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지속가능한 디자인을 위한 학제간 교육의 단계적 연계 방안 -패션디자인과 실내디자인 교육을 중심으로-
권성하 ( Kwon¸ Sung-ha ),손희주 ( Son¸ Hee-joo ) 커뮤니케이션디자인학회 2021 커뮤니케이션 디자인학연구 Vol.76 No.-
본 연구는 학제간 연계 하에 통합적 사고와 교류를 기반으로 한 지속가능한 디자인 교육의 필요성에 의해 구상되었다. 지속가능성과 지속가능한 디자인 개념을 정리하고 Erich Jantsch의 다학제 체계의 학제간협력 및 협업 관계 이론을 기본으로 지속가능한 디자인을 위한 단계별 학제간 교육 커리큘럼을 패션디자인과 실내디자인 전공을 중심으로 계획하였다. 다학제적 교육에 대한 여러 선행연구 분석을 통해 학부에서 필요한 교육 특성과 방향을 정리하였는데, 문제 해결을 목적으로 한 프로젝트 기반 수업, 실습 위주, 그룹 수업, 타전공 및 외부전문가와의 공동 티칭, 단계별 깊어지는 학제간 연계수업으로 정리할 수 있었다. 1학년 때에는 multi-disciplinary 단계로 지속가능성에 대한 공통의 문제 의식을 공유할 수 있는 전공교양의 공동 수강, 2학년 때에는 pluri-disciplinary 단계, 3학년 때에는 cross-disciplinary로 협업의 정도를 확장하여, 4학년 때 심화된 inter-disciplinary 단계로 학년이 높아질수록 단계적으로 심화, 연계되는 학제간 교육 구성을 제안하였다. 이를 바탕으로 현재의 학제를 유지하면서 학년별 연계 교과목을 따로 두어 단계적으로 심화되는 학제간 교육을 가능하게 하고자 하였고 지속가능한 디자인의 실무적 학제 교류를 기반으로 한 교육방법이 될 수 있을 것이라 사료된다. This study was conceived by the need for sustainable design education based on integrated thinking and exchange under interdisciplinary links. Based on Erich Jantsch's theory of interdisciplinary cooperation and collaboration relationships, a phased linkage of interdisciplinary education curriculum for sustainable design was designed to focus on fashion design and interior design. A number of prior research analyses on interdisciplinary education organized the educational characteristics and directions needed by the undergraduate department, including project-based, practice-oriented, group classes, team teaching with other majors and external experts, and phased interdisciplinary classes. Based on this, expanding the degree of collaboration between major courses will be developed, a multidiscipline level of sharing a common sense and theory of sustainability in the first grade, pluridiscipline in the second grade, and crossdiscipline in the third grade and the interdiscipline level developed in the fourth grade. While maintaining the current education system, interdisciplinary subjects for each grade to enable advanced convergence education can be settled, and it is believed that it will be a method based on practical educational exchange of sustainable design.
Development of Single-tractor Integrated Multi-purpose Forage Harvester
( Sungha Hong ),( Daein Kang ),( Deayean Kim ),( Yongjin Cho ),( Kyouseung Lee ) 한국농업기계학회 2016 바이오시스템공학 Vol.41 No.4
To improve the insufficient mechanized forage harvesting system, an integrated forage harvester that produces midsize round bales was developed. Methods: The harvesting performance of the developed harvester was tested in a forage plantation. The harvesting performance was evaluated by investigating the bale production performance and residue ratios of the harvester at three levels of tractor driving speeds. Results: The bales outputs per hour by driving speed shown by the harvester were 30 bales (6.8 MT) at 2.3 km/h, 36 bales (8.4 MT) at 3.2 km/h, and 44 bales (10.5 MT) at 5.1 km/h in the case of rye-straw. In the case of rice-straw, they were 43 bales (8.8 MT) at 4.3 km/h, 44 bales (9.7 MT) at 5.0 km/h, and 48 bales (10.7 MT) at 6.2 km/h. In the case of Italian ryegrass (IRG), they were 35 bales (10.7 MT) at 7.0 km/h, 37 bales (12.0 MT) at 8.3 km/h, and 38 bales (13.2 MT) at 9.5 km/h. The average ratios of residues to the available quantities were 2.61% in the case of rye-straw, 1.89% in the case of rice-straw, and 1.57% in the case of IRG. When residues smaller than 200 mm, which cannot be collected, were excluded, the residue ratios of all crops were good, as they did not exceed 1.0%. Conclusions: Since the baling and wrapping functions, which had been separately operated, were integrated into the developed harvester, the developed harvester is expected to maximize bale production efficiency and increase labor productivity, thereby increasing farming profitability.
Support Vector Machine based on Stratified Sampling
Sunghae Jun 한국지능시스템학회 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.2
Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.
Improvement of SOM using Stratification
Sunghae Jun 한국지능시스템학회 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.1
Self organizing map(SOM) is one of the unsupervised methods based on the competitive learning. Many clustering works have been performed using SOM. It has offered the data visualization according to its result. The visualized result has been used for decision process of descriptive data mining as exploratory data analysis. In this paper we propose improvement of SOM using stratified sampling of statistics. The stratification leads to improve the performance of SOM. To verify improvement of our study, we make comparative experiments using the data sets form UCI machine learning repository and simulation data.