• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.81-94
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• 2022

This study analyzed the effect of the entrepreneurship planning course provided by an women's university in Seoul on the enhancement of the core competencies of entrepreneurship of university students. To this end, pre- and post-test of core entrepreneurship competency were conducted on 63 female university students (32 in experimental group, 31 in control group) and then the results were analyzed. The course in which the experimental group participated was a team-based project learning course and it required a team of three people to draw an entrepreneurship plan containing social problem solving as the final result. The course was operated for a total of 8 weeks. To measure the level of entrepreneurship core competency in the pre- and post- test, the survey tool that was developed by the Ministry of Education and Korea Entrepreneurship Foundation (2020) was used. This tool composed by 'value creation', 'challenge', 'self-directed', and 'group creativity' competencies. As analyses methods, i) covariance analysis was performed using the pretest as a covariate, and then a two-way ANOVA was performed with treatment (experimental group, control group) and time point (pre test, post test) as two independent variables. Results show while there was no significant difference between the experimental group and the control group in the value creation competency, it significantly contributed to the enhancement of challenge, self-directed, and collective creativity competencies. Based on these results, implications and limitations were discussed, followed by future research direction.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• Proceedings of the CALSEC Conference
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• 2002.01a
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• pp.86-106
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• 2002

Informatization is a process that corporation's external environmental factors and internal environmental factors influence as complex. is a phenomenon that appears via this process. To evaluate that informatization was propeled well or informatization level is high can be dangerous work extremely by only once-over-lightly some factors, organization information ability is superior or infrastructure is constructed well. Therefore, an evaluation for industrial information systems that consider corporation's external environment and internal environment configurationally and objective estimation through this is required in national dimension. This research sorted types of business using types of business classification of 2001 EIII(Evaluation Indices of Industrial Informatization) laying stress on corporation's product and product production process for reflecting various industrial classification. And we are dividing whole our country corporations by manufacture industry, the construction industry, distribution industry, service industry, banking industry 5 types of business. To see such classed types industry classification from consistent viewpoint, we saw them within new framework, purchase, operation, physical distribution, marketing and sale. service etc. laying stress on primary businesses except support businesses of planning, financial management etc. To draw special quality of business center from primary business of each types of business, we draw industry classification Key Capability that centers when plans corporation's corporate strategy and information strategy. And we deducted industrial classification key production business connected with industry classification Key Capability. After drawing an evaluation items for industrial information systems in informatization analysis viewpoint laying stress on drawn businesses. Finally we did Case Study by making out an evaluation for industrial information systems questionnaire that considers special quality of manufacturing industry. Through EIII that consider the industrial classification, we could know that it explains the corporation's purchase, production, distribution in general and detail.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.1
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• pp.44-50
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• 2013

The persons in charge of management request diagnosis and prescription to tree hospitals in order to get consultation about the problems like blight that occur in landscape tree management. This study aims to analyze what the main problems and questions raised by landscape gardeners are and those concerned in landscape tree management. This is done by investigating landscape tree-related questions and answers uploaded on the online consultation boards of the plant diagnostic centers approved in Korea including the Seoul National University Plant Clinic, the Chungbuk National University Plant Hospital, and the Kangwon Diagnostic Center. As a result, those concerned in landscape occupied the most as 81.4% among the questioners. However, only 11.5% did explain the plant management history or surrounding environment, which is essential for landscape tree diagnosis when asking questions. This shows that those concerned in landscape lack basic knowledge or interest about plant diagnosis. Among 263 questions about landscape trees, questions about physiological damage included 94 cases that were the most taking up 35.8%. Moreover, the next were damage by insects and damage by disease in order. It is thought that due to the characteristics of physiological problems that occur by various sorts of stress and with no signs, they tend to request diagnosis or prescription the most. The most frequent reasons for physiological damage are water stress and temperature stress. About damage by disease, there exist many types of diseases, and there are many complex damages accompanied by physiological causes. About damage by insects, the most common include damage by moths. In consideration of this result, universities or technician training centers should provide education for landscape tree management so that landscape technicians and students can acquire essential knowledge and information about landscape tree management and increase their interest in it. In particular, it is necessary to provide profound learning opportunities for plant physiology, and the technicians should make efforts themselves. In addition, it is needed to build organizations to which they can ask technical questions about landscape planting and management in order to understand landscape industry in general and the actual status of landscape planting technique and the actual field. Moreover, to elevate systemicity and expertise in the area of landscape tree management not yet equipped with the foundation, it is needed to cultivate the technicians intensively and conduct research by those concerned both in academic and industrial circles.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Cartoon and Animation Studies
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• s.47
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• pp.31-52
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• 2017

NCS education was created to realize a society in which skills and abilities are respected, such as transcending specifications, establishing recruitment systems, and developing and disseminating national incompetence standards. At the university level, special lectures and job training are being strengthened to raise industrial experts. Especially, in the field of animation, new technologies are rapidly emerging and demanding convergent talents with various fields. In order to meet these social demands, there is a limit to the existing one-class teaching method. In order to solve this problem, it is necessary to participate in a variety of specialized teachers. In other words, rather than solving problems of students' job training and job creation, It is aimed to solve jointly, Team teaching was suggested as a method for this. The expected effects that can be obtained through this are as follows. First, the field of animation is becoming more diverse and complex. The ability to use NCS job-related skills pools can be matched with professors from other departments to enable a wider range of professional instruction. Second, it is possible to use partial professorships in other departments by actively utilizing professors in the university. This leads to the strengthening of the capacity of teachers in universities. Third, it is possible to build a broader and more integrated educational system through cooperative teaching of professors in other departments. Finally, the advantages of special lectures and mentor support of college professors' pools are broader than those of field specialists. A variety of guidance for students can be made with responsible professors. In other words, time and space constraints can be avoided because the mentor is easily met and guided by the university.

• Journal of Korean Society of Forest Science
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• v.109 no.3
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• pp.259-270
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• 2020

Extreme weather events, such as heat and drought, have occurred frequently over the past two decades. This has led to continuous reports of cases of forest damage due to physiological stress, not pest damage. In 2014, pine trees were collectively damaged in the forest genetic resources reserve of Sogwang-ri, Uljin, South Korea. An investigation was launched to determine the causes of the dieback, so that a forest management plan could be prepared to deal with the current dieback, and to prevent future damage. This study aimedto 1) understand the topographic and structural characteristics of the area which experienced pine tree dieback, 2) identify the main causes of the dieback, and 3) predict future risk areas through the use of machine-learning techniques. A model for identifying risk areas was developed using 14 explanatory variables, including location, elevation, slope, and age class. When three machine-learning techniques-Decision Tree, Random Forest (RF), and Support Vector Machine (SVM) were applied to the model, RF and SVM showed higher predictability scores, with accuracies over 93%. Our analysis of the variable set showed that the topographical areas most vulnerable to pine dieback were those with high altitudes, high daily solar radiation, and limited water availability. We also found that, when it came to forest stand characteristics, pine trees with high vertical stand densities (5-15 m high) and higher age classes experienced a higher risk of dieback. The RF and SVM models predicted that 9.5% or 115 ha of the Geumgang Pine Forest are at high risk for pine dieback. Our study suggests the need for further investigation into the vulnerable areas of the Geumgang Pine Forest, and also for climate change adaptive forest management steps to protect those areas which remain undamaged.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.3
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• pp.50-66
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• 2016

This study attempts to understand and explain how "Directed Attention Restorative Environment (DARE)" is managed and fostered in "Gang-Hak (講學)" and "Yu-Sik (遊息)" spaces both inside and outside of Oksan Seowon Confucian Academy, Gyeongju. Directed Attention is a pivotal element in human information processing so that its restoration is crucial for effective thinking and learning. According to Kaplan & Kaplan's Attention Restoration Theory, an environment, in order to be restorative, should have four elements: 'Being Away,' 'Extent,' 'Fascination,' and 'Compatibility.' We could confirm OkSan Seowon Confucian Academy has an inner logic that integrates two basically different spacial concepts of "Jangsu" and "Yusik" and thus fosters the Attention Restorative Environment. Particularly, the Four Mountains and Five Platforms (四山五臺) surrounding the premises provides an excellent learning environment, and is in itself educational in terms of the Neo-Confucian epistemology with "Attaining Knowledge by way of Positioning Things (格物致知)" as its principle precept, and of its aesthetics with "Connectedness with Nature" as its central tenet. This study attempts to recapture the value of Korea's cultural heritage concerning the Human/Nature relationship; and it may provide useful insights and practical guidelines/grounds in designing today's schools and campuses, where the young people's needs for the Directed Attention- and Attention Restorative- Servicescapes seem to be greater than ever.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6234-6241
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• 2014

The purpose of this study was to examine the status of medical staff stress and accommodating manners on the death of patients in a hospital setting for serving the basic information to develop a death education program of medical personnel from April 1 to April 30, 2014. A survey was performed on 353 medical personnel at K university hospital, located in Daejeon metropolitan city. Frequency analysis, chi-square test, and independent t-test were used to analyze the data. The results showed that 'to understand the value of the time and preparedness of a meaningful future' were the most important perspectives on the contents of death education (p<0.05), 'in order to change perceptions and attitudes toward death positively' was the most important reason why they required death education'(p<0.05), 'case-based teaching and problem-based learning' was the most effective way of death education (p<0.05), 'negative or hostile response of a patient's guardian to medical personnel' was the largest stress that medical personnel confront upon witnessing a death'(p<0.05). An understanding of the death of patients by medical personnel and an awareness of the need for death education will help improve the understanding of the patient, their guardian, and medical personnel themselves. The main findings will contribute to the development of a specific death education program on the medical personnel in a hospital setting.