• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.3-42
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• 2021

Conventional global magnetic field maps, such as daily updated synoptic maps, have been constructed by merging together a series of observations from the Earth's viewing direction taken over a 27-day solar rotation period to represent the full surface of the Sun. It has limitations to predict real-time farside magnetic fields, especially for rapid changes in magnetic fields by flux emergence or disappearance. Here, we construct accurate synchronic magnetic field maps using frontside and AI-generated farside data. To generate the farside data, we train and evaluate our deep learning model with frontside SDO observations. We use an improved version of Pix2PixHD with a new objective function and a new configuration of the model input data. We compute correlation coefficients between real magnetograms and AI-generated ones for test data sets. Then we demonstrate that our model better generate magnetic field distributions than before. We compare AI-generated farside data with those predicted by the magnetic flux transport model. Finally, we assimilate our AI-generated farside magnetograms into the flux transport model and show several successive global magnetic field data from our new methodology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.881-891
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• 2023

Recently, various studies on malicious URL detection using artificial intelligence have been conducted, and most of the research have shown great detection performance. However, not only does classical machine learning require a process of analyzing features, but the detection performance of a trained model also depends on the data analyst's ability. In this paper, we propose a DL-ML Fusion Hybrid Model for malicious web site URL detection based on URL lexical features. the propose model combines the automatic feature extraction layer of deep learning and classical machine learning to improve the feature engineering issue. 60,000 malicious and normal URLs were collected for the experiment and the results showed 23.98%p performance improvement in maximum. In addition, it was possible to train a model in an efficient way with the automation of feature engineering.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4607-4616
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• 2023

We implement machine learning regression models to predict peak pressures of primary and secondary systems, a major safety concern in Loss Of Condenser Vacuum (LOCV) accident. We selected the Multi-dimensional Analysis of Reactor Safety-KINS standard (MARS-KS) code to analyze the LOCV accident, and the reference plant is the Korean Optimized Power Reactor 1000MWe (OPR1000). eXtreme Gradient Boosting (XGBoost) is selected as a machine learning tool. The MARS-KS code is used to generate LOCV accident data and the data is applied to train the machine learning model. Hyperparameter optimization is performed using a simulated annealing. The randomly generated combination of initial conditions within the operating range is put into the input of the XGBoost model to predict the peak pressure. These initial conditions that cause peak pressure with MARS-KS generate the results. After such a process, the error between the predicted value and the code output is calculated. Uncertainty about the machine learning model is also calculated to verify the model accuracy. The machine learning model presented in this paper successfully identifies a combination of initial conditions that produce a more conservative peak pressure than the values calculated with existing methodologies.

• Journal of the Semiconductor & Display Technology
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• v.23 no.3
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• pp.102-107
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• 2024

Previous research on solar power generation forecasting has generally relied on meteorological data, leading to lower prediction accuracy. This study, in contrast, uses actual measured power generation data to train various ANN (Artificial Neural Network) models and compares their prediction performance. Additionally, it describes the characteristics and advantages of each ANN model. The paper defines the principles of solar power generation, the characteristics of solar panels, and the model equations, and it also explains the I-V characteristics of solar cells. The results include a comparison between calculated and actual measured power generation, along with an evaluation of the accuracy of power generation predictions using artificial intelligence. The findings confirm that the LSTM (Long Short-Term Memory) model performs better than the MLP (Multi- Layer Perceptron) model in handling time-series data.

• Journal of Drive and Control
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• v.17 no.1
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• pp.1-12
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• 2020

This study was to develop a simulation model of a compound planetary gear reducer for the final driving shaft using a gear analysis software (KISSsoft, Version 2017, KISSsoft AG, Switzerland). The aim of this study is to analyze transmission error and the tooth load distribution through micro-geometry using the simulation model. The tip and root relief were modified with Micro-geometry in the profile direction, and crowning was modified with Micro-geometry in the lead direction. The transmission error was analyzed using the PPTE (Peak to Peak Transmission Error) value, and the tooth load distribution was analyzed for the concentrated stress on the tooth surface. As a result of modifying tip and relief in the profile direction, the transmission error was reduced up to 40.7%. In the case of modifying crowning in the lead direction, the tooth load was more evenly distributed than before and decreased the stress on the tooth surface. After modifying the profile direction for the 1st and 2nd planetary gear train, the bending and contact safety factors were increased by 31.7% and 17%, and 18.3% and 12.5% respectively. Moreover, the bending and safety factors after modifying lead direction were increased by 59.5% and 32.7%, respectively for the 1st planetary gear train, and 59.6% and 43.6%, respectively for the 2nd planetary gear train. In future studies, the optimal design of a compound planetary gear reducer for the final driving shaft is needed considering both the transmission error and tooth load distribution.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.501-506
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• 2015

Background: To evaluate the effectiveness of the National Train-the-Trainers Program for Hospice and Palliative Care Experts (TTHPC) sponsored by the National Cancer Center of Korea between 2009 and 2012. This program was developed to improve the teaching skills of those in the field of hospice and palliative care (HPC). Materials and Methods: Training was offered in eight 1-day sessions between 2009 and 2012. The effect of the program was measured using Kirkpatrick's model of educational outcomes. First, levels 1 and 2 were evaluated immediately after the 1-day program (n=120). In 2012, the level-3 evaluation test was administered to trainers who offered at least one HPC training (n=78) as well as to their trainees (n=537). Results: The level-1 evaluation addressed participant reactions to and satisfaction with the program. Participants (n=120) were generally satisfied with the content, the method, and the overall course (mean range: 3.94-4.46 on a five-point Likert scale). The level-2 evaluation (learning) showed that participants gained knowledge and confidence related to teaching HPC (4.24 vs. 4.00). The level-3 evaluation (behavioral), which assessed trainers' application of teaching skills to HPC, showed that trainees rated the teaching methods of trainers (mean range: 4.03-4.08) more positively than did trainers (p<0.05). Female trainers were more likely than were male trainers to plan sessions in consideration of their trainees' characteristics (4.11 vs. 3.58; p<0.05), and nurse trainers were more likely than physician trainers to use a variety of instructional methods (4.05 vs. 3.36; p<0.05) Conclusions: We conducted systematic evaluations based on Kirkpatrick's model to assess the effectiveness of our train-the-trainers program. Our educational program was practical, effective, and followed by our HPC experts, who needed guidance to learn and improve their clinical teaching skills.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.350-356
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• 2018

The contact force, which is the dynamic interaction between the pantograph and the catenary, is an important indicator for evaluating the current collecting quality, which is a stable power supply characteristic to the vehicle. In this study, dynamic contact force characteristics of pantograph of HEMU-430X vehicle, which is a power-distributed high-speed train test vehicle, were analyzed according to the catenary tension and compared with the analytical results using the pantograph-catenary interaction model. As a result of comparing the test results with the analytical results, it was confirmed that the average contact force and the standard deviation of the contact force, which are the main dynamic contact force characteristics, coincide relatively well. Using the analytical model, the relationship between the catenary tension and the contact force is presented according to the vehicle speed, and the optimal catenary tension for each operation speed is presented and compared with the international standard. As a result, it was found that the results obtained from the analysis are comparable to those recommended by international standards.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.507-518
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• 2020

With the rapid development of rail transit, rail transit noise needs to be paid more and more attention. In order to accurately and effectively analyze the characteristics of low-frequency noise, a prediction model of vibration of box girder was established based on the hybrid FE-SEA method. When the train speed is 140 km/h, 200 km/h and 250 km/h, the vibration and noise of the box girder induced by the vertical wheel-rail interaction in the frequency range of 20-500 Hz are analyzed. Detailed analysis of the energy level, sound pressure contribution, modal analysis and vibration loss power of each slab at the operating speed of 140 km /h. The results show that: (1) When the train runs at a speed of 140km/h, the roof contributes more to the sound pressure at the far sound field point. Analyzing the frequency range from 20 to 500 Hz: The top plate plays a very important role in controlling sound pressure, contributing up to 70% of the sound pressure at peak frequencies. (2) When the train is traveling at various speeds, the maximum amplitude of structural vibration and noise generated by the viaduct occurs at 50 Hz. The vibration acceleration of the box beam at the far field point and near field point is mainly concentrated in the frequency range of 31.5-100 Hz, which is consistent with the dominant frequency band of wheel-rail force. Therefore, the main frequency of reducing the vibration and noise of the box beam is 31.5-100 Hz. (3) The vibration energy level and sound pressure level of the box bridge at different speeds are basically the same. The laws of vibration energy and sound pressure follow the rules below: web

• Journal of Korean Society of Steel Construction
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• v.18 no.6
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• pp.793-804
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• 2006

Various PSC and steel-concrete composite railway bridges are being developed for short-medium spans with structural and economic efficiency. According to the design concept, the prestressed composite girder bridge has the advantages of being lightweight and having low girder depth, with the capacity for long spans. However, the dynamic behavior under a passing train is one of the critical issues concerning these railway bridges designed with more flexibility. Therefore, it is very important to evaluate the modal parameters before performing dynamic analyses. In this paper, real-scale prestressed composite girders were fabricated as a test model and modal testing was carried out to evaluate modal parameters including natural frequency and modal damping ratio. During the modal testing, a digitally controlled vibration exciter as well as an impact hammer was applied to obtain frequency-response functions, and the modal parameters were also evaluated after the fracture of test models. With application of reliable properties from modal tests, the estimation of dynamic performances of prestressed composite girder railway bridges can be obtained from various parametric studies on dynamic behavior under the passage of a moving train.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.13-25
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• 2016

High-speed railway (Korea Train Express) has had a deep impact on the regional economy of Korea. Current high-speed rail research is mostly theoretical, there is a lack of quantitative research using a precise algorithm to study the effect of high-speed railway on the regional economy. This paper analyses the influence of high-speed rail on the regional economy, with a focus on the Daegu area. Quantitative analysis using department store indexes and regional medical records is performed to calculate the economic influence of high-speed rail. The result shows that high-speed railway effects the regional economy though regional consumption growth and medical care trends.