• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.34-34
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• 2023

The Chah-Nimeh reservoirs, which are a sort of natural lakes located in the border of Iran and Afghanistan, are the main drinking and agricultural water resources of Sistan arid region. Considering the occurrence of intense seasonal wind, locally known as levar wind, this study aims to explore the possibility to provide a TSM (Total Suspended Matter) monitoring model of Chah-Nimeh reservoirs using multi-temporal satellite images and in-situ wind speed data. The results show that a strong correlation between TSM concentration and wind speed are present. The developed empirical model indicated high performance in retrieving spatiotemporal distribution of the TSM concentration with R²=0.98 and RMSE=0.92g/m³. Following this observation, we also consider a machine learning-based model to predicts the average TSM using only wind speed. We connect our in-situ wind speed data to the TSM data generated from the inversion of multi-temporal satellite imagery to train a neural network based mode l(Wind2TSM-Net). Examining Wind2TSM-Net model indicates this model can retrieve the TSM accurately utilizing only wind speed (R²=0.88 and RMSE=1.97g/m³). Moreover, this results of this study show tha the TSM concentration can be estimated using only in situ wind speed data independent of the satellite images. Specifically, such model can supply a temporally persistent means of monitoring TSM that is not limited by the temporal resolution of imagery or the cloud cover problem in the optical remote sensing.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.9-16
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• 2023

Technological advancement is being advanced in sports such as electronic protection of taekwondo competition and VAR of soccer. However, a person judges and guides the posture by looking at the posture, so sometimes a judgment dispute occurs at the site of the competition in Taekwondo Poomsae. This study proposes an artificial intelligence model that can more accurately judge and evaluate Taekwondo movements using artificial intelligence. In this study, after pre-processing the photographed and collected data, it is separated into train, test, and validation sets. The separated data is trained by applying each model and conditions, and then compared to present the best-performing model. The models under each condition compared the values of loss, accuracy, learning time, and top-n error, and as a result, the performance of the model trained under the conditions using ResNet50 and Adam was found to be the best. It is expected that the model presented in this study can be utilized in various fields such as education sites and competitions.

• Smart Media Journal
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• v.12 no.9
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• pp.159-170
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• 2023

It takes a lot of time and manpower to search for the missing. As part of the solution, a missing person search AI system was implemented using a YOLO-based model. In order to train object detection models, the model was learned by collecting recognition images (road fixation) of drone mobile objects from AI-Hub. Additional mountainous terrain datasets were also collected to evaluate performance in training datasets and other environments. In order to optimize the missing person search AI system, performance evaluation based on model size and hyperparameters and additional performance evaluation for concerns about overfitting were conducted. As a result of performance evaluation, it was confirmed that the YOLOv5-L model showed excellent performance, and the performance of the model was further improved by applying data augmentation techniques. Since then, the web service has been applied with the YOLOv5-L model that applies data augmentation techniques to increase the efficiency of searching for missing people.

• Clinical Nutrition Research
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• v.12 no.2
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• pp.138-153
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• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.667-671
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• 2024

Professional job training for government officers emphasizes establishing desirable values as public officials and improving professionalism in public service. To provide customized education, some studies are analyzed factors affecting education satisfaction. However, there is a lack of research predicting education satisfaction with educational contents. Therefore, we propose a deep learning-based regression model that predicts government officer education satisfaction with educational contents. We use education information data for government officer. We use one-hot encoding to categorize variables collected in text format, such as education targets, education classifications, and education types. We quantify the education contents stored in text format as TF-IDF. We train our deep learning-based regression model and validate model performance with 10-Fold Cross Validation. Our proposed model showed 99.87% accuracy on test sets. We expect that customized education recommendations based on our model will help provide and improve optimized education content.

• Journal of Navigation and Port Research
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• v.32 no.1
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• pp.47-56
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• 2008

With rapid growing of the Northeastern Asia, the interest for the connection of Infrastructure that was behind of interesting until now is getting larger. In a line of same connection, UN-ESCAP are forwarding transcontinental railway project, asian highway project et al.. And this study aimed at analysis on the effect that extended to a space by Korea-Japan undersea tunnel project. In aspect of a national land balanced-development to solve various problems such as overcrowding in capital region, unbalanced state by regions, weak exchange between South and North Korea, and weakness of national land basis to prepare for unification et al., this study consulted the economic potentiality model as a analysis method to examine an effect. In this analysis, I used 24 scenarios including all cases by combination of 3 scenarios for Korea-Japan undersea tunnel, 4 scenarios for transportation modes in the section of undersea tunnel, and 2 scenarios for adjacency infrastructure. Transportation modes in the section of undersea tunnel are railway, car-train, mixing way of railway and car-train, and mixing way of road and railway. Adjacency infrastructure applied railway and road. In all scenarios, Korea showed higher growth potentiality than Japan. Also, proposal plan C route relatively showed better in national land balanced-development than other proposal plans. The growth potentiality relatively appeared higher by buildup of a connection together with non-capital regions from the construction of Korea-Japan undersea tunnel. In aspect of Northeastern Asia, it resulted in a increasing of trade and chance of network formation in the region of Asia through infrastructure connection. But, in considering passenger and various factors that extended to the economic growth, this analysis have some limitation. Therefore, I hope that deep studies will continuously perform with various factors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.237-246
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• 2019

The Electro-Mechanical Brake (EMB) is the next generation braking system for automobiles and railway vehicles. Current brake systems for high-speed trains generate a braking force using a pneumatic cylinder, but EMB systems produce that force through a combination of an electric motor and a gear. In this study, an EMB operation mechanism capable of generating a high braking force was proposed, and structural and vibration analyses of the gears and shafts, which are the core parts of the mechanisms, were performed. Dynamic structural analysis confirmed that the maximum stress in the analysis model was within the yield strength of the material. In addition, the design that maximizes the diameter of the motor shaft was found to be advantageous in strength, and large shear stress could be generated in the bolt fixing the gear and eccentric shaft. In addition, a test apparatus that can reproduce the mechanism of the analytical model was fabricated to measure the strain of the fixed bolt part, which is the most vulnerable part. The strain measurement results showed that the error between the analysis and measurement was within 10%, which could verify the accuracy of the analytical model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.9
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• pp.1078-1085
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• 2011

Prediction of a minimum crack size for growth, which is defined as a crack size that grows fast enough to keep ahead of its removal by contact wear and periodic grinding, is the most demanding work to prevent rail from fatigue failure and develop cost effective railway maintenance strategy In this study, we investigated the wheel load increment due to a rail defect during a train ran over it, and its effect on the minimum crack size for growth. For this purpose, we developed simulation software based on the Fletcher and Kapoor's "2.5D" model and measured wheel load increment during a train passed over a defect. A maximum contact pressure and contact patch size were calculated by 3D FEM and crack growth analyses were performed by varying two of dominant contact contributors; surface friction coefficient(0.1, 0.2, 0.3 and 0.4) and crack aspect ratio. The minimum crack sizes for growth were calculated from 0.29 to 1.44mm depending on the contact conditions. They were decreasing with increasing surface friction coefficient and decreasing with crack aspect ratio(a/b).

• Journal of the Korean Society for Railway
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• v.13 no.2
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• pp.173-178
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• 2010

Electric railroad systems consist of supply system of electric power and electric locomotive. The electric locomotive is adapted to high speed driving and mass transportation due to obtaining high traction force. The electric locomotive is operated by motor blocks and traction motors. Train speed is controlled by suppling power from motor blocks to traction motors according to reference speed. Speed control of the electric locomotive is efficient by spending minimum energy between motor blocks and traction motors. Recently, induction motors have been used than DC and synchronized motors as traction motors. Speed control of induction motors are used by vector control techniques. In this paper, speed of the induction motor is controlled by using the vector control technique. Control system model is presented by using Simulink. Pulse is controlled by PI and hysteresis controller. IGBT inverter is used for real-time control and system performance is demonstrated by simulating the induction motor which has 210[kW] on the output power.