• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1414-1419
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• 2019

Patent indicators were used to analyze the movements of local industries in order to derive blank technologies due to rapid changes in technology in the 4th Industrial Revolution and to discover promising technologies. Currently, Changwon-si is gathering a lot of technical information to develop hydrogen electric vehicle technology as a future regional flagship industry to discover it as a promising technology in the future. Collecting technical information has many problems in terms of time and cost due to classification methods, technical trends, and similar technologies. Therefore, a systematic classification of technical information and a method for easily deriving technical trends are needed. In this paper, we analyzed the blank technology and promising technology trends for the future core industries of the region through the method of measuring the growth rate of patents and the frequency of patent application through the patent indicators.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.120-125
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• 2009

본 논문에서는 통계적 분류방법을 이용하여 문화재 자료의 분석을 수행하였다. 분류방법으로는 선형판별분석, 로지스틱회귀분석, 의사결정나무분석, 신경망분석, SVM분석을 사용하였다. 각각의 분류방법에 대한 개념 및 이론에 대해 간략히 소개하고, 실제자료 분석에서는 "지역별 문화재 통계분석 및 모형개발 연구 1차(2008)"에 사용된 자료 중 익산시 자료를 근거로 매장문화재에 대한 분류방법별 적합모형을 구축하였다. 구축된 모형과 모의실험의 결과를 통해 각각의 적합모형에 대한 비교를 수행하여 모형의 성능을 비교하였다. 분석에 사용된 도구로는 최근 가장 관심을 갖는 R-project를 사용하였다.

• Convergence Security Journal
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• v.17 no.3
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• pp.21-29
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• 2017

Accurate encoding of local patterns is a very important factor in texture classification. However, LBP based methods w idely studied have fundamental problems that are vulnerable to noise. Recently, LDP method using edge response and dire ction information was proposed in facial expression recognition. LDP is more robust to noise than LBP and can accommod ate more information in it's pattern code, but it has drawbacks that it is sensitive to rotation transforms that are critical to texture classification. In this paper, we propose a new local pattern coding method called Rotation Invariant Local Direc tional Pattern, which combines rotation-invariant transform to LDP. To prove the texture classification performance of the proposed method in this paper, texture classification was performed on the widely used UIUC and CUReT datasets. As a result, the proposed RILDP method showed better performance than the existing methods.

• Journal of the Korean Geotechnical Society
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• v.39 no.6
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• pp.5-12
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• 2023

Proactive assessment of landslide susceptibility is necessary for minimizing casualties. This study proposes a methodology for classifying the landslide safety factor using a classification algorithm based on machine learning techniques. The high-risk area model is adopted to perform the classification and eight geotechnical parameters are adopted as inputs. Four classification algorithms-namely decision tree, k-nearest neighbor, logistic regression, and random forest-are employed for comparing classification accuracy for the safety factors ranging between 1.2 and 2.0. Notably, a high accuracy is demonstrated in the safety factor range of 1.2~1.7, but a relatively low accuracy is obtained in the range of 1.8~2.0. To overcome this issue, the synthetic minority over-sampling technique (SMOTE) is adopted to generate additional data. The application of SMOTE improves the average accuracy by ~250% in the safety factor range of 1.8~2.0. The results demonstrate that SMOTE algorithm improves the accuracy of classification algorithms when applied to geotechnical data.

• The Korean Journal of Petroleum Geology
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• v.9 no.1_2 s.10
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• pp.40-45
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• 2001

국내 대륙붕 제 6-1광구 고래 I지역에 대한 AVO분석 (OFFSET에 따른 진폭변화 연구: 주로 유체성분분석)을 수행하였다. 특히, 관심을 끌었던 고래 I지역의 다층에 대한 AVO 분석결과, 물을 함유한 층인, 다층에서는 가스를 함유한 저류층인 가층에 비해 가스를 함유할 가능성이 더 높게 나타났다. 하지만, 시추결과에 따르면 다층은 물로 채워진 층으로 판명되었다. 본 연구에서는, 가스를 함유하지 않은 다층이 더 뚜렷한 AVO 현상을 나타나게 된 원인을 분석 및 고찰하였다. 그 방법으로 다양한 AVO 분석 방법 (PxG stack, psedo-Poisson's ratio stack, Scaled-S-Wave reflectivity stack 분석 법 및 Cross Plot등)을 통해 가스층과 물을 함유한 층의 분류 가능성에 대한 연구를 수행하였다. 그 결과, 일반적인 AVO 분석 방법에 의해서는 가스층과 물을 함유한층의 분류가 어려웠다. 따라서, AVO 분석시 나타나는 AVO 현상에 대한 심도있는 고찰을 위해서는 AVO 모델링 기법의 적용을 고려해 볼 수 있으며, 이를 통해 탐사 위험도를 낮출 수 있을 것으로 기대된다. 또한 새로운 유망구조에 대한 상기 AVO 분석방법을 적용하여 유망구조의 가스함유 가능성에 대한 연구가 가능할 것으로 판단된다. 그 실례로, 고래 I지역에 대한 새로운 유망구조에서의 가스 함유가능성에 대한 연구를 수행하였다. 연구 방법으로는 상기에서 언급한 다양한 AVO 분석 방법을 적용하였으며, 그 결과 유망구조에서의 가스 발견 가능성은 높은 것으로 사료된다. 따라서, 향후, 가스층 탐사시 (물론, 연구결과 얻어진 가능성에 대한 시추결과가 있어야 하겠지만)축적된 AVO 분석기법을 적용 시 석유탐사에서 위험률 제고에 기여할 수 있을 것으로 기대된다.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.369-376
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• 2007

This paper proposes a hybrid genetic algorithm(HGA) for the classifier ensemble selection. HGA is added a local search operation for increasing the fine-turning of local area. This paper apply hybrid and simple genetic algorithms(SGA) to the classifier ensemble selection problem in order to show the superiority of HGA. And this paper propose two methods(SSO: Sequential Search Operations, CSO: Combinational Search Operations) of local search operation of hybrid genetic algorithm. Experimental results show that the HGA has better searching capability than SGA. The experiments show that the CSO considering the correlation among classifiers is better than the SSO.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.861-867
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• 2006

The objective of this study is to analyze the landslide hazard areas by combining LRA (Lgistic Regression Analysis) and AHP (Analytic Hierarchy Program) methods with Remote Sensing and GIS data in Anseong-si. In order to classify landslide hazard areas of seven levels, six topographic factors (slope, aspect, elevation, soil drain, soil depth, and land use) were used as input factors of LRA and AHP methods. As results, high-risk areas for landslide (1 and 2 levels) by LRA and AHP of its own were classified as 46.1% and 48.7%, respectively. A new method by applying weighting factors to the results of LRA and AHP was suggested. High-risk areas for landslide (1 and 2 levels) form the new method was classified as 58.9%.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.219-226
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• 2014

Fractional values resulted from the spectral mixture analysis could be used to classify not only urban area with various materials but also forest area in more detailed spatial scale. Especially South Korea is largely consist of mixed forest, so the spectral mixture analysis is suitable as a classification method. For the successful classification using spectral mixture analysis, extraction of optimal endmembers is prerequisite process. Though geometric endmember selection has been widely used, it is barely suitable for forest area. Therefore, in this study, we modified Iterative Error Analysis (IEA), one of the most famous algorithms of image endmember selection which extracts pure pixel directly from the image. The endmembers which represent deciduous and coniferous trees are automatically extracted. The experiments were implemented on two sites of Compact Airborne Spectrographic Imager (CASI) and classified forest area into two types. Accuracies of each classification results were 86% and 90%, which mean proposed algorithm effectively extracted proper endmembers. For the more accurate classification, another substances like forest gap should be considered.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.223-232
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• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.265-267
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• 2004

경제 여건의 향상 및 생활양식의 변화로 최근 우리나라에서도 당뇨병 환자가 늘어남에 따라 당뇨병의 예측 및 치료가 중요한 관심사가 되고 있다. 본 논문은 1993년과 1995년 두 차례에 걸쳐 경기도 연천 지역 주민들의 여러 가지 신체 지수 등을 조사한 데이터를 대상으로, 1차 년도의 데이터로부터 동일한 환자가 2차 년도에 정상상태를 유지하는지 흑은 당뇨병으로 진행이 되는지를 예측하는 문제를 다룬다. 혈당량, 허리둘레 등의 수치가 당뇨병의 발병에 영향을 끼치는 것은 알려진 사실이므로, 현재의 데이터로부터 앞으로의 발병 가능성을 예측하는 것이 가능하며, 이는 환자에게 보다 정확한 정보를 알려줄 수 있으므로 의미가 있는 일이다. 예측을 위해 본 논문에서는 분류기를 사용하며, 예측율을 높이기 위해 여러 분류기를 BKS로 결합하였다. BKS (behavior knowledge space) 결합 방법은 분류기간의 독립 가정이 필요 없으며, 데이터 크기가 크고 전형적인 경우에 좋은 결과를 낼 수 있는 방법이다. BKS 결합 방법을 통해 실험을 해본 결과 단일 분류기로 실험을 한 결과보다 향상된 성능을 얻을 수 있었으며, 투표 결합 방법과 비교하여 더 좋은 성능을 보였다.