• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2004년도 춘계종합학술대회
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• pp.158-161
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• 2004

RBF 네트워크의 중간층은 클러스터링하는 층이다. 즉, 이 충의 목적은 주어진 자료 집합을 유사한 클러스터들(homogenous cluster)로 분류하는 것이다. 여기서 유사하다는 것은 입력 데이터들에 대한 특징 벡터 공간사이에서 한 클러스터내의 벡터들 간에 거리를 측정하여 정해진 반경 내에 존재하면 같은 클러스터로 분류하고 정해진 반경 내에 존재하지 않으면 다른 클러스터로 분류한다. 그러나 정해진 반경 내에서 클러스터링하는 것은 잘못된 클러스터를 선택하는 단점을 가지게 된다. 그러므로 중간층을 결정하는 .것은 RBF 네트워크의 전반적인 효율성에 큰 영향을 준다. 따라서 본 논문에서는 효율적으로 중간층을 결정하기 위한 방법으로 퍼지 C-Means 클러스터링 알고리즘을 적용한 퍼지 RBF 네트워크를 제안한다. 제안된 퍼지 RBF 네트워크의 학습은 크게 두 단계로 구분된다. 첫 번째 단계는 입력층과 중간층 사이에 퍼지 C-Means 알고리즘이 수행되고, 두 번째 단계는 중간층과 출력층 사이에 지도학습이 수행된다. 제안된 방법의 학습 성능을 평가하기 위하여 실제 주민등록증에서 추출한 숫자패턴에 적용한 결과, 기존의 RBF네트워크 보다 학습 성능이 개선된 것을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2013년도 춘계학술발표대회
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• pp.687-690
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• 2013

소프트웨어 버스마크 기법은 도용이 의심되는 소프트웨어의 소스 코드를 얻을 수 없을 때 사용할 수 있는 소프트웨어 도용 탐지 기법이다. 이 기법은 프로그램의 바이너리나 자바 클래스 파일에서 프로그램 고유의 특징인 버스마크를 추출한 다음 프로그램간 버스마크 유사도 측정을 통해 도용을 탐지한다. 이 논문에서는 선행 연구된 k그램 버스마크 기법에 연산자 요약이라는 아이디어를 접목한 연산자 요약 k그램 버스마크 기법을 제안한다. 연산자 요약이란 연산자 우선순위가 같은 연산자의 JVM 명령어를 묶어 요약번호로 나타내는 것이다. 연산자 요약 k그램 버스마크 기법은 연산자 요약과 제어 흐름을 고려하여 생성한 연속된 k개의 요약번호 시퀀스 집합을 버스마크로 정의한다. 버스마크를 평가하기 위해 선택 정렬 메소드와 버블 정렬 메소드를 대상으로 신뢰도 실험과 강인도 실험을 하였다. 실험 결과 연산자 요약 k그램 버스마크 기법이 선행 연구된 Tamada 버스마크 기법과 k그램 버스마크 기법보다 높은 신뢰도와 강인도를 보였다.

• The Transactions of the Korea Information Processing Society
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• 제5권11호
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• pp.2884-2896
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• 1998

For on-line signature verification, the local shape of a signature is an important information. The current approaches, in which signatures are represented into a function of time or a feature vector without regarding of local shape, have not used the various features of local shapes, for example, local variation of a signer, local complexity of signature or local difficulty of forger, and etc. In this paper, we propose a new technique for on-line signature verification based on a structural signature representation so as to analyze local shape and to make a selection of important local parts in matching process. That is. based on a structural representation of signature, a technique of important of local weighting and personalized decision threshold is newly introduced and its experimental results under different conditions are compared.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.167-170
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• 2007

동일한 집단에 속하는 개체를 다른 집단에 속하는 개체로부터 구별할 수 있는 염기의 특징을 해당 집단의 시그너쳐라고 한다. 학습 데이터는 두 집단에 속하는 염기서열들이고, 염기서열에 대한 시그너쳐는 개체를 다른 집단과 구별할 수 있는 위치의 염기들로 구성된 서열이다. 제안한 방법에서는 각 집단에 대해서 위치별로 염기의 발생빈도를 계산하고, 가장 발생빈도가 높은 염기를 결정한 다음, 다른 집단의 대응 위치에서 해당 염기의 빈도를 계산하여, 빈도차이가 지정한 분류임계값 이상이면, 해당 위치의 염기를 시그너쳐를 구성하는 특징으로 간주한다. 시그너쳐를 대한 임의의 염기서열에 대한 부합정도는 시그너쳐에 속하는 염기의 학습집단에서의 상대빈도값을 가중치로 하여 계산한다. 임의의 염기서열이 특정 집단에 속하는지 판단하기 위해서는 해당 집단의 시그너쳐에 대한 부합정도를 계산하게 되는데, 부합정도가 얼마이상이 되어야 해당 집단에 속하는 것으로 간주할지 기준이 되는 임계값을 엄밀도 임계값이라고 한다. 엄밀도 임계값은 학습 데이터 집합에 대해서 주어진 시그너쳐에 대한 엄밀도 임계값이 민감도와 특이도를 최대로 하는 것을 선택한다. 제안한 방법을 구현한 바이오인포매틱스 도구를 개발하여, 한국형 HIV-1 바이러스 시그너쳐 추출에 적용하여 분류특성이 우수한 시그너쳐를 추출할 수 있음을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• 제8권2호
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• pp.82-95
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• 1998

In this paper, we propose a text-independent speaker identification system which has a classifier composed of two parts; to calculate the degree of likeness of each speech frame and to select the most probable speaker from the entire speech duration. The first part is realized using RBFN which is selforganized through learning and in the second part the speaker is determined using a con-tbination of MAXNET and delayed summings. And we use features from linear speech production model and features from fractal geometry. Closed-set speaker identification experiments on 13 male homogeneous speakers show that the proposed techniques can achieve the identification ratio of 100% as the number of delays increases.

• Journal of the Institute of Electronics Engineers of Korea SC
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• 제37권6호
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• pp.50-63
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• 2000

In this paper, a new EMG pattern classification method based on soft computing techniques is proposed to help the disabled and the elderly handle rehabilitation robotic arm systems. First, it is shown that EMG is more useful than existing input devices such as voice, a laser pointer and a keypad in view of naturality, extensibility, and applicability. Then, a new procedure is proposed to select the minimal feature set. As methods of classifying the pre-defined motions, a fuzzy pattern classification and fuzzy min-max neural networks (FMMNN) are designed using the selected features. As results, the motions are recognized with success rates of 83 percent and 90 Percent using fuzzy pattern classification and FMMNN, respectively.

• Korean Journal of Remote Sensing
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• 제15권3호
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• pp.183-194
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• 1999

The Brightness, color and texture included in a multispectral satellite data are used as important factors to analyze and to apply the image data for a proper use. One of the most significant process in the satellite data analysis using texture or color information is to extract features effectively expressing the information of original image. It was described in this paper that six features were introduced to extract useful features from the analysis of the satellite data, and also a classification network using the back-propagation neural network was constructed to evaluate the classification ability of each vector feature in SPOT imagery. The vector features were adopted from the training set selection for the interesting region, and applied to the classification process. The classification results showed that each vector feature contained many merits and demerits depending on each vector's characteristics, and each vector had compatible classification ability. Therefore, it is expected that the color and texture features are effectively used not only in the classification process of satellite imagery, but in various image classification and application fields.

• Journal of the Institute of Electronics Engineers of Korea TC
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• 제48권9호
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• pp.1-9
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• 2011

In this paper, we propose an efficient method to create clusters of geographically distributed base stations which cooperatively transmit signals in cellular mobile communication systems. The proposed method utilizes a sequential selection approach to choose candidate base stations which can provide maximum weighted sum-rate gain when they participate in the cooperative transmission with the existing cluster. In particular, the proposed method limits the maximum number of base stations in a cluster by considering the system operational and implementation complexities. Moreover, the combinations of clusters dynamically change along with variations of channel environments. Through computer simulations, performance of the proposed method is verified by comparing with the non-cooperative transmission method and the static clustering method. Numerical result shows that the proposed sequential selection based clustering method is especially advantageous for the performance improvement of lower percentile users in terms of average throughput, and thus the proposed method can effectively improve the fairness among users.

• Journal of the Korean Association of Geographic Information Studies
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• 제9권1호
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• pp.1-12
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• 2006

Approaches to spatial analysis differ from the type of spatial objects to be treated. Especially, in here, the case where two spatial data sets coexist is considered. The goal of such case lies on detecting a subset of spatial objects out of a large set that affects the distribution of the other object. However, it is not easy to extract a subset from a large set by visualization just with the help of GIS since huge amount of data are provided nowadays. In this research, therefore, relationship between two different spatial data are analyzed by quantitative measure in the case study of marketing geography. A purchase history data of a small retail store and the location of its competitors are given as source data for the analysis. The goal of analysis from the aspect of this case study is to extract strong competitors of the store that affects the sales amount of the store among many competitors. With the result, therefore, it is expected that market area pattern and competitive power of stores under micro scale retail environment would be understood by quantitative measure.

• Journal of the Institute of Electronics and Information Engineers
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• 제50권11호
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• pp.206-216
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• 2013

The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.