• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3482-3497
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• 2021

Artificial intelligence has emerged as the core of the 4th industrial revolution, and large amounts of data processing, such as big data technology and rapid data analysis, are inevitable. The most fundamental and universal data interpretation technique is an analysis of information through regression, which is also the basis of machine learning. Ridge regression is a technique of regression that decreases sensitivity to unique or outlier information. The time-consuming calculation portion of the matrix computation, however, basically includes the introduction of an inverse matrix. As the size of the matrix expands, the matrix solution method becomes a major challenge. In this paper, a new algorithm is introduced to enhance the speed of ridge regression estimator calculation through series expansion and computation recycle without adopting an inverse matrix in the calculation process or other factorization methods. In addition, the performances of the proposed algorithm and the existing algorithm were compared according to the matrix size. Overall, excellent speed-up of the proposed algorithm with good accuracy was demonstrated.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.681-691
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• 2010

LiDAR waveform decomposition plays an important role in LiDAR data processing since the resulting decomposed components are assumed to represent reflection surfaces within waveform footprints and the decomposition results ultimately affect the interpretation of LiDAR waveform data. Decomposing the waveform into a mixture of Gaussians involves two related problems; 1) determining the number of Gaussian components in the waveform, and 2) estimating the parameters of each Gaussian component of the mixture. Previous studies estimated the number of components in the mixture before the parameter optimization step, and it tended to suggest a larger number of components than is required due to the inherent noise embedded in the waveform data. In order to tackle these issues, a new LiDAR waveform decomposition algorithm based on the sequential approach has been proposed in this study and applied to the ICESat waveform data. Experimental results indicated that the proposed algorithm utilized a smaller number of components to decompose waveforms, while resulting IMP value is higher than the GLA14 products.

• Physical Therapy Korea
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• v.11 no.4
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• pp.61-69
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• 2004

EMG is used in rehabilitation research to provide a method to infer muscle function. This paper will present an introduction to interpretation of electromyography (EMG) data for physical therapists. It is important for the physical therapist to have an understanding of the collection and reduction of raw electrical data from the muscle to allow the physical therapist to interpret findings in a research report, and improve planning of clinical research projects with respect to data collection. We will discuss factors that affect the type of EMG collected and the ways in which various common methods of data reduction will impact the findings from a study that uses EMG.

• Fire Science and Engineering
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• v.11 no.4
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• pp.25-31
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• 1997

In this study, EMTP(Electromagnetic Transients Program) which is one of the most well-known computer simulation methods is used to collect the data for a power plant fault. EMTP is the program for an interpretation of the phenomena of electric transients, and is designed to manifest the data of the electric current and voltage etc. at the time of a power plant fault. By EMTP, I analyze the properties of the heat energy which are transferred from the electrics when a power plant fault brings out. In terms of the results of this study, it is able to measure the heat energy at a power plant fault (power transformer) and to be acquired of the related data. And moreover, these data are expected to be used as a standard for the protection of the fire owing to a high voltage power transformer fault.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.65
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• pp.41-50
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• 2001

Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and intercluster similarity is minimized. The discovered clusters from clustering process are used to explain the characteristics of the data distribution. In this paper we propose a new methodology for clustering related transactions with categorical attributes. Our approach starts with transforming general relational databases into a transactional databases. We make use of inter-dimensional association rules for composing hypergraph edges, and a hypergraph partitioning algorithm for clustering the values of attributes. The clusters of the values of attributes are used to find the clusters of transactions. The suggested procedure can enhance the interpretation of resulting clusters with allocated attribute values.

• Economic and Environmental Geology
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• v.21 no.1
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• pp.85-96
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• 1988

Geophysical well logging at various coal fields were carried out to study the characteristic response of domestic coal seams. Also a computer program is developed for quantitative analysis of coal logging data. Most coal seams penetrated by the drill holes, where the well logging were carried out, showed poor thickness and quality, and were severely altered. Therefore, majority of log data are inadequate for detailed quantitative analysis. The logs show, however, typical characteristics with related to coal seams, but interpretation should be made with caution because certain log response of demestic coals, mostly anthracite, are quite different to those of foreign coals, mostly bituminous. The developed comuter program has been proved as an effective one for identification of coal seams and lithology anslysis, and is expected to be succesfully used for coal quality analysis in cases of more diversified log data of good quality being obtained.

• Economic and Environmental Geology
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• v.22 no.3
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• pp.267-276
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• 1989

This paper presents results of interpretaton of gravity data by iterative nonlinear inversion methods. The gravity data are obtained by a theoretical formula for two-dimensional 2-layer structure. Depths to the basement of the structure are determined from the gravity data by four interative inversion methods. The four inversion methods used here are the Gradient, Gauss-Newton, Newton-Raphson, and Full Newton methods. Inversions are performed by using different initial guesses of depth for the over-determined, even-determined, and under-determined cases. This study shows that the depth can be determined well by all of the methods and most efficiently by the Newton-Raphson method.

• ETRI Journal
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• v.36 no.6
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• pp.1023-1031
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• 2014

Failure analysis is necessary to clarify the root cause of a failure, predict the next time a failure may occur, and improve the performance and reliability of a system. However, it is not an easy task to analyze and interpret failure data, especially for complex systems. Usually, these data are represented using many attributes, and sometimes they are inconsistent and ambiguous. In this paper, we present a scalable approach for the analysis and interpretation of failure data of high-performance computing systems. The approach employs rough sets theory (RST) for this task. The application of RST to a large publicly available set of failure data highlights the main attributes responsible for the root cause of a failure. In addition, it is used to analyze other failure characteristics, such as time between failures, repair times, workload running on a failed node, and failure category. Experimental results show the scalability of the presented approach and its ability to reveal dependencies among different failure characteristics.

• Economic and Environmental Geology
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• v.33 no.4
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• pp.283-293
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• 2000

A set of geophysical survey results over an area in Bookil-myun, Chungwon-Gun, Chungcheongbuk-Do is presented; resistivity logging, d.c. sounding, dipole-dipole resistivity, and controlled-source magnetotelluric (CSMT) surveys. These surveys were chosen in this research for the estimation of the basement depth and the delineation of the hydrogeologic structure over the survey area. The results provide an optimal input to a hydrogeologic modeling analysis using the strategies built in GIS software. A total of 14 lines of dipole-dipole resistivity surveys, 25 stations of d.c. sounding and 6 stations of CSMT sounding were performed. In addition 10 boreholes were chosen for resistivity logging to correlate the logs to the surface data. A quantitative information on the hydrogeologic structure over the area is provided by synthesizing the results from various geophysical data and attribute layers are constructed by utilizing a GIS software Arc/ Info. The constructed layers match well to the hydrogeologic structures, which were outlined from the drilling data. The methodology tested and adopted in this study would be useful for providing a more reliable input to the hydrogeologic model setup.