• 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 Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Science and Technology Studies
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• v.19 no.2
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• pp.169-206
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• 2019

This article seeks to explore new relationships and ethics of human and technology by analyzing a cultural imaginary produced by artificial intelligence. Drawing on theoretical reflections of the Feminist Scientific and Technological Studies which understand science and technology as the matter of care(Puig de la Bellacas, 2011), this paper focuses on the fact that artificial intelligence and robots materialize cultural imaginary such as autonomy. This autonomy, defined as the capacity to adapt to a new environment through self-learning, is accepted as a way to conceptualize an authentic human or an ideal subject. However, this article argues that artificial intelligence is mediated by and dependent on invisible human labor and complex material devices, suggesting that such autonomy is close to fiction. The recent growth of the so-called 'assistant technology' shows that it is differentially visualizing the care work of both machines and humans. Technology and its cultural imaginary hide the care work of human workers and actively visualize the one of the machine. And they make autonomy and agency ideal humanness, leaving disabled bodies and dependency as unworthy. Artificial intelligence and its cultural imaginary negate the value of disabled bodies while idealizing abled-bodies, and result in eliminating the real relationship between man and technology as mutually dependent beings. In conclusion, the author argues that the technology we need is not the one to exclude the non-typical bodies and care work of others, but the one to include them as they are. This technology responsibly empathizes marginalized beings and encourages solidarity between fragile beings. Inspired by an art performance of artist Sue Austin, the author finally comes up with and suggests 'artificial intelligence in wheelchair' as an alternative figuration for the currently dominant 'autonomous artificial intelligence'.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.39 no.2
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• pp.165-170
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• 1994

High quality rapeseed cultivars including Nojeokchae, Yeongsanyuchae Halla-yuchae and Tamrayuchae have been released and recommended as a zero erucic acid variety to Cheju farmers for 13 years, where is a major rapeseed production area in korea. However, rapeseeds produced in Cheju island in 1992 and 1993 contained 47.7% and 37.0% of erucic acid respectively resulting in poor quality oil being not adequate for edible oil. It was considered that the zero erucic acid varieties did not have an opportunity to be cultivated in Cheju island by farmers living in the Island. Thus, the new rapeseed varieties without erucic acid should be bred and recommended to the farmers of southern area of Korea as a multiple cropping crop just after rice harvest, and for large scale mechanized and labour-serving rapeseed culture. The change of rapeseed breeding goal would be desirable for fatty acid composition improvement of rapeseed to develop varieties adaptable to southern part of Korea, and to produce rapeseed oil directly used as an edible oil safely.

• Journal of Korea Entertainment Industry Association
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• v.13 no.3
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• pp.193-202
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• 2019

This study focuses on what changes are required as to a curriculum of dance department in the university in the age of the 4th industrial revolution. By comparing and analyzing the curricula of dance department in the five universities in Seoul, five academic subjects as to curricula of dance department, which covers what to learn for dance education in the age of the 4th industrial revolution, are presented. First, dance integrative education, the integration of creativity and science education, can be referred to as a subject that stimulates ideas and creativity and raises artistic sensitivity based on STEAM. Second, the curriculum characterized by prediction of the future prospect through Big Data can be utilized well in dealing with dance performance, career path of dance-majoring people, and job creation by analyzing public opinion, evaluation, and feelings. Third, video education. Seeing the images as modern major media tends to occupy most of the expressive area of art, dance by dint of video enables existing dance work to be created as new form of art, expanding dance boundaries in academic and performing art viewpoint. Fourth, VR and AR are essential techniques in the era of smart media. Whether upcoming dance studies are in the form of performance or education or industry, for VR and AR to be digitally applied into every relevant field, keeping with the time, learning about VR and AR is indispensable. Last, the 4th industrial revolution and the curriculum of dance art are needed to foresee the changes in the 4th industrial revolution and to educate changes, development and seeking in dance curriculum.

• Journal of radiological science and technology
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• v.38 no.3
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• pp.295-304
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• 2015

This study intended to examine the correlation of career self-regulation (plan and check-up, positive thinking, career feedback, environment formation for career) and career identity (career decision, indecisiveness, career indecision) caused by parental career support (informative, emotional, financial, and empirical) among freshmen, sophomores, and juniors in the radiotechnology department. For assessment, a survey was conducted and according to the results, there existed correlation as follows. Regarding parental career support, emotional support is plan and check-up (r=.25, p<.001), Career feedback (r=.54, p<.001), and positive thinking (r=.46, p<.001) showed high positive correlation while informative support showed correlation in all factors showing high correlation with environment formation for career (r=.22, p<.001), plan and check-up (r=.20, p<.001), career feedback (r=.24, p<.001), and positive thinking (r=.26, p<.001). Financial support career feedback (r=.33, p<.001) and positive thinking (r=.34, p<.001) showed somewhat higher correlation. All factors of environment formation for career (r=.18, p<.001), plan and check-up (r=.25, p<.001), career feedback (r=.37, p<.001), and positive thinking (r=.30, p<.001) showed high correlation. Informative support showed high correlation only with career decision (r=.27, p<.001) and financial support also showed high correlation only with career decision (r=.18, p<.001). Also, empirical support was somewhat highly correlated only with career decision (r=.23, p<.001). Regarding school-year difference depending on parental career support, there was significant difference between emotional support (F=8.52, p<.001), financial support (F=8.97, p<.001), and empirical support (F=5.36, p<.05) while informative support was dismissed. Regarding school-year difference depending on career self-regulation, there was significant difference between career feedback (F=8.48, p<.001) and positive thinking (F=16.29, p<.001) while environment formation for career and plan and check-up were dismissed. Regarding school-year difference depending on career identity, there was significant difference between career indecision (F=4.01, p<.05) and career decision (F=11.72, p<.001) while indecisiveness was dismissed. According to the analysis results, parents' active support to their child like respecting and listening to their opinion on career, provision of career related experience or information, and provision of necessary financial aid for their study or academic preparation made the students plan and exploring their career, examine accomplishment progress, have positive idea to realize their objectives. In addition, the students were able to establish the objective of their career by forming the environment that helped them realize their objectives by seeking advices and encouragement from surroundings. Meanwhile, the parents' attitude to respect and listen to their child's career related opinion affected their career decision and indecision. Although informative support helped the students' career decision, financial and empirical support caused effect only to career decision.

• Journal of Technologic Dentistry
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• v.17 no.1
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• pp.41-57
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• 1995

This study was conducted to collect and analyze previous information in order to manage efficience, improve experience effect and promote employment rate. The questionnaire interview with 27 chief of dental Laboratory refered clinical experience in technology department about clinical experience in 14 Jumior colleges were also investigated. The results were summarried as follows : The portion of age of 35-39 among chief of dental Laboratory was 40.7% which was the highest, that of male was 96.3%, that of junior college graduate was 97.5%, that of 10years experience was 92.6% and that of ceramic technician was 85.2%, 63.0% dental laboratory for clinical experience was a bore space of 30pyong. Aspect of dental laboratory management, manufacturing all part of prosthetic restoration was 29.6%, othodontic appliance and ceramic restoration was 7.4%, 3.8%, each. The percentage of 40.7 was having connection with 30-3a dental clinics and referring case per day was 10-19 cases(40.7%), manufacturing time of referred prosthetic restoration was 3-4 days(77.8%), places preparing seminar room for education was 29.6%, above a place of 40pyong was 11.1% 30-34 pyong and 35-39 pyong was 7.4% each. During training of 2 years education course student, 18.5% was rack of thorough occupational career. While 44.4% will want the more salary among 3years education course student, 74.1% will expect the more dental techmicians would engaged in their field, 51.9% will hope improve of their theory and practice, 29.6% be expected better skill and 14.8% be expected better theory. Attitude of clinical experience places was distributed by 59.3% of offering only experience chance, 25.9% of wasting time and 29.0% of annoying. The big emphasis of climical experience was thorough occupational career(44.4%). The clinical experience places of our college were selected after direct visiting, so their condition of management was not that bad but most of dental laboratory were poor in management state and working environment. Therefore it is difficult to choose appropriate places and dental Laboratory are also limited manpower and time as suppliers. So that it recommended to induce flexible management of experience period by interval and rotation of experience places among college and to applicate intern-system for employment ant industry-college cooperation aspect.