• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.949-953
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• 2004

In this paper, Power Flow Boundary Element Method(PFBEM) is studied as the numerical method for the vibration and sound predictions of complex structures in medium-to-high frequency ranges. NASPFA, the sound analysis software based on PFBEM, is developed and is used for the vibro-acoustic analysis. And also the developed software is used for the prediction of interior and exterior sound fields of vibrating structures and for the analysis of the multi-domain problems. To verify the accuracy, NASPFA is applied to the prediction of the energy distribution in the simple structures, and its results are compared with exact PFA solutions. And various practical vehicle systems are modeled and the distributions of the acoustical energy density are successfully predicted.

• Advances in aircraft and spacecraft science
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• v.11 no.1
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• pp.1-21
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• 2024

The stiffness reduction of cross-ply composite laminates featuring a transverse cracking and delamination within the mid-layer is predicted through utilization of a modified shear-lag model, incorporating a stress perturbation function. Good agreement is obtained by comparing the prediction models and experimental data. The material characteristics of the composite are affected by fluctuations in temperature and transient moisture concentration distribution in desorption case, based on a micro-mechanical model of laminates. The transient and non-uniform moisture concentration distribution induces a stiffness reduction. The obtained results demonstrate the stiffness degradation dependence on factors such as cracks density, thickness ratio and environmental conditions. The present study underscores the significance of comprehending the degradation of material properties in the failure progression of laminates, particularly in instances of extensive delamination growth.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.434-437
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• 2008

In this study, we developed a fuel moisture variation prediction model on each day after precipitation during a spring forest fire exhibition period. For this research, we selected plots in pine forest on Sam-Chuck si and Dong-hae si in Kangwon do according to a forest density(low, mediate, high) and classified a surface woody fuel by a diameter.(below 0.6cm, $0.6{\sim}3cm$, $3{\sim}6cm$, and above 6cm). A validity of this model was verified by applying a fuel moisture variation after precipitation in this spring. In the result, $R^2$ was $0.76{\sim}0.92$. This model will be a useful for improvement of a forest fire danger rate forcast through a prediction a fule moisture in forest.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.823-830
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• 2010

This study was designed to examine the morphological measurement and characteristics of trabecullae based on femoral radiographic image for prediction of osteoporosis. Study subjects were 34 females (average age of 62.1 years) and 6 males (average age of 60.1 years), they were categorized into normal group and osteoporosis group in accordance with the T-score value. Measurement of the bone density of femoral bone was measured with DEXA(Dual Energy X-ray absorptiometry). ROI(Region of interests) was selected on femoral neck and trochanter. Characteristics of trabecullae was analyzed by using the skeletonization analysis of trabecular image. Morphological measurement was analyzed through femoral radiographic image in order to examine the correlation with osteoporosis. The result demonstrated statistically significant correlation between neck cortical thickness, shaft width, shaft cortical thickness, periphery, mean gray level and trabeculae area with BMD average (T-score) of femoral part. The results show that morphological measurement and characteristics of trabecullae based on femoral radiographic images for osteoporosis prediction could be effective.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.91-103
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• 2018

This study developed a prediction model using machine learning technology and predicted the success of health consulting by using life log data generated through u-Health service. The model index of the Random Forest model was the highest using. As a result of analyzing the Random Forest model, blood pressure was the most influential factor in the success or failure of metabolic syndrome in the subjects of u-Health service, followed by triglycerides, body weight, blood sugar, high cholesterol, and medication appear. muscular, basal metabolic rate and high-density lipoprotein cholesterol were increased; waist circumference, Blood sugar and triglyceride were decreased. Further, biometrics and health behavior improved. After nine months of u-health services, the number of subjects with four or more factors for metabolic syndrome decreased by 28.6%; 3.7% of regular drinkers stopped drinking; 23.2% of subjects who rarely exercised began to exercise twice a week or more; and 20.0% of smokers stopped smoking. If the predictive model developed in this study is linked with CBR, it can be used as case study data of CBR with high probability of success in the prediction model to improve the compliance of the subject and to improve the qualitative effect of counseling for the improvement of the metabolic syndrome.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.1
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• pp.7-12
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• 2017

Most of existing buried pipes are composed of reinforced concrete. Reinforced concrete pipes have many problems such as aging, corrosion, leaking, etc. The polyethylene (PE) pipes have advantages to solve these problems. The plastic pipes buried underground are classified into a flexible pipe. National standard that has limited the long-term vertical deformation of the pipe to 5% for flexible pipes including PE pipe. This study presents a prediction for the long-term behavior of the polyethylene pipe based on ASTM D 5365. This prediction method is presented to estimate by using the statistical method from the initial deflection measurement data. We predict the behavior of long-term performance on the double-wall pipe and multi-wall pipe. As a result, it was found that the PE pipe will be sound enough more than 50 years if the compaction of soil around the pipe is more than 95% of the standard soil compaction density.

• Geomechanics and Engineering
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• v.22 no.1
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• pp.25-33
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• 2020

In metropolitan areas, the quantity and density of the underground structure increase rapidly in recent years. Even though most damage incidents of the underground structure were minor, there were still few incidents causing a great loss in lives and economy. Therefore, the safety evaluation of the underground structure becomes an important issue in the disaster prevention plan. Liquefaction induced uplift is one important factor damaging the underground structure. In order to perform a preliminary evaluation on the safety of the underground structure, simplified prediction equations were introduced to provide a first order estimation of the liquefaction induced uplift. From previous studies, the input motion is a major factor affecting the magnitude of the uplift. However, effects of the input motion were not studied and included in these equations in an appropriate and rational manner. In this article, a numerical simulation approach (FLAC program with UBCSAND model) is adopted to study effects of the input motion on the uplift. Numerical results show that the uplift and the Arias Intensity (Ia) are closely related. A simple modification procedure to include the input motion effects in the Sasaki and Tamura prediction equation is proposed in this article for engineering practices.

• Macromolecular Research
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• v.15 no.4
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• pp.315-323
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• 2007

The present manuscript deals with the prediction of the lifetime of NBR compound based rubber gaskets for use as fuel cells. The material was investigated at 120, 140 and $160^{\circ}C$, with aging times from 3 to 600 h and increasing $H_2SO_4$ concentrations of 5, 6, 7 and 10 vol%. Both material and accelerated acid-heat aging tests were carried out to predict the useful life of the NBR rubber gasket for use as a fuel cell stack. To investigate the effects of acid-heat aging on the performance characteristics of the gaskets, the properties of the NBR rubber, such as crosslink density and elongation at break, were studied. The hardness of the NBR rubber was found to decrease with decreasing acid concentration at both $120\;and\;140^{\circ}C$, but at $160^{\circ}C$, the hardness of the NBR rubber increased abruptly in a very short time at different acid concentrations. The tensile strength and elongation at break were found to decrease with increases in both the $H_2SO_4$ concentration & temperature. The observed experimental results were evaluated using the Arrhenius equation.

• Transactions of the Korean hydrogen and new energy society
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• v.14 no.1
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• pp.61-68
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• 2003

This paper presents the methodology to analyze flow duration characteristics and performance prediction technique for small hydro power(SHP) Plants and its application. The flow duration curve can be decided by using monthly rainfall data at the most of the SHP sites with no useful hydrological data. It was proved that the monthly rainfall data can be characterized by using the cumulative density function of Weibull distribution and Thiessen method were adopted to decide flow duration curve at SHP plants. And, the performance prediction technique has been studied and development. One SHP plant was selected and performance characteristics was analyzed by using the developed technique, Primary design specfications such as design flowrate, plant capacity, operational rate and annual electricity production for the SHP plant were estimated, It was found that the methodology developed in this study can be a useful tool to predict the performance of SHP plants and candidate sites in Korea.