• Journal of applied mathematics & informatics
• /
• 제22권1_2호
• /
• pp.307-316
• /
• 2006

Incomplete LU factorization preconditioning techniques often have difficulty on indefinite sparse matrices. We present hybrid reordering strategies to deal with such matrices, which include new diagonal reorderings that are in conjunction with a symmetric nondecreasing degree algorithm. We first use the diagonal reorderings to efficiently search for entries of single element rows and columns and/or the maximum absolute value to be placed on the diagonal for computing a nonsymmetric permutation. To augment the effectiveness of the diagonal reorderings, a nondecreasing degree algorithm is applied to reduce the amount of fill-in during the ILU factorization. With the reordered matrices, we achieve a noticeable improvement in enhancing the stability of incomplete LU factorizations. Consequently, we reduce the convergence cost of the preconditioned Krylov subspace methods on solving the reordered indefinite matrices.

• 한국통신학회논문지
• /
• 제16권5호
• /
• pp.468-574
• /
• 1991

분산 너버우전탐색 트리구성에서 통신복잡성은 몇가지 임계값에 기초한 효과적인 통신동기방식에 의해서 개선될 수 있다. 본 논문에서는 분산 그래프의 밀도함수로서 임계값을 설정하고 그 임계값을 이용하여 통신 동기방식에 의거한 분산 너비우선탐색 트리 구성 알고리즘을 제안하였다. 제안된 알고리즘은 밀도가 낮은 그래프에서 기존의 알고리즘보다 통신 복잡성면성 면에서 수학적 분석을 통해개선됨을 입증하였으며, 밀도가 높은 그래프에서는 현재와 동일함을 입증함으로써 본 논문에서 제안한 알고리즘이 종래의 알고리즘들보다 통신 복잡성에서 가장 효율적임을 보았다.

• 방송공학회논문지
• /
• 제18권3호
• /
• pp.349-361
• /
• 2013

본 논문에서는 깊이 정보만을 이용하여 얼굴을 고속으로 추적하는 방법을 제안하다. 그 방법으로는 템플릿 매칭 방법을 사용하며, 템플릿 매칭 방법의 문제점인 과다한 수행시간의 문제를 해결하여 고속으로 얼굴을 추적하기 위하여 조기종료 기법과 sparse 탐색 기법을 적용하고, 그에 따른 추적오류를 보정하고자 주변 화소들을 대상으로 매칭보정을 수행한다. 얼굴의 움직임에 따른 깊이의 변화를 보정하기 위해 추적할 얼굴의 깊이 값을 추정하고 그 결과에 따라 템플릿의 크기를 조정한다. 또한 조정된 템플릿의 크기에 따라 템플릿 매칭을 수행할 탐색영역을 조정한다. 자체 제작한 테스트 시퀀스들을 사용하여 추적에 필요한 파리미터들을 결정하였으며, 또 다른 자체 제작한 테스트 시퀀스들과 MPEG에서 제공한 다시점 테스트 시퀀스를 제안한 방법에 적용하는 실험을 수행하였다. 실험결과 Kinect을 이용하여 자체제작($640{\times}480$) 시퀀스에서는 약 3%의 추적오류와 2.45ms의 수행시간을 보였으며, Lovebird1($1024{\times}768$) 시퀀스에서는 약 1%의 추적 오류와 7.46ms의 수행시간을 보였다.

• Nuclear Engineering and Technology
• /
• 제55권8호
• /
• pp.3030-3038
• /
• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.86-89
• /
• 2003

To model a numerical problem space under the limitation of available data, we need to extract sparse but key points from the space and to efficiently approximate the space with them. This study proposes a sampling method based on the search process of genetic algorithm and a space modeling method based on least-squares approximation using the summation of Gaussian functions. We conducted simulations to evaluate them for several kinds of problem spaces: DeJong's, Schaffer's, and our original one. We then compared the performance between our sampling method and sampling at regular intervals and that between our modeling method and modeling using a polynomial. The results showed that the error between a problem space and its model was the smallest for the combination of our sampling and modeling methods for many problem spaces when the number of samples was considerably small.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집(한일합동학술편); 한국과학기술대학, 충남; 16-17 Oct. 1987
• /
• pp.791-796
• /
• 1987

In this paper, an efficient algorithm to estimate the volume and surface area from ultrasonic imaging and a reconstruction algorithm to generate three-dimensional graphics are presented. The computing efficiency is Improved by using the graph theory and the algorithm to determine proper contour points is performed by applying several tolerances. The search for contour points is limited by the change in curvature in order to provide an efficient search of the minimum cost path. These algorithms are applied to a selected mathematical model of ellipsoid. The results show that the measured value of the volume and surface area for the tolerances of 1.0005, 1.001 and 1.002 approximate to the measured values for the tolerance of 1.000 resulting in small errors. The reconstructed 3-dimensional Images are sparse and consist of larger triangular tiles between two cross sections as tolerance is increased.

• 정보처리학회논문지D
• /
• 제8D권2호
• /
• pp.125-131
• /
• 2001

우리 사회에는 많은 종류의 재해가 발생한다. 일반적으로 재해가 발생하면 귀중한 생명 또는 소중한 재산들을 보호하기 위해 즉각적인 조치가 필요하다. 재해 또는 사고가 발생하면 사고 내용을 즉시 재해 또는 사고 대책 본부에 보고되고 즉각적인 처리를 위한 명령들이 관할 기관에 전달된다. 이 논문은 먼저 능동 데이터베이스의 트리거 기능을 이용한 지능현 무인 비상대책 시스템을 소개한다. 무인 비상 대첵 싯쳄은 사람의 직접적인 참여 없이 저장하고 있는 지식을 이요하여 다양한 종류와 사고를 처리한다. 이 시스템의 핵심 기능 중 하나는 적절한 처리 명령이 전달될 위치를 신속히 찾아내는 기능이다. 본 논문에서는 무인 대책 시스템과 같은 능동 시스템에서의 명령이 전달될 위치를 신속히 찾기 취해서 전형화 방법인 Z-ordering 방법을 사용하여 희소 공간에서 효과적인 영역질의와 최근접 질의를 할 수 있는 새로운 방법을 제시ㅏ였다. 뿐만 아니라 최소 강간에 존재하는 위치 사이의 거리가 직선거리가 아닌 실제 상황을 고려한 경우에도 처리할 수 있는 방법을 제시하였다.

• 천문학회보
• /
• 제42권2호
• /
• pp.54.2-54.2
• /
• 2017

While quasars are sparse in number density, they reside at relatively high-redshift as compared to e.g. luminous red galaxies. Hence, they are likely to be less non-linearly evolved than the galaxy population, and thus have a distribution that more closely mirrors the primordial density field. Therefore, they offer an intriguing opportunity to search for Baryonic Acoustic Oscillations (BAO). To this end, the 3-point correlation function (3PCF) is an excellent statistical tool to detect BAO. In this work, we will make the first-ever measurement of the large-scale quasar 3PCF from the SDSS-IV DR14 quasar sample (spanning the largest volume to-date). This work will use the order N2-time 3PCF algorithmof Slepian & Eisenstein (2015), with N the number of objects.

• 천문학회보
• /
• 제42권1호
• /
• pp.53.3-53.3
• /
• 2017

While quasars are sparse in number density, they reside at relatively high-redshift as compared to galaxies. Hence, they are likely to be less non-linearly evolved than the galaxy population, and thus have a distribution that more closely mirrors the primordial density field. Therefore, they offer an intriguing opportunity to search for primordial non-Gaussianity (PNG). To this end, the 3-point correlation function (3PCF) is an excellent statistical tool to detect departures from Gaussianity, vanishing for a Gaussian field. In this work, we will make the first-ever measurement of the large-scale quasar 3PCF from the SDSS-IV DR14 quasar sample (spanning the largest volume to-date) to place constraints on PNG through the usual f_NL-type parametrization. This work will use the order N^2-time 3PCF algorithm of Slepian & Eisenstein (2015), with N the number of objects.

• 음성과학
• /
• 제10권1호
• /
• pp.71-84
• /
• 2003

In the acoustic modeling for large vocabulary speech recognition, a sparse data problem caused by a huge number of context-dependent (CD) models usually leads the estimated models to being unreliable. In this paper, we develop a new clustering method based on the C45 decision-tree learning algorithm that effectively encapsulates the CD modeling. The proposed scheme essentially constructs a supervised decision rule and applies over the pre-clustered triphones using the C45 algorithm, which is known to effectively search through the attributes of the training instances and extract the attribute that best separates the given examples. In particular, the data driven method is used as a clustering algorithm while its result is used as the learning target of the C45 algorithm. This scheme has been shown to be effective particularly over the database of low unknown-context ratio in terms of recognition performance. For speaker-independent, task-independent continuous speech recognition task, the proposed method reduced the percent accuracy WER by 3.93% compared to the existing rule-based methods.