• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.6
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• pp.327-340
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• 2017

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2016. 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 flow, heat and mass transfer, the reduction of pollutant exhaust gas, cooling and heating, the renewable energy system and the flow around buildings. CFD schemes were used more for all research areas. (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 of the long-term performance variation of the plate-type enthalpy exchange element made of paper, design optimization of an extruded-type cooling structure for reducing the weight of LED street lights, and hot plate welding of thermoplastic elastomer packing. In the area of pool boiling and condensing, the heat transfer characteristics of a finned-tube heat exchanger in a PCM (phase change material) thermal energy storage system, influence of flow boiling heat transfer on fouling phenomenon in nanofluids, and PCM at the simultaneous charging and discharging condition were studied. In the area of industrial heat exchangers, one-dimensional flow network model and porous-media model, and R245fa in a plate-shell heat exchanger were studied. (3) Various studies were published in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, subjects include mobile cold storage heat exchanger, compressor reliability, indirect refrigeration system with $CO_2$ as secondary fluid, heat pump for fuel-cell vehicle, heat recovery from hybrid drier and heat exchangers with two-port and flat tubes. In the alternative refrigeration/energy system category, subjects include membrane module for dehumidification refrigeration, desiccant-assisted low-temperature drying, regenerative evaporative cooler and ejector-assisted multi-stage evaporation. In the system control category, subjects include multi-refrigeration system control, emergency cooling of data center and variable-speed compressor control. (4) In building mechanical system research fields, fifteenth 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, renewable energies, etc. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which could be help for improving 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 analyses of indoor thermal environments controlled by portable cooler, the effects of outdoor wind pressure in airflow at high-rise buildings, window air tightness related to the filling piece shapes, stack effect in core type's office building and the development of a movable drawer-type light shelf with adjustable depth of the reflector. The subjects of building energy were worked on the energy consumption analysis in office building, the prediction of exit air temperature of horizontal geothermal heat exchanger, LS-SVM based modeling of hot water supply load for district heating system, the energy saving effect of ERV system using night purge control method and the effect of strengthened insulation level to the building heating and cooling load.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.97-106
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• 2004

It is a Known fact that driving through long tunnel increases possibility of traffic accident because of psychological feeling of insecurity and dispersion of drivers' concentration since driving in narrow and limited space for a longtime. It, therefore, results in raising transportation and environment problems, such as traffic accident difficult to be properly dealt with and ventilation. This study aims at proposing a method of augmenting driving amenity by improving the internal lighting facilities in the tunnel. The study is conducted by investigating internal landscapes of tunnels by lighting colors, which are currently being operated. The Color Planning System (CPS), developed by SHARP Co. Ltd, is exploited for selecting adjective that express the sensitivity image on lighting colors. The CPS is an example that applies to sensitivity of human body for products design development. The CPS takes the following process to define the color : 1) expressing "Pvoduct's Image" as "A Word (adjective)" and 2) referring "A Word" to "Image Scale", and 3) determining the color through this "Image Panel". The study is processed by making a questionnaire using the semantic differential (SD) scale, grasping the consciousness structure of experimental persons through the Factor Analysis, and building a model in which dependent variable is "Degree of Preference" about internal landscape in tunnel using LISREL(LInear Structural RELations).

• Journal of Labour Economics
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• v.28 no.3
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• pp.141-166
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• 2005

The purpose of this study is to evaluate the status of youth overeducation and to analyze the impact on the wage system, before and after the financial crisis. In this study, we adapt the following method; first, we investigate the year 1996 (before financial crisis) and year 2000 (after financial crisis) data from "the Survey Report on the Wage structure", based on the data from "the Occupational Dictionary" by occupation group. So we could evaluate the difference between the youth over-educational status, before and after financial crisis. Second, we analyze the reason why the difference occurs, with financial crisis dummy variable and other variables such as sex, occupation, industry. Third, we try to find the difference between the impact of the overeducation on the wage rate, before and after financial crisis. The main findings are as follows; first, the degree of overeducation in year 2000 is more than in year 1996. So the financial crisis plays the important role in deepening the degree of overeducation. Second, the wage rate of the overeducated worker is higher than that of the required-educated worker. Also, the both wage rates are increased after financial crisis. However, the difference of both wage rates' has declined over the financial crisis. Such a finding means that even though the both wage rates of the overeducated and the required-educated worker are increased, the wage rate of the required-educated worker has increased much more than that of overeducated worker, after the financial crisis.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.4
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• pp.307-313
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• 2016

When liquefied natural gas (LNG) is stored in a tank, it is necessary to maintain low temperature. It is very important that insulation techniques are applied to the LNG cargo because of this extreme environment. Hence, laminated wood, especially plywood, is widely used as the structural member and insulation material in LNG cargo containment systems (CCS). However, fracture of plywood has been reported recently, owing to sloshing effect. Therefore, it is necessary to increase the strength of the structural member for solving the problem. In this study, compressed wood, which is used as a support in LNG independent type B tanks, was considered as a substitute for plywood. Compression and bending tests were performed on compressed wood under ambient and cryogenic temperatures to estimate the mechanical behaviors and fracture characteristics. In addition, the direction normal to the laminates surface was considered as an experimental variable. Finally, the feasibility of using compressed wood for an LNG CCS was evaluated from the test results.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.27-34
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• 2016

In the train system, railway vehicles are connected in a line. Therefore, this feature should be considered in composing network topology in a train system. Besides, inter-car communication should be distinguished from in-car communication. As for the inter-car communication, the hybrid topology was proposed to use rather than the conventional ring, star, daisy-chain, and bus topologies. In the hybrid topology, a number of cars are bound to be a group. Then star topology is used for the communication in a group and daisy-chain topology is used for the communication between groups. Hybrid topology takes the virtue of both star and daisy-chain topologies. Hence it maintains communication speed with reducing the number of connecting cables between cars. Therefore, it is important to choose the number of cars in a group to obtain higher performance. In this paper, we focus on the optimization of hybrid topology for railway cars. We first assume that the size of data and the frequency of data production for each car is identical. We also assume that the importance for the maximum number of cables to connect cars is variable as well as the importance of the communication speed. Separated weights are granted to both importance and we derive the optimum number of cars in a group for various number of cars and weights.

• Archives of design research
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• v.15 no.1
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• pp.191-202
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• 2002

As family types have been diverse, patterns of living and living space became diverse as much as users are. Therefore, it is needed to provide various remodeled design of living space corresponding to changes of users'living patterns, and to provide these remodeling process to users directly on the web. In this paper, use scenario for the Internet-based Virtual Apartment Remodeling Simulator is researched as an export system to remodel space in accordance with users diverse lifestyle paradigm and the website is developed. The study consists of four parts. First, the general concept of remodeling, including the range and types of remodeling, are defined, and the misleading terms in this field are reviewed and organized by secondary research Second, fixed factors and variable factors are differentiated in the complex building for residence and business that was decided as a basic building type in this study. Third, there needed a database for consulting, final material, pre-estimation real estimation for simulation of remodeling. This database was introduced along with floor plan and elevation. Finally, the remodeling simulator is presented by the case study developed on the web. The system structure and use scenario are also presented. In order to present and inspect design alternatives, prototype was produced. The Final simulator was enhanced by defeating problems regarding interface efficiency and missing information of existing online site.

• Korean Journal of Construction Engineering and Management
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• v.18 no.3
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• pp.74-83
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• 2017

The recently introduced public rental housing REITs was just different the business structure from the existing public rental housing system and the basic supply system is the same. So the ownership conversion for public house over 10 years rental duration is possible after half of the obligated rental duration according to the agreement between lessor and lessee. However rental business operators are likely to have a negative attitude to the early ownership conversion because of less expected profit. Thus, there is a need for an analysis of proper early ownership conversion moment that can achieve public purposes while ensuring the profitability of public rental housing REITs. In this study, the characteristics of the ownership conversion rights that can lessee to exercise considered to be options. Also the nature of 'REITs', 'public rental housing REITs' is considered to be affected by the macroeconomic variables. Thus, this study analyzed the value for ownership conversion in the public rental housing REITs according to real option scenarios reflecting macroeconomic variables. As a result, according to the change of the variation rate of the macroeconomic variables, it was found that with adjustable early ownership conversion time using the DCF(Discounted Cash Flow) model. Therefore, it is possible to ensure profitability of early ownership conversion by predicting the variation of variables.

• Land and Housing Review
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• v.12 no.3
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• pp.67-78
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• 2021

After World War II, modular housing was developed as a means of quickly and efficiently meeting the housing supply demand. For the past 30 plus years, efforts have been made to improve modular housing in South Korea and to increase their competitiveness in the housing market. This study investigated modular houses based on a steel framed rahem structure which provides a flexible floor plan where walls are easily reconfigured to create rooms of various sizes and functions. Similar to the factory production methods used in the automotive industry, the modular housing industry can also benefit by standardizing such aspects as building components, manufacturing and construction methods, materials, process management, and floor plans. This study examined the feasibility of using a 3m × 3m module for developing various floor plans which are easy to produce and transport. Each 3m × 3m module can be configured to meet different living needs resulting in a complete home when multiple modules are connected. The module configurations can be varied to meet ground transportation and crane limitations. This study found that a 3m × 3m steel framed modular unit is a promising step towards providing residents with plans that meet their living preferences while improving and increasing the supply of modular houses.