• Journal of the Korean Society for Precision Engineering
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• v.18 no.1
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• pp.162-170
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• 2001

In torch weaving in arc welding on V groove, the heat input distribution on groove surface is a main factor determining the bead shape and the weld quality with and without the weld defects such as undercut, overlap, etc. In this study, we calculate the heat input varying with the welding current, arc voltage, welding speed and the touch weaving condition using numerical method. And we investigate the heat input distribution on groove surface while applying the various grooves having 2 dimensional heat sources.

• Proceedings of the KIEE Conference
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• summer
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• pp.39-41
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• 2004

This paper presents a analytical method for the reliability evaluation of distribution system, including the distributed generations. Unlike the generators of transmission system, those of distribution system have complexities in analyzing and determining the operation. In the process of evaluate reliability, it could be shown that the analytical method is simpler than the monte-carlo simulation. Modeling technique of distributed generation to analysis distribution system reliability using frequency and duration method is proposed in this paper, and is compared with the simulation method as a result of the distribution system reliability evaluation.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1275-1301
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• 2023

This paper introduced the Weibull Marshall-Olkin Power Lomax (WMOPL) distribution. The statistical aspects of the proposed model are presented, such as the quantiles function, moments, mean residual life and mean deviations, variance, skewness, kurtosis, and reliability measures like the residual life function, and stress-strength reliability. The parameters of the new model are estimated using six different methods, and simulation research is illustrated to compare the six estimation methods. In the end, two real data sets show that the Weibull Marshall-Olkin Power Lomax distribution is flexible and suitable for modeling data.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.109-122
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• 2018

The composite lognormal-generalized Pareto distribution (LN-GPD) is a mixture of right-truncated lognormal and GPD for a given threshold value. Scollnik (Scandinavian Actuarial Journal, 2007, 20-33, 2007) shows that the composite LN-GPD is adequate to describe body distribution and heavy-tailedness. This paper considers time-varying modeling of the LN-GPD based on local polynomial maximum likelihood estimation. Time-varying model provides significant detailed information of time dependent data, hence it can be applied to disciplines such as service engineering for staffing and resources management. Our work also extends to Beirlant and Goegebeur (Journal of Multivariate Analysis, 89, 97-118, 2004) in the sense of losing no data by including truncated lognormal distribution. Our proposed method is shown to perform adequately in simulation. Real data application to the service time of the Israel bank call center shows interesting findings on the staffing policy.

• Journal of Powder Materials
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• v.27 no.1
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• pp.44-51
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• 2020

Powder characteristics, such as density, size, shape, thermal properties, and surface area, are of significant importance in the powder bed fusion (PBF) process. The powder required is exclusive for an efficient PBF process. In this study, the particle size distribution suitable for the powder bed fusion process was derived by modeling the PBF product using simulation software (GeoDict). The modeling was carried out by layering sintered powder with a large particle size distribution, with 50 ㎛ being the largest particle size. The results of the simulation showed that the porosity decreased when the mean particle size of the powder was reduced or the standard deviation increased. The particle size distribution of prepared titanium powder by the atomization process was also studied. This study is expected to offer direction for studies related to powder production for additive manufacturing.

• Journal of Korea Water Resources Association
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• v.43 no.4
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• pp.337-351
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• 2010

There is a growing dissatisfaction with use of conventional statistical methods for the prediction of extreme events. Conventional methodology for modeling extreme event consists of adopting an asymptotic model to describe stochastic variation. However asymptotically motivated models remain the centerpiece of our modeling strategy, since without such an asymptotic basis, models have no rational for extrapolation beyond the level of observed data. Also, this asymptotic models ignored or overestimate the uncertainty and finally decrease the reliability of uncertainty. Therefore this article provide the research example of the extreme rainfall event and the methodology to reduce the uncertainty. In this study, the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) and the MLE (Maximum Likelihood Estimation) methods using a quadratic approximation are applied to perform the at-site rainfall frequency analysis. Especially, the GEV distribution and Gumbel distribution which frequently used distribution in the fields of rainfall frequency distribution are used and compared. Also, the results of two distribution are analyzed and compared in the aspect of uncertainty.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.1
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• pp.111-122
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• 2014

The provided habitat of many services from natural capital is important. But because most ecosystem services tools qualitatively evaluated biodiversity or habitat quality, this study quantitatively analyzed those aspects using the species distribution model (MaxEnt). This study used location point data of the goat(Naemorhedus caudatus), marten(Martes flavigula), leopard cat(Prionailurus bengalensis), flying squirrel(Pteromys volans aluco) and otter(Lutra lutra) from the 3rd National Ecosystem Survey. Input data utilized DEM, landcover classification maps, Forest-types map and digital topographic maps. This study generated the MaxEnt model, randomly setting 70% of the presences as training data, with the remaining 30% used as test data, and ran five cross-validated replicates for each model. The threshold indicating maximum training sensitivity plus specificity was considered as a more robust approach, so this study used it to conduct the distribution into presence(1)-absence(0) predictions and totalled up a value of 5 times for uncertainty reduction. The test data's ROC curve of endangered mammals was as follows: growing down goat(0.896), otter(0.857), flying squirrel(0.738), marten(0.725), and leopard cat(0.629). This study was divided into two groups based on habitat: the first group consisted of the goat, marten, leopard cat and flying squirrel in the forest; and the second group consisted of the otter in the river. More than 60 percent of endangered mammals' distribution probability were 56.9% in the forest and 12.7% in the river. A future study is needed to conduct other species' distribution modeling exclusive of mammals and to develop a collection method of field survey data.

• Journal of Applied Biological Chemistry
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• v.43 no.1
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• pp.25-30
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• 2000

The main form of nitrogen fertilizer applied to lowland rice is urea, but little is known about its transport in waterlogged soil. This study was conducted to investigate the transport of urea in waterlogged soil column using WAVE (simulation of the substances Water and Agrochemicals in the soil, crop and Vadose Environment) model which includes the parameters for urea adsorption and hydrolysis, The adsorption distribution coefficient and hydrolysis rate of urea were measured by batch experiments. A transport experiment was carried out with the soil column which was pre-incubated for 45 days under flooded condition. The urea hydrolysis rate (k) was $0.073h^{-1}$. Only 5% of the applied urea remained in soil column at 4 days after urea application. The distribution coefficient ($K_d$) of urea calculated from adsorption isotherm was $0.21Lkg^{-1}$, so it was assumed that urea that urea was a weak-adsorbing material. The maximum concentration of urea was appeared at the convective water front because transport of mobile and weak-adsorbing chemicals, such as urea, is dependent on water convective flow. The urea moved down to 11 cm depth only for 2 days after application, so there is a possibility that unhydrolyzed urea could move out of the root zone and not be available for crops. A simulated urea concentration distribution in waterlogged soil column using WAVE model was slightly different from the measured concentration distribution. This difference resulted from the same hydrolysis rate applied to all soil depths and overestimated hydrodynamic dispersion coefficient. In spite of these limitations, the transport of urea in waterlogged soil column could be predict with WAVE model using urea hydrolysis rate (k) and distribution coefficient ($K_d$) which could be measured easily from a batch experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.809-817
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• 2020

In the design and development process of Small-Sat power distribution and transmission module, the stability of dynamic resources was evaluated by a deep learning algorithm. The requirements for the stability evaluation consisted of the power distribution function of the power distribution module and demand module to the SAR radar in Small-Sat. To verify the performance of the switching power components constituting the power module PDM, the reliability was verified using a dynamic neural network. The adoption material of deep learning for reliability verification is the power distribution function of the payload to the power supplied from the small satellite main body. Modeling targets for verifying the performance of this function are output voltage (slew rate control), voltage error, and load power characteristics. First, to this end, the Coefficient Structure area was defined by modeling, and PCB modules were fabricated to compare stability and reliability. Second, Levenberg-Marquare based Two-Way NARX neural network Sigmoid Transfer was used as a deep learning algorithm.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.1
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• pp.54-65
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• 2022

Species distribution models are a useful tool for predicting future distribution and establishing a preemptive response of invasive species. However, few studies considered the possibility of habitat for the aquatic organism and the number of target sites was relatively small compared to the area. Environmental DNA (eDNA) is the emerging tool as the methodology obtaining the bulk of species presence data with high detectability. Thus, this study applied eDNA survey results of Micropterus salmoides and Lepomis macrochirus to species distribution modeling by seasons in the Anyang stream network. Maximum Entropy (MaxEnt) model evaluated that both species extended potential distribution area in October compared to July from 89.1% (12,110,675 m²) to 99.3% (13,625,525 m²) for M. salmoides and 76.6% (10,407,350 m²) to 100% (13,724,225 m²) for L. macrochirus. The prediction value by streams was varied according to species and seasons. Also, models elucidate the significant environmental variables which affect the distribution by seasons and species. Our results identified the potential of eDNA methodology as a way to retrieve species data effectively and use data for building a model.