• Nuclear Engineering and Technology
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• v.41 no.7
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• pp.893-906
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• 2009

The present work presents the development of a Large Eddy Simulation (LES) methodology viable for complex geometries and suitable for the simulation of rod-bundles. The use of LES and Direct Numerical Simulation (DNS) allows for a deeper analysis of the flow field and the use of stochastical tools in order to obtain additional insight into rod-bundle hydrodynamics. Moreover, traditional steady-state CFD simulations fail to accurately predict distributions of velocity and temperature in rod-bundles when the pitch (P) to diameter (D) ratio P/D is smaller than 1.1 for triangular lattices of cylindrical pins. This deficiency is considered to be due to the failure to predict large-scale coherent structures in the region of the gap. The main features of the code include multi-block capability and the use of the fractional step algorithm. As a Sub-Grid-Scale (SGS) model, a Dynamic Smagorinsky model has been used. The code has been tested on plane channel flow and the flow in annular ducts. The results are in excellent agreement with experiments and previous calculations.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.423-433
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• 2014

For the proper decision making and responsibility enhancement for an unexpected accident in large-scale facilities, it is important to train operators or first responders to minimize potential human errors and consequences resulted from them. Simulation technologies, including human-computer interaction and virtual reality, enables personnel to participate in simulated hazardous situations with a safe, interactive, repetitive way to perform these training activities. For the development of accident response training simulator, it is necessary to define components comprising the simulator and to integrate them for the given training purpose. In this paper, we analyze requirements of the training simulator, derive key components, and design the training simulator. Based on the design, we developed a prototype training simulator and verified the simulator through experiments.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2558-2561
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• 2008

In the present study, a parallel Taylor-Galerkin/level set based two-phase flow code was developed using finite element discretization and domain decomposition method based on MPI (Message Passing Interface). The proposed method can be utilized for the analysis of a large scale free surface problem in a complex geometry due to the feature of FEM and domain decomposition method. Four-step fractional step method was used for the solution of the incompressible Navier-Stokes equations and Taylor-Galerkin method was adopted for the discretization of hyperbolic type redistancing and advection equations. A Parallel ILU(0) type preconditioner was chosen to accelerate the convergence of a conjugate gradient type iterative solvers. From the present parallel numerical experiments, it has been shown that the proposed method is applicable to the simulation of large scale free surface flows.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.37-50
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• 2021

Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.

• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.78-85
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• 2024

We proposed a method for visual place recognition that represents images using objects as visual words. Visual words represent the various objects present in urban environments. To detect various objects within the images, we implemented and used a zero-shot detector based on a large-scale image language model. This zero-shot detector enables the detection of various objects in urban environments without additional training. In the process of creating histograms using the proposed method, frequency-based weighting was applied to consider the importance of each object. Through experiments with open datasets, the potential of the proposed method was demonstrated by comparing it with another method, even in situations involving environmental or viewpoint changes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3942-3961
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• 2019

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9B
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• pp.832-844
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• 2008

Distributed localization algorithms are necessary for large-scale wireless sensor network applications. In this paper, we introduce an efficient node distribution based localization algorithm that emphasizes simple refinement and low system load for low-cost and low-rate wireless sensors. Each node adaptively chooses neighbor nodes for sensors, update its position estimate by minimizing a local cost function and then passes this update to the neighbor nodes. The update process considers a distribution of nodes for large-scale networks which have same density in a unit area for optimizing the system performance. Neighbor nodes are selected within a range which provides the smallest received signal strength error based on the real experiments. MATLAB simulation showed that the proposed algorithm is more accurate than trilateration and les complex than multidimensional scaling. The implementation on MicaZ using TinyOS-2.x confirmed the practicality of the proposed algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.24-35
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• 2017

This paper reports the results of afield appliance study of the hydraulic well method to prevent embankment seepage, the large-scale embankment experiment and seepage analysis to examine the traits of the seepage pressure. The experimental procedure was focused on the pore pressure after examining the detected value of the pore pressure gage. The inner water levels of hydraulic well were compared with the pore pressure data, which were used to inspect the seepage variations. Two different large-scale experiments were conducted according to the installation points of the hydraulic wells. The decrease in seepage pressure reached a maximum of 37% from the experimental results. The experimental pore pressure results were similar to those of the analyses. In addition, the pore pressure oriented from the water level variations of the hydraulic well showed similar patterns between the experiment and analysis, but if the hydraulic well was deeper, the analyzed water levels were larger than the experimental values.

• Journal of KIISE
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• v.43 no.9
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• pp.963-973
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• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.239-248
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• 2005

The reliability and efficiency of network must be considered in the large scale wireless sensor networks. Broadcast method must be used rather than unicast method to enhance the reliability of networks. In recently proposed GRAB (GRAdient Broadcast) can certainly enhance reliability of networks fy using broadcast but its efficiency regarding using energy of network is low due to using only one sink. Hence, the lifetime of networks is reduced. In the paper we propose the scheme of SMSGB (Selective Multi Sink Gradient Broadcast) which uses single sink of multi-sink networks. The broadcast based SMSGB can secure reliability of large scale wireless sensor networks. The SMSGB can also use the network's energy evenly via multi sink distribution. Our experiments show that using SMSGB was reliable as GRAB and it increased the network's lifetime by 18% than using GRAB.