• The Journal of the Korea Contents Association
• /
• v.22 no.5
• /
• pp.288-304
• /
• 2022

Walking is an eco-friendly and sustainable means of transportation that promotes health and endurance. Despite the positive health benefits of walking, pedestrian safety is a serious problem in Korea. Therefore, it is necessary to investigate with various studies to reduce pedestrian-vehicle crashes. In this study, the cognitive characteristics affecting pedestrian-vehicle crashes were considered by applying deep learning semantic segmentation. The main results are as follows. First, it was found that the risk of pedestrian-vehicle crashes increased when the ratio of buildings among cognitive factors increased and when the ratio of vegetation and the ratio of sky decreased. Second, the humps were shown to reduce the risk of pedestrian-related collisions. Third, the risk of pedestrian-vehicle crashes was found to increase in areas with many neighborhood roads with lower hierarchy. Fourth, traffic lights, crosswalks, and traffic signs do not have a practical effect on reducing pedestrian-vehicle crashes. This study considered existing physical neighborhood environmental factors as well as factors in cognitive aspects that comprise the visual elements of the streetscape. In fact, the cognitive characteristics were shown to have an effect on the occurrence of pedestrian- related collisions. Therefore, it is expected that this study will be used as fundamental research to create a pedestrian-friendly urban environment considering cognitive characteristics in the future.

• Journal of Korean Institute of Industrial Engineers
• /
• v.37 no.1
• /
• pp.64-73
• /
• 2011

Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.

• The Journal of Korean Association of Computer Education
• /
• v.7 no.4
• /
• pp.15-25
• /
• 2004

Previous researches on the effect of programming learning upon logical thinking ability have used a standardized test to measure logical thinking ability in the general and comprehensive aspect after programming learning. Considering that the areas of intelligence are separated from one another and work independently, the existing standardized tool to measure general and comprehensive logical thinking ability has a limitation in measuring a logical thinking ability required at specific areas. Thus this study extracted logical thinking and its sub-factors related to computer programming and suitable for the level of cognitive development through analyzing standardized test sheets at home and abroad, and developed logical thinking test I and II of the same form according to a development procedure model. The result of verifying the developed logical thinking tests proved that the two tests are logical thinking tests of the same form. The developed tests can be utilized in identifying the effect of programming learning upon logical thinking and its sub-factors.

• Journal of Digital Convergence
• /
• v.19 no.6
• /
• pp.213-223
• /
• 2021

In this study, a smartphone utilization system was designed to explore ways to use smartphones owned by more than 95% of Korean teenagers in school classes. First, the necessary functions were selected based on the factors related to adolescents and the factors of distraction, dependence, motivation, and cooperation in the preceding studies on the use of smartphone classes, and then three versions of the application were composed according to the educational roles of students, teachers, and parents. By using these applications, students can learn self-study, motivate learning, and actively interact, teachers can be more closely with students and facilitate lessons while giving detailed feedback, and parents can check the student's safe learning using smartphones. It is hoped that this measure will help to increase the educational effect by activating the use of smartphones in school classes.

• 한국정보교육학회:학술대회논문집
• /
• 2021.08a
• /
• pp.199-204
• /
• 2021

Along with the development of technology in the fourth industrial revolution, the fields that can apply artificial intelligence technology are rapidly increasing. In order to improve computational thinking, overseas countries such as the U.S. and the U.K. are already using various AI education platforms to provide artificial intelligence education. Therefore, there is an increasing need for elementary school pre-service teachers in Korea to strengthen their AI education capabilities along with the existing software education. However, it may be difficult for learners with low levels of programming experience and AI education experience to choose an AI education platform that can sustain their learning motivation. Therefore, in this study, the factors related to learning motivation in the AI education platform were explored using the ARCS model. Through this, we present the factors required by the AI education platform for motivation and sustain of learning.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_2
• /
• pp.771-783
• /
• 2023

This study aims to compare the performance of each machine learning model for preparing a grid-based disaster risk map related to flooding in Jung-gu, Ulsan, for Typhoon Chaba which occurred in 2016. Dynamic data such as rainfall and river height, and static data such as building, population, and land cover data were used to conduct a risk analysis of flooding disasters. The data were constructed as 10 m-sized grid data based on the national point number, and a sample dataset was constructed using the risk value calculated for each grid as a dependent variable and the value of five influencing factors as an independent variable. The total number of sample datasets is 15,910, and the training, verification, and test datasets are randomly extracted at a 6:2:2 ratio to build a machine-learning model. Machine learning used random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) techniques, and prediction accuracy by the model was found to be excellent in the order of SVM (91.05%), RF (83.08%), and KNN (76.52%). As a result of deriving the priority of influencing factors through the RF model, it was confirmed that rainfall and river water levels greatly influenced the risk.

• The Journal of the Korea Contents Association
• /
• v.18 no.5
• /
• pp.123-130
• /
• 2018

The present study aims to investigate how Korean EFL learners' views of form-focused instruction, integrated and isolated FFI (form-focused instruction), are related to their beliefs about grammar and attention and how different these relationships are between high and low proficiency levels and between males and females in Korean college contexts. The findings indicated the participants' strong preference for integrated FFI, which was significantly correlated with two factors, attention in English class and English proficiency. On the other hand, the isolated FFI was strongly correlated with their beliefs about grammar learning, that is, independent learning of grammar and importance of learning grammar rules. In conclusion, the integrated FFI was associated with students' proficiency and attention, while the isolated FFI was related to their views of grammar learning. In conclusion, it is suggested that we need to use integrated FFI in Korean EFL contexts considering students' levels of proficiency and attention.

• Journal of Gifted/Talented Education
• /
• v.25 no.3
• /
• pp.401-420
• /
• 2015

The purpose of this study was to perform a comparative analysis of the research trends in STEAM education in gifted education and suggest educational implications to improve the current STEAM education for the gifted. The results were as follows. First, STEAM education has been increasing in the past couple of years and gifted and talented education took up relatively high proportion of it. This demonstrates that gifted education closely related to creative and versatile individuals plays a leading role in STEAM education. Second, researches on STEAM education and STEAM education for the gifted targeted elementary school students the most. Third, researches on the development of STEAM program for the gifted have been mainly addressing science-oriented convergence programs. Among them, programs including all the five combined factors(Science, Technology, Engineering, Arts and Mathematics) were the most common. In terms of learning types, a criterion-referenced teaching-learning model has been developing and there were diverse learning types which applied teaching-learning models tailored to characteristics of a gifted child. The researches related to STEAM programs'application effects on creativity were most dominant.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.208-216
• /
• 2023

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.17 no.3
• /
• pp.387-394
• /
• 2011

Purpose: The purpose of this study was to investigate self-directed learning and critical thinking disposition which influence clinical competence in nursing students. Method: As a descriptive study, a total of 271 third year students were recruited from a nursing college in Daegu. A structured questionnaire was self-administered from June 7, 2011 to June 10, 2011. The data were analyzed using SPSS/WIN version 12. Results: In self-directed learning, there were significant differences in gender (t=2.26, p=.025), satisfaction of major (F=7.77, p=.001), and satisfaction of clinical experience (F=10.39, p<.001). Critical thinking disposition differed in gender (t=2.82, p=.005). Moreover gender (t=4.00, p<.001), satisfaction of achievement (F=6.50, p=.002), satisfaction of major (F=4.24, p=.015), and satisfaction of clinical experience (F=9.54, p<.001) differed with clinical competence. Clinical competence was positively related to self-directed learning (r=.45, p<.001) and critical thinking disposition (r=.51, p<.001). According to the result of multiple regression, critical thinking disposition (t=5.80, p<.001), satisfaction of achievement (t=3.33, p=.001), gender (t=2.93, p=.004) and self-directed learning (t=2.35, p=.019) were significant factors of clinical competence explaining 33.0% of the variances. Conclusion: Critical thinking disposition, satisfaction of clinical experience, gender and self-directed learning had a positive effect on clinical competence in nursing students. To enhance clinical competence for nursing students, it is necessary to develop self-learning teaching strategy and curriculum.