• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.11 no.4
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• pp.109-116
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• 1997

This paper describes the surge protective device combined with varistor and LC low pass filter on AC power circuits. Up to now varistors alone have been used as a surge protective device on AC power circuits, but it gives negative effects on the equipments to be protected due to the steep rise and high remnant voltage. Therefore in this work, for the purpose of improving the cutoff performance of surge protective device on AC power circuits, the hybrid-type surge protective device with varistor and LC filter was designed and prepared. And the experiments for operational characteristics and clamping performance of the surge protective device were carried out with an 1.2/50[$\mu$s] impulse generator. As a result, the proposed surge protective device with the combination of varistors and LC filter can limit the surge voltage to lower value and reduce the value of dV /dt to very small values. Also the attenuation of high-frequency voltages across the output port is effectively provided by LC filter. Therefore this result may be all the more helpful avoiding both damage and upset of electronic systems.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.427-438
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• 2003

In a data warehouse, users typically search for trends, patterns, or unusual data behaviors by issuing queries interactively. The OLAP range-sum query is widely used in finding trends and in discovering relationships among attributes in the data warehouse. In a recent environment of enterprises, data elements in a data cube are frequently changed. The problem is that the cost of updating a prefix sum cube is very high. In this paper, we propose a novel algorithm which reduces the update cost significantly by an index structure called the Δ-tree. Also, we propose a hybrid method to provide either approximate or precise results to reduce the overall cost of queries. It is highly beneficial for various applications that need quick approximate answers rather than time consuming accurate ones, such as decision support systems. An extensive experiment shows that our method performs very efficiently on diverse dimensionalities, compared to other methods.

• Journal of KIISE
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• v.42 no.3
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• pp.320-327
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• 2015

Although service robots in various fields are being commercialized, most of them have problems that depend on explicit commands by users and have difficulty to generate robust reactions of the robot in the unstable condition using insufficient sensor data. To solve these problems, we modeled mirror neuron and theory of mind systems, and applied them to a robot agent to show the usefulness. In order to implement quick and intuitive response of the mirror neuron, the proposed intention-response model utilized behavior selection networks considering external stimuli and a goal, and in order to perform reactions based on the long-term action plan of theory of mind system, we planned behaviors of the sub-goal unit using a hierarchical task network planning, and controled behavior selection network modules. Experiments with various scenarios revealed that appropriate reactions were generated according to external stimuli.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.5
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• pp.661-666
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• 2011

Recently, there are active research activities regarding to power systems of marine traffic facilities using a hybrid generation system which includes a photovoltaic generation system and a wave power generation system. However, when the marine traffic system is operated on a maritime environment for a substantial period of time, it was reported that the shellfish was adhered inside the water column. To tackle this problem, researches on the AFS (Anti-Fouling System) are on the progress. However, use of the single channel AFS resulted in frequent replacement of anode. Thus, the paper proposes a multi-channel method on AFS and experiments have been taken place correspondently. To improve the reliability of the experiment, the melting anode result was applied to our simulation program. The outcome of the simulation illustrates that the proposed multi-channel AFS's anode in the buoy have been ionized equally.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.125-132
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• 2012

Urea-SCR system, which converts nitrogen oxides to nitrogen and water in the presence of a reducing agent, usually AdBlue urea solution, is known as one of the powerful NOx reduction systems for mobile as well as stationary applications. For its consistent and reliable operation in mobile applications, such various problems as transient injection, ammonia slip, and freezing in cold weather have to be resolved. In this work, therefore, numerical study on three-dimensional unsteady heating problems were analyzed to understand the melting and heat transfer characteristics such as urea liquid volume fraction, temperature profiles and generated natural convection behavior in urea solution by using the commercial software Fluent 6.3. After validating by comparing numerical and experimental data with pure gallium melting phenomena, numerical experiment for urea melting is conducted with three different coolant heating models named CH1, 2, and 3, respectively. Finally, it can be found that the CH3 model, in which more coolant is concentrated on the lower part of the urea tank, has relatively better melting capability than others in terms of urea quantity of $1{\ell}$ for start-up schedule.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.757-764
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• 2017

As the reduction of greenhouse gases(GHGs) emission has become a global issue, the microgrid markets are growing rapidly. With the sudden changes in the market, Korean government suggested a new business model called 'Self-Sufficient Energy Islands'. Its main concern is a stand-alone microgrid composed of Distributed Energy Resources(DERs) such as Renewable Energy Sources(RESs), Energy Storage System(ESS) and Fuel Cell, in order to minimize the emission of GHGs. According to these trend, this paper is written to propose an optimal sizing method of DERs in a stand-alone microgrid by using Genetic Algorithm(GA), one of the representative stochastic methods. It is to minimize the net present cost with the variables, size of RESs and ESS. In the process for optimization, the sunless days are considered as additional constraints. Through the case study analysis, the size of DERs installed in a microgrid system has been computed using the proposed method in MATLAB. And the result of MATLAB is compared with that of HOMER(Hybrid Optimization of Multiple Energy Resources), a well-known energy modeling software.

• Smart Structures and Systems
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• v.14 no.5
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• pp.847-867
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• 2014

There are continuous efforts to mitigate structural losses from earthquakes and manage risk through seismic risk assessment; seismic fragility curves are widely accepted as an essential tool of such efforts. Seismic fragility curves can be classified into four groups based on how they are derived: empirical, judgmental, analytical, and hybrid. Analytical fragility curves are the most widely used and can be further categorized into two subgroups, depending on whether an analytical function or simulation method is used. Although both methods have shown decent performances for many seismic fragility problems, they often oversimplify the given problems in reliability or structural analyses owing to their built-in assumptions. In this paper, a new method is proposed for the development of seismic fragility curves. Integration with sophisticated software packages for reliability analysis (FERUM) and structural analysis (ZEUS-NL) allows the new method to obtain more accurate seismic fragility curves for less computational cost. Because the proposed method performs reliability analysis using the first-order reliability method, it provides component probabilities as well as useful byproducts and allows further fragility analysis at the system level. The new method was applied to a numerical example of a 2D frame structure, and the results were compared with those by Monte Carlo simulation. The method was found to generate seismic fragility curves more accurately and efficiently. Also, the effect of system reliability analysis on the development of seismic fragility curves was investigated using the given numerical example and its necessity was discussed.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.260-268
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• 2008

Optical diffuse reflectance sensing has potential for rapid and reliable on-site estimation of soil properties. For good results, proper calibration to measured soil properties is required. One issue is whether it is necessary to develop calibrations using samples from the specific area or areas (e.g., field, soil series) in which the sensor will be applied, or whether a general "factory" calibration is sufficient. A further question is if specific calibration is required, how many sample points are needed. In this study, these issues were addressed using data from 42 paddy fields representing 14 distinct soil series accounting for 74% of the total Korean paddy field area. Partial least squares (PLS) regression was used to develop calibrations between soil properties and reflectance spectra. Model evaluation was based on coefficient of determination ($R^2$) root mean square error of prediction (RMSEP), and RPD, the ratio of standard deviation to RMSEP. When sample data from a soil series were included in the calibration stage (full information calibration), RPD values of prediction models were increased by 0.03 to 3.32, compared with results from calibration models not including data from the test soil series (calibration without site-specific information). Higher $R^2$ values were also obtained in most cases. Including some samples from the test soil series (hybrid calibration) generally increased RPD rapidly up to a certain number of sample points. A large portion of the potential improvement could be obtained by adding about 8 to 22 points, depending on the soil properties to be estimated, where the numbers were 10 to 18 for pH, 18-22 for EC, and 8 to 22 for total C. These results provide guidance on sampling and calibration requirements for NIR soil property estimation.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.636-645
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• 2008

OneNAND flash is a high-performance hybrid flash memory that combines the advantages of both NAND flash and NOR flash. OneNAND flash has not only all virtues of NAND flash but also greatly enhanced read performance which is considered as a downside of NAND flash. As a result, it is widely used in mobile applications such as mobile phones, digital cameras, PMP, and portable game players. However, most of the general purpose operating systems, such as Linux, can not exploit the read performance of OneNAND flash because of the restrictions imposed by their virtual memory system and block I/O architecture. In order to solve that problem, we suggest a new approach called virtual I/O segment. By using virtual I/O segment, the superior read performance of OneNAND flash can be exploited without modifying the existing block I/O architecture and MTD subsystem. Experiments by implementations show that this approach can reduce read latency of OneNAND flash as much as 54%.