• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Korean Home Economics Education Association
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• v.16 no.3
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• pp.131-146
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• 2004

The Purpose of this study is to suggest strategies for environmental education through the Practical Arts(TechnologyㆍHome economics) Subject in a viewpoint of the clothing & textiles resources to resolve problems in the clothing life area. For this, this study was carried out through review of literature which is related with the consumption, the environmental problems, the environmental policies, and regulations of the government and new environmental technologies, of clothing & textiles industries and environmental education. The major findings of the study were as follows; 1) The environmental education system model in a viewpoint of the Clothing & Textiles resources was developed. This model system is consisted with interactions on school, government, industry, home and non-government organizations. Thus, the fact that Practical Arts(TechnologyㆍHome economics) Subject were the most effective subject to teaching the environmental education viewpoint of the Clothing & Textiles resources was confirmed. 2) The standards were analysed out to analyse the contents in the clothing area of the Practical Arts(TechnologyㆍHome economics) Subject. It were consist of 4 factors and 12 elements under the factors: Awareness of clothing & textile resources(clothing consumption, production of clothing & textile and environmental problems). Planning and buying of clothing(planning, buying), Management of clothing(understand of textile. human body & environment, laundering and Environmental pollution, arrangement & conservation) Recycling & exhaust of clothing(contribution, redesign, recycling, exhaust) 3) Analysing the current Practical Arts (TechnologyㆍHome economics) subject from the Environmental education in the clothing section, the environmental education related with clothing were taught the most in the middle school course, and environmental contents were concentrated in the recycling factors. but not so much on other factors. 4) After analysing the Practical Arts (TechnologyㆍHome economics) subject, the strategies were suggested for reinforcing the environmental education in the clothing of the Practical Arts(TechnologyㆍHome economics) subject.

• Journal of Korean Home Economics Education Association
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• v.21 no.4
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• pp.71-89
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• 2009

The purpose of this paper was to suggest effective management of clothing department in vocational high schools. Bibliographic research on ten vocational high schools' clothing departments was done. Also a survey was done to figure out what students and teachers think of clothing department education. The results were as follows. There are ten clothing departments in vocational high schools countrywide. 1493 students are enrolled in the department and they are taught by 51 teachers. Each school has two to seven laboratories. Professional subject time assignment of clothing department varies from 82 to 112 hours. Students chose clothing department in order to enter the university and their satisfaction on the department was "fair." Students' satisfaction level of professional education courses was "fair" and they felt difficulties both in theory and practice. Students answered that Embroidery/Knitting courses should be closed and Fashion Coordination be opened. Students were "fairly" satisfied with facilities for practice and felt job training was necessary. Most of them wanted to enter university after high school graduation. Teachers answered professional education curriculum was "fair." They found it necessary to improve the textbooks and felt both theory and practice difficult. They wanted Embroidery/Knitting courses to be closed and Fashion CAD course to be installed. From the teachers' view, students' prospect was better to enter university than to get a job. Teachers suffered from too much teaching-unrelated workload and wanted the number of teachers to be increased. Considering the above results, vocational high schools should concentrate their efforts on making students continue to study by extending the way to enter the same department of university as department of vocational high schools. In addition, they should develop various practice programs through field practice and educational-industrial relationship to help students get jobs. Both teachers and students think that the clothing department curriculum is difficult now. Therefore, the level of the curriculum should be adjusted. Finally, it was necessary to increase the number of teachers and to carry out teachers' training to raise quality in education.