• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.131-157
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• 2014

Special track systems used to divert a train to other directions or other tracks are generally called 'railway turnout'. A traditional turnout system consists of steel rails, switches, crossings, steel plates, fasteners, screw spikes, timber bearers, ballast and formation. The wheel rail contact over the crossing transfer zone has a dip-like shape and can often cause detrimental impact loads on the railway track and its components. The large impact also emits disturbing noises (either impact or ground-borne noise) to railway neighbors. In a brown-field railway track where an existing aged infrastructure requires renewal or maintenance, some physical constraints and construction complexities may dominate the choice of track forms or certain components. With the difficulty to seek for high-quality timbers with dimensional stability, a methodology to replace aged timber bearers in harsh dynamic environments is to adopt an alternative material that could mimic responses and characteristics of timber in both static and dynamic loading conditions. A critical review has suggested an application of an alternative material called fibre-reinforced foamed urethane (FFU). The full-scale capacity design makes use of its comparable engineering characteristics to timber, high-impact attenuation, high damping property, and a longer service life. A field trial to investigate in-situ behaviours of a turnout grillage system using an alternative material, 'fibre-reinforced foamed urethane (FFU)' bearers, has been carried out at a complex turnout junction under heavy mixed traffics at Hornsby, New South Wales, Australia. The turnout junction was renewed using the FFU bearers altogether with new special track components. Influences of the FFU bearers on track geometry (recorded by track inspection vehicle 'AK Car'), track settlement (based on survey data), track dynamics, and acoustic characteristics have been measured. Operational train pass-by measurements have been analysed to evaluate the effectiveness of the replacement methodology. Comparative studies show that the use of FFU bearers generates higher rail and sleeper accelerations but the damping capacity of the FFU help suppress vibration transferring onto other track components. The survey data analysis suggests a small vertical settlement and negligible lateral movement of the turnout system. The static and dynamic behaviours of FFU bearers appear to equate that of natural timber but its service life is superior.

• 한국가시화정보학회:학술대회논문집
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• 2007.11a
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• pp.116-118
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• 2007

Three-dimensional (3D) velocity field information of a laminar flow in a curved micro tube of circular cross-section has been measured using a digital micro holographic particle tracking velocimetry (HPTV). The temporal evolution of instantaneous velocity field of a water flow in a curved micro tube of $100\;{\mu}\;m$ and $300\;{\mu}\;m$ in inner diameter was obtained. The 3D mean velocity field distribution was obtained quantitatively by statistical-averaging of instantaneous velocity fields. At low Dean number (De), a secondary flow was not generated in the curved tube. With increasing Dean number, the secondary flow constituted of two large-scale counter-rotating vortices was formed due to enhanced centrifugal force. To reveal the flow characteristics of high Dean numbers, trajectories of fluid particles were evaluated experimentally from the 3D velocity fields data measured by the HPTV technique. The present experimental results, especially the 3D particle trajectories, would be helpful to design and to understand the mixing phenomena in 3D curved passages of various curved micro-tubes or micro-channels.

• Geomechanics and Engineering
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• v.19 no.2
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• pp.167-178
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• 2019

Open-pit (OP) and underground (UG) mining are usually used to exploit shallow and deep ore deposits, respectively. When mine deposit starts from shallow subsurface and extends to a great depth, sequential use of OP and UG mining is an efficient and economical way to maintain mining productivity. However, a transition from OP to UG mining could induce significant rock movements that cause the slope instability of the open pit. Based on Yanqianshan Iron Mine, which was in the transition from OP to UG mining, a large-scale two-dimensional (2D) model test was built according to the similar theory. Thereafter, the UG mining was carried out to mimic the process of transition from OP to UG mining to disclose the triggered rock movement as well as to assess the associated slope instability. By jointly using three-dimensional (3D) laser scanning, distributed fiber optics, and digital photogrammetry measurement, the deformations, movements and strains of the rock slope during mining were monitored. The obtained data showed that the transition from OP to UG mining led to significant slope movements and deformations that can trigger catastrophic slope failure. The progressive movement of the slope could be divided into three stages: onset of micro-fracture, propagation of tensile cracks, and the overturning and/or sliding of slopes. The failure mode depended on the orientation of structural joints of the rock mass as well as the formation of tension cracks. This study also proved that these non-contact monitoring technologies were valid methods to acquire the interior strain and external deformation with high precision.

• Journal of the korean society of oceanography
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• v.38 no.4
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• pp.173-184
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• 2003

Major differences between wind speeds from atmospheric pressure maps (Na wind) and near­surface wind speeds derived from satellite scatterometer (NSCAT) observations over the East (Japan) Sea have been examined. The root­mean­square errors of Na wind and NSCAT wind speeds collocated with Japanese Meteorological Agency (JMA) buoy winds are about $3.84\;ms^{-1}\;and\;1.53\;ms^{-1}$, respectively. Time series of NSCAT wind speeds showed a high coherency of 0.92 with the real buoy measurements and contained higher spectral energy at low frequencies (>3 days) than the Na wind. The magnitudes of monthly Na winds are lower than NSCAT winds by up to 45%, particularly in September 1996. The spatial structures between the two are mostly coherent on basin­wide large scales； however, significant differences and energy loss are found on a spatial scale of less than 100 km. This was evidenced by the temporal EOFs (Empirical Orthogonal Functions) of the two wind speed data sets and by their two­dimensional spectra. Since the Na wind was based on the atmospheric pressures on the weather map, it overlooked small­scale features of less than 100 km. The center of the cold­air outbreak through Vladivostok, expressed by the Na wind in January 1997, was shifted towards the North Korean coast when compared with that of the NSCAT wind, whereas NSCAT winds revealed its temporal evolution as well as spatial distribution.

• Journal of The Korean Astronomical Society
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• v.47 no.2
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• pp.49-67
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• 2014

A novel method to characterize the topology of the early-universe intergalactic medium during the epoch of cosmic reionization is presented. The 21-cm radiation background from high redshift is analyzed through calculation of the 2-dimensional (2D) genus. The radiative transfer of hydrogen- ionizing photons and ionization-rate equations are calculated in a suite of numerical simulations under various input parameters. The 2D genus is calculated from the mock 21-cm images of high-redshift universe. We construct the 2D genus curve by varying the threshold differential brightness temperature, and compare this to the 2D genus curve of the underlying density field. We find that (1) the 2D genus curve reflects the evolutionary track of cosmic reionization and (2) the 2D genus curve can discriminate between certain reionization scenarios and thus indirectly probe the properties of radiation-sources. Choosing the right beam shape of a radio antenna is found crucial for this analysis. Square Kilometre Array (SKA) is found to be a suitable apparatus for this analysis in terms of sensitivity, even though some deterioration of the data for this purpose is unavoidable under the planned size of the antenna core.

• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.367-371
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• 2006

Multilayer percepteon has the advantage of learning their optimal parameters and efficiency. However, MLP shows some drawbacks when dealing with high dimensional data within the input space. Also, it Is very difficult to find the optimal parameters when the input data are highly correlated such as large scale face dataset. In this paper, we propose a novel technique for face recognition based on LDA and local MLP. To resolve the main drawback of MLP, we calculate the reduced features by LDA in advance. And then, we construct a local MLP per group consisting of subset of facedatabase to find its optimal learning parameters rather than using whole faces. Finally, we designed the face recognition system combined with the local MLPs. From various experiments, we obtained better classification performance in comparison with the results produced by conventional methods such as PCA and LDA.

• Journal of Platform Technology
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• v.8 no.3
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• pp.10-21
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• 2020

Partial discharge occurs a lot in high-voltage power equipment such as switchgear, transformers, and switch gears. Partial discharge shortens the life of the insulator and causes insulation breakdown, resulting in large-scale damage such as a power outage. There are several types of partial discharge that occur inside the product and the surface. In this paper, we design a predictive model that can predict the pattern and probability of occurrence of partial discharge. In order to analyze the designed model, learning data for each type of partial discharge was collected through the UHF sensor by using a simulator that generates partial discharge. The predictive model designed in this paper was designed based on CNN during deep learning, and the model was verified through learning. To learn about the designed model, 5000 training data were created, and the form of training data was used as input data for the model by pre-processing the 3D raw data input from the UHF sensor as 2D data. As a result of the experiment, it was found that the accuracy of the model designed through learning has an accuracy of 0.9972. It was found that the accuracy of the proposed model was higher in the case of learning by making the data into a two-dimensional image and learning it in the form of a grayscale image.

• Analytical Science and Technology
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• v.24 no.6
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• pp.510-518
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• 2011

Recent developments and improvements of multiple technological elements including mass spectrometry (MS) instrument, multi-dimensional chromatographic separation, and software tools processing MS data resulted in benefits of large scale proteomics analysis. However, its throughput is limited by the speed and reproducibility of the protein digestion process. In this study, we demonstrated a new method for rapid proteolytic digestion of proteins using acoustic technology. Tryptic digests of BSA prepared at various conditions by super acoustic for optimization time and intensity were analyzed by LC-MS/MS showed higher sequence coverage in compared with traditional 16 hrs digestion method. The method was applied successfully for complex proteins of a breast cancer cells at 30 min of digestion at intensity 2. This new application reduces time-consuming of sample preparation with better efficiency, even with large amount of proteins, and increases high-throughput process in sample preparation state.