• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2007년도 추계학술대회 논문집
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• pp.47-51
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• 2007

The present paper reports the life time prediction of Acrylonitrile-Butadiene rubber (NBR) fuel cell gasket materials as a function of operational variables like acid concentration, ageing time and temperature. 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. The acid ageing of the gasket compounds has been investigated at 120, 140 and $160^{\circ}C$, with aging times from 3 to 600 h and increasing acid ($H_2SO_4$) concentrations of 5, 6, 7 and 10 vol%. Material characteristics the gas compound such as cross-link density, tensile strength 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 $160^{\circ}C$ interestingly 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 increase in both the acid concentrate ion & temperature. The life time of the compounds were evaluated using the Arrhenius equation.

• 한국멀티미디어학회논문지
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• 제20권4호
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• pp.593-605
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• 2017

In applications which are covered wide range, it is possible that one or more number of Unmanned Aerial Vehicle(UAV) squadrons are used to perform a mission. In this case, it is most important to communicate seamlessly between the UAV squadrons. In this paper, we applied the modified OLSR(OSLR-Pds) which can prediction for state of the link for the communication in UAV squadron, and applied the modified AOMDV which can build multi-path for the communication between UAV Squadrons. The mobility of nodes are modeled using Gauss-Markov algorithm, and relative speed between nodes were calculated by derive equation of movement, and thereby we can predict link state for in a squadron and between squadrons. An experiment for comparing AODV, AOMDV and the proposed routing protocol was conducted by three factors such as packet delivery ratio, end to end delay, and routing overhead. In experiment result, we make sure that the proposed protocol performance are superior in these three factors. However, if the density of the nodes constituting FANET are too low, and if the moving speed of node is very slow, there is no difference to others protocols.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 춘계 학술발표회
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• pp.888-895
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• 2009

Compaction is a process of increasing soil density using physical energy. It is intended to improve the strength and stiffness of soil. In embankment, degree of compaction affects the construction time, money, also method of soil improvement. In large scale embankment project, difficulties of embankment should change due to uncertainty of settlement. So it is very important to predict the final settlement and factor of safety induced by embankment. In many construction site, there are primarily design of high embankment using in-situ soil. Therefore numerical analyses are necessary for valid evaluation of the settlement prediction. But due to the construction cost and schedule, there were lacking in properties of soil and also limited number of in-situ test were performed. So we proposed the method that can easily estimate the proper soil parameters and suggest the proper method of numerical analysis. From this, two-dimensional finite-difference numerical analysis was conducted to investigate the settlement and factor of safety induced by embankment with various case of compaction rate and embankment height.

• Bulletin of the Korean Chemical Society
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• 제34권4호
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• pp.1043-1046
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• 2013

Prediction of physicochemical properties of organic molecules is an important process in chemistry and chemical engineering. The MSEP approach developed in our lab calculates the molecular surface electrostatic potential (ESP) on van der Waals (vdW) surfaces of molecules. This approach includes geometry optimization and frequency calculation using hybrid density functional theory, B3LYP, at the 6-31G(d) basis set to find minima on the potential energy surface, and is known to give satisfactory QSPR results for various properties of organic molecules. However, this MSEP method is not applicable to screen large database because geometry optimization and frequency calculation require considerable computing time. To develop a fast but yet reliable approach, we have re-examined our previous work on organic molecules using two semi-empirical methods, AM1 and PM3. This new approach can be an efficient protocol in designing new molecules with improved properties.

• 대한디지털의료영상학회논문지
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• 제14권2호
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• pp.63-67
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• 2012

Calcium density in human bones decreases as people are getting older due to the interior or exterior environmental factors. Bone aging forms osteoporosis. And this can bring out various spine fractures which develops a complications. Thus the prediction of seniliy is one of the important factors in spine diseases. Once spine aged, diverse fractures occur such as compression fracture and micro fracture. Side images of the spine by the digital radiography (DR) were prepared, and pixel arbitrary unit with Image J was measured from one spot in the lumbar bone part. By calculating pixel arbitrary unit of the simple contrast, it was obtained that the value of pixel arbitrary unit decreased as seniliy of bones increased. By simply applying Image J to the seniliy of patient's spine, the seniliy of bones predicts the level of danger with only digital radiography(2D) image. consequently we show that Image J value of pixel arbitrary unit index for predicts the level of precaution of osteoporosis patient.

• 한국농공학회논문집
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• 제62권4호
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• pp.45-51
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• 2020

The poisson's ratio was obtained from the effective vertical stress and horizontal stress of consolidation-undrained test. It was analyzed void ratio verse poisson's ratio. At the result, the effective friction angle was increase with relative density increased, was decreased the poisson's ratio. The empirical equation of void ratio and poisson's ratio was showed very high correlation r²=0.846. The empirical equation was showed that the smaller the void ratio in the fine grained soil than granular soil. In the case of 0.85 times the correlation analysis equation of granular and fine grained soil, the experimental results were shown very similarly. In especially, the poisson's ratio prediction results was shown within 5% of the error range, was revalidation 0.85 times the correlation analysis equation using the void ratio. In this study, correlation analysis equation of the granular and fine grained soil was more reliability of the poisson's ratio prediction results apply to the void ratio than dry unit weight.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 추계학술대회 논문집
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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.

• 대한원격탐사학회지
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• 제21권5호
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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.

• Nuclear Engineering and Technology
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• 제53권6호
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• pp.1796-1809
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• 2021

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.29-36
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• 2022

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.