• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.5
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• pp.299-306
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• 2015

A conventional phased array system can control an ultrasonic beam electronically by adjusting the excitation time delay of individual elements in a multi-element probe and produce an ultrasonic image. In Contrast, full matrix capture (FMC) is a data acquisition process that allows receiving ultrasonic signals from one single shot of the phased array transducer element through all the other elements and captures the complete dataset from every possible transmit-receive combination. This FMC data can be used to create the ultrasonic image in post processing. It is possible to produce not only images equivalent to conventional phased array image but also total focusing method (TFM) images with improved resolution and sharpness, which is virtually focused at any point in a region of interest. In this paper, the system that can perform FMC by using a conventional phased array instrument is developed, and a study was conducted on the imaging algorithms to reconstruct sector B-scan and TFM images from FMC dataset.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.167-171
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• 2007

Application areas of Knowledge Discovery in Database (KDD) have been expanded into many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has recently fully utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not cheap, korean language process not available, and user's demands not reflected. To solve these problems, Korea Institute of Science and Technology Information (KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. Knowledge Matrix main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. KnowledgeMatrix show better performances and offer more various functions than extant systems.

• KIISE Transactions on Computing Practices
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• v.20 no.11
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• pp.610-614
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• 2014

Like many types of scientific data, results from simulations of volcanic ash diffusion are of a clustered sparse matrix in the netCDF format. Since these data sets are large in size, they generate high storage and transmission costs. In this paper, we suggest a new method that reduces the size of the data of volcanic ash diffusion simulations by converting the multi-dimensional index to a single dimension and keeping only the starting point and length of the consecutive zeros. This method presents performance that is almost as good as that of ZIP format compression, but does not destroy the netCDF structure. The suggested method is expected to allow for storage space to be efficiently used by reducing both the data size and the network transmission time.

• Journal of Korean Society for Quality Management
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• v.42 no.2
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• pp.221-234
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• 2014

Purpose: The Purpose of this study is to develop a model for predicting agent churn group in the cosmetics industry. We develope two models, pattern model and matrix model, which are compared regarding the prediction accuracy of churn agents. Finally, we try to conclude if there is statistically significant difference between two models by empirical study. Methods: We develop two models using the part of RFM(Recency, Frequency, Monetary) method which is one of customer segmentation method in traditional CRM study. In order to ensure which model can predict churn agents more precisely between two models, we used CRM data of cosmetics company A in China. Results: Pattern model and matrix model have been developed. we find out that there is statistically significant differences between two models regarding the prediction accuracy. Conclusion: Pattern model and matrix model predict churn agents. Although pattern model employed the trend of monetary mount for six months, matrix model that used the amount of sales per month and the duration of the employment is better than pattern model in prediction accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.4
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• pp.53-68
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• 2008

We study on the practical privacy-preserving techniques by matrix-based randomization approach. We clearly examine the relationship between the two parameters associated with the measure of privacy breach and the condition number of matrix in order to achieve the optimal transition matrix. We propose a simple formula for efficiently calculating the inverse of transition matrix which are needed in the re-construction process of random substitution algorithm, and deduce some useful connections among standard error and another parameters by obtaining condition numbers according to norms of matrix and the expectation and variance of the transformed data. Moreover we give some experimental results about our theoretical expressions by implementing random substitution algorithm.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.421-431
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• 2014

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.393-396
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• 2011

In this paper, we propose a constrained alternating least squares nonnegative matrix factorization algorithm (cALSNMF) to detect active brain regions from single subject's task-related fMRI data. In cALSNMF, we define a new cost function which considers the uncorrelation and noisy problems of fMRI data by adding decorrelation and smoothing constraints in original Euclidean distance cost function. We also generate a novel training procedure by modifying the update rules and combining with optimal brain surgeon (OBS) algorithm. The experimental results on visuomotor task fMRI data show that our cALSNMF fits fMRI data better than original ALSNMF in detecting task-related brain activation from single subject's fMRI data.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.1
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• pp.79-92
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• 2000

We investigate numerical properties of Newton's algorithm for improving an eigenpair of a real matrix A using only fixed precision arithmetic. We show that under natural assumptions it produces an eigenpair of a componentwise small relative perturbation of the data matrix A.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.10
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• pp.141-146
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• 2000

We introduce the algorithms of 3-D position estimation using a laser sensor for automatic die remodeling. First, a vision sensor based on the optical triangulation was used to collect the range data of die surface. Second, line vector equations were constructed by the measured range data, and an analytic algorithm was proposed for recognizing the die location with these vector equations. This algorithm could make the transformation matrix without any specific corresponding points. To ascertain this algorithm, folded SUS plate was measured by the laser vision sensor attached to a 3-axis cartesian manipulator and the transformation matrix was calculated.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2
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• pp.68-72
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• 2000

Matched field processing technique is performed by considering complex underwater environments. Specially, the performance of minimum variance processor is greatly degraded by eigenvalue problem. In this paper, we propose the minimum variance matched field processor using shaping matrix. This shaping matrix makes that the input covariance matrix is invertible and enhances the desired acoustic source component. It was proved effectively range/depth localization of the proposed method with simulated data and vertical array data collected by NATO SACLANT Center north of the island of Elba off the Italian west coast.