• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.779-790
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• 2021

Film literacy comprises the process of producing a new creation through understanding the elements that make up a film, the content of a film, and a critical and creative thinking process. Film literacy is employed in fields such as composition, science, social studies, and geography, and, additionally, it is used to cultivate humanities literacy and critical thinking skills. Yet despite the large proportion of the film script in the movie, it is not easy to find literacy education cases that use film English as a teaching method. Film English is a practical and authentic material, and is suitable as an English learning material in an EFL context like Korea. However, the approach of using films to teach and learn differs according to the content and genre of a film. Thus, the teacher may have a difficult time organizing and preparing for class. This study suggests six class activities that can be commonly applied to English classes using films based on the areas of critical, cultural, and creative (3Cs) activities. Four hundred and five college students taking Movie English classes participated in the present study and frequency analysis was conducted to find out their preferences through a questionnaire survey. The results from conducting class activities in university liberal arts classes suggest that the most preferred activities of students are related to cultural, critical, and creative, in that order. Creative activities that are far beyond English instruction utilizing various digital tools or providing additional reading materials can be a burden on learners.

• Journal of Korean Elementary Science Education
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• v.40 no.2
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• pp.162-174
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• 2021

The purpose of this study was to investigate the types of questions raised by pre-service elementary teachers when planning a science class utilizing smart technology and the characteristics of their lesson plans. For this purpose, lesson plans and questions written by the 96 pre-service teachers were collected. The results of this study can be summarized as follows: (1) Pre-service teachers used simulation apps, information offering apps, clicker evaluation apps, astronomical observation apps. Simulation apps and clicker evaluation apps were used the most in the introduction stage of the class, simulation apps in the development stage, and clicker evaluation apps in the finishing stage. (2) In the lesson plans, the activities that elementary school students use smart technology showed more than those used by teachers, and its characteristics were more prominent in the development stage of the class. (3) As for the content type of smart technology, experience type was the highest, followed by tool type and resource type. In comparison, there were relatively few interaction support types and learning opportunity extensions. (4) There were many cases in which pre-service teachers replaced elementary school students with virtual experiences using experience type instead of providing opportunities to experiment or experience directly. (5) Pre-service teachers asked various questions while planning science class utilizing smart technology, and a total of 25 question types appeared in 7 categories.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.148-153
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• 2020

In order to obtain high accuracy with an convolutional neural network(CNN), it is necessary to set the optimal hyperparameters. However, the exact value of the hyperparameter that can make high performance is not known, and the optimal hyperparameter value is different based on the type of the dataset, therefore, it is necessary to find it through various experiments. In addition, since the range of hyperparameter values is wide and the number of combinations is large, it is necessary to find the optimal values of the hyperparameters after the experimental design in order to save time and computational costs. In this paper, we suggest an algorithm that use the design of experiments and grid search algorithm to determine the optimal hyperparameters for a classification problem. This algorithm determines the optima values of the hyperparameters that yields high performance using the factorial design of experiments. It is shown that the amount of computational time can be efficiently reduced and the accuracy can be improved by performing a grid search after reducing the search range of each hyperparameter through the experimental design. Moreover, Based on the experimental results, it was shown that the learning rate is the only hyperparameter that has the greatest effect on the performance of the model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.336-344
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• 2021

The purpose of this study was to identify the self-awareness of smartphone dependence among undergraduate students and their response to the same. The data was drawn from a survey on smartphone overdependence conducted by the Ministry of Science and information and communications technology (ICT) and the National Information Society Agency in 2017. The responses of 1,735 undergraduate students were analyzed by frequency, percentage, mean, standard deviation, minimum-maximum value, ��2-test, independent t-test, Pearson's correlation coefficient, and stepwise multiple regression analysis. The results indicated that 22.3% of participants were at risk of smartphone dependence, and 63.6% of them were unaware of their dependence on smartphones. The perception of smartphone dependence was significantly associated with a higher risk of smartphone dependence (��=.35, p=.000) and the increasing use of applications such as games (��=.19, p=.000), television/video (��=.11, p=.000), and learning (��=.11, p=.000). Of the participants with dependence awareness, only a few knew about the existence of centers to prevent smartphone and internet dependence. Moreover, they rarely utilized these centers. However, the participants felt the need for more counseling agencies (26.8%), programs for dealing with oneself (23.2%), information about smartphone dependence (14.9%), and help to overcome dependence (10.9%). These findings show the need to establish public services so that students can easily access correct information on smartphone dependence and address this problem.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.17-36
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• 2022

The purpose of this study is to diagnose problems in the regulation and management of Restricted Development Zone and to prepare a construction plan to convert it to a data-based monitoring system. Unlike other land-use zones, the Restricted Development Zone is a exceptional zone that prohibits all development activities other than the minimum maintenance and must be strictly controlled and managed by the local government. However, the current Restricted Development Zone management is distributed according to the conditions of each local government, and it is not possible to monitor changes in the entire Restricted Development Zone as shown in the survey results. In particular, in this study, by introducing an AI-based monitoring system, MOLIT sends the results of detecting changes across the country at regular time points(monthly and quarterly) to the local governments based on the same regulation standards, and the local governments can be trusted while inputting the regulation results into the system. To propose this methodology, first, a survey and interview were conducted with local government officials and experts. Second, we analyzed cases in which AI analysis was applied to local governments and proposed a plan to improve the efficiency of regulation work according to the introduction of the monitoring system. Third, a plan was prepared to establish a monitoring system based on the advancement of the management information system. This monitoring system can be expanded and applied to land that needs periodic regulation and management in the future, and this study tried to propose a methodology and policy for this.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.57-74
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• 2022

Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.

• Journal of Korean Home Economics Education Association
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• v.33 no.4
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• pp.1-13
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• 2021

The purpose of this study is to identify trends and potential themes of research on consumption of children and adolescents for 20 years by analyzing keywords. The keywords of 869 studies on consumption of children and adolescents published in journals listed in Korean Citation Index were analyzed using text mining techniques. The most frequent keywords were found in the order of youth, youth consumers, consumer education, conspicuous consumption, consumption behavior, and character. As a result of analyzing the frequency of keywords by dividing into five-year periods, it was confirmed that the frequency of consumer education was significantly higher betwn 2006 and 2010. Research on ethical consumption has been active since 2011, and research has been conducted on various topics instead of without a prominent keyword during the most recent 5-year period. Looking at the keywords based on the TF-IDF, the keywords related to the environment and the Internet were the main keywords between 2001 and 2005. From 2006 to 2010, the TF-IDF values of media use, advertisement education, and Internet items were high. From 2011 to 2015, fair trade, green growth, green consumption, North Korean defector youths, social media, and from 2016 to 2020, text mining, sustainable development education, maker education, and the 2015 revised curriculum appeared as important themes. As a result of topic modeling, eight topics were derived: consumer education, mass media/peer culture, rational consumption, Hallyu/cultural industry, consumer competency, economic education, teaching and learning method, and eco-friendly/ethical consumption. As a result of network analysis, it was found that conspicuous consumption and consumer education are important topics in consumption research of children and adolescents.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.561-568
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• 2022

The purpose of this study is to explore the narrative of VR disaster and safety Education introduced by disaster film narrative. VR(Virtual Reality) is be suitable technology for disaster and safety Education due to media characteristics as 'immersion', 'presence', 'interactivity', 'pleasure'. Disaster film narrative is able to be worth VR disaster and safety education as a variety of stories and educational effect. For this study, examine a theoretical study and a visiting research of 'Busan 119 Safety & Experience Center'. This study concludes that Firstly need to introduce catharsis effect, Secondly, build 'interactive narratives' that ensure active participation of users, Thirdly, introduce an 'adventure game' narrative element, Fourthly, introduce a hero-shaped narrative in which the user becomes a one-man hero, And lastly, need education as use user's multiple access and group experience learning. Therefore, This thesis is of academic value in that it suggest a desirable new direction of narrative in VR disaster and safety education.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.