Earticle

Since introduction, the Support Vector Machines (SVM) has been popularly used in machine learning and data mining tasks due to their strong mathematical background and promising result. Nevertheless, they are noticeably slow in the prediction stage. The speed is influenced by number of support vectors determined in the training phase. Motivated by this fact, several studies are done to reduce the number of support vectors. The reduction should consider the degeneration of learning quality and preserve it at much as possible. Most previous methodologies either reduce the training set or apply a post-processing step to reduce the number of support vectors. In this paper, we proposed a new SVM cost function called Step Regularized Support Vector Machine (SRSVM), which is a standard SVM with extra constrained to reduce the number of support vectors, which can be defined by user. Experimental results are done to evaluate the efficiency and speed of proposed algorithm. SRSVM are also compared to other related SVM algorithms. The comparisons showed that the proposed method is effective in reducing number of support vectors while preserving the high performance of the classifier.

In this paper, we propose a new method for reducing the number of distance calculations in the LBG (Linde, Buzo, Gray) algorithm, which is widely used method to construct a codebook in vector quantization of speech recognition system. The proposed algorithm can reduce the distance calculation between input vector and codeword by utilizing the observation that codewords are quickly stabilized as the number of iteration increases. From the simulation results, it is shown that we can reduce the running times over $43.77\%$ on average in comparison with current LBG algorithm without sacrificing the performance of codebook.

보안을 위협하는 요소들에 대하여 기존의 보안기술들은 응용계층 기술의 한계를 드러내고 있다. 이를 극복하기 위한 방법으로 보안 운영체제에 대한 연구가 활발히 진행되고 있지만, 보안정책 설정의 복잡성 때문에 일반 사용자들이 보안정책을 설정하여 적용하기가 어렵다. 본 논문에서는 대표적인 보안 운영체제인 SELinux의 보안모델과 이와 관련된 연구들을 살펴보고, SELinux의 정책 복잡성 개선을 위한 SELinux 보안정책 템플릿을 제안한다.

In this paper, we propose the computational complexity reduction methods of delta pitch search that is used in G.723.1 vocoder. In order to decrease the computational complexity in delta pitch search the characteristic of proposed algorithms is as the following. First, scheme to reduce the computational complexity in delta pitch search uses NAMDF. Developed the second scheme is the skipping technique of lags in pitch searching by using the threshold value. By doing so, we can reduce the computational amount of pitch searching more than 64% with negligible quality degradation.

본 논문에서는 H.26L 동영상 압축 표준화 방식에서 블록화 및 링 현상을 동시에 제거하는 계산량이 감소된 일차원 loop filter를 제안한다. 새로운 일차원 정규화 완화 함수가 정의되고 두 개의 인접 방향에서 완화의 정도를 조절하는 정규화 매개변수는 부호화와 복호화 부에서 이용 가능한 코드화 블록 패턴과 양자화 스텝크기로 정의된다. 그러므로, 정규화 매개변수를 정의하고 압축된 동영상으로부터 복원된 영상을 얻기 위한 기타 정보는 필요하지 않다. 실험결과로부터 제안된 알고리즘의 성능은 확인할 수 있었다.