• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.203-210
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• 2014

This study proposes adaptive decision making method having feed-back structure of regression and simulation models to support the quick decision making of production managers by managing and integrating the mutual relationship among historical data. For that, from historical data that have extracted and accumulated from each process, we first selected major constraint resources that are used as independent variables in regression model. The regression model is designed by using the dependent variables (objectives) that defined above by managers and independent variables selected in previous step and simulation model that are composed of constraint resources is designed. In process of simulation run, we obtain the multiple feasible solutions (alternatives) by using meta-heuristic method. Each solution is substituted by regression equation and we found the optimal solution that is minimum of difference between values obtained by regression model and simulation results. The optimal solution is delivered and incorporated to production site and current operation results from production site is used to generate new regression model after that time.

• Nuclear Engineering and Technology
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• v.19 no.1
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• pp.10-21
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• 1987

Uncertainty analysis of the FLB accident is performed for KNU-1 using the response surface methodology and Monte Carlo simulation. The FLB analyses using the RELAP4/Mod6 were performed a number of times to generate the data base for the uncertainty analysis, along with the EM calculation for comparison purpose. Two kinds of input sets are utilized for response surface method to investigate and compare the effects of the uncertainty of input variables on the RCS peak pressure following a FLB. The first set is composed of six major plant operational parameters and the second set is composed of five major modelling parameters. It is found through the analysis of results that the uncertainties of modelling parameters have more influence on the RCS peak pressure than the uncertainties of plant operational parameters and that the extra margin of 9% of peak pressure is gained. And one of the assumptions of EM calculation, which is usually accepted as conservative is found to be erroneous, that is, the initial core inlet temperature is found to act negatively on the RCS pressure following a FLB.

• Journal of Plant Biotechnology
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• v.47 no.3
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• pp.194-202
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• 2020

The sweetpotatoes (Ipomoea batatas) generate adventitious roots (ARs) from cut stems that develop into storage roots and make for an important means of propagation. However, few studies have investigated the hormones involved in AR development in sweetpotato. In this study, the expression patterns of hormone-related genes involved in AR formation were identified using the transcriptome data. RNA-seq data from stems grown for 0 and 3 days after cutting were analyzed. In addition, hormone-related genes were identified among differentially expressed genes (DEGs) and filtered genes, and cluster analysis was used to characterize expression patterns by function. Most hormone-related regulated genes expressed 3 days after growing the cut stems were abscisic acid (ABA)-related genes, followed by ethylene- and auxin-related genes. For ABA, the biosynthesis genes (including genes annotated to NINE-CIS-EPOXYCAROTENOID DIOXYGENASE 3 (NCED3)) and signal transduction and perception genes (including genes annotated to PROTEIN PHOSPHATASE 2Cs (PP2Cs)) tended to decrease. Expression patterns of auxin- and ethylene-related genes differed by function. These results suggest that ABA, auxin, and ethylene genes are involved in AR formation and that they may be regulated in a hormone function-dependent manner. These results contribute to the identification of hormone functions during AR formation and may contribute to understanding the mechanism of AR formation in the sweetpotato.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.156-163
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• 2003

Most watermarking techniques insert watermarks into transform coefficients in the frequency domain because we can consider robust or imperceptible frequency bands against malicious attacks to remove them. However, parameterization of 3-D data is not easy because of irregular attribution of connectivity information, while 1-I) or 2-D data is regular. In this paper we propose a new watermarking scheme for 3-D polygonal mesh models in the DCT domain. After we generate triangle strips by traversing the 3-D model and transform its vertex coordinates into the DCT domain, watermark signals are inserted into mid-frequency bands of AC coefficients for robustness and imperceptibility. We demonstrate that our scheme is robust against additive random noise, the affine transformation, and geometry compression by the MPEG-4 SNHC standard.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.377-379
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• 2007

To reduce the chattering errors of reed switch sensors used for automatic remote measurement of water supply system, a reed switch sensor was analyzed and improved. The operation of reed switch sensors can be described as a mechanical contact by approximation of permanent magnet piece to generate an electrical pulse. The reed switch sensors are used in measurement application by detecting the rotational or translational displacement. To apply for flow measurement devices, the reed switch sensors should keep high reliability. They are applied for the electronic digital type of water flow meters. The reed switch sensor is just installed simply on the mechanical type flow meter. A small magnet is attached on a pointer of the water meter counter rotor. Inside the reed sensor, two steel leaf springs make mechanical contact and apart as rotation of flow meter counter. The counting electrical contact pulses can be converted as the water flow amount. The MCU sends the digital flow rate data to the server using the wireless communication network. But it occurs data difference or errors by chattering noise. The reed switch sensor contains chattering error by it self at the force equivalent position. The vibrations such as passing car near to the switch sensor installed location. In order to reduce chattering error, most system uses just software methods for example using filter and also statistical calibration methods. The chattering errors were reduced by changing leaf spring structure using mechanical hysteresis characteristics.

• Journal of Korea Game Society
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• v.11 no.1
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• pp.131-138
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• 2011

Recently in terrain rendeing, most researches introduce mipmap-based out-of-core methods for handling large sized DEM data which does not fit in main memory of general computer. However, mipmap-based LOD(level-of-detail) methods occur geometric errors which appear in data simplifying the higher LOD level. These geometric errors cause geometric popping effects where LOD level changes when viewpoint moves. In this paper, we propose vertex cohesion map for reducing geometric error. In preprocessing step, we generate vertex cohesion map, which is a texture that stores the vectors. By these vectors, each vertex will be cohered into the position in which the difference of gradient value is bigger than others. Therefore in terrain rendering, using vertex cohesion map can dramatically reduce the geometry popping effects rather than using mipmap.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Journal of Korean Tunnelling and Underground Space Association
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• v.7 no.1
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• pp.89-96
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• 2005

Due to fast development of digital equipments, various information techniques have been applied to the tunneling and a decision aid system based on IT has also been used during excavation stage. A PDA based informative tunneling method is, therefore, studied in this paper and the decision aids for tunneling using digital face mapping data as well as geologic information in terms of digital data is developed. For this, wireless network, mobile computer, CDMA and digital camera have been combined to generate the digital map of the tunnel face and reinforcement or excavation pattern can be estimated based on digitalized geologic conditions. Future studies will be concentrated on the enhancement of the PDA S/W so that reinforcement method as well as the amount of reinforcements can also be stored in the same DB. Furthermore, field application of the S/W will be undertaken and a virtual reality technique will also be introduced to visualize all the tunneling work on the computer monitor.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.301-316
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• 2020

Algorithm journalism refers to the practices of automated news generation using algorithms that generate human sounding narratives. Algorithm journalism is known to have strengths in automating repetitive tasks through rapid and accurate analysis of data, and has been actively used in news domains such as sports and finance. In this paper, we propose an interactive card news system that generates personalized local election articles in 2018. The system consists of modules that collects and analyzes election data, generates texts and images, and allows users to specify their interests in the local elections. When a user selects interested regions, election types, candidate names, and political parties, the system generates card news according to their interest. In the study, we examined how personalized card news are evaluated in comparison with text and card news articles by human journalists, and derived implications on the potential use of algorithm in reporting political events.

• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.435-442
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• 2017

The runway visual range affected by fog and so on is one of the important indicators to determine whether aircraft can take off and land at the airport or not. In the case of airports where transportation airplanes are operated, major weather forecasts including the runway visual range for local area have been released and provided to aviation workers for recognizing that. This paper proposes a runway visual range estimation model with a deep neural network applied recently to various fields such as image processing, speech recognition, natural language processing, etc. It is developed and implemented for estimating a runway visual range of local airport with a deep neural network. It utilizes the past actual weather observation data of the applied airfield for constituting the learning of the neural network. It can show comparatively the accurate estimation result when it compares the results with the existing observation data. The proposed model can be used to generate weather information on the airfield for which no other forecasting function is available.