• Journal of the Korean Institute of Landscape Architecture
• /
• v.32 no.4
• /
• pp.28-38
• /
• 2004

Korean goral (Nemorhaedus caudatus raddeanus) is one of the endangered species in Korea, and the rugged terrain of the Soraksan National Park (373㎢) is a critical habitat for the species. But the goral population is threatened by habitat fragmentation caused by roads and hiking trails. The objective of this study was to develop a potential habitat suitability model for Korean goral in the park, and the model was based on the concepts of fuzzy set theory and multi-criteria evaluation. The process of the suitability modeling could be divided into three steps. First, data for the modeling was collected by using field work and a literature survey. Collected data included 204 points of GPS data obtained through a goral trace survey and through the number of daily visitors to each hiking trail during the peak season of the park. Second, fuzzy set theory was employed for building a GIS data base related to environmental factors affecting the suitability of the goral habitat. Finally, a multiple-criteria evaluation was performed as the final step towards a goral habitat suitability model. The results of the study were as follows. First, characteristics of suitable habitats were the proximity to rock cliffs, scattered pine (Pinus densiflora) patches, ridges, the elevation of 700∼800m, and the aspect of south and southeast. Second, the habitat suitability model had a high classification accuracy of 93.9％ for the analysis site, and 95.7％ for the validation site at a cut off value of 0.5. Finally, 11.7％ of habitatwith more than 0.5 of habitat suitability index was affected by roads and hiking trails in the park.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.4
• /
• pp.435-442
• /
• 2007

To reduce damage from high concentration ozone in the air, we have researched how to predict high concentration ozone before it occurs. High concentration ozone is a rare event and its reaction mechanism has nonlinearities and complexities. In this paper, we have tried to apply and consider as many methods as we could. We clustered the data using the fuzzy c-mean method and took a rejection sampling to fill in the missing and abnormal data. Next, correlations of the input component and output ozone concentration were calculated to transform more correlated components by modified log transformation. Then, we made the prediction models using Dynamic Polynomial Neural Networks. To select the optimal model, we adopted a minimum bias criterion. Finally, to evaluate suggested models, we compared the two models. One model was trained and tested by the transformed data and the other was not. We concluded that the modified transformation effected good to ideal performance In some evaluations. In particular, the data were related to seasonal characteristics or its variation trends.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2006.06a
• /
• pp.179-179
• /
• 2006

산업기술의 고도화에 따른 IT 산업의 급속한 발전으로 각종 전자, 정보통신기기에 대해 더욱 소형화 고성능화를 요구하고 있다. 이와 같은 경향에 따라 더욱 향상된 기능을 가지고 각종 소자 부품의 개발과 동시에 유독 물질 발생이 없는 청정생산기술 개발에 대한 요구가 끊임없이 제기 되어 왔다. 이러한 요구에 부응하여 기술들이 개발되고 있으며 그 중의 하나로 잉크젯 프린팅 기술이 연구되고 있다. 특히 Dod(Drop on Demand) 방식의 잉크젯은 가정용 프린터로 개발되어 널리 보급된 기술이지만, 이 기술을 PCB 제조기술에 전용하면 친환경 생산공정으로 부품 성장밀도를 증대 시킬 수 있다. 기존의 PCB 제조기술은 전극과 신호 패턴을 형성시키기 위하여 노광공정과 에칭공정을 반복적으로 사용하고 있는데, 노광공정에서 쓰이는 마스크와 유틸리티 설비 유지 비용의 문제가 대두되고 있다. 노즐로부터 분사된 잉크 액적들의 집합으로 기판위에 점/선/면의 인쇄이미지를 구현하게 된다. 그러므로 인쇄 해상도는 잉크액적 및 인쇄 방법, 기판과의 상호작용에 크게 의존하게 된다. 잉크 액적과 기판의 상호작용에 영향을 미치는 요소로는 잉크의 물리화학적 물성(밀도, 점도, 표면장력), 잉크 액적의 충돌 조건(액적 지름, 부피, 속도), 그리고 기판의 특성(친수/소수성, Porous/Nonporous, 표면조도 등)을 들 수 있겠다. 우선적으로 노즐을 통과해서 분사되는 액적의 크기에 따라 기판위에 형성되는 라인의 두께 및 폭이 결정된다. 떨어진 액적이 기판위에서 퍼지는 것을 UV 조사를 통한 가경화 과정을 통해서 최종적으로 라인의 투께 및 폭을 조절하려고 한다. 따라서 선폭 $75{\mu}m$의 일정한 미세 배선을 형성시키기 위해 액적 크기 조절과 탄착 resist 액적 표면의 UV 가경화 조건으로 구현하려고 한다. 또한 DPI(Dot Per Inch) 조절을 통한 인쇄로 탄착 resist의 두께 확보 후 에칭시 박리되는 현상을 억제 시키려 한다.

• Journal of KIISE:Software and Applications
• /
• v.29 no.4
• /
• pp.270-277
• /
• 2002

Text filtering is a task of deciding whether a document has relevance to a specified topic. As Internet and Web becomes wide-spread and the number of documents delivered by e-mail explosively grows the importance of text filtering increases as well. The aim of this paper is to improve the accuracy of text filtering systems by using machine learning techniques. We apply AdaBoost algorithms to the filtering task. An AdaBoost algorithm generates and combines a series of simple hypotheses. Each of the hypotheses decides the relevance of a document to a topic on the basis of whether or not the document includes a certain word. We begin with an existing AdaBoost algorithm which uses weak hypotheses with their output of 1 or -1. Then we extend the algorithm to use weak hypotheses with real-valued outputs which was proposed recently to improve error reduction rates and final filtering performance. Next, we attempt to achieve further improvement in the AdaBoost's performance by first setting weights randomly according to the continuous Poisson distribution, executing AdaBoost, repeating these steps several times, and then combining all the hypotheses learned. This has the effect of mitigating the ovefitting problem which may occur when learning from a small number of data. Experiments have been performed on the real document collections used in TREC-8, a well-established text retrieval contest. This dataset includes Financial Times articles from 1992 to 1994. The experimental results show that AdaBoost with real-valued hypotheses outperforms AdaBoost with binary-valued hypotheses, and that AdaBoost iterated with random weights further improves filtering accuracy. Comparison results of all the participants of the TREC-8 filtering task are also provided.