• Korean Journal of Computational Design and Engineering
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• v.10 no.3
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• pp.179-187
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• 2005

The usage of mixed 2D and 3D CAD data of commercial CAD systems is required in the construction practice. Sometimes 3D wireframe model is required by end-users when 2D CAD data is delivered. However, current KOSDIC can not represent 3D CAD data, because it has been developed as a 2D drawing delivery standard. Therefore, this study is to provide exchange and sharing of mixed 2D and 3D CAD data that add 3D wireframe model in the KOSDIC. To achieve this purpose, the authors have investigated the 3D CAD entities of commercial CAD systems, and have analyzed STEP standards providing 3D wireframe model. The result, the authors have extracted 3D CAD common entities based wireframe model which shall be added in the KOSDIC. This study can be beneficial by using the developed data model for heterogeneous CAD systems, and by providing the representation of mixed 2D and 3D CAD data in construction practice such as GIS, piping system, and so forth.

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• ETRI Journal
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• v.32 no.2
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• pp.292-301
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• 2010

Increasing convergence among heterogeneous radio networks is expected to be a key feature of future ubiquitous services. The convergence of radio networks in combination with dynamic spectrum allocation (DSA) could be a beneficial means to solve the growing demand for radio spectrum. DSA might enhance the spectrum utilization of involved radio networks to comply with user requirements for high-quality multimedia services. This paper proposes a simple spectrum allocation algorithm and presents an analytical model of dynamic spectrum resource allocation between two networks using a 4-D Markov chain. We argue that there may exist a break-even point for choosing whether or not to adopt DSA in a system. We point out certain circumstances where DSA is not a viable alternative. We also discuss the performance of DSA against the degree of resource sharing using the proposed analytical model and simulations. The presented analytical model is not restricted to DSA, and can be applied to a general resource sharing study.

• KIEAE Journal
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• v.10 no.3
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• pp.3-10
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• 2010

As indoor air quality directly affects health and comforts of the residents, researchers from different countries have continued to explore criteria by which indoor air quality can be indicated in a scientific and quantitative manner over the past several decades. However, there are many possibilities that can deteriorate indoor air quality. Due to the uncertainty of influence factors, it is quite difficult to develop a correct evaluation model and quantitative method. Furthermore, the effects from the indoor air pollutants have different levels, leading to the difficulties to apply the regular standard. This study aims to propose evaluation criteria by using the FD-AHP analysis. Obtained findings will be beneficial to construct apartment buildings, commercial buildings and others health performance evaluation framework.

• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.276-284
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• 2004

This paper presents the results of the TEXAS-V computer code simulations of FARO L-14, L-28, and L-33. The old break-up model and new break-up model are tested to compare the respective simulations of each. As these experimental data sets cover a wide range of ambient pressures, sub-cooling of the water pool, and the melt jet diameters, the results of the simulations will be beneficial in assessing the TEXAS-V code's capability to predict the steam explosion phenomena in a prototypical reactor case. The current model was found to have some deficiencies, and the modules for the fragmentation, the equation of state, and the interfacial area for each flow regime in TEXAS-V were improved for the simulation of FARO L28 and FARO L-33.

• Nuclear Engineering and Technology
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• v.50 no.5
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• pp.758-766
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• 2018

We developed numerical models to efficiently simulate the low-cycle fatigue behavior of a pipe elbow. To verify the model, in-plane cyclic bending tests of pipe elbow specimens were conducted, and a through crack occurred in the vicinity of the crown. Numerical models based on the erosion method and tie-break method are developed, and the numerical results are compared with experimental results. The calculated results of both models are in good agreement with experimental results, and the model using the tie-break method possesses two times faster calculation speed. Therefore, the numerical model based on the tie-break method would be beneficial to evaluate the strength of piping systems under seismic loadings.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1171-1178
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• 2019

This study proposes a deep neural network model based on an encoder-decoder structure for visual dialogs. Ongoing linguistic understanding of the dialog history and context is important to generate correct answers to questions in visual dialogs followed by questions and answers regarding images. Nevertheless, in many cases, a visual understanding that can identify scenes or object attributes contained in images is beneficial. Hence, in the proposed model, by employing a separate person detector and an attribute recognizer in addition to visual features extracted from the entire input image at the encoding stage using a convolutional neural network, we emphasize attributes, such as gender, age, and dress concept of the people in the corresponding image and use them to generate answers. The results of the experiments conducted using VisDial v0.9, a large benchmark dataset, confirmed that the proposed model performed well.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Korean Journal of Exercise Nutrition
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• v.24 no.4
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• pp.1-6
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• 2020

[Purpose] Exercise is a fundamental way to maintain and improve health and physical fitness. Many human studies have demonstrated the beneficial effects of exercise on various biological parameters. However, studies investigating the effects of exercise in dogs are limited. This review summarized the current data from studies that examined the effects of different exercise conditions (treadmill vs. non-treadmill and acute vs. chronic) on physiological and hematological parameters in dogs. [Methods] Papers addressing the effects of exercise in dogs published from January 2000 to October 2020 were retrieved from the online databases of Scopus, Google Scholar, and PubMed and were selected and reviewed. [Results] The exercise conditions differentially affected physiological and hematological responses and adaptation in dogs. Therefore, the development and comprehensive evaluation of scientific exercise programs for dogs are necessary. [Conclusion] The dog would be a valuable exercise science animal model, and studies aiming at the optimal health, well-being, and quality of life of dogs need to be conducted.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.3-14
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• 1992

The efficiency of marketing channel of distribution between a manufacturer with several customers can be increase by influencing the order quantity of customer. Manufacturer reduces average inventory holding cost by penalizing the large order quantity from the customer. Such a penalty is significant only if the manufacturer's unit inventory holding cost is relatively large. Conditions under which such penalizing can be beneficial to both parties are derived.