• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.279-290
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• 2013

In digital forensics log files have been stored as a form of large data for the purpose of tracing users' past behaviors. It is difficult for investigators to manually analysis the large log data without clues. In this paper, we propose a text mining technique for extracting intrusion logs from a large log set to recommend reliable evidences to investigators. In the training stage, the proposed method extracts intrusion association words from a training log set by using Apriori algorithm after preprocessing and the probability of intrusion for association words are computed by combining support and confidence. Robinson's method of computing confidences for filtering spam mails is applied to extracting intrusion logs in the proposed method. As the results, the association word knowledge base is constructed by including the weights of the probability of intrusion for association words to improve the accuracy. In the test stage, the probability of intrusion logs and the probability of normal logs in a test log set are computed by Fisher's inverse chi-square classification algorithm based on the association word knowledge base respectively and intrusion logs are extracted from combining the results. Then, the intrusion logs are recommended to investigators. The proposed method uses a training method of clearly analyzing the meaning of data from an unstructured large log data. As the results, it complements the problem of reduction in accuracy caused by data ambiguity. In addition, the proposed method recommends intrusion logs by using Fisher's inverse chi-square classification algorithm. So, it reduces the rate of false positive(FP) and decreases in laborious effort to extract evidences manually.

• Annals of Clinical Neurophysiology
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• v.15 no.1
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• pp.27-29
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• 2013

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.71-78
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• 2005

Recently an important issue in Bayesian methodology is determination of noninformative prior distributions, often required when there is no idea of prior information. In this thesis attention is focused on the development of noninformative priors for stationary AR(1) model. The noninformative priors primarily discussed are the Jeffreys prior, and the reference priors. The remarkable points in the result are that the Jeffreys prior coincides with the reference prior for the case that $\rho$ is the parameter of interest.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.10a
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• pp.203-205
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• 2012

해양레저보트와 마리나에 대한 관심이 몇 년 전부터 급증하고 있고 이에 따라 마리나항만 기본계획까지 수립되어 있으나, 사업비 마련이 용이하지 않아서 계획대로 추진이 되지 않고 있다. 우리나라 보다 일찍 해양레저활동을 시작한 일본의 레저선박 인프라 현황과 규모 그리고 관련 정책과 제도 등에 대한 흐름을 파악하고자 한다. 이를 바탕으로 일본 내 레저선박으로 인한 문제점을 파악하고 앞으로 해양레저 활성화를 위해서는 어떤 점이 필요한지를 생각해 보고자 한다.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.29-36
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• 2006

This paper proposes a new algorithm of MS-EIDV (modal sensitivity-effective independence distribution vector) for determining optimal accelerometer locations (OAL) by using the Fisher Information Matrix (FIM) derived from mode-shape sensitivities. Also, the paper provides a reasonable guideline for selecting OAL which can reflect dynamic responses of a structure effectively. Since OAL should be determined with known values of structural parameters but since the parameters can be estimated by applying an inverse method such as SI (system identification) using measured response, the paper proposes a statistical method to overcome the paradox by considering the error bound of the structural parameters. To examine the proposed methods, a frequency-domain SI method has been applied. By using the identified results, the minimum necessary number of accelerometers could be selected depending on the number of target measurable modes. Through simulation studies, the results by applying EIDV method directly using the information of mode shapes were compared with those by applying the proposed MS-EIDV.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.35-46
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• 2011

In this paper, we proposed the method of emotion recognition using staged classification model and Fisher's linear discriminant. By organizing the staged classification model, the proposed method improves the classification rate on the Fisher's feature space with high complexity. The staged classification model is achieved by the successive combining of binary classification model which has simple structure and high performance. On each stage, it forms Fisher's linear discriminant according to the two groups which contain each emotion class, and generates the binary classification model by using Adaboost method on the Fisher's space. Whole learning process is repeatedly performed until all the separations of emotion classes are finished. In experimental results, the proposed method provides about 72% classification rate on 8 classes of emotion and about 93% classification rate on specific 3 classes of emotion.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.783-788
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of the variability in scale, location, orientation and pose. The difficulties in visual detection and recognition are caused by the variations in viewpoint, viewing distance, illumination. In this paper, we present an efficient linear discriminant for multi-view face detection and face location. We define the training data by using the Fisher`s linear discriminant in an efficient learning method. Face detection is very difficult because it is influenced by the poses of the human face and changes in illumination. This idea can solve the multi-view and scale face detection problems. In this paper, we extract the face using the Fisher`s linear discriminant that has hierarchical models invariant size and background. The purpose of this paper is to classify face and non-face for efficient Fisher`s linear discriminant.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.558-565
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• 2013

Mammography, the process of using X-ray to examine the woman breast, is the one of the effective tools for detecting breast cancer at an early state. In screening mammogram, Computer-Aided Detection(CAD) system helps radiologist to diagnose cases by detecting malignant masses. A mass is an important lesion in the breast that can indicate a cancer. Due to various shapes and unclear boundaries of the masses, detecting breast masses is considered a challenging task. To this end, CAD system detects a lot of regions of interest including normal tissues. Thus it is important to develop the well-organized classifier. In this paper, we propose an enhanced sparse representation (SR) based classifier using Fisher discrimination dictionary learning. Experimental results show that the proposed method outperforms the existing support vector machine (SVM) classifier.

• International Commerce and Information Review
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• v.3 no.1
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• pp.277-294
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• 2001

본 연구는 환율과 금리 및 주가간의 균형관계를 나타내는 분석모형(分析模型)을 구축 제시하고, 그 분석모형을 구성하고 있는 일별(日別) 국내 외 금융지표의 변동간에 장기관계(長期關係)가 성립하는지 즉, 공적분(共積分) 관계(關係)의 성립여부를 분석하며, 이들 금융지표간 균형관계에 대한 안정성(安定性)(stationary) 여부를 검증함으로써 제시한 분석모형의 유효성(有效性) 즉, 장기모형관계(長斯模型關係)에 대한 성립여부를 분석하는 데 중점을 두고 있다. 분석결과는 분석모형의 변수간 장기관계 즉, 공적분 관계가 존재한다 할지라도 다변량 가설검증에 의하여 분석모형이 장기모형관계(長斯模型關係)로부터 유의적(有意的)으로 이탈할 수도 있다는 사실을 확충하고 있다.

• The Journal of Information Systems
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• v.16 no.1
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• pp.1-21
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• 2007

신용평점을 위한 부도예측의 분류 문제를 다루는데 있어서 통계적 판별분석 및 인공신경망 및 유전자알고리즘 등을 이용한 데이터 마이닝의 방법들이 일반적으로 고려되어왔다. 이 연구에서는 수리계획법을 응용하여 classification gap을 고려한 이단계 수리계획 접근방법을 신용평가에 적용하는 방법론을 제안하여 수리계획법을 통한 신용평가모형 구축의 가능성을 제시한다. 1단계에서는 선형계획법을 이용해서 대출 신청자에게 대출을 허가할 것 인지의 여부를 결정하게 되는 대출 심사 filtering으로의 적용단계이고, 2단계에서는 정수계획법을 이용하여 오분류 비용이 최소가 되도록 하는 판별점수를 찾는 과정으로 모형을 구성한다. 개인 대출 신청자의 데이터(German Credit Data)에 대하여 피셔의 선형 판별함수, 로지스틱 회귀모형 및 기존의 수리계획 기법들과의 비교를 통해서 제안된 모델의 성능을 평가한다. 이단계 수리계획 접근법의 평가 결과를 통하여 신용평가모형에의 적용가능성을 기존 통계적인 접근방법 및 수리계획 접근법과 비교하여 제시하고 있다.