• 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 The Korean Association For Science Education
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• v.35 no.1
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• pp.159-167
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• 2015

RI-Biomics is a promising radiation convergence technology that combines radiation with bio science as new growth power technology. Many developed countries are focusing active support and constant exertion to dominate the RI-Biomics market in advance. In order to achieve global leadership in the RI-Biomics field, we need more highly advanced technologies and professional manpower. In fact, we have less manpower compared to technology we currently hold. In this study, we established a basic infrastructure to train professional manpower in the RI-Biomics field by developing/operating optimum training program through expert interviews and survey. The developed program has four organized sections to understand overall procedure of RI-Biomics. To evaluate our training program, we performed test operations with eight students who have a major related to RI-Biomics for three weeks in KARA (Seoul) and KAERI (Jung-eup). In detail, radioisotope usage and safety management were conducted for one week as basic course, RI-Biomics application technology was conducted for two weeks as professional course. To verify performance results of training program, we conducted to journal research, daily reports, and survey on participants. The results show a high level of satisfaction with training programs and continuous intention of involvement in our program. We also need to develop an intensive course to train high-quality human resources and to operate training program continuously. This training program will be used as basic materials for the development of RI-Biomics curriculum for university. Hence, we will expect that our training program contributes in training a professional manpower and develop RI-Biomics technology.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2895-2901
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• 2011

SeoulMetro(line number 1 to 4) for the first half of the year. Therefore air quality in the subway is very important. It is passengers, such as sneezing and respiratory vital activities, Suspended due to skin keratin microbial action, and Microbial contaminants such as viruses. Hypersensitivity disorders, an atopic dermatitis, infectious diseases, allergic diseases, and can cause respiratory diseases. Ministry of Environment and National Institute of Environmental Research is managed so the life bacteria. It is emerging as the occupational health problems. Introduction of an appropriate ventilation system for cooling and dehumidification is needed. In line number 2, commuting and normal trains are measured in-room floating microbes. Suspended bacteria and fungi suspended in 2011 for 85 ~ 385$cfu/m^3$, 67 ~ 98$cfu/m^3$ is lower than baseline. Suspended to prevent microbial contamination and air conditioning equipment performance is a substantial improvement. Suspended micro-organisms and the impact on passenger room ventilation is increased. Electric car how to improve air quality substantially investigated.

• Journal of the Korean Geotechnical Society
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• v.30 no.5
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• pp.47-54
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• 2014

Although shear wave velocity ($V_s$) is an important design factor in seismic design, the measurement is not usually made in typical field investigation due to time and economic limitations. In the present study, an investigation was made to predict sand $V_s$ based on the standard penetration test (SPT) results by using artificial neural network (ANN) model. A total of 650 dataset composed of SPT-N value ($N_{60}$), water content, fine content, specific gravity for input data and $V_s$ for output data was used to build and train the ANN model. The sensitivity analysis was then performed for the trained ANN to examine the effect of the input variables on the $V_s$. Also, the ANN model was compared with seven existing empirical models on the performance. The sensitivity analysis results revealed that the effect of the SPT-N value on $V_s$ is significantly greater compared to other input variables. Also, when compared with the empirical models using Nash-Sutcliffe Model Efficiency Coefficient (NSE) and Root Mean Square Error (RMSE), the ANN model was found to exhibit the highest prediction capability.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.4 s.304
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• pp.33-42
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• 2005

This paper proposes a classification method for gene expression data, using membership function and neural network. The gene expression is a process to produce mRNA and protains which generate a living body, and the gene expression data is important to find out the functions and correlations of genes. Such gene expression data can be obtained from DNA 칩 massively and quickly. However, thousands of gene expression data may not be useful until it is well organized. Therefore a classification method is necessary to find the characteristics of gene data acquired from the gene expression. In the proposed method, a set of gene data is extracted according to the fisher's criterion, because we assume that selected gene data is the well-classified data sample. However, the selected gene data does not guarantee well-classified data sample and we calculate feature values using membership function to reduce the influence of outliers in gene data. Feature vectors estimated from the selected feature values are used to train back propagation neural network. The experimental results show that the clustering performance of the proposed method has been improved compared to other existing methods in various gene expression data.

• Korean Journal of Applied Biomechanics
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• v.18 no.1
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• pp.213-226
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• 2008

E. H. KIM, E. S. CHOI, D, H. NAM, S. S. KIM, J. W. CHUNG and T. W. KIM, The Instrumenfal Development for Pulling . Reaping Training & Measuring in Judo.Korean Jiurnal of Sport Biomechanics, Vol. 18, No. 1, pp. 213-226, 2008. The purpose of this study was to develop a judo-doll uke(partner : doll-uke) for training and measurement applicable to pulling, pushing and reaping in judo. In Judo the most common techniques consist of the pulling, pushing and sweep which all need to be practiced with a partner. So the research needs to develop a measurement system that can be used to evaluate the forces involved with these techniques. Also the Doll-Uke must be developed so that judokas can train alone. After the manufacture of Doll-Uke the usefulness of it must be evaluated. The height of a Doll-Uke is l70cm and its weight is 50kg. Doll-Uke was developed with a trunk angle of 55 and the lower extremities of an angle of 45. The Doll-Uke can also measure the forces developed during the pulling, pushing and sweep. Due to the ability of the system to measure the forces while preforming Judo techniques feedback can be provided to the Judokas to improve their performance.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.3
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• pp.257-266
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• 2015

This study introduce that we studied optimization and possibility of smoke spread prevention with air-curtain system in undersea tunnel named from Ho-Nam to Jeju line in domestic if a fire break out in train. To verify performance, air-curtain system is installed between rescue station platform and each door of passenger car to provide safety route to evacuator and we studied simulation model of various cases about 15 MW fire severity considering domestic specifications. As a result we verified the fact that CASE1(air jet with 15degree toward passenger car) and CASE 5 (air jet with 15degree toward passenger car and pressure air blast from cross passage) is best Smoke Spread Prevention and less inflow carbon monoxide. Through above results, we expect that air-curtain system is one of the facilities for fire safety and provide us safety platform route in undersea tunnel.

• Journal of Biomedical Engineering Research
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• v.34 no.3
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• pp.148-155
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• 2013

This study is about the development of the wrist rehabilitation system for the patient who has limited capability of movement after stroke. Electromyography triggered training system (ETTS) can play the role between complete passive training and patient activating training system. Surface EMG was measured on pronator teres muscle and biceps brachii muscle for wrist pronation and supination. Our system detects whether the subject makes muscular effort for pronation or supination or nothing in every 50 ms. When the effort level exceeds the preset percentage of maximal voluntary contraction, the motor rotates according to the direction of the intention of the subject. EMG triggers the motor rotation for the wrist rehabilitation training until the preset angle. To evaluate its performance, the maximum voluntary contraction level was measured for 4 subjects at first. With the audio-visual instruction to rotate the wrist (pronation or supination) the subjects made effort to follow the instruction. After calculating root mean square (RMS) for 50 ms, the controller determines whether there was muscular effort to rotate while holding the motor. When there was an effort to rotate, the controller rotates the motor 0.8 degree. By comparing the RMS values from two channels of EMG, the controller determines the rotational direction. The onset delay is $0.76{\pm}0.24$ s and offset delay is $0.65{\pm}0.22$ s for pronation. For supination the onset delay is $1.24{\pm}0.41$ s and offset delay is $0.77{\pm}0.22$ s. The system responded fast enough to be used for rehabilitation training. The controller perceived the direction of rotation 100% correctly for the pronation and 97.5% correctly for supination. ETTS was developed and the fundamental functions were validated for normal subjects. The clinical validation should be done with patients for real world application. With ETTS, the subjects can train voluntarily over the limitation of the range of motion which increases the effectiveness of the rehabilitation training.