• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.334-335
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• 2005

Ternary tellurite glassy systems ($Li_2O-V_2O_5-P_2O_5$) have been synthesised using Vanadium oxide as a network former and Lithium oxide as network modifier. The addition of a metal? oxide makes them electric or mixed electric-ionic conductors, which are of potential interest as cathode' materials for solid-state batteries. This glass-ceramics crystallized from the $Li_2O-V_2O_5-P_2O_5$ system are particularly interesting, because they exhibit high conductivity (up to $5.95\times10^{-4}$ S/cm) at room temperature. the glass samples were prepared by quenching the melt on the copper plate and the glass-ceramics were heat-treated at crystallizing temperature determined from differential thermal analysis (DTA). The electric D.C conductivity result have been analyzed in terms of a small polaron-hopping model.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.842-844
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• 2004

Ternary tellurite glassy systems $(CuO-V_2O_5-TeO_2)$ have been synthesised using tellurium oxide as a network former and copper oxide as network modifier. The addition of a transition-matal oxide makes them electric or mixed electric-ionic conductors, which are of potential interest as cathode materials for solid-state batteries. This glass-ceramics crystallized from the $CuO-V_2O_5-TeO_2$ system are particularly interesting, because they exhibit high conductivity ( up to $6.03{\times}10^{-3}S/cm$) at room temperature. the glass samples were prepared by quenching the melt on the copper plate and the glass-ceramics were heat-treated at crystallizing temperature determined from differential thermal analysis (DTA). The electric D.C conductivity result have been analyzed in terms of a small polaron-hopping model.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.6
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• pp.416-426
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• 2008

The boiler tube failure often experienced in the superheater of a utility boiler can seriously affect the economic and safe operation of the power plant. It has been known that this failure is mainly caused by the thermal load deviation in the superheater tube system, and deeply intensified by the non-uniform distribution of steam flow rates. The nonuniform steam flow is distinctively prominent at low power load rather than at full power load. In this paper, we analyze the steam flow distribution in the superheater tube system by using one dimensional flow network model. At 30% power load, the deviation of steam flow rate is predicted to be within 0.8% of the averaged flow rate. This deviation can be reduced to 0.1% and 0.07% by assuming two cases, that is, the removal of 13th tube at each tube rows and the installation of intermediate header, respectively. The assumed two cases would be effective for the uniform steam flow distribution across 85 superheater tube rows.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Progress in Superconductivity and Cryogenics
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• v.9 no.1
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• pp.37-42
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• 2007

We are developing a 22.9 kV/25 MVA superconducting fault current limiting(SFCL) system for a power distribution network. A Bi-2212 bulk SFCL element, which has the merits of large current capacity and high allowable electric field during fault of the power network, was selected as a candidate for our SFCL system. In this work, we experimentally investigated important characteristics of the 190 kVA Bi-2212 SFCL element in its application to the power grid e.g. DC voltage-current characteristic, AC loss, current limiting characteristic during fault, and so on. Some experimental data related to thermal and electromagnetic behaviors were also compared with the calculated ones based on numerical method. The results show that the total AC loss at rated current of the 22.9 kV/25 MVA SFCL system, consisting of one hundred thirty five 190 kVA SFCL elements, becomes likely 763 W, which is excessively large for commercialization. Numerically calculated temperature of the SFCL element in some sections is in good agreement with the measured one during fault. Local temperature distribution in the190 kVA SFCL element is greatly influenced by non-uniform critical current along the Bi-2212 bulk SFCL element, even if its non-uniformity becomes a few percentages.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.1-10
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• 2023

Open surface water body extraction is gaining popularity in recent years due to its versatile applications. Multiple techniques are used for water detection based on applications. Different applications of Radar as LADAR, Ground-penetrating, synthetic aperture, and sounding radars are used to detect water. Shortwave infrared, thermal, optical, and multi-spectral sensors are widely used to detect water bodies. A stereo camera is another way to detect water and different methods are applied to the images of stereo cameras such as deep learning, machine learning, polarization, color variations, and descriptors are used to segment water and no water areas. The Satellite is also used at a high level to get water imagery and the captured imagery is processed using various methods such as features extraction, thresholding, entropy-based, and machine learning to find water on the surface. In this paper, we have summarized all the available methods to detect water areas. The main focus of this survey is on water detection especially in small patches or in small areas. The second aim of this survey is to detect water hazards for unmanned vehicles and off-sure navigation.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4776-4797
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• 2022

In the one-dimensional thermal-hydraulic (TH) codes, a subcooled boiling model to predict the void fraction profiles in a vertical channel consists of wall heat flux partitioning, the vapor condensation rate, the bubbly-to-slug flow transition criterion, and drift-flux models. Model performance has been investigated in detail, and necessary refinements have been incorporated into the Safety and Performance Analysis Code (SPACE) developed by the Korean nuclear industry for the safety analysis of pressurized water reactors (PWRs). The necessary refinements to models related to pumping factor, net vapor generation (NVG), vapor condensation, and drift-flux velocity were investigated in this study. In particular, a new NVG empirical correlation was also developed using artificial neural network (ANN) techniques. Simulations of a series of subcooled flow boiling experiments at pressures ranging from 1 to 149.9 bar were performed with the refined SPACE code, and reasonable agreement with the experimental data for the void fraction in the vertical channel was obtained. From the root-mean-square (RMS) error analysis for the predicted void fraction in the subcooled boiling region, the results with the refined SPACE code produce the best predictions for the entire pressure range compared to those using the original SPACE and RELAP5 codes.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.60.4-60.4
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• 2019

Small bodies in the solar system are pristine leftovers of planetesimals since the formation epoch (~4.6 Gyr ago). After the formation, icy planetesimals have been preserved in the distant cold place beyond 30 au (i.e., Trans-Neptunian region) until recently without any catastrophic processes but have just been injected into inner region (<~5 au from the Sun) to be observed as comets. On the contrary, asteroids are rocky primitive objects (although some of them contains icy volatiles) distributing in the mainbelt between Mars and Jupiter orbits. Because of frequent encounters in the mainbelt, asteroids have experienced a number of repeated impacts until the present day. Namely, it is important to investigate thermal alternation process of cometary volatiles and refractories in the solar radiation field, whereas collisional and subsequence phenomena of asteroidal bodies. Although recent spacecraft observations revealed the physical natures on the surfaces of comets and asteroids, their interiors still remain largely unexplored. It is likely that a sudden brightening of a comet is associated with rapid sublimation of internal CO and CO2 or phase transition of amorphous H2O. An episodic dust ejection from an asteroid is causally related to an impact among asteroids, sudden sublimation of remaining subsurficial volatiles, etc. Because these transient phenomena provide rare opportunities to investigate their interiors, immediate observations using any optical instruments are particular important. In my presentation, I will review some examples of such transient phenomena in the solar system and propose possible collaborative research using the Korean Small Telescope Network.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.791-802
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• 2023

The manual inspection of photovoltaic (PV) panels to meet the requirements of inspection work for large-scale PV power plants is challenging. We present a hot spot detection and positioning method to detect hot spots in batches and locate their latitudes and longitudes. First, a network based on the YOLOv3 architecture was utilized to identify hot spots. The innovation is to modify the RU_1 unit in the YOLOv3 model for hot spot detection in the far field of view and add a neural network residual unit for fusion. In addition, because of the misidentification problem in the infrared images of the solar PV panels, the DeepLab v3+ model was adopted to segment the PV panels to filter out the misidentification caused by bright spots on the ground. Finally, the latitude and longitude of the hot spot are calculated according to the geometric positioning method utilizing known information such as the drone's yaw angle, shooting height, and lens field-of-view. The experimental results indicate that the hot spot recognition rate accuracy is above 98%. When keeping the drone 25 m off the ground, the hot spot positioning error is at the decimeter level.

• Journal of the Korean Society of Visualization
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• v.22 no.1
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• pp.49-60
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• 2024

The purpose of this study establishes the deep neural network (DNN) and Decoder models to predict the flow and thermal fields of three-dimensional wavy wings as a passive flow control. The wide ranges of the wavy geometric parameters of wave amplitude and wave number are considered for the various the angles of attack and the aspect ratios of a wing. The huge dataset for training and test of the deep learning models are generated using computational fluid dynamics (CFD). The DNN and Decoder models exhibit quantitatively accurate predictions for aerodynamic coefficients and Nusselt numbers, also qualitative pressure, limiting streamlines, and Nusselt number distributions on the surface. Particularly, Decoder model regenerates the important flow features of tiny vortices in the valleys, which makes a delay of the stall. Also, the spiral vortical formation is realized by the Decoder model, which enhances the lift.