• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.103-108
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• 2011

Generally, Miller-Rabin method has been the most popular primality test. This method arbitrary selects m at k-times from m=[2, n-1] range and (m,n)=1. Miller-Rabin method performs $k{\times}r$ times and reports prime as $m^d\;{\equiv}\;1(mod\;n)$ or $m^{2^rd}\;{\equiv}\;-1(mod n)$ such that n-1=$2^sd$, $0\;{\leq}\;r\;{\leq}\;s-1$. This paper suggests more simple primality test than Miller-Rabin method. This test method computes c=$p^{\frac{n-1}{2}}(mod\;n)$ for k times and reports prime as c=-1. The proposed primality test method reduces $k{\times}r$ times of Miller-Rabin method to k times.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.103-109
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• 2013

Miller-Rabin method is the most prevalently used primality test. However, this method mistakenly reports a Carmichael number or semi-prime number as prime (strong lier) although they are composite numbers. To eradicate this problem, it selects k number of m, whose value satisfies the following : m=[2,n-1], (m,n)=1. The Miller-Rabin method determines that a given number is prime, given that after the computation of $n-1=2^sd$, $0{\leq}r{\leq}s-1$, the outcome satisfies $m^d{\equiv}1$(mod n) or $m^{2^rd}{\equiv}-1$(mod n). This paper proposes a step-by-step primality testing algorithm that restricts m=2, hence achieving 98.8% probability. The proposed method, as a first step, rejects composite numbers that do not satisfy the equation, $n=6k{\pm}1$, $n_1{\neq}5$. Next, it determines prime by computing $2^{2^{s-1}d}{\equiv}{\beta}_{s-1}$(mod n) and $2^d{\equiv}{\beta}_0$(mod n). In the third step, it tests ${\beta}_r{\equiv}-1$ in the range of $1{\leq}r{\leq}s-2$ for ${\beta}_0$ > 1. In the case of ${\beta}_0$ = 1, it retests m=3,5,7,11,13,17 sequentially. When applied to n=[101,1000], the proposed algorithm determined 96.55% of prime in the initial stage. The remaining 3% was performed for ${\beta}_0$ >1 and 0.55% for ${\beta}_0$ = 1.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.151-156
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• 2009

This paper proposes a Gaussian mixture model(GMM)-based music discrimination system for FM broadcasting. The objective of the system is automatically archiving music signals from audio broadcasting programs that are normally mixed with human voices, music songs, commercial musics, and other sounds. To improve the system performance, make it more robust and to accurately cut the starting/ending-point of the recording, we also added a post-processing module. Experimental results on various input signals of FM radio programs under PC environments show excellent performance of the proposed system. The fixed-point simulation shows the same results under 3MIPS computational power.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.12a
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• pp.299-306
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• 2003

본 논문에서는 현재 사용되고 있는 소수성 검정 알고리즘의 효율성을 비교하여 효과적인 알고리즘 사용에 관한 방향을 제시하려 한다. 현재 가장 일반적으로 사용하고 있는 Miller-Rabin 소수성검정법(Miller-Rabin primality test)에 대하여, Miller-Rabin 소수성 검정법 이외에 다른 확률적 소수성 검정법으로 제안된 Frobenius-Grantham 소수성 검정법(Frobenius-Grantham primality test) 이 있다. 그러나 합성수 판별에 대한 확률적 우세함에도 불구하고, Miller-Rabin 소수성 검정법을 대체하고 있지 못하는 이유는 시간복잡도(time complexity)가 Randomized polynomial time이기 때문에 같은 확률에 대한 평균 실행 속도가 Miller-Rabin 소수성 검정법보다 크게 효율적이지 못하기 때문이다. 또한, 2002년 Manindra Agrawal이 제시한 AKS 알고리즘(AKS algorithm)은 최초의 다항식 시간내 결정적 소수성 검정법(Polynomial time deterministic primality test)이지만, 시간 복잡도에서 다항식의 차수가 높기 때문에 현재 사용되고 있는 확률적 소수성 검정법(Probabilistic primality test)을 대체하지 못할 것으로 사료된다. 본 논문에서는 최근 발표된 소수성 검정법인 Frobenius-Grantham 소수성 검정법, AKS 알고리즘과 기존의 Miller-Rabin 소수성 검정법의 장단점을 비교·분석해 보고자 한다.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.595-602
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• 2007

This paper proposes a method that discriminates signal peptide and predicts the cleavage site of the secretory proteins cleaved by the signal peptidase I. The preprocessing stage uses hydrophobicity scales of amino acids in order to predict the presence of signal sequence and the cleavage site. The preprocessing enhances the performance of the prediction method by eliminating the non-secretory proteins in the early stage of prediction. for the effective use of support vector machine for the signal sequence prediction, the biologically relevant distance between the amino acid sequences is defined by using the hydrophobicity and substitution matrix; the hydrophobicity can be used to Predict the location of amino acid in a cell and the substitution matrix represents the evolutionary relationships of amino acids. The proposed method showed 98.9% discrimination rates from signal sequences and 88% correct rate of the cleavage site prediction on Swiss-Prot release 50 protein database using the 5-fold-cross-validation. In the comparison tests, the proposed method has performed significantly better than other prediction methods.

• The KIPS Transactions:PartC
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• v.18C no.1
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• pp.1-6
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• 2011

The performance and practicality of cryptosystem for encryption, decryption, and primality test is primarily determined by the implementation efficiency of the modular exponentiation of $a^b$(mod n). To compute $a^b$(mod n), the standard binary squaring still seems to be the best choice. But, the d-ary, (d=2,3,4,5,6) method is more efficient in large b bits. This paper suggests m-numeral system modular exponentiation. This method can be apply to$b{\equiv}0$(mod m), $2{\leq}m{\leq}16$. And, also suggests the another method that is exit the algorithm in the case of the result is 1 or a.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.599-609
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• 2021

Data with a large difference in the number of objects between clusters are called unbalanced data. In discriminant analysis of unbalanced data, it is more important to classify objects in minority categories than to classify objects in majority categories well. However, objects in minority categories are often misclassified into majority categories. In this study, we propose a method that combined hierarchical DBSCAN (HDBSCAN) and SMOTE to solve this problem. Using HDBSCAN, it removes noise in minority categories and majority categories. Then it applies SMOTE to create new data. Area under the roc curve (AUC) and F1 scores were used to compare performance with existing methods. As a result, in most cases, the method combining HDBSCAN and synthetic minority oversampling technique (SMOTE) showed a high performance index, and it was found to be an excellent method for classifying unbalanced data.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.251-256
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• 2007

성형외과에서는 웹 방문자를 늘리기 위하여 다양한 노력을 하고 있지만, 웹 사이트의 어떠한 속성이 웹 방문자 수를 증대시키는지에 대한 체계적인 연구는 찾아보기 어렵다. 본 논문에서는 방문자 수가 많은 성형외과 웹 사이트와 방문자 수가 적은 웹 사이트를 구분하는 속성을 규명하였다. 다중 판별 분석과 의사결정 나무 기법, 신경망 분석 기법을 이용하여 방문자의 다소 (多少)를 구분하는 속성들을 도출하였다. 웹 사이트의 속성 중 '가상성형프로그램', '정보추천' 등 소수의 속성이 방문자 수의 다소(多少)를 설명하는 것으로 드러났다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.37-39
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• 2020

인터넷 상에서의 허위정보 생산과 유통은 주로 가짜 뉴스를 통하여 이루어진다. 과거에는 신문이나 공중파 TV등 뉴스 기사의 생산과 유통이 매우 제한적이었지만 지금은 인터넷의 발달로 누구나 쉽게 뉴스를 생산하고 유통할 수 있다. 뉴스 생산의 용이성은 정보 공유의 즉각성과 수월성이라는 장점을 제공하지만 반대로 불확실한 뉴스 남발로 인한 정보의 신뢰성 하락과 선량한 피해자를 양산하는 단점 또한 존재한다. 이는 가짜 뉴스가 사회적 문제로 대두되고 있는 이유이다. 에이전트나 스파이더 등의 소프트웨어를 통해 인터넷으로 급속도로 전파되는 가짜 뉴스를 전통 방식인 소수의 전문가가 수동으로 잡아내는 것은 불가능하다. 이에 기술발달로 잡아내기 힘들어진 가짜뉴스에 대해, 역으로 발달된 기술을 활용하여 잡아내려는 시도가 늘어나고 있다. 본 논문에서는 가짜뉴스를 판별하는 다양한 기법들을 탐색하고 해결방안을 제시하고자 한다.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.235-245
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• 2012

Logistic discrimination is an useful statistical technique for quantitative analysis of financial service industry. Especially it is not only easy to be implemented, but also has good classification rate. Generalized additive model is useful for credit scoring since it has the same advantages of logistic discrimination as well as accounting ability for the nonlinear effects of the explanatory variables. It may, however, need too many additive terms in the model when the number of explanatory variables is very large and there may exist dependencies among the variables. Mixtures of factor analyzers can be used for dimension reduction of high-dimensional feature. This study proposes to use the low-dimensional factor scores of mixtures of factor analyzers as the new features in the generalized additive model. Its application is demonstrated in the classification of some real credit scoring data. The comparison of correct classification rates of competing techniques shows the superiority of the generalized additive model using factor scores.