• Journal of the Korean Mathematical Society
• /
• v.52 no.4
• /
• pp.797-819
• /
• 2015

A trapdoor discrete logarithm group is a cryptographic primitive with many applications, and an algorithm that allows discrete logarithm problems to be solved faster using a pre-computed table increases the practicality of using this primitive. Currently, the distinguished point method and one extension to this algorithm are the only pre-computation aided discrete logarithm problem solving algorithms appearing in the related literature. This work investigates the possibility of adopting other pre-computation matrix structures that were originally designed for used with cryptanalytic time memory tradeoff algorithms to work as pre-computation aided discrete logarithm problem solving algorithms. We find that the classical Hellman matrix structure leads to an algorithm that has performance advantages over the two existing algorithms.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1994.06c
• /
• pp.42-47
• /
• 1994

A pitch detector is an essential component in a variety of speech processing systems. Besides providing valuable insights into the nature of the exciation source for speech production, the pitch contour of an utterance is useful for recognizing speakers, aids-to-the handicapped, and is required in almost all speech analysis-synthesis system. Because of the importance of the pitch detection, a wide variety algorithms for pitch detection have been proposed in speech procesing literature. Thus, in this paper we discuss th evarious type of pitch detection algorithms which have been proposed until now. Then we provide th eperformance measurements for seven pitch detection algorithms.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.11
• /
• pp.8-15
• /
• 1992

This paper analyzes the properties of such algorithm that corresponds to the LMS algorithm with additional update terms, parameterized by the scalar factors $\alpha$ and $\beta$, and presents its structure. The analysis of convergence leads to complex eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The computational cmomplexities of MLMS algorithms are compared with those of MADF, sign and the conventional LMS algorithms. In application of the system identification the second order momentum MLMS algorithm has faster convergence speed than LMS and the first order MLMS algorithms.

• The Journal of the Acoustical Society of Korea
• /
• v.3 no.1
• /
• pp.25-35
• /
• 1984

This paper analysis the Dynamic Time Warping algorithms for time normalization of speech pattern and discusses the Dynamic Programming algorithm for spoken Korean isolated digits recognition. In the DP matching, feature vectors of the reference and test pattern are consisted of first three formant frequencies extracted by power spectrum density estimation algorithm of the ARMA model. The major differences in the various DTW algorithms include the global path constrains, the local continuity constraints on the path, and the distance weighting/normalization used to give the overall minimum distance. The performance criterias to evaluate these DP algorithms are memory requirement, speed of implementation, and recognition accuracy.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1998.10a
• /
• pp.468-473
• /
• 1998

The objective of this paper is to develop a consistent algorithm for the finite element analysis for behavior of concrete under cyclic loading using viscoplastic-damage model. For modeling the behavior of concrete under cyclic loading, consistent algorithms of rate-dependent viscoplastic-damage are employed with a Willam-Warnke 5-parameter failure criterion which can consider the softening behavior of concrete and consistent tangent moduli are derived. Using finite element program implemented with the developed algorithms, the algorithms are verified and the behaviors of concrete under cylic loading are simulated and compared with experimental data.

• Communications of the Korean Mathematical Society
• /
• v.26 no.4
• /
• pp.685-693
• /
• 2011

General framework for proximal point algorithms based on the notion of (A, ${\eta}$)-maximal monotonicity (also referred to as (A, ${\eta}$)-monotonicity in literature) is developed. Linear convergence analysis for this class of algorithms to the context of solving a general class of nonlinear variational inclusion problems is successfully achieved along with some results on the generalized resolvent corresponding to (A, ${\eta}$)-monotonicity. The obtained results generalize and unify a wide range of investigations readily available in literature.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.967-968
• /
• 2008

In this paper we demonstrate the effect of sparse decomposition on various Independent Component Analysis (ICA) algorithms for separating simultaneous linear mixture of independent 2-D signals (images). We will show using simulated results that sparse decomposition before Kernel ICA (Sparse Kernel ICA) algorithm produces the best results as compared to other ICA algorithms.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.259-259
• /
• 2000

In the recent, the size of hardware is smaller and the structure is simpler, without reducing the performance of the digital controller. Accordingly, the fixed-point arithmetic is very important in the digital controller. This paper presents simulation to apply the robust control algorithms to DVDR servo controller using the floating-point and fixed-point arithmetic from the matlab. Also, it analyses and compares the performance of control algorithms in the each of point calculation and presents a method for improvement of drop in the performance, quantization error and overflow/underflow from using the fixed-point arithmetic

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.04b
• /
• pp.289-291
• /
• 2002

Host of source separation methods focus on stationary sources so higher-order statistics is necessary In this paler we consider a problem of source separation when sources are second-order nonstationary stochastic processes . We employ the natural gradient method and develop learning algorithms for both 1inear feedback and feedforward neural networks. Thus our algorithms possess equivariant property Local stabi1iffy analysis shows that separating solutions are always locally stable stationary points of the proposed algorithms, regardless of probability distributions of

• Journal of the Korea Safety Management & Science
• /
• v.8 no.3
• /
• pp.1-9
• /
• 2006

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare algorithms for data analysis of industrial accidents and this paper provides an optimal predicting model of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. Also, this paper provides an approximation model for an optimal predicting model based on NN. The approximation model provided in this study can be utilized for easy interpretation of data analysis using NN. This study uses selected ten independent variables to group injured people according to a dependent variable in a way that reduces variation. In order to find an optimal predicting model among 5 algorithms, a retrospective analysis was performed in 67,278 subjects. The sample for this work chosen from data related to industrial accidents during three years ($2002\;{\sim}\;2004$) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.