• 대한공업교육학회지
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• v.35 no.2
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• pp.82-111
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• 2010

The goal of this paper was to understand the skill-up experiences of ex-participants of the customized training program in Technical High Schools for Small and Medium Business during first 2 years in Company through qualitative interviews with 3 purposefully selected ex-participants. Their core skill-up experience in this period was assumed as the shift from' dependent worker' to 'independent worker' on the base of literature review. The results of this study were following : 1. The small and medium companies offered a few formal training for newcomers, production-site orientation through short job rotation, linking them with skilled workers and job manuals or job standards. 2. Authentic skill-up experiences were combined with a structured reprimand, peer learning, deep learning through reflection on one's own experiences. 3. There were a few handicap conditions that disturbed their skill-up activities: the skilled worker don't open their skill toward new corner; the ex-participants in company had no time to learn anything meaningful to up-grade their competency.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.149-154
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• 2024

Research on particulate matter is advancing in real-time, and various methods are being studied to improve the accuracy of prediction models. Furthermore, studies that take into account various factors to understand the precise causes and impacts of particulate matter are actively being pursued. This paper trains an LSTM model using seasonal data and another LSTM model using concentration-based data. It compares and analyzes the PM_2.5 prediction performance of the two models. To train the model, weather data and air pollutant data were collected. The collected data was then used to confirm the correlation with PM_2.5. Based on the results of the correlation analysis, the data was structured for training and evaluation. The seasonal prediction model and the concentration-specific prediction model were designed using the LSTM algorithm. The performance of the prediction model was evaluated using accuracy, RMSE, and MAPE. As a result of the performance evaluation, the prediction model learned by concentration had an accuracy of 91.02% in the "bad" range of AQI. And overall, it performed better than the prediction model trained by season.

• Korean Journal of Comparative Education
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• v.24 no.5
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• pp.71-95
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• 2014

This is a pilot study of multifaceted longitudinal research project to explore educational experiences of students with multicultural family backgrounds in Korea. Especially for this pilot study, I selected three foreign-born immigrant youths and tried to explore how these youths describe constraints of their learning experience in different culture. The data for this study were mainly collected through qualitative research methods. For a better understanding of the research participants' perceptions in this study, narrative inquiry and series of semi-structured in-depth interviews were conducted for a period of four months which corresponds to one semester of school system in Korea. As a result, this study found that there is an urgent need to establish a systemic and developmentaly appropriate language education programs that ensure educating the language to the foreign-born immigrant youths because their academic achievement, interpersonal relationships, and future depend greatly on the fluency of Korean language. It was also found that it is necessary to take appropriate educational actions in supporting alternative schools where the foreign-born immigrant youths can fully and seriously considered as a whole person. Although this study has some limitations in examining every single aspect of the current state of education of students with multicultural backgrounds in Korea, it provides deep insight into some of their initial educational experiences and proposes several ways to improve these educational programs for them.

• Journal of the Korean Geotechnical Society
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• v.40 no.1
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• pp.15-28
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• 2024

This paper proposes a method to acquire image data inside tunnel structures and a method to structure the acquired image data. By improving the conditions by which image data are acquired inside the tunnel structure, high-quality image data can be obtained from area type tunnel scanning. To improve the data acquisition conditions, a longitudinal rail of the tunnel can be installed on the tunnel ceiling, and image data of the entire tunnel structure can be acquired by moving the installed rail. This study identified 0.5 mm cracked simulation lines under a distance condition of 20 m at resolutions of 3,840 × 2,160 and 720 × 480 pixels. In addition, the proposed image-data-structuring method could acquire image data in image tile units. Here, the image data of the tunnel can be structured by substituting the application factors (resolution of the acquired image and the tunnel size) into a relationship equation. In an experiment, the image data of a tunnel with a length of 1,000 m and a width of 20 m were obtained with a minimum overlap rate of 0.02% to 8.36% depending on resolution and precision, and the size of the local coordinate system was found to be (14 × 15) to (36 × 34) pixels.

• Research in Plant Disease
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• v.28 no.3
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• pp.113-121
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• 2022

Rice blast is a major plant disease that occurs worldwide and significantly reduces rice yields. Rice blast disease occurs periodically in Korea, causing significant socio-economic damage due to the unique status of rice as a major staple crop. A disease outbreak prediction system is required for preventing rice blast disease. Epidemiological investigations of disease outbreaks can aid in decision-making for plant disease management. Currently, plant disease prediction and epidemiological investigations are mainly based on quantitatively measurable, structured data such as crop growth and damage, weather, and other environmental factors. On the other hand, text data related to the occurrence of plant diseases are accumulated along with the structured data. However, epidemiological investigations using these unstructured data have not been conducted. The useful information extracted using unstructured data can be used for more effective plant disease management. This study analyzed news articles related to the rice blast disease through text mining to investigate the years and provinces where rice blast disease occurred most in Korea. Moreover, the average temperature, total precipitation, sunshine hours, and supplied rice varieties in the regions were also analyzed. Through these data, it was estimated that the primary causes of the nationwide outbreak in 2020 and the major outbreak in Jeonbuk region in 2021 were meteorological factors. These results obtained through text mining can be combined with deep learning technology to be used as a tool to investigate the epidemiology of rice blast disease in the future.