• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.705-714
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• 2019

Precision positioning is necessary for various use of high-resolution UAV images. Basically, GCP is used for this purpose, but in case of emergency situations or difficulty in selecting GCPs, the data shall be obtained without GCPs. This study proposed a method of improving positional accuracy for x, y coordinate of UAV based 3 dimensional point cloud data generated without GCPs. Road vector file by the public data (Open Data Portal) was used as reference data for improving location accuracy. The geometric correction of the 2 dimensional ortho-mosaic image was first performed and the transform matrix produced in this process was adopted to apply to the 3 dimensional point cloud data. The straight distance difference of 34.54 m before the correction was reduced to 1.21 m after the correction. By confirming that it is possible to improve the location accuracy of UAV images acquired without GCPs, it is expected to expand the scope of use of 3 dimensional spatial objects generated from point cloud by enabling connection and compatibility with other spatial information data.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.17 no.4
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• pp.418-425
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• 1988

Since conventional computer program is workable only with velocity boundary condition, in practical fluid passage such as clean room which usually have wide inlets and outlets, it is not easy to measure velocity itself because of its vector property. Furthermore a certain assumption of velocity at boundaries may lead to physically unreasonable results. From this motivation, we have developed a computer program to predict whole flow field imposed on pressure-based boundary condition which can be measured by relatively simple method. The only additional velocity boundary condition that should be imposed on to make the problem unique, are no slip condition at all walls and zero cross stream velocity at inlet. The result of present study was compared with that by Bernoulli equation being used practically. They were coincident well each other within 5%, therefore the validity of the present method is proved. In the present work, the flow field in a clean room subject to pressure-based boundary conditions at an inlet and two exits was predicted numerically. The pressure difference between the inlet and the left exit which keeps relatively low pressure among two exits is fixed as 150[Pa] and the pressure at the right exit is varied from zero to 150[Pa] by the increment of 25[Pa]. For each cases the flow characteristics in the clean room, the velocity profile at the inlet, and the flow rate through the two exits was predicted. The flow rate through the right exit imposed on relatively higher pressure than the left exit decreased linearly according to the increase of pressure of the right exit. When the pressure of the right exit is increased enough to cause back flow at the exit, the flow rate is rapidly decreased.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.71-78
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• 2021

Purpose: To evaluate the inter-fractional position and respiratory reproducibility of lung and liver tumors using pressure conserving type(P-type) abdominal compressor in volumetric modulated arc therapy(VMAT). Materials and methods: Six lung cancer patients and three liver cancer patients who underwent VMAT using a P-type abdominal compressor were included in this study. Cone-beam computed tomography(CBCT) images were acquired before each treatment and compared with planning CT images to evaluate the inter-fractional position reproducibility. The position variation was defined as the difference of position shift values between target matching and bone matching. 4-dimensional cone-beam computed tomography(4D CBCT) images were acquired weekly before treatment and compared with planning 4DCT images to evaluate the inter-fractional respiratory reproducibility. The respiratory variation was calculated by the magnitude of excursions by breathing. Results: The mean ± standard deviation(SD) of overall position variation values, 3D vector in the three translational directions were 1.1 ± 1.4 mm and 4.5 ± 2.8 mm for the lung and liver, respectively. The mean ± SD of respiratory variation values were 0.7 ± 3.4 mm (p = 0.195) in the lung and 3.6 ± 2.6 mm (p < 0.05) in the liver. Conclusion: The use of P-type compressor in lung and liver VMAT was effective for stable control of inter-fractional position and respiratory variation by reproduction of abdominal compression. Appropriate PTV margin must be considered in treatment planning, and image guidance before each treatment are required in order to obtain more stable reproducibility

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• Economic Analysis
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• v.27 no.3
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• pp.1-32
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• 2021

This study empirically analyzes whether bank capital channel and risk-taking channel for monetary policy work for domestic banks in South Korea by analyzing the impact of the expansionary monetary policy on the rate spread between deposit and loan, capital ratio, and loan amount. For the empirical analysis, the Uhlig (2005)'s sign-restricted SVAR(Structural Vector Auto-Regression) model is used. The empirical results are as follows: the bank's interest rate margin increases, the capital ratio improves, risk-weighted asset ratio increases, and the amount of loans increases in response to expansionary monetary shock. This empirical results confirm that bank capital channel and risk-taking channel work in domestic banks, similar to the previous research results. The implications of this study are as follows. Although the expansionary monetary policy has the effect of improving the bank's financial soundness and profitability in the short term as bank capital channel works, it could negatively affect the soundness of banks by encouraging banks to pursue risk in the long run as risk-taking channel works. It is necessary to note that the capital ratio according to the BIS minimum capital requirement of individual banks may cause an illusion in supervising the soundness of the bank. So, the bank's aggressive lending expansion may lead to an inherent weakness in the event of a crisis. Since the financial authority may have an illusion about the bank's financial soundness if the low interest rate persists, the authority needs to be actively interested in stress tests and concentration risk management in the pillar 2 of the BIS capital accord. In addition, since system risk may increase, it is necessary to conduct regular stress tests or preemptive monitoring of assets concentration risk.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.199-208
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• 2023

Taguchi method is one of the most popular approaches for design optimization such that performance characteristics become robust to uncontrollable noise variables. However, most previous Taguchi method applications have addressed a single-characteristic problem. Problems with multiple characteristics are more common in practice. The multi-criteria decision making(MCDM) problem is to select the optimal one among multiple alternatives by integrating a number of criteria that may conflict with each other. Representative MCDM methods include TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey Relational Analysis), PCA(Principal Component Analysis), fuzzy logic system, and so on. Therefore, numerous approaches have been conducted to deal with the multi-characteristic design problem by combining original Taguchi method and MCDM methods. In the MCDM problem, multiple criteria generally have different measurement units, which means that there may be a large difference in the physical value of the criteria and ultimately makes it difficult to integrate the measurements for the criteria. Therefore, the normalization technique is usually utilized to convert different units of criteria into one identical unit. There are four normalization techniques commonly used in MCDM problems, including vector normalization, linear scale transformation(max-min, max, or sum). However, the normalization techniques have several shortcomings and do not adequately incorporate the practical matters. For example, if certain alternative has maximum value of data for certain criterion, this alternative is considered as the solution in original process. However, if the maximum value of data does not satisfy the required degree of fulfillment of designer or customer, the alternative may not be considered as the solution. To solve this problem, this paper employs the desirability function that has been proposed in our previous research. The desirability function uses upper limit and lower limit in normalization process. The threshold points for establishing upper or lower limits let us know what degree of fulfillment of designer or customer is. This paper proposes a new design optimization technique for multi-characteristic design problem by integrating the Taguchi method and our desirability functions. Finally, the proposed technique is able to obtain the optimal solution that is robust to multi-characteristic performances.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1009-1014
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• 2023

Comprehensive observations of the Euler method and the Lagrangian method were performed in order to obtain high-resolution observation data in space and time for the complex environment of new city. The two radiosondes, which measure meteorological parameters using Lagrangian methods, produced air pressure, wind speed and wind direction. They were generally consistent with each other even if the observation points or times were different. The temperature measured by the sensor exposed to the air during the day was relatively high as the altitude increased due to the influence of solar radiation. The temporal difference in wind direction and speed was found in the comparison of Euler's wind profiler data with radiosonde data. When the wind field is horizontally in homogeneous, this result implies the need to consider the advection component to compare the data of the two observation methods. In this study, a method of using observation data at different times for each altitude section depending on the observation period of the Euler method is proposed to effectively compare the data of the two observation methods.

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.69-84
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• 2024

Objective : This study aimed to categorize the lifestyles of older adults into two types - healthy and unhealthy, and use machine learning to identify the personal values that influence these lifestyles. Methods : This cross-sectional study targeting middle-aged and older adults (55 years and above) living in local communities in South Korea. Data were collected from 300 participants through online surveys. Lifestyle types were dichotomized by the Yonsei Lifestyle Profile (YLP)-Active, Balanced, Connected, and Diverse (ABCD) responses using latent profile analysis. Personal value information was collected using YLP-Values (YLP-V) and analyzed using machine learning to identify the relative importance of personal values on lifestyle types. Results : The lifestyle of older adults was categorized into healthy (48.87%) and unhealthy (51.13%). These two types showed the most significant difference in social relationship characteristics. Among the machine learning models used in this study, the support vector machine showed the highest classification performance, achieving 96% accuracy and 95% area under the receiver operating characteristic (ROC) curve. The model indicated that individuals who prioritized a healthy diet, sought health information, and engaged in hobbies or cultural activities were more likely to have a healthy lifestyle. Conclusion : This study suggests the need to encourage the expansion of social networks among older adults. Furthermore, it highlights the necessity to comprehensively intervene in individuals' perceptions and values that primarily influence lifestyle adherence.

• The Korean Journal of Applied Statistics
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• v.37 no.5
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• pp.643-661
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• 2024

This study investigates the effectiveness of 3D skeleton data for human action recognition by comparing the classification performance of machine learning and deep learning models. We use the subset of the NTU RGB+D dataset, containing only frontal-view recordings of 40 individuals performing 60 different actions. Our study uses linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) as machine learning models, while the deep learning models are hierarchical bidirectional RNN (HBRNN) and semantics-guided neural network (SGN). To evaluate model performance, cross-subject cross-validation is conducted. Our analysis demonstrates that action type significantly impacts model performance. Cluster analysis by action category shows no significant difference in classification performance between machine learning and deep learning models for easily recognizable actions. However, for actions requiring precise differentiation based on frontal-view joint coordinates such as 'clapping' or 'rubbing hands', deep learning models show a higher performance in capturing subtle joint movements compared to machine learning models.