• Journal of applied mathematics & informatics
• /
• v.20 no.1_2
• /
• pp.35-59
• /
• 2006

In this paper, we generalized the results of [23, 26], and get the results of the condition number of the W-weighted Drazin-inverse solution of linear system W AW\chi=b, where A is an $m{\times}n$ rank-deficient matrix and the index of A W is $k_1$, the index of W A is $k_2$, b is a real vector of size n in the range of $(WA)^{k_2}$, $\chi$ is a real vector of size m in the range of $(AW)^{k_1}$. Let $\alpha$ and $\beta$ be two positive real numbers, when we consider the weighted Frobenius norm $\|[{\alpha}W\;AW,\;{\beta}b]\|$(equation omitted) on the data we get the formula of condition number of the W-weighted Drazin-inverse solution of linear system. For the normwise condition number, the sensitivity of the relative condition number itself is studied, and the componentwise perturbation is also investigated.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.10
• /
• pp.1317-1322
• /
• 2021

Nonlocal similarity of natural images leads to the fact that a patch matrix whose columns are similar patches of the reference patch has a low rank. Images corrupted by additive white Gaussian noises (AWGN) make their patch matrices to have a higher rank. The noise in the image can be reduced by obtaining low rank approximation of the patch matrices. In this paper, an image denoising algorithm is proposed, which first constructs the patch matrices by combining the similar patches of each reference patch, which is a part of the noisy image. For each patch matrix, the proposed algorithm calculates its low rank approximate, and then recovers the original image by aggregating the low rank estimates. The simulation results using widely accepted test images show that the proposed denoising algorithm outperforms four recent methods.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.714-717
• /
• 1996

This paper presents a fault detection and diagnosis methodologies based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. At the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model(WSM) is used to generate those candidates. The weight is determined from dynamic simulation. Using WSMs, the methodology can generate the cause candidates and rank them according to the probability. Secondly, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies and showed satisfactory diagnostic resolution.

• Communications for Statistical Applications and Methods
• /
• v.19 no.3
• /
• pp.367-379
• /
• 2012

In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.5
• /
• pp.520-525
• /
• 1997

This paper presents a fault detection and diagnosis methodology based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. In the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model is used to generate those candidates. The weight is determined from dynamic simulation. Using WSM, the methodology can generate the cause candidates and rank them according to the probability. Second, the fault propagation trends identified from the partial or complete sequence of measurements are compared with the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies, and the results showed satisfactory diagnostic resolution.

• Journal of the Korean Society for information Management
• /
• v.41 no.3
• /
• pp.511-533
• /
• 2024

This study aims to develop novel centrality measures applicable to networks that include both directional and weighted information, such as interlibrary loan networks and logistics transportation networks. While weighted PageRank has traditionally been used in such cases, experimental results reveal that it yields similar outcomes to neighborhood centrality, which measures local centrality. However, triangle betweenness centrality (TBC), despite assessing global centrality in weighted networks, does not consider link directions. To address these limitations, we propose two modified versions of the existing TBC measure: TBC-T for trust networks and TBC-F for flow networks. Applying these measures to two interlibrary loan networks, we find that TBC-T considers only the weights of inlinks, while TBC-F incorporates both inlink and outlink weights. These newly developed measures are expected to be useful for measuring node global centrality in weighted directed networks.

• International Journal of Contents
• /
• v.12 no.3
• /
• pp.22-28
• /
• 2016

Tensor with missing or incomplete values is a ubiquitous problem in various fields such as biomedical signal processing, image processing, and social network analysis. In this paper, we considered how to reconstruct a dataset with missing values by using tensor form which is called tensor completion process. We applied Tucker factorization to solve tensor completion which was built base on optimization problem. We formulated the optimization objective function using components of Tucker model after decomposing. The weighted least square matric contained only known values of the tensor with low rank in its modes. A first order optimization method, namely Nonlinear Conjugated Gradient, was applied to solve the optimization problem. We demonstrated the effectiveness of the proposed method in EEG signals with about 70% missing entries compared to other algorithms. The relative error was proposed to compare the difference between original tensor and the process output.

• Annual Conference of KIPS
• /
• 2022.11a
• /
• pp.354-356
• /
• 2022

소셜 네트워크 및 웹 데이터와 같은 대규모 그래프 데이터를 처리하기 위해 병렬 처리 기반의 기법들이 많이 사용되어 왔다. 본 연구에서는 그래프 형식의 대규모 교통 데이터를 하둡 맵리듀스를 이용하여 처리하는 효과적인 기법을 제안한다. 제안하는 방식에서는 도시의 유동 인구 흐름을 가중치로 고려할 수 있도록 Weighted PageRank 알고리즘을 기반으로 하는 병렬 그래프 알고리즘을 사용하며, 해당 알고리즘을 하둡 맵리듀스에 적용하여 주거 및 근무지 등의 지역을 분류하도록 결과를 분석하였다. 제안 기법을 통한 분석 결과를 기반으로 지역 간 유동 인구 그래프 데이터에서 각 도시의 영향력을 측정하는 페이지랭크, 하둡 맵리듀스 기반의 기법을 제시한다.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.3
• /
• pp.569-580
• /
• 2015

Log-rank is widely used for testing equality of two survival functions, and this method is efficient only under the proportional hazard assumption. However, crossing survival functions are common in practice. Therefore, many approaches have been suggested to test equality of them. This study considered several methods; Renyi type test, modified Kolmogorov-Smirnov and Cramer-von Mises test, and weighted Log-rank test, which can be applied when the survival functions cross, and simulated power of those methods. Based on the simulation results, we provide the useful information to choose a suitable approach in a given situation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.5
• /
• pp.1163-1185
• /
• 2024

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.