• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.363-369
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• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• ETRI Journal
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• v.38 no.4
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• pp.735-745
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• 2016

The Paris agreement at the 21st Conference of the Parties (COP21) emphasizes the reduction of greenhouse gas emissions and increase in energy consumption in all areas. Thus, an important aspect is energy saving in buildings where the lighting is a major component of the electrical energy consumption. This paper proposes a building energy management system employing visible light communication (VLC) based on LED lighting. The proposed management system has key characteristics including personalization and localization by utilizing such VLC advantages as secure communication through light and location-information transmission. Considering the efficient implementation of an energy-consumption adjustment using LED luminaires, this paper adopts variable pulse position modulation (VPPM) as a VLC modulation scheme with simple controllability of the dimming level that is capable of providing a full dimming range. This paper analyzes the VPPM performances according to variable dimming for several schemes, and proposes a VPPM demodulation architecture based on dimming-factor acquisition, which can obtain an improved performance compared to a 2PPM-based scheme. In addition, the effect of a dimming-factor acquisition error is analyzed, and a frame format for minimizing this error effect is proposed.

• Convergence Security Journal
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• v.7 no.1
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• pp.45-53
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• 2007

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, Goel-Okumoto and Yamada-Ohba-Osaki model was reviewed, proposes the hyper-exponential distribution reliability model, which maked out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method. For model determination and selection, explored goodness of fit (the error sum of squares). The methodology developed in this paper is exemplified with a software reliability random data set introduced by of Weibull distribution (shape 0.1 & scale 1) of Minitab (version 14) statistical package.

• The KIPS Transactions:PartA
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• v.12A no.2 s.92
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• pp.145-150
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• 2005

Recently, many Processor manufacturers have implemented simultaneous multi treading technology, which can simultaneously execute independent threads in one processor cycle, as a way of increasing processor efficiency, ana one particular example is Hyper Threading. Hyper Threading technology, which enables many logical processors to reside a physical processor, differs from the current multiprocessing environment which has many independent processors, and calls for a particular work assignment method optimized for Hyper Threading environment Thus, in this paper, We have proposed a scheduling algorithm compatible with Hyper Threading technology and analyzed its performance using various methods. As a result, we shall expect its efficient performance by properly understanding and managing Hyper Threading system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.111-116
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• 2021

The hyper-spectral image is data obtained by dividing the electromagnetic wave band in the infrared region into hundreds of wavelengths. It is used to find or classify objects in various fields. Recently, deep learning classification method has been attracting attention. In order to use hyper-spectral image data as deep learning training data, a processing technique is required compared to conventional visible light image data. To solve this problem, we developed a software that selects specific wavelength images from the hyper-spectral data cube and performs the ground truth task. We also developed software to manage data including environmental information. This paper describes the configuration and function of the software.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.6
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• pp.75-80
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• 2013

In this study, a forced ignition method with a plasma jet torch is studied in Mach 2 laboratory scaled wind-tunnel. The hyper-mixer is used as a mixer. For two normal injection cases, the one is collided against a wedge plate of the hyper-mixer and the other is directly injected into the cold main flow. For the first case, the hyper-mixer disperses the injected fuel, leading to the mixing enhancement. Furthermore, the fuel-air mixture is provided into the plasma hot gas, which enhances the combustion performance. However, the direct injection into the main flow method spends amount of fuel without ignition in the cold supersonic flow. In the end, for the forced combustion, it is important to supply the fuel-air mixture into the heat source.

• The KIPS Transactions:PartA
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• v.9A no.3
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• pp.311-316
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• 2002

LZW is a popular variant of LZ78 to compress text documents. LZW yields a high compression rate and is widely used by many commercial programs. Its core idea is to assign most probably used character group an entry in a dictionary. If a group of character which is already positioned in a dictionary appears in the streaming data, then an index of a dictionary is replaced in the position of character group. In this paper, we propose a new efficient method to find least used entries in a dictionary using counter. We also achieve higher compression rate by preassigning widely used tags in hyper-text documents. Experimental results show that the proposed method is more effective than V.42bis and Unix compression method. It gives 3∼8％ better in the standard Calgary Corpus and 23∼24％ better in HTML documents.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1381-1394
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• 2006

Generation methods of data cube have been studied for many years in data warehouse which supports decision making using stored data. There are two previous studies, one is multi-way array algorithm and the other is H-cubing algorithm which is based on the hyper-tree. The multi-way array algorithm stores all aggregation data in arrays, so if the base data is increased, the size of memory is also grow. The H-cubing algorithm which is based on the hyper-tree stores all tuples in one tree so the construction cost is increased. In this paper, we present an efficient data cube generation method based on hash table using weight mapping table and record hash table. Because the proposed method uses a hash table, the generation cost of data cube is decreased and the memory usage is also decreased. In the performance study, we shows that the proposed method provides faster search operation time and make data cube generation operate more efficiently.

• ETRI Journal
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• v.40 no.5
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• pp.664-676
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• 2018

Given the rapidly increasing market penetration of photovoltaic (PV) systems in many fields, including construction and housing, the effective maintenance of PV systems through remote monitoring at the panel level has attracted attention to quickly detect faults that cause reductions in yearly PV energy production, and which can reduce the whole-life cost. A key point of PV monitoring at the panel level is cost-effectiveness, as the installation of the massive PV panels that comprise PV systems is showing rapid growth in the market. This paper proposes an implementation method that involves the use of a panel-level wireless PV monitoring module (WPMM), and which assesses the cost-effectiveness of this approach. To maximize the cost-effectiveness, the designed WPMM uses a voltage-divider scheme for voltage metering and a shunt-resistor scheme for current metering. In addition, the proposed method offsets the effect of element errors by extracting calibration parameters. Furthermore, a design method is presented for portable and user-friendly PV monitoring, and demonstration results using a commercial 30-kW PV system are described.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.379-382
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• 1997

We present a hybrid self-tuning method using hyper elliptic Gaussian membership function. The proposed method applies a GA to identify the structure and the parameters of a fuzzy inference system. The parameters obtained by a GA, however, are near optimal solutions. So we solve this problem through a backpropagation-type gradient method. It is called GA hybrid self-tuning method in this paper. We provide a numerical example to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.