• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.847-856
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• 2013

In this paper, we deal with testing goodness-of-fit for normal distribution based on parametric and nonparametric entropy estimators. The minimum variance unbiased estimator for the entropy of the normal distribution is derived as a parametric entropy estimator to be used for the construction of a test statistic. For a nonparametric entropy estimator of a data-generating distribution under the alternative hypothesis sample entropy and its modifications are used. The critical values of the proposed tests are estimated by Monte Carlo simulations and presented in a tabular form. The performance of the proposed tests under some selected alternatives are investigated by means of simulations. The results report that the proposed tests have better power than the previous entropy-based test by Vasicek (1976). In applications, the new tests are expected to be used as a competitive tool for testing normality.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.63-73
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• 2010

Malicious bot programs, the source of distributed denial of service attack, are widespread and the number of PCs which were infected by malicious bot program are increasing geometrically thesedays. The continuous distributed denial of service attacks are happened constantly through these bot PCs and some financial incident cases have found lately. Therefore researches to response distributed denial of service attack are necessary so we propose an effective feature generation method for distributed denial of service attack detection using entropy. In this paper, we apply our method to both the DARPA 2000 datasets and also the distributed denial of service attack datasets that we composed and generated ourself in general university. And then we evaluate how the proposed method is useful through classification using bayesian network classifier.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.293-301
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• 2014

In this paper, we propose a new measure based on the confidence of Bayesian posterior probability so as to reduce unimportant information in the clustering process. Because the performance of clustering is up to selecting the important degree of attributes within the databases, the concept of information entropy is added to posterior probability for attributes discernibility. Hence, The same value of attributes in the confidence of the proposed measure is considerably much less due to the natural logarithm. Therefore posterior probability-based clustering algorithm selects the minimum of attribute reducts and improves the efficiency of clustering. Analysis of the validation of the proposed algorithms compared with others shows their discernibility as well as ability of clustering to handle uncertainty with ACME categorical data.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.343-349
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• 2013

Rough set theory comes to derive optimal rules through the effective selection of features from the redundancy of lots of information in data mining using the concept of equivalence relation and approximation space in rough set. The reduction of attributes is one of the most important parts in its applications of rough set. This paper purports to define a information-theoretic measure for determining the most important attribute within the association of attributes using rough entropy. The proposed method generates the effective reduct set and formulates the core of the attribute set through the elimination of the redundant attributes. Subsequently, the control rules are generated with a subset of feature which retain the accuracy of the original features through the reduction.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1846-1847
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• 2007

본 논문에서는 비선형 공정의 모델링 성능을 향상시키기 위하여 퍼지 엔트로피 분석을 통해 새로운 클러스터를 생성하고, 이 클러스터를 퍼지 모델의 새로운 규칙으로 추가하는 자기구성 퍼지 모델을 제안한다. 퍼지 엔트로피가 상대적으로 큰 데이터 집합으로 새로운 클러스터를 구성하면 퍼지 모델의 애매모호한 정도가 작아져서 모델링 오차가 줄어들 가능성이 크게 된다. 제안한 방법의 유용성을 입증하기 위해 이를 Box-Jenkins의 가스로 공정에 적용하여 퍼지 규칙수의 증가에 따른 모델링 성능의 변화를 보이고, 기존의 방법에 의한 모델링 결과와 비교한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.383-385
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• 2007

웨이블릿 화상 코딩 기법은 자연적으로 레이어드된 비트스트림을 생성해 내기 때문에, 주파수 제한적인 채널 상황에서 에러에 강한 성능을 나타내고 있다. 본 논문에서는 Progressive Image Coder의 성능을 비교하고 평가하는 새로운 기법인 시각적 엔트로피를 이용해, 웨이블릿 영역에서 시각적인 가중치를 이용해 정량화하려고 한다. 이 시각적인 가중치는 주파수 영역과 공간 영역에서, 인간의 시각 체계(HVS, human visual system)에 기반 하여 만들어진 것으로, 웨이블릿 계수들의 코딩 순서를 결정하는 기준으로 사용되고, 이렇게 해서 시각적인 화질을 개선할 수가 있다. 정규화된 채널 용량이 0.3일 때, 전송 이득은 시각적 엔트로피로 측정해 보았을 때 23% 이상 얻을 수 있다.

• Journal of the KSME
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• v.23 no.5
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• pp.335-343
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• 1983

새로운 방법에 의한 열역학의 기본법칙의 공식화과정 및 검증, 실제문제의 적용실례로 부터 다 음과 같은 제안을 할 수 있다. (1) 열역학의 기본법칙의 공식화에 있어서 재래방법의 사용도 좋으나 이해와 적용가능성의 측 방법에서 좀더 일반화된 사실에서 출발한 새로운 방법의 도입이 바람직하다. (2) 열역학적 가역에, 비가역성과 관련하여 기본법칙의 공식화과정에서의 시간개념의 도입은 중 요하다. (3) 동력기관의 해석에 있어서 이론적인 최대효율의 관점뿐 아니라 실제적인 최대일의 발생이 병행 취급되어야 하며 생성엔트로피의 개념에 의해 효과적으로 설명될 수 도 있다. (4) 단순한 사이클이 아닌 열역학적 과정에 대한 문제 및 주위조건을 고려한 해석의 경우, 가용 에너지, 엑서지의 보편화가 필요하다. (5) 생성엔트로피개념을 다양한 열역학문제에 적용하여 기존 해석방법에 대한 보완 및 검토가 요구된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10b
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• pp.903-906
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• 2000

웨이브렛 영역에서 벡터 양자화(vector quantization)를 수행하여 생성된 VQ 인덱스들을 엔트로피 부호화(entropy coding)하면 영상의 코딩 효율을 높일 수 있다. 본 논문에서는 벡터 양자화 이전에 VQ 인덱스들의 중복성을 높이기 위해 다중해상도 코드북의 코드 워드들을 에너지 크기 순으로 재정렬하는 기법을 제안한다. 코드 워드들의 평균과 편차를 이용한 재정렬 방법과 제안된 기법을 벡터 양자화 후 생성되는 VQ 인덱스에 DPCM/Huffman 기법을 적용하여 각각에 대한 코딩 효율을 비교한다.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.385-395
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• 2005

Long sentence analysis has been a critical problem in machine translation because of high complexity. The methods of intra-sentence segmentation have been proposed to reduce parsing complexity. This paper presents the intra-sentence segmentation method based on maximum entropy probability model to increase the coverage and accuracy of the segmentation. We construct the rules for choosing candidate segmentation positions by a teaming method using the lexical context of the words tagged as segmentation position. We also generate the model that gives probability value to each candidate segmentation positions. The lexical contexts are extracted from the corpus tagged with segmentation positions and are incorporated into the probability model. We construct training data using the sentences from Wall Street Journal and experiment the intra-sentence segmentation on the sentences from four different domains. The experiments show about $88\%$ accuracy and about $98\%$ coverage of the segmentation. Also, the proposed method results in parsing efficiency improvement by 4.8 times in speed and 3.6 times in space.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.334-340
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• 2002

This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.