• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.4
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• pp.414-420
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• 2018

The digital convergence looking for new ways to engage visitors by superimposing virtual content on projection over the real world captured media contents. This paper propose the Light ID based interactive 3D immersive exhibition things view using HMD AR technology. This approach does not required to add any additional infrastructure to be built-in to enable service and uses the installed Lighting or displays devices in the exhibit area. In this approach, the Light ID can be used as a Location Identifier and communication medium to access the content unlike the QR Tag which supports provide the download information through web interface. This utilize the advantages of camera based optical wireless communication (OWC) to receive the media content on smart device to deliver immersive 3D content visualization using AR. The proposed exhibition method is emulated on GALAXY S8 smart phone and the visual performance is evaluated for Jeonju Hanok Village. The experimental results shows that the proposed method can give immersive 3D view for exhibit things in real-time.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.8
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• pp.1190-1196
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• 2013

The purpose of this study was to investigate the mechanism and effects of different types of ginseng on memory improvement in an experimental rat model. In this study, SD rats were induced for memory deficits through scopolamine treatment (1 mg/kg, i.p.) then administrated with ginseng extract for 7 weeks. The rats were divided into five groups: saline (1 mL/kg, NC: negative control), white ginseng (300 mg/kg, WG), red ginseng (300 mg/kg, RG), black ginseng (300 mg/kg, BG), and scopolamine (1 mg/kg, PC: positive control). The step through latency of the BG and RG groups was significantly longer than the PC group in the retention trial of multiple trial passive avoidance test. In the spatial reference memory triads of the Morris water maze test, the latency time of BG and RG was significantly lower than the PC group. In addition, in the prove test, the time spent in the platform quadrant of BG and RG groups were significantly longer than the PC group. Brain choline acetyltransferase (ChAT) activities BG and RG groups significantly increased compared to other groups. On the other hand, the levels of malondialdehyde (MDA) were significantly lower in the BG and RG groups compared to other groups. These result suggested that black ginseng could be useful to enhance learning memory and cognitive function by regulation of cholinergic enzymes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.6
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• pp.979-990
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• 2016

This study presents a nonlinear finite element procedure involving a phenomenological model to validate the tensile strain capacity of the X80 line pipe developed for the strain-based design purpose. The procedure is based on the Gurson-Tvergaard-Needleman (GTN) model, which models nucleation, growth and coalescence of void volume fraction occurred inside a metal. In this study, the user-defined material module (UMAT) is implemented in the commercial finite element platform ABAQUS and is applied to the nonlinear damage analysis of steel specimens. Material parameters for the nonlinear damage analysis of base and weld metals are calibrated from numerical simulations for the tensile tests of round bar and full thickness specimens. They are then employed in the numerical simulations for SENT (Single Edge Notch Tension) test and CWPT (Curved Wide Plate Test) and in the simulations, the tensile strain capacities are naturally evaluated. Comparison of the numerical results with the experimental results and the conventional empirical formulae shows that the proposed numerical procedure can fairly well predict the tensile strain capacity of X80 line pipe. So, it is readily expected to be effectively applied to the strain-based design procedure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.643-651
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• 2020

A capsule train runs inside a sub-vacuum tube and can reach very high speed due to the low air resistance. A capsule train uses a superconducting electrodynamic suspension (SC-EDS) method for levitation, which allows for a large levitation gap and does not require gap control. However, SC-EDS has inherent characteristics such as the large gap variation and a small damping effect in the levitation force, which can degrade the running stability and ride comfort. To overcome this, a stability improvement device should be designed and applied based on dynamic analysis. In this study, a 1/10 small-scale testbed was developed to replicate the dynamic characteristics of a capsule train and investigate the performance of stability improvement devices. The testbed is composed of a 6-degree-of-freedom Stewart platform for the realization of bogie motion, a secondary suspension with a running stabilization device, and a carbody. Based on the dynamic similarity law proposed by Jaschinski, the small-scale testbed was manufactured, and a bogie motion algorithm was applied with the consideration of guideway irregularity and levitation stiffness. The experimental results from the testbed were compared with simulation results to investigate the performance of the testbed.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.97-103
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• 2018

In oriental paintings, there is Luo-kuan that expressed in a single picture by compressing the artist's information. Such Luo-kuan includes various information such as the title of the work or the name of the artist. Therefore, information about Luo-kuan is considered important to those who collect or enjoy oriental paintings. However, most of the letters in the Luo-kuan are difficult kanji, kanzai, or various shapes, so it is difficult for the ordinary people to interpret. In this paper, we developed an Luo-kuan search application to easily check the information of the Luo-kuan. The application uses a search algorithm that analyzes the captured Luo-kuan image and sends it to the server to output information about the Luo-kuan candidates that are most similar to the Luo-kuan images taken from the database in the server. We also compared and analyzed the accuracy of the algorithm based on 170 Luo-kuan data in order to find out the ranking of the Luo-kuan that matched the Luo-kuan among the candidates. Accuracy Analysis Experimental Results The accuracy of the search algorithm of this application is confirmed to be about 90%, and it is anticipated that it will be possible to develop a platform to automatically analyze and search images in a big data environment by supplementing the optimizing algorithm and multi-threading algorithm.

• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.658-669
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• 2015

The paradigm of game development changes with technological trends. If the system can analyze and determine undefined inputs, users' input choices are not restricted. Therefore, game scenarios can have multifarious flows depending upon the user's input data. In this paper, we propose a method of including an output plan in the game system that is based on the user's input but is not restricted to predefined choices. We have implemented an experimental game on the Android platform by combining network communication and APIs. The game interface works as follows: first, the user's input data is transmitted to the server using HTTP protocol; then, the server carries out an analysis on the input data; and finally, the server returns the decision result to the game device. The game can provide users a scenario that corresponds to the decision results. In this paper, we used an image file for the user's input data format. The server calculates similarities between the user's image file and reference images obtained from the Naver Image Search API and then returns determination results. We have confirmed the value of integrating the game development framework with other computing technologies demonstrating the potential of the proposed methods for application to various future game interfaces.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3068-3084
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• 2021

Investigations of large commercial aircraft impact effect on nuclear power plant (NPP) buildings have been drawing extensive attentions, particularly after the 9/11 event, and this paper aims to experimentally assess the damage and vibrations of NPP buildings subjected to aircraft crash. In present Part I, two shots of reduce-scaled model test of aircraft impacting on NPP building were carried out. Firstly, the 1:15 aircraft model (weighs 135 kg) and RC NPP model (weighs about 70 t) are designed and prepared. Then, based on the large rocket sled loading test platform, the aircraft models were accelerated to impact perpendicularly on the two sides of NPP model, i.e., containment and auxiliary buildings, with a velocity of about 170 m/s. The strain-time histories of rebars within the impact area and acceleration-time histories of each floor of NPP model are derived from the pre-arranged twenty-one strain gauges and twenty tri-axial accelerometers, and the whole impact processes were recorded by three high-speed cameras. The local penetration and perforation failure modes occurred respectively in the collision scenarios of containment and auxiliary buildings, and some suggestions for the NPP design are given. The maximum acceleration in the 1:15 scaled tests is 1785.73 g, and thus the corresponding maximum resultant acceleration in a prototype impact might be about 119 g, which poses a potential threat to the nuclear equipment. Furthermore, it was found that the nonlinear decrease of vibrations along the height was well reflected by the variations of both the maximum resultant vibrations and Cumulative Absolute Velocity (CAV). The present experimental work on the damage and dynamic responses of NPP structure under aircraft impact is firstly presented, which could provide a benchmark basis for further safety assessments of prototype NPP structure as well as inner systems and components against aircraft crash.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1834-1845
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• 2021

Liquid lead-bismuth cooled fast reactor is one of the most promising reactor types among the fourth-generation nuclear energy systems. The flow and heat transfer characteristics of lead-bismuth eutectic (LBE) are completely different from ordinary fluids due to its special thermal properties, causing that the traditional Reynolds analogy is no longer recommended and appropriate. More accurate turbulence flow and heat transfer model for the liquid metal lead-bismuth should be developed and applied in CFD simulation. In this paper, a specific CFD solver for simulating the flow and heat transfer of liquid lead-bismuth based on the k - 𝜀 - k_𝜃 - 𝜀_𝜃 model was developed based on the open source platform OpenFOAM. Then the advantage of proposed model was demonstrated and validated against a set of experimental data. Finally, the simulation of LBE turbulent flow and heat transfer in a 7-pin wire-wrapped rod bundle with the k - 𝜀 - k_𝜃 - 𝜀_𝜃 model was carried out. The influence of wire on the flow and heat transfer characteristics and the three-dimensional distribution of key thermal hydraulic parameters such as temperature, cross-flow velocity and Nusselt number were studied and presented. Compared with the traditional SED model with a constant Pr_t = 1.5 or 2.0, the k - 𝜀 - k_𝜃 - 𝜀_𝜃 model is more accurate on predicting the turbulence flow and heat transfer of liquid lead-bismuth. The average relative error of the k - 𝜀 - k_𝜃 - 𝜀_𝜃 model is reduced by 11.1% at most under the simulation conditions in this paper. This work is meaningful for the thermal hydraulic analysis and structure design of fuel assembly in the liquid lead-bismuth cooled fast reactor.

• Smart Media Journal
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• v.8 no.2
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• pp.58-65
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• 2019

Automatic text summarization is a process of shortening a text document by either extraction or abstraction. The abstraction approach inspired by deep learning methods scaling to a large amount of document is applied in recent work. Abstractive text summarization involves utilizing pre-generated word embedding information. Low-frequent but salient words such as terminologies are seldom included to dictionaries, that are so called, out-of-vocabulary(OOV) problems. OOV deteriorates the performance of Encoder-Decoder model in neural network. In order to address OOV words in abstractive text summarization, we propose a copy mechanism to facilitate copying new words in the target document and generating summary sentences. Different from the previous studies, the proposed approach combines accurate pointing information and selective copy mechanism based on bidirectional RNN and bidirectional LSTM. In addition, neural network gate model to estimate the generation probability and the loss function to optimize the entire abstraction model has been applied. The dataset has been constructed from the collection of abstractions and titles of journal articles. Experimental results demonstrate that both ROUGE-1 (based on word recall) and ROUGE-L (employed longest common subsequence) of the proposed Encoding-Decoding model have been improved to 47.01 and 29.55, respectively.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.87-96
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• 2019

In this paper, we propose a MapReduce-supported clustering technique for collecting and classifying distributed workflow enactment event logs as a preprocessing tool. Especially, we would call the distributed workflow enactment event logs as Workflow BIG-Logs, because they are satisfied with as well as well-fitted to the 5V properties of BIG-Data like Volume, Velocity, Variety, Veracity and Value. The clustering technique we develop in this paper is intentionally devised for the preprocessing phase of a specific workflow process mining and analysis algorithm based upon the workflow BIG-Logs. In other words, It uses the Map-Reduce framework as a Workflow BIG-Logs processing platform, it supports the IEEE XES standard data format, and it is eventually dedicated for the preprocessing phase of the ${\rho}$-Algorithm that is a typical workflow process mining algorithm based on the structured information control nets. More precisely, The Workflow BIG-Logs can be classified into two types: of activity-based clustering patterns and performer-based clustering patterns, and we try to implement an activity-based clustering pattern algorithm based upon the Map-Reduce framework. Finally, we try to verify the proposed clustering technique by carrying out an experimental study on the workflow enactment event log dataset released by the BPI Challenges.