• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.54.2-54.2
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• 2016

We present a machine learning approach to estimating stellar atmospheric parameters, effective temperature (Teff), surface gravity (log g), and metallicity ([Fe/H]) for stars observed during the course of the Sloan Digital Sky Survey (SDSS). For training a neural network, we randomly sampled the SDSS data with stellar parameters available from SEGUE Stellar Parameter Pipeline (SSPP) to cover the parameter space as wide as possible. We selected stars that are not included in the training sample as validation sample to determine the accuracy and precision of each parameter. We also divided the training and validation samples into four groups that cover signal-to-noise ratio (S/N) of 10-20, 20-30, 30-50, and over 50 to assess the effect of S/N on the parameter estimation. We find from the comparison of the network-driven parameters with the SSPP ones the range of the uncertainties of 73~123 K in Teff, 0.18~0.42 dex in log g, and 0.12~0.25 dex in [Fe/H], respectively, depending on the S/N range adopted. We conclude that these precisions are high enough to study the chemical and kinematic properties of the Galactic disk and halo stars, and we will attempt to apply this technique to Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which plans to obtain about 8 million stellar spectra, in order to estimate stellar parameters.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.225-230
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• 2002

The constrained off-line competitive deletion problem is a simple form of the set manipulation operations problem. It excludes the insertion operation from the off-line competitive deletion problem. An optimal sequential algorithm and a CREW PRAM algorithm which runs $O(log^2nloglogn)$ time using O(n/loglogn) processors were already presented in the literature. In this paper, we present a reconfigurable mesh algorithm for the constrained off-line competitive deletion problem. The proposed algorithm is executed in a constant time on an $n{\times}n$ reconfigurable mesh, and the result is $AT^2$ optimal.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.383-389
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• 2003

Clay minerals show a distinct induced-polarization phenomenon, which is one of the most important factors for predicting groundwater flow and contaminant transport. This paper presents a step-by-step process to estimate Cole-Cole parameters from spectral induced-polarization (IP) data measured on the surface of three-dimensional earth. First, the inversion of low-frequency resistivity survey data is made to identify the dc resistivity ${\rho}_dc$ of a volume having IP effects. The other parameters, chargeability m, time constant $\tau$, and frequency dependence c, are sought for the polarizable volume. Next, using multi-frequency data, c can be obtained as high or low asymptotes of the slope of log phase vs. log frequency. Further, for low m, intrinsic $\tau$ is approximated by apparent one, ${\tau}_a$, which is derived from the relation ${{\omega}{\tau}}_a$=1 at an angular frequency $\omega$, where the imaginary component of spectral IP data has an extreme value. Finally, to obtain intrinsic m a two-step linearized procedure has been derived. For a body of given $\tau$ and c, forward modeling with a progression of m values yields a plot of observed vs. intrinsic imaginary components for a frequency. Since this plot is essentially linear, to extract the intrinsic imaginary component is quite simple with an observed value. Using the plot of intrinsic imaginary component vs. m, intrinsic m is determined. We present a synthetic example to illustrate that the Cole-Cole parameters can be recovered from spectral IP data.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.399-406
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• 2013

Recently, studies about process mining for creating and analyzing business process models from log data have received much attention from BPM (Business Process Management) researchers. Process mining is a kind of method that extracts meaningful information and hidden rules from the event log of enterprise information systems such as ERP and BPM. In this paper, repair processes of electronic devices are analyzed using ProM which is a process mining tool. And based on the analysis of repair processes, the method for finding major failure patterns is proposed by multi-dimensional data analysis beyond simple statistics. By using the proposed method, the reliability of electronic device can be increased by providing the identified failure patterns to design team.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.241-252
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• 2011

During the last decades, other parts of operating systems, storage devices, and media are changed steadily, whereas filesystem is changed little. As data is grown bigger, the number of files to be managed also increases in geometrically. Researches about new filesystem schemes are being done widely to support these files efficiently. In web document search area, there are many researches about finding meaningful documents using semantic search. Many researches tried to apply these schemes, which is been proven in web document search previously, to filesystems. But they've focused only on higher layer of filesystem, that is not related seriously to storage media. Therefore they're not well tuned to physical characteristics of new flash memory based SSD which has different features against traditional HDD. We enhance log structured filesystem, that is already well known to work better in SSD, by putting semantic search scheme to and with multi logging point.

• The Journal of the Acoustical Society of Korea
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• v.25 no.3E
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• pp.101-109
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• 2006

Besides dorsal-aspect target strength (TS) of the fish, side-aspect TS information is also acoustically important parameter in fisheries acoustics. In this study, the side-aspect TS of 11 black sea bream (Acanthopagurus schlegeli) were measured using a split beam echosounder of 120, 200, and 420 kHz; total length of the fish ranged from 12.4 to 23.7 cm, and wet weight from 27.5 to 229.8g. For the precise TS measurement with side-aspect angle, we used anesthetized and tethered specimens of known size while being rotated through $360^{\circ}C$ by means of a carousel structure. The side-aspect TS measurements of the fish were conducted by rotating the fish in the horizontal plane at 50 interval. The ping interval was 0.2 second and elapsed time at each angle was 30-60 second. As a result, the measured side-aspect TS data were fitted by sinusoidal function. The relationships between fish length and near full side-aspect TS were as follows: $TS_{120kHz}= 21.46 log (TL)-67.5 (r = 0.70), \;TS_{200 kHz}= 31.03 log (TL)-76.9 (r=0.83),\;TS_{420 kHz}= 30.79 log (TL)-72.2 (r = 0.77)$. For comparison, theoretically estimated side-aspect TS from the Kirchhoff ray mode (KRM) model, which based on swimbladder and body morphology, were compared with the measured TS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1312-1317
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• 2003

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in[10] to handle the pose variations. For a given color input image, the detector encloses a face in a complex scene within a circular boundary and indicates the position of the nose. Next, for recognition, a radial grid mapping centered on the nose yields a feature vector within the circular boundary. As the width of the color segmented region provides an estimated size for the face, the extracted feature vector is scale normalized by the estimated size. The feature vector is input to a trained neural network classifier for face identification. The system was evaluated using a database of 20 person's faces with varying scale and pose obtained on different complex backgrounds. The performance of the face recognizer was also quite good except for sensitivity to small scale face images. The integrated system achieved average recognition rates of 87% to 92%.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.593-610
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• 2015

Adaptive Rejection Sampling (ARS) method is a well-known random number generator to acquire a random sample from a probability distribution, and has the advantage of improving the proposal distribution during the sampling procedures, which update it closer to the target distribution. However, the use of ARS is limited since it can be used only for the target distribution in the form of the log-concave function, and thus various methods have been proposed to overcome such a limitation of ARS. In this paper, we attempt to compare five random number generators based on ARS in terms of adequacy and efficiency. Based on empirical analysis using simulations, we discuss their results and make a comparison of five ARS-based methods.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.909-920
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• 2003

Nowadays, IT for CRM has been growing and developed rapidly. Typical techniques are statistical analysis tools, on-line multidimensional analytical processing (OLAP) tools, and data mining algorithms (such neural networks, decision trees, and association rules). Among customer data, web log data is very important and to use these data efficiently, applying OLAP technology to analyze multi-dimensionally. To make OLAP cube, we have to precalculate multidimensional summary results in order to get fast response. But as the number of dimensions and sparse cells increases, data explosion occurs seriously and the performance of OLAP decreases. In this paper, we presented why the web log data sparsity occurs and then what kinds of sparsity patterns generate in the two and t.he three dimensions for OLAP. Based on this research, we set up the multidimensional data models and query models for benchmark with each sparsity patterns. Finally, we evaluated the performance of three OLAP systems (MS SQL 2000 Analysis Service, Oracle Express and C-MOLAP).

• Journal of Environmental Health Sciences
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• v.39 no.2
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• pp.187-195
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• 2013

Objectives: For the practical application of the dielectric barrier discharge plasma reactor, a plasma reactor able to manage large volumes of water is needed. This study investigated the possibility of the practical application of a multi-plasma reactor which is a scaled-up version of a single plasma reactor. Methods: The multi-plasma reactor consists of several high-voltage transformers and plasma modules (discharge, ground electrodes and quartz dielectric tubes). The effects of water characteristics such as voltage (30-120 V), air flow rate (1-5 l/min), number of high-voltage transformers and plasma modules, and water quality on Escherichia coli (E. coli) disinfection and decrease of COD and $UV_{254}$ absorbance were investigated. Results: The experimental results showed that at a voltage of over 80 V, most of the E. coli were disinfected within 90 seconds. E. coli inactivation was not affected by the air flow rate. E. coli disinfection in the multiplasma process showed the traditional log-linear form of the disinfection curve. E. coli inactivation performance by transformer 3-Reactor 5 and transformer 3-Reactor 3 were similar. The disinfection performance of the UV process was affected by artificial sewage water. However, the plasma process was less affected by the artificial sewage within the standards for effluent water quality. Conclusions: Disinfection performance with several low voltages and plasma modules of three to five in number applied to the plasma process was higher than that concentrating a small amount of high voltage through a single plasma reactor. Removal of COD, $UV_{254}$ absorbance, and E. coli disinfection with the plasma process were better than with the UV process.