• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.723-730
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• 2009

Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random variables and predicted by the expectation of the unknown values given the data. The purpose of this study is to impute missing values which are regarded as the maximum likelihood estimator and random variable in incomplete data and to compare with two methods using ARMA model. For illustration, the Mumps data reported from the national capital region monthly over the years 2001 ${\sim}$ 2006 are used, and results from two methods are compared with using SSF(Sum of square for forecasting error).

• Journal of The Korean Astronomical Society
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• v.36 no.2
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• pp.67-72
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• 2003

The basic objective of helioseismology is to determine the structure and the dynamics of the Sun by analysing the frequency spectrum of the solar oscillations. Accurate frequency measurements provide information that enables us to probe the solar interior structure and the dynamics. Therefore the frequency of the solar oscillation is the most fundamental and important information to be extracted from the solar oscillation observation. This is why many efforts have been put into the development of accurate data analysis techniques, as well as observational efforts. To test one's data analysis method, a realistic artificial data set is essential because the newly suggested method is calibrated with a set of artificial data with predetermined parameters. Therefore, unless test data sets reflect the real solar oscillation data correctly, such a calibration is likely incomplete and a unwanted systematic bias may result in. Unfortunately, however, commonly used artificial data generation algorithms insufficiently accommodate physical properties of the stochastic excitation mechanism. One of reason for this is that it is computaionally very expensive to solve the governing equation directly. In this paper we discuss the nature of solar oscillation excitation and suggest an efficient algorithm to generate the artificial solar oscillation data. We also briefly discuss how the results of this work can be applied in the future studies.

• Journal of Korean Library and Information Science Society
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• v.41 no.3
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• pp.351-372
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• 2010

This study was conducted to improve the classification schemes of KDC in the fields of motion picture materials. Classification schemes of the KDC, DDC and LCC for motion picture materials were analyzed and compared. From the results, KDC was found to be the least suitable classification system to handle motion picture materials because it had very poorly detailed subdivisions of items and incomplete subsidiary tables to develop some subject items to represent various aspects of motion picture fields. Some suggestions to cover the deficiencies of KDC for motion picture fields were made in terms of 9 subdivisions and 53 sub-items. Particularly, new subdivisions of ‘types of genre', ’motion picture festival', 'worldwide motion pictures' and ’people related to motion pictures' were added.

• 한국연소학회:학술대회논문집
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• 2003.12a
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• pp.97-102
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• 2003

Experimental investigations were carried out in an atmospheric pressure, optically accessible and laboratory-scale dump combustor operating on natural gas. The objective of this study is to obtain the phase-resolved gas temperatures at different phases of the oscillating pressure cycle during unstable combustion. CARS temperature measurements were made at several spatial locations under lean premixed conditions to get the information on temperature field within the combustor. Also the effect of incomplete fuel-air mixing on phase-resolved temperature fluctuation was investigated. Results including phase-resolved averaged temperature, normalized standard deviation and temperature probability distribution functions (PDFs) were provided in this paper. Temperature PDFs give an insight on the flame behavior. And strong correlation between phase-resolved temperature profile and pressure cycle was observed. Results of the phase-resolved high temperature give an additional information on the perturbation of equivalence ratio at flame as well as the effect of mixing quality on NOx emission characteristics.

• Journal of Ship and Ocean Technology
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• v.4 no.3
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• pp.1-12
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• 2000

A number of numerical methods like Computational Fluid Dynamics(CFD) have been developed to predict the flow fields of a vessel but the present study is developed to infer the wake fields on propeller plane by Statistical Fluid Dynamics(SFD) approach which is emerging as a new technique over a wide range of industrial fields nowadays. Neural network is well known as one prospective representative of the SFD tool and is widely applied even in the engineering fields. Further to its stable and effective system structure, generalization of input training patterns into different classification or categorization in training can offer more systematic treatments of input part and more reliable result. Because neural network has an ability to learn the knowledge through the external information, it is not necessary to use logical programming and it can flexibly handle the incomplete information which is not easy to make a definition clear. Three dimensional stern hull forms and nominal wake values from a model test are structured as processing elements of input and output layer respectively and a neural network is trained by the back-propagation method. The inferred results show similar figures to the experimental wake distribution.

• Journal of the Korea Society of Computer and Information
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• v.20 no.10
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• pp.91-97
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• 2015

In this paper, we propose a MCFQ I/O scheduler that is implemented by modifying the existing Linux CFQ I/O scheduler. MCFQ observes whether the user requested I/O bandwidth weight is well distributed. Based on the I/O bandwidth observation, we improved I/O performance of the existing bandwidth distribution ability by dynamically controlling the I/O time-slice of the virtual machine. The use of SSDs as storage has been increasing dramatically in recent computer systems due to their fast performance and low power usage. As the usage of SSD increases and prices fall, virtualized system administrators can take advantage of SSDs. However, studies on guaranteeing SLA(Service Level Agreement) services when multiple virtual machines share the SSD is still incomplete. In this paper was conducted to improve performance of the bandwidth distribution when multiple virtual machine are sharing a single SSD storage in a virtualized environment. In particular, it was observed that the performance of the bandwidth distribution varied widely when garbage collection occurs in the SSD. In order to reduce performance variance, we add a MoTS(Manager of Time Slice) on existing CFQ I/O scheduler.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1940-1946
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• 2013

The Object of smart healthcare service is focused on preventive healthcare and wellness; It also aims for the user oriented active service. Recently, Smart healthcare using smart-phone can support various services like basal physical fitness, caloric intake, checking heartbeat and so on. However, it has not yet commercially viable service model because of incomplete personal health devices standardization. We designed and implemented Android smart healthcare Manager to support ISO/IEEE 11073 by Bluetooth HDP using oximetry System.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.711-713
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• 2017

Fiber Brag grating sensors were written in standard Ge-doped telecom optical fiber (Corning SMF-28) using an 800nm femtosecond laser and a phase mask. It were exposed to gamma-radiation up to a dose of 100 kGy to evaluate the radiation effect. The fs-FBG-2 sensor showed incomplete optical characteristics during irradiation, but the fs-FBG-1 sensor showed excellent radiation resistance with Bragg wavelength shift(BWS) of less than 10pm at a dose of 100 kGy.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.263-273
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• 2010

Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random variables and predicted by the conditional expectation of the unknown values given the data. The purpose of this study is to impute missing values which are regarded as the maximum likelihood estimator and random variable in incomplete data and to compare with two methods using ARMA and STAR model. For illustration, the Mumps data reported from the national capital region monthly over the years 2001~2009 are used, and estimate precision of missing values and forecast precision of future data are compared with two methods.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.393-404
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• 2005

As multi-source spatial data fusion mainly deal with various types of spatial data which are specific representations of real world with unequal reliability and incomplete knowledge, proper data representation and uncertainty analysis become more important. In relation to this problem, this paper presents and applies an advanced data representation methodology for different types of spatial data such as categorical and continuous data. To account for the uncertainties of both categorical data and continuous data, fuzzy boundary representation and smoothed kernel density estimation within a fuzzy logic framework are adopted, respectively. To investigate the effects of those data representation on final fusion results, a case study for landslide hazard mapping was carried out on multi-source spatial data sets from Jangheung, Korea. The case study results obtained from the proposed schemes were compared with the results obtained by traditional crisp boundary representation and categorized continuous data representation methods. From the case study results, the proposed scheme showed improved prediction rates than traditional methods and different representation setting resulted in the variation of prediction rates.