• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.11-14
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• 1995

A Fuzzy Classifier which deals with very confusing objects is proposed. Naturally this classifier heavily relies on the nulti-feature decision-making procedure. For a simple example, this classifier is applied to the recognition of confusing handwritten numerals 4,6 and 9 The characteristic variables used in this paper are the existence of a loop and the relative location of the starting or ending points(SEP). Thus each sample of handwritten numerals 4, 6 and 9 is classified in one of the 6 groups which are divided according to the sample structure. Each group has its own classifying rules. Also the method of rule-generation using genetic algorithms in each group is proposed.

• 한국군사과학기술학회지
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• 제17권1호
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• pp.41-49
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• 2014

In this paper, we consider a method to access the number of aircraft requirement which is a strategic variable in national security. This problem becomes more important considering the F-X and KF-X project in ROKAF. Traditionally, ATO(Air Tasking Order) and fighting power index have been used to evaluate the number of aircrafts required in ROKAF. However, those methods considers static aspect of aircraft requirement. This paper deals with a model to accommodate dynamic feature of aircraft requirement using absorbing Markov chain. In conclusion, we suggest a dynamic model to evaluate the number of aircrafts required with key decision variables such as destroying rate, failure rate and repair rate.

• 생태와환경
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• 제49권3호
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• pp.236-243
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• 2016

본 논문은 국립공원 아고산대 침엽수림을 효과적으로 보전하고 관리하는 데 기초자료로 활용하고자 진행된 사례연구로서 지리산국립공원과 속리산국립공원의 아고산대에서 현지 조사된 구상나무 (Abies koreana Wilson) 210개체의 서식실태 자료를 바탕으로, 입지환경의 기본이 되는 지리적 위치와 지형적 특성이 구상나무의 생장에 미치는 영향을 분석하였다. 이를 위해서 구상나무의 생장과 관련된 변수는 수고 및 흉고직경으로 하고, 지형적 특성은 GIS 공간분석을 이용하여 추출된 지리적 위도, 해발고도, 산지경사, 사면향 그리고 지형습윤지수로 하였다. 두 변수군의 연관성의 유무와 정도를 평가하기 위해서 정준상관분석을 이용하고, 다중회귀분석을 이용하여 지리 지형적 특성이 구상나무의 생장구조에 미치는 영향을 평가하였다. 구상나무 생장구조를 나타내는 흉고직경 및 수고는 지형의 수직적인 분포보다 지리 위도적인 분포와 연관성이 더 크고, 지리 지형요소는 수고보다 흉고직경과 연관성이 더 큰 것으로 나타났다. 구상나무의 생장구조변수와 지리 지형변수는 유의한 상관관계가 있고, 지리 지형변수가 생장구조변수를 18.1% 정도 설명하는 것으로 나타났다. 구상나무의 흉고직경에 영향을 주는 변수는 지리적 위도, 지형습윤지수, 사면향 그리고 해발고도의 순으로 통계적 유의성이 있는 것으로 나타났다. 위도가 높아질수록 흉고직경은 작아지고 지형적 요소에 따라서는 커지는 것으로 나타났다. 수고에 영향을 주는 변수는 지형습윤지수만이 유의미한 것으로 나타났다. 전반적으로, 구상나무의 생장구조와 관련된 수고와 흉고직경은 지리적인 특성의 영향이 가장 클 것으로 나타났다. 특히 지형적 특성 중에서 수분상태의 영향이 다른 지형요소에 비해서 더 클 것으로 예측되었다. 이러한 결과로 볼 때, 지리 지형적인 특성은 구상나무의 생장에 중요한 요인이 될 것으로 추측된다. 비록 지리 지형적 특성만을 고려하고 GIS를 이용하여 제작된 공간분석 자료를 사용한 한계가 있다고 해도, 본 연구결과는 향후 국립공원 아고산대에서 서식하고 있는 침엽수림의 생장환경을 조사하고 연구하는 데 유용하고, 구상나무 등 상록침엽교목을 효과적으로 관리하고 보전하기 위해서 대책을 수립하는 데 기초자료로 활용될 것으로 기대된다.

• 한국조리학회지
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• 제22권8호
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• pp.109-121
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• 2016

This study analyzed the effect of five sub-variables' attribute of HMR: features of information, diversity, promptness, price and convenience, on the consumer purchasing intention. In addition, the regulation effect of positive reviews and negative reviews of consumers' online reviews between HMR selection attribute and purchasing intention was also tested. Results are following. First, convenience feature (B=.577, p<.001) and diversity feature (B=.093, p<.01) among the effect of HMR selection attribute had a positive (+) effect on purchasing intention. On the other hand, promptness feature (B=.235, p<.001) and price feature (B=.161, p<.001), and information feature (B=.288, p<.001) were not significant effect on purchasing intention. Second, result of regulation effect of the positive reviews of consumer's online review between the selection attribute of the HMR product and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product is input as an independent variable, there was a significant positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In addition, there was significant positive (+) main effect (B=.472, p<.001) in the second step model in which the consumers' positive reviews, that is a regulation variable. Furthermore, the feature of price (B=.068, p<.05) had a significant positive (+) effect in the third stage in which the selection attribute of the HMR product that is an independent variable and the interaction of the positive review. However, the feature of information (B=-.063, p<.05) showed negative (-) effect, and there was no effect on the features of convenience, diversity, and promptness. Third, as a result of testing the regulation effect of the negative reviews of consumers' online reviews between HMR product selection attribute and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product was a positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In the second-stage model in which consumers' negative reviews (B=-.113, p<.001) had negative (-) effect. In the third-stage in which the selection attribute of the HMR product and the interactions of the negative reviews was a positive (+) effect with the feature of price (B=.113, p<.01). Last, there was no effect at all on the features of convenience, promptness, and information.

• 한국컴퓨터정보학회논문지
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• 제25권6호
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• pp.35-47
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• 2020

박스 오피스 예측은 영화 이해관계자들에게 중요하다. 따라서 정확한 박스 오피스 예측과 이에 영향을 미치는 주요 변수를 선별하는 것이 필요하다. 본 논문은 영화의 박스 오피스 예측 정확도 향상을 위해 다변량 시계열 데이터 분류와 주요 변수 선택 방법을 제안한다. 연구 방법으로 한국 영화 일별 데이터를 KOBIS와 NAVER에서 수집하였고, 랜덤 포레스트(Random Forest) 방법으로 주요 변수를 선별하였으며, 딥러닝(Deep Learning)으로 다변량 시계열을 예측하였다. 한국의 스크린 쿼터제(Screen Quota) 기준, 딥러닝을 이용하여 영화 개봉 73일째 흥행 예측 정확도를 주요 변수와 전체 변수로 비교하고 통계적으로 유의한지 검정하였다. 딥러닝 모델은 다층 퍼셉트론(Multi-Layer Perceptron), 완전 합성곱 신경망(Fully Convolutional Neural Networks), 잔차 네트워크(Residual Network)로 실험하였다. 결과적으로 주요 변수를 잔차 네트워크에 사용했을 때 예측 정확도가 약 93%로 가장 높았다.

• 한국국방경영분석학회지
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• 제30권1호
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• pp.1-14
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• 2004

In this paper, we propose a new data-mining-based methodology for military occupational specialty assignment. The proposed methodology consists of two phases, feature selection and man-power assignment. In the first phase, the k-means partitioning algorithm and the optimal variable weighting algorithm are used to determine attribute weights. We address limitations of the optimal variable weighting algorithm and suggest a quadratic programming model that can handle categorical variables and non-contributory trivial variables. In the second phase, we present an integer programming model to deal with a man-power assignment problem. In the model, constraints on demand-supply requirements and training capacity are considered. Moreover, the attribute weights obtained in the first phase for each specialty are used to measure dissimilarity. Results of a computational experiment using real-world data are provided along with some analysis.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.283-285
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• 2005

To account for the importance of variable in multi-sensor data fusion, random forests are applied to supervised land-cover classification. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. Its distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Supervised classification with a multi-sensor remote sensing data set including optical and polarimetric SAR data was carried out to illustrate the applicability of random forests. From the experimental result, the random forests approach could extract important variables or bands for land-cover discrimination and showed good performance, as compared with other non-parametric data fusion algorithms.

• 한국안전학회지
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• 제30권2호
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• pp.70-76
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• 2015

This study deals with the traffic accident of circular intersections. The purpose of the study is to develop the accident models by traffic violation type. In pursuing the above, this study gives particular attention to analyzing various factors that influence traffic accident and developing such the optimal models as Poisson and Negative binomial regression models. The main results are the followings. First, 4 negative binomial models which were statistically significant were developed. This was because the over-dispersion coefficients had a value greater than 1.96. Second, the common variables in these models were not adopted. The specific variables by model were analyzed to be traffic volume, conflicting ratio, number of circulatory lane, width of circulatory lane, number of traffic island by access road, number of reduction facility, feature of central island and crosswalk.

• 한국콘텐츠학회논문지
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• 제7권1호
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• pp.65-72
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• 2007

본 연구에서는 초음파 검사를 기반으로 하는 비파괴검사 방법을 사용하였으며, 용접결함의 패턴인식 알고리즘으로서 역전파 신경망과 확률 신경망을 비교하였다. 이러한 목적을 위한 과정에서 두 가지 알고리즘에 동일한 변수를 적용하였으며, 여기서 사용된 특징변수는 용접결함으로부터 반사된 시간영역 상의 전체 결함신호로부터 결함부분만을 분리한 신호파형을 사용하였다. 이상의 절차를 통하여 두 가지 알고리즘의 적용방안을 확인하였으며, 두 가지 알고리즘에 대하여 각각의 장단점을 비교하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권1호
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.