• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.583-593
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• 2022

The harvesting system in South Korea faces the problems of aging workers and high wages, so it is necessary to improve the operation system and train workers to use high-performance forestry machines. This study compared the effectiveness and costs of yarding and processing operations between a multi-operation system using a tower yarder (HAM300) and a processor (KESLA 20SH) with those of a single-system using a forestry combi-machine. A whole-tree (cable) yarding operation was conducted in the clear-cutting area located at Compartment 15, Gwangneung Experimental Forest, National Institute of Forest Science, and the productivity and cost of multi- and single-system were analyzed. The productivity of the single-system was 1.5 m³/PMH and 1.6 m³/PMH higher than that of the multi- system because the single-system produced 1 log/cycle more than the multi-system in the yarding operation. The cost was approximately 12.1% lower for the single-system (￦36,113/m³) than for the multi-system (￦41,065/m³). The costs of the single-system and multi-system were decreased by maximums of 22.6% and 15.9%, respectively, by decreasing the idle time.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.525-539
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• 2024

Massive multiplayer online role playing game (MMORPG) is a common type of game these days. Predicting user churn in MMORPG is a crucial task. The retention rate of users is deeply associated with the lifespan and revenue of the service. If the churn of a specific user can be predicted in advance, targeted promotions can be used to encourage their stay. Therefore, not only the accuracy of churn prediction but also the speed at which signs of churn can be detected is important. In this paper, we propose methods to identify early signs of churn by utilizing the daily predicted user retention probabilities. We train various deep learning and machine learning models using log data and estimate user retention probabilities. By analyzing the change patterns in these probabilities, we provide empirical rules for early identification of users at high risk of churn. Performance evaluations confirm that our methodology is more effective at detecting high risk users than existing methods based on login days. Finally, we suggest novel methods for customized marketing strategies. For this purpose, we provide guidelines of the percentage of accessed users who are at risk of churn.

• 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.

• 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.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.674-682
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• 2020

Recently, the problem of air pollution has become a global concern due to industrialization and overcrowding. Air pollution can cause various adverse effects on human health, among which respiratory diseases such as asthma, which have been of interest in this study, can be directly affected. Previous studies have used clinical data to identify how air pollutant affect diseases such as asthma based on relatively small samples. This is high likely to result in inconsistent results for each collection samples, and has significant limitations in that research is difficult for anyone other than the medical profession. In this study, the main focus was on predicting the actual asthmatic occurrence, based on data on the atmospheric environment data released by the government and the frequency of asthma outbreaks. First of all, this study verified the significant effects of each air pollutant with a time lag on the outbreak of asthma through the time-lag Pearson Correlation Coefficient. Second, train data built on the basis of verification results are utilized in Deep Learning algorithms, and models optimized for predicting the asthmatic occurrence are designed. The average error rate of the model was about 11.86%, indicating superior performance compared to other machine learning-based algorithms. The proposed model can be used for efficiency in the national insurance system and health budget management, and can also provide efficiency in the deployment and supply of medical personnel in hospitals. And it can also contribute to the promotion of national health through early warning of the risk of outbreak by atmospheric environment for chronic asthma patients.

• Journal of Environmental Health Sciences
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• v.38 no.1
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• pp.51-56
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• 2012

Objectives: High concentrations of airborne particulate matters (PM) can affect the health of passengers using public transportation. The objectives of this research were to develop a PM control system for a railway cabin and to evaluate the performance of the device under conditions of an actual journey. Methods: This study measured the concentrations of $PM_{10}$ and $PM_{2.5}$ simultaneously in a reference cabin and a cabin with the PM control device. Results: The average $PM_{10}$ concentration in the reference cabin was 100 ${\mu}g/m^3$, and the $PM_{10}$ concentration in the cabin with the control device was 79 ${\mu}g/m^3$. While the overall control efficiency of the control device was 15.4%, reduction was more effective for peak $PM_{10}$ concentration. However, $PM_{2.5}$ levels did not differ greatly between the reference cabin and the cabin with the control device. The ratio of $PM_{2.5}$ to $PM_{10}$ was 0.37. $PM_{10}$ concentrations in cabins were not associated with ambient concentrations, indicating that the main sources of $PM_{10}$ were present in cabins. Additionally, average $CO_2$ concentration in the cabins was 1,359 ppm, less than the maximum of 2,000 ppm set out by the Korean Ministry of Environment's guideline. The $CO_2$ concentration in cabins was significantly associated with the number of passengers: the in-cabin concentration = $23.4{\times}N+460.2$, where N is the number of passengers. Conclusions: Application of the PM control device can improve $PM_{10}$ concentration, especially at peak levels but not $PM_{2.5}$ concentration.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.4
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• pp.125-135
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• 2015

The tendency of more longer span length being required economical in railway bridges is studying about PSC I shaped girder. In this case, it is important to analyze and choose the effective girder section for stiffness of bridge. This study investigates the dynamic properties and safety of PSC I shaped girder being used wide upper flange whose selection based on radii and efficiency factor of flexure for railway bridge in different span type. In addition, 40m PSC Box girder bridge adopted in Honam high speed railway is further analyzed to compare dynamic performance of PSC I shaped girder railway bridge with same span length. Time history response is acquired based on the mode superposition method. Static analysis is also analyzed using standard train load combined with the impact factor. Consequently, the result met limit values in every case including vertical displacement, acceleration and distort.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.469-476
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• 1998

In the case of acquisition and displaying medical Images such as ultrasonography and endoscopy on VGA monitor of PC system, image degradation of tear-drop appears through scan conversion. In this study, we compare several methods which can solve this degradation and implement the hardware system that resolves this problem in real-time with PC. It is possible to represent high quality image display and real-time processing and acquisition with specific de-interlacing device and PCI bridge on our hardware system. Image quality is improved remarkably on our hardware system. It is implemented as PC-based system, so acquiring, saving images and describing text comment on those images and PACS networking can be easily implemented.metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.706-714
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• 2012

The Korea Meteorological Administration provided the recent four-years records of weather dataset for our very short-term heavy rainfall prediction. We divided the dataset into three parts: train, validation and test set. Through feature selection, we select only important features among 72 features to avoid significant increase of solution space that arises when growing exponentially with the dimensionality. We used a differential evolution algorithm and two classifiers as the fitness function of evolutionary computation to select more accurate feature subset. One of the classifiers is Support Vector Machine (SVM) that shows high performance, and the other is k-Nearest Neighbor (k-NN) that is fast in general. The test results of SVM were more prominent than those of k-NN in our experiments. Also we processed the weather data using undersampling and normalization techniques. The test results of our differential evolution algorithm performed about five times better than those using all features and about 1.36 times better than those using a genetic algorithm, which is the best known. Running times when using a genetic algorithm were about twenty times longer than those when using a differential evolution algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.179-186
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• 2021

Manipulation of complex objects with an anthropomorphic robot hand like a human hand is a challenge in the human-centric environment. In order to train the anthropomorphic robot hand which has a high degree of freedom (DoF), human demonstration augmented deep reinforcement learning policy optimization methods have been proposed. In this work, we first demonstrate augmentation of human demonstration in deep reinforcement learning (DRL) is effective for object manipulation by comparing the performance of the augmentation-free Natural Policy Gradient (NPG) and Demonstration Augmented NPG (DA-NPG). Then three DRL policy optimization methods, namely NPG, Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO), have been evaluated with DA (i.e., DA-NPG, DA-TRPO, and DA-PPO) and without DA by manipulating six objects such as apple, banana, bottle, light bulb, camera, and hammer. The results show that DA-NPG achieved the average success rate of 99.33% whereas NPG only achieved 60%. In addition, DA-NPG succeeded grasping all six objects while DA-TRPO and DA-PPO failed to grasp some objects and showed unstable performances.