• Journal of Radiation Industry
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• v.11 no.1
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• pp.33-38
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• 2017

RI-Biomics technology is advanced convergence technologies that can be measured in real time and track in vivo behavior and metabolism of substances using characteristics of the radioactive isotope. Its application fields are increasing such as drug development, agriculture, development of new materials and their utilization, etc. In addition, according to domestic and international developments and changes in the RI-Biomics environment, RI-Biomics professionals are needed to train continuously. To develop systematic human resources basement and competency-based curriculum, we perform competency modeling of pedagogical perspective to targeted at high-performance on RI-Biomics. Furthermore, we redefine the competency model and verified by industry experts with focus group interviews. In the result, two general competencies and three professional competencies were extracted by interview. Each competencies are organized six sub-competencies and nine sub-competencies. In the finial steps, the same procedures were repeated to obtain the consensus of experts on derived competencies and behavioral objectives. The results of the study are applicable to enhance human resource management and to develop the curriculum for RI-Biomics expert training. It is expected to be used as reference material of long term-planning for RI-Biomics professional.

• Journal of Distribution Science
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• v.19 no.9
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• pp.5-17
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• 2021

Purpose: As mobile devices are commonly used and contact-free services are widespread due to the COVID-19 pandemic in the recent distribution environment, this study suggests retail strategies for consumers using high-speed railways. To this end, we analyzed how consumer perception on technologies necessary for use of mobile apps is related to the attitude that drives consumers to continue using the app services. Research design, data and methodology: Based on the extended unified theory of technology acceptance and use of technology model by Venkatesh, Morris, Davis and Davis (2003), we added variables proposed by existing theories that studied the technology acceptance model from multiple perspectives and empirically analyzed the relationship between user satisfaction and use intention with structural equation modeling. Results: As expected, factors necessary for the use of app services such as performance expectancy, social influence, price value, facilitating conditions, security, and aesthetics had positive effects on user satisfaction, whereas the effect of effort expectancy on user satisfaction was rejected. And user satisfaction was found to have a significant effect on intention to use. Conclusions: The results provide implications that strategic retail management of the above factors can motivate passengers to continuously use high-speed railways.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• ETRI Journal
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• v.44 no.6
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• pp.1004-1019
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• 2022

For autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under a generative adversarial network framework. Furthermore, we devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model for improving the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving, in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.

• New & Renewable Energy
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• v.8 no.1
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• pp.44-51
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• 2012

Due to a high tidal range of up to 10 m on the west coast of Korea, numerous tidal current projects are being planned and constructed. The turbine, which initially converts the tidal energy, is an important component because it affects the efficiency of the entire system. Its performance is determined by design variables such as the number of blades, the shape of foils, and the size of a hub. To design a turbine that can extract the maximum power on the site, the depth and duration of current velocity with respect to direction should be considered. Verifying the performance of a designed turbine is important, and requires a circulating water channel (CWC) facility. A physical model for the performance test of the turbine should be carefully designed and compared to results from computational fluid dynamics (CFD) analysis. In this study, a horizontal axis tidal current turbine is designed based on the blade element theory. The proposed turbine's performance is evaluated using both CFD and a CWC experiment. The sealing system, power train, measuring devices, and generator are arranged in a nacelle, and the complete TCP system is demonstrated in a laboratory scale.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.332-340
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• 2022

Recently, with the development of deep learning and artificial neural network technologies, research on the application of neural network has been actively conducted in the field of video coding. In particular, deep learning-based intra prediction is being studied as a way to overcome the performance limitations of the existing intra prediction techniques. This paper presents a method of context-adaptive neural network-based intra prediction model training and its coding performance analysis. In other words, in this paper, we implement and train a known intra prediction model based on convolutional neural network (CNN) that predicts a current block using contextual information from reference blocks. Then, we integrate the trained model into HM16.19 as an additional intra prediction mode and evaluate the coding performance of the trained model. Experimental results show that the trained model gives 0.28% BD-rate bit saving over HEVC in All Intra (AI) coding mode. In addition, the coding performance change of training considering block partition is also presented.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.2
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• pp.331-346
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• 2018

In the tunnel of domestic high - speed railway, the main fire - fighting facility, fire - extinguishing passageway, is installed. However, due to the high pressure of the high - speed train, frequent breakage and maintenance are caused by strong shock and long - term vibration. In order to solve these problems, it is necessary to improve the fire door, but in Korea, it is installed by submitting a certificate by simple KS F 2296 performance test. At present, it is developed as a simple test certification by producing a real scale fireproof door without the theoretical examination in advance, so that a high cost for improvement is occurring in Korea. Therefore, through this study, structural analysis study which can preliminary structure review was carried out in order to design the refuge connection passage fire door and to improve the performance improvement. In order to secure the reliability of the result value, the official authentication test (KS F 2296) were compared.

• Korean Institute of Interior Design Journal
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• v.26 no.6
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• pp.97-105
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• 2017

Korea is promoted STEAM education since 2011. Furthermore, in high school education, based on the in-depth elective course's teaching and learning contents of science. The STEAM program can improve students' competence because it encourages to self-directed learning through the vocational project performance. Therefore, in this study, we researched a design education program for the experience of fusion and complex design based on STEAM education concept. We developed an education program to design insulation wall systems using up-cycling concepts to increase energy efficiency. As a result, the characteristics of the fusion education and the theoretical study about the learner-centered education curriculum, the analysis of the high school curriculum, the STEAM elements, The program was revised and supplemented through consultation with STEAM experts. In addition, the developed program was applied to high school students, and each step were analyzed based on the educational method theory. The following results were obtained. First, this study presented a program to cope with the needs of high school intensive education. Second, it provided learning motivation by combining flipped-learning as a way to train STEAM education contents. Third, it is required to develop differentiated and continuous program development and data sharing Fourth, in order to operate and promote the future environment design STEAM school, it is necessary to expand educational programs for high school students in the region through linkage with various universities.

• Journal of the Korean Society for Railway
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• v.18 no.3
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• pp.252-260
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• 2015

Ballasted track on an asphalt roadbed can be beneficial for its various effects such as (i) decreasing of roadbed thickness by dispersing train load; (ii) prevention of both strength reduction and weakening in roadbed system by preventing rainwater penetration; and (iii) reducing maintenance cost by preventing roadbed mud-pumping and frostbite. With these beneficial effects, ballasted track on asphalt roadbed has been widely used in Europe and Japan, and relevant research for applying such ballasted track on asphalt roadbed systems in Korea is ongoing. In this study, full-scale static and dynamic train load tests were performed to compare the performance of ballasted track on asphalt roadbed and ballasted track. The optimum thickness levels of asphalt and reinforced roadbeds, corresponding to the design criteria for reinforced roadbed of high-speed railway, was estimated using the FEM program ABAQUS. Test results show that the earth pressure on reinforced roadbed of ballasted track on the asphalt roadbed was relatively low compared with that of simple ballasted track. The elastic and plastic displacements of simple ballasted track on the asphalt roadbed were also lower than those of ballasted track. These test results may indicate that the use of ballasted track on asphalt roadbed is an advantageous system in view of long-term maintenance.

• Design & Manufacturing
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• v.15 no.1
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• pp.48-56
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• 2021

The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.