 m from the sea surface were converted to 10-m neutral winds using the LKB (Liu-Katsaros-Businger) model. The matchup procedure produced 5,544 and 10,051 collocation points for MetOp-A and MetOp-B, respectively. The root mean square errors (RMSE) were 1.36 and 1.28 m s^-1, and bias errors amounted to 0.44 and 0.65 m s^-1 for MetOp-A and MetOp-B, respectively. The wind directions of both scatterometers exhibited negative biases of -8.03° and -6.97° and RMSE values of 32.46° and 36.06° for MetOp-A and MetOp-B, respectively. These errors were likely associated with the stratification and dynamics of the marine-atmospheric boundary layer. In the seas around the Korean Peninsula, the sea-surface winds of the ASCAT tended to be more overestimated than the in-situ wind speeds, particularly at weak wind speeds. In addition, the closer the distance from the coast, the more the amplification of error. The present results could contribute to the development of a prediction model as improved input data and the understanding of air-sea interaction and impact of typhoons in the coastal regions around the Korean Peninsula.