• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.604-607
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• 2005

In the electro-discharge machining the machining performance is closely related to the characteristics of discharge which can be identified from electrical behavior in gap between workpiece and electrode. Therefore, the accurate prediction of electrical behavior in electro-discharge machining (EDM) is useful to process control and optimization. However, any simulation model fur prediction of electrical behavior in EDM process has never been reported until now. In this study, a simulation model is developed to analyze the electrical behavior of electro-discharge plasma which significantly influences electrical behavior in EDM process. For the purpose of this the fundamentals of electro-discharge mechanism such as inception, propagation, formation of plasma channel and termination are investigated to accurately predict the cycle of discharge plasma in EDM. As a result, a mathematical model of electro-discharge plasma is constructed with considering the fundamentals of electro-discharge plasma. Consequently, it is demonstrated that the developed model can predict the electrical behavior of plasma such as electron density in various conditions.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.143-150
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• 2017

Absorption of microwave radio frequency signal by atmospheric rain is prevalent at frequencies above 10 GHz. This paper presents the studies on rain attenuation at 18 GHz for 3.2 km experimental link between Khumdang (Korea Telecom, KT station) and Icheon (National Radio Research Agency, RRA station). The received signal data for rain attenuation and rain rate were collected at 10 second intervals over a three year periods from 2013 to 2015. Out of several models, the paper present discussion and comparison of ITU-R P.530-16 model, Moupfouma model, Da Silva Mello model along with suitable rain attenuation prediction method. The limitation of research lies on the experimental system that is set up in only one location, however, the preliminary results indicate the application of suitable 1-minute rain attenuation model for specific site. The method provides useful information for microwave engineers and researchers in making decision over the choice of most suitable rain attenuation prediction in terrestrial links.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.144-152
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• 1996

Recently, as the demands for radio spectrum are growing and the number of cell sites is increasing rapidly, the spectrum engineering plays an important role in estimating frequency sharing and reuse. The radio propagation analysis is essential in the basic technology of radio network design such as deciding the service area and selecting the position of the base station. But, domestic propagation environment in which mountainous region is occupying over 70% of our terrain does not allow us to apply foreign studies which are deduced in highly different environments. Therefore, we need to have our propagation analysis system derived from our own terrain condition. In this paper, we propose the propagation prediction model which issuitable toour propagation environment, and also usinghis model, we implement thesimulator based on GIS(Geographic Information System)which can be applied to both spectrum engineering and radio propagation analysis. We showed that this simulator can well be applied to frequency assignment, propagation network design as well as other radio services. Considering the results of our analysis, we could guarantee the standard deviation of error between the measured data and predicted results as 5 to 7 dB.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.129-137
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• 2005

Criticality prediction models that identify fault-prone spots using system design specifications play an important role in reducing development costs of large systems such as telecommunication systems. Many criticality prediction models using complexity metrics have been suggested. But most of them need training data set for model training. And they are classification models that can only classify design entities into fault-prone group and non fault-prone group. To solve this problem, this paper builds a new prediction model, HMM, using two styled hybrid metrics. HMM has strong point that it does not need training data and it enables comparison between design entities by criticality. HMM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering internal characteristics and accuracy of prediction.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.33-40
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• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development costs because the problems in early phases largely affect the quality of the late products. Real-time systems such as telecommunication systems are so large that criticality prediction is mere important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing causes of the prediction results and low extendability. This paper builds a new prediction model, GAM, based on Genetic Algorithm. GAM is different from other models because it produces a criticality function. So GAM can be used for comparison between entities by criticality. GAM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering Internal characteristics and accuracy of prediction.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.22 no.3
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• pp.163-170
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• 2010

Both scatterer method and transfer matrix method are compared to analyze their characteristics, which are wave propagation models due to topographic change based on plane wave approximation. Results from the scatterer method are closer to the results obtained by the more accurate existing models and it is appraised that the scatterer method gives the clearer explanation about physical process involved in the wave transformation. Since both methods have analytical solutions, in the computational point of view they are very fast and easy to be implemented. Both methods give a good prediction for wave scattering by relatively simple bedform.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.252-254
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• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. ill addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.6
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• pp.661-669
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• 2018

Engineering system problems consist of multi-objective optimisation and the performance analysis is generally time consuming. To optimise the system concerning its performance, many researchers perform the optimisation using an approximation model. The Response Surface Method (RSM) is usually used to predict the system performance in many research fields, but it shows prediction errors for highly nonlinear problems. To create an appropriate metamodel for marine systems, Lee (2015) compares the prediction accuracy of the approximation model, and multi-objective optimal design framework is proposed based on a confirmed approximation model. The proposed framework is composed of three parts: definition of geometry, generation of approximation model, and optimisation. The major objective of this paper is to confirm the applicability/usability of the proposed optimal design framework and evaluate the prediction accuracy based on sensitivity analysis. We have evaluated the proposed framework applicability in derrick structure optimisation considering its structural performance.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.4 no.2
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• pp.83-90
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• 1992

A wave prediction model of DP type with shallow water effects is presented. An intercom-parison study of the shallow water wave models has been made to verify applicability of this model which has source functions of Inoue, propagation scheme by Gadd and dissipation functions due to bottom friction. The energy distribution shows reasonable results and for the bottom friction JONS-WAP decay function seems to be more appropriate.

• Structural Engineering and Mechanics
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• v.16 no.1
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• pp.83-99
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• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).