• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1902-1912
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• 2024

This paper presents results from system-level modeling of a water-based reactor cavity cooling system using RELAP5-3D. The computational model is benchmarked with experimental data from a half-scale RCCS test facility at Argonne National Laboratory. The model prediction is first compared with a two-phase oscillatory baseline experimental case where mixed accuracy is obtained. The model shows reasonable prediction of mass flow rate, pressure, and temperature but significant overprediction of void fraction. The model prediction is then compared with a fault case where the inlet of the risers is gradually reduced using a throttling valve. As the valve is closed, the model is able to predict some major flow phenomena observed in the experiment such as the dampening of oscillations, the reintroduction of oscillations, as well as boiling, flashing, and geysering in the risers. However, the timeline of these events are not well captured by the model. The model is also used to investigate the evolution of flow regime in the chimney. This work highlights that the semi-empirical constitutive relations used in RELAP-3D could have a strong influence on the accuracy of the model in two-phase oscillatory flows.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.491-500
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• 2024

In hopes of resolving the issue of poor quality of information input for teaching spoken English online, the study creates an English teaching assistance model based on a recognition algorithm named dynamic time warping (DTW) and relies on automated voice recognition technology. In hopes of improving the algorithm's efficiency, the study modifies the speech signal's time-domain properties during the pre-processing stage and enhances the algorithm's performance in terms of computational effort and storage space. Finally, a simulation experiment is employed to evaluate the model application's efficacy. The study's revised DTW model, which achieves recognition rates of above 95% for all phonetic symbols and tops the list for cloudy consonant recognition with rates of 98.5%, 98.8%, and 98.7% throughout the three tests, respectively, is demonstrated by the study's findings. The enhanced model for DTW voice recognition also presents higher efficiency and requires less time for training and testing. The DTW model's KS value, which is the highest among the models analyzed in the KS value analysis, is 0.63. Among the comparative models, the model also presents the lowest curve position for both test functions. This shows that the upgraded DTW model features superior voice recognition capabilities, which could significantly improve online English education and lead to better teaching outcomes.

• Journal of Korean Society on Water Environment
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• v.32 no.3
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• pp.243-252
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• 2016

In cases of water pollution accidents, accurate prediction for arrival time and concentration of contaminants in a river is essential to take proper measures and minimize their impact on downstream water intake facilities. It is critical to fully understand the behavior characteristics of contaminants on river surface, especially in case of oil spill accidents. Therefore, in this study, the effects of main parameters of advection and diffusion of contaminants were analyzed and validated by comparing the results of Lagrangian particle tracking (LPT) simulation of Environmental Fluid Dynamic Code (EFDC) model with those of Global Position System (GPS)-equipped drifter experiment. Prevention scenario modeling was accomplished by taking cases of movable weir operation into account. The simulated water level and flow velocity fluctuations agreed well with observations. There was no significant difference in the speed of surface particle movement between 5 and 10 layer modeling. Therefore, 5 layer modeling could be chosen to reduce computational time. It was found that full three dimensional modeling simulated wind effects on surface particle movements more sensitively than depth-averaged two dimensional modeling. The diffusion range of particles was linearly proportional to horizontal diffusivity by sensitivity analysis. Horizontal diffusivity estimated from the results of GPS-equipped drifter experiment was 0.096 m²/sec, which was considered to be valid for applying the LPT module in this area. Finally, the scenario analysis results showed that particle movements could be stagnant when discharge from the upstream weir was reduced, implying the possibility of securing time for mitigation actions such as oil boom installation and wiping oil contaminants. The outcomes of this study can help improve the prediction accuracy of particle tracking simulation to establish the most suitable mitigation plan considering the combination of movable weir operation.

• Journal of Bio-Environment Control
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• v.17 no.2
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• pp.83-89
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• 2008

A wind tunnel test was conducted at Protected Horticulture Experiment Station of National Horticultural Research Institute in Busan to find the aerodynamic resistance and quadratic resistance coefficient of chrysanthemum in greenhouse. The internal plants of the CFD model has been designed as a porous media because of the complexity of its physical shapes. Then the aerodynamic resistance value should be input for analyzing CFD model that crop is considered while the value varies by crops. In this study, the aerodynamic resistance value of chrysanthemum canopy was preliminarily found through wind tunnel test. The static pressure at windward increased as wind velocity and planting density increased. The static pressure at leeward decreased as wind velocity increased but was not significantly affected by planting density. The difference of static pressure between windward and leeward increased as wind velocity and planting density increased. The aerodynamic resistance value of chrysanthemum canopy was found to be 0.22 which will be used later as the input data of Fluent CFD model. When the planting distances were $9{\times}9\;cm$, $11{\times}11\;cm$, and $13{\times}13\;cm$, the quadratic resistance coefficients of porous media were found to be 2.22, 1.81, and 1.07, respectively. These values will be used later as the input data of CFX CFD model.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Korean Journal of Cognitive Science
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• v.15 no.4
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• pp.1-14
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• 2004

When two objects are tying in the same visual direction there occurs abrupt depth change between two objects, which is against the assumption of the computational model for stereopsis on the surfaces in a natural scene. For this reason, this stimulus configuration is popularly used in the studies for the effectiveness of the constraints employed in the computational model. Contrary to the results from two nails (or objects) tying in the same visual direction, the two different surfaces from random-dot stereogram (RDS) in the same situation can be seen simultaneously in the different depth. The seemingly contradictory results between two situations my reflect the different strategies imposed by binocular mechanism for each situation during binocular matching process. Otherwise, the surfaces tying in the same visual direction is not equivalent situation to two objects tying in the same visual direction with regards to matching process. In order to examine above possibilities, the stereoscopic depth of the surface was measured after manipulating the visual direction of the surface elements. The visual direction of each dot pair from different surfaces in RDS (in Experiment 1) or the visual direction of line (hawing rectangle with regard to that of the vertical line (in Experiment 2) was manipulated. The stereoscopic depth of the surface was found to be varied depending on visual direction of the surface elements in both RDS and line hawing stimulus. Similar to the results from two nails situation depth of the surface was greatly reduced when each surface element was tying in the same visual direction as that of the other surface element or the other object. These results suggest that binocular mechanism imposes no different strategy in resolving correspondence problem in both two objects and two surfaces situation. And the results were discussed in the context of usefulness of the constraints employed in the computational model for stereopsis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.51-59
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• 2012

This paper suggests an efficient method which tracks articulated human body modeled with markov network using disparity map derived from stereo images. The conventional methods which only use color information to calculate likelihood for energy function tend to fail when background has same colors with objects or appearances of object are changed during the movement. In this paper, we present a method evaluating likelihood with both disparity information and color information to find human body parts. Since the human body part are cylinder projected to rectangles in 2D image plane, we use the properties of distribution of disparity of those rectangles that do not have discontinuous distribution. In addition to that we suggest a conditional-messages-update that is able to reduce unnecessary message update of belief propagation. Since the message update has comprised over 80% of the whole computation in belief propagation, the conditional-message-update yields 9~45% of improvements of computational time. Furthermore, we also propose an another speed up method called three dimensional dynamic models assumed the body motion is continuous. The experiment results show that the proposed method reduces the computational time as well as it increases tracking accuracy.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.2
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• pp.139-146
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• 2013

This study is to update of vibration power generating model and to analyze electromotive force on subway. Analysis of electromotive force using power generation depending on classification of locations which are ballast bed and concrete bed. As the section between Seocho and Bangbae in the line 2 subway was changed from ballast bed to concrete bed, it could be analyzed at same condition, train, section. Induced electromotive force equation by Faraday's law was updated using Bayesian regression and correlation analysis with calculate value and experiment value. Using the updated model, it could get 40mV per one power generation in ballast bed, and it also could get 4mV per one power generation in concrete bed. If the updated model apply to subway or any train, it will be more effective to get electric power. In addition to that, it will be good to reduce greenhouse gas and to build a green traffic network.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.21-26
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• 2011

Feature ranking is useful to gain knowledge of data and identify relevant features. In this study, we proposed a use of feature ranking for classification of neuro-degeneration and vascular dementia in micro-Raman spectra of platelet. The entire region of the spectrum is divided into local region including several peaks, followed by Gaussian curve fitting method in the region to be modeled. Local minima select from the subregion and then remove the background based on the position by using interpolation method. After preprocessing steps, significant features were selected by feature ranking method to improve the classification accuracy and the computational complexity of classification system. PCA (principal component analysis) transform the selected features and the overall features that is used classification with the number of principal components. These were classified as MAP (maximum a posteriori) and it compared with classification result using overall features. In all experiments, the computational complexity of the classification system was remarkably reduced and the classification accuracy was partially increased. Particularly, the proposed method increased the classification accuracy in the experiment classifying the Parkinson's disease and normal with the average 1.7 %. From the result, it confirmed that proposed method could be efficiently used in the classification system of the neuro-degenerative disease and vascular dementia of platelet.

• Journal of the Korean Society for Marine Environment & Energy
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• v.20 no.2
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• pp.107-116
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• 2017

When drilling operation is being performed, many physical and chemical changes are occurred near wellbore. To handle various changes of well condition and keep drilling process safe, additives of bulk, such as bentonite for increasing density of drilling mud, barite for increasing viscosity of drilling mud, polymer for chemical control, or surfactant, are added into drilling mud through a mud shear mixer. Because the achievement of the required material property through mud mixing system is essential to stabilize drilling system, it is of importance to analyze multi-phase flow during mud mixing process, which is directly related to increase mixing performance of the system and guarantee the safety of the whole drilling system. In this study, a series of liquid-solid flow simulation based on a computational fluid dynamics (CFD) are performed with comparing to solid concentration in experiment by Gilles et al. [2004] to understand the characteristics of liquid-solid mixing in a mud shear mixer. And then, the simulation-based design of shear mixer are carried out to improve mixing performance in a mud handling system.