• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.12
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• pp.962-968
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• 2011

In this study, we investigated the effects of Mn dopant (0.1~3.0 at% $Mn_3O_4$ sintered at 1000$^{\circ}C$ for 1 h in air) on the bulk trap (i.e. defect) and grain boundary properties of ZnO, ZM(0.1~3.0) using admittance spectroscopy (AS), and impedance-modulus spectroscopy (IS & MS). As a result, three kinds of defect were found below the conduction band edge of ZnO as 0.09~0.14 eV (attractive coulombic center), 0.22~25 eV ($Zn^{{\cdot}{\cdot}}_i$), and 0.32~0.33 eV ($V^{\cdot}_o$). The oxygen vacancy increased with Mn doping. In ZM, an electrically single grain boundary as double Schottky barrier was formed with 0.82~1.0 eV of activation energies by IS & MS. We also find out that the barriers of grain boundary of Mn-doped ZnO (${\alpha}$-factor=0.13) were more stabilized and homogenized with temperature compared to pure ZnO.

• Particle and aerosol research
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• v.6 no.2
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• pp.51-59
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• 2010

ZnO quantum dots embedded in a silica matrix without agglomeration were synthesized from $TEOS:Zn(NO_3)_2$ solutions in one-step process by aerosol-gel method. It was successfully demonstrated that the size of ZnO Q-dots could be controlled from 2 to 7 mm verified by a high resolution transmission electron microscope observation. The line scanning energy dispersive X-ray spectroscopy(EDS) revealed that the Q-dots existed preferentially inside SiO2 sphere when Zn/Si < 0.5. However, the Q-dots distributed homogeneously all over the sphere when Zn/Si > 1.0. Blue-shifted UV/Vis absorption peak observation confirmed the quantum size effect on the optical properties. The photoluminescence(PL) emission peaks of the powders at room temperature were consistent with previous reports in the following aspects: 1) PL characteristics are dominated by two peaks of deep-level defect-related emissions at 2.4 - 2.8 eV, 2) the first defect-related peak at 2.4 eV was blue shifted due to the quantum size effect with decreasing the concentration of $Zn(NO_3)_2$(decreasing the size of ZnO q dots). More interestingly, the existence of surface-exposed ZnO q dots affects greatly the second defect PL peak at 2.8 eV.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2016.11a
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• pp.181-181
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• 2016

Hydrogenated microcrystalline silicon (${\mu}c-Si:H$) films have attracted much attention as materials of the bottom-cells in Si thin film tandem photovoltaics due to their low bandgap and excellent stability against light soaking. However, in PECVD, the source gas $SiH_4$ must be highly diluted by $H_2$, which eventually results in low deposition rate. Moreover, it is known that high-rate ${\mu}c-Si:H$ growth is usually accompanied by a large number of dangling-bond (DB) defects in the resulting films, which act as recombination centers for photoexcited carriers, leading to a deterioration in the device performance. During film deposition, Si nanoparticles generated in $SiH_4$ discharges can be incorporated into films, and such incorporation may have effects on film properties depending on the size, structure, and volume fraction of nanoparticles incorporated into films. Here we report experimental results on the effects of nonoparticles incorporation at the different substrate temperature studied using a multi-hollow discharge plasma CVD method in which such incorporation can be significantly suppressed in upstream region by setting the gas flow velocity high enough to drive nanoparticles toward the downstream region. All experiments were performed with the multi-hollow discharge plasma CVD reactor at RT, 100, and $250^{\circ}C$, respectively. The gas flow rate ratio of $SiH_4$ to $H_2$ was 0.997. The total gas pressure P was kept at 2 Torr. The discharge frequency and power were 60 MHz, 180 W, respectively. Crystallinity Xc of resulting films was evaluated using Raman spectra. The defect densities of the films were measured with electron spin resonance (ESR). The defect density of fims deposited in the downstream region (with nonoparticles) is higher defect density than that in the upstream region (without nanoparticles) at low substrate temperature of RT and $100^{\circ}C$. This result indicates that nanoparticle incorporation can change considerably their film properties depending on the substrate temperature.

• Journal of the Korea Institute of Building Construction
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• v.11 no.2
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• pp.154-161
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• 2011

In increasingly competitive market conditions, domestic construction companies are struggling to secure a competitive edge by increasing consumer satisfaction. In these circumstances, a greater emphasis needs to be put on providing an upgraded level of quality in the area of addressing defects to the satisfaction of customers, which is considered to have a great impact on customer satisfaction. This empirical study of the relationships among service quality, customer satisfaction, and repurchase intention aims to provide basis data for increasing service quality, particularly in dealing with the defect issues facing construction companies. To this end, a literature review was carried out on the concept of service quality along with defect issues, and customer satisfaction and influential factors were identified. Building on these foundations and findings, a survey was administered to residents in housing dwellings such as apartments. The results of this study are expected to heighten awareness of the need for increased service quality, which may have a critical influence on customer satisfaction and repurchase intention, and will thereby contribute to enhancing competitiveness and other aspects of the current construction industry.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.3
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• pp.251-257
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• 2017

In this paper, an algorithm for detecting defects in thin-film-transistor liquid-crystal display (TFT-LCD) cell images is presented. TFT-LCD cell images typically contain periodic cell patterns that make it difficult to detect defects. We propose an efficient and powerful algorithm for eliminating the cell patterns using magnitude spectrum analysis. The first step was to obtain a spectrum for a cell image using the Fourier transform while eliminating larger coefficients using an adaptive filter. Next, an image without the cell pattern was obtained by using the inverse Fourier transform. Finally, the defect pixels were detected using the STD algorithm. The validity of the proposed method was investigated using real TFT-LCD cell images. The experimental results indicate that the proposed technique is extremely effective for detecting defects in TFT-LCD cell images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• Journal of Magnetics
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• v.22 no.1
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• Journal of Surface Science and Engineering
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• v.51 no.6
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• pp.421-429
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• 2018

In this study, various electrochemical experiments were carried out to compare the corrosion characteristics of AA5052-O, AA5083-H321 and AA6061-T6 in seawater. The electrochemical impedance and potentiostatic polarization measurements showed that the corrosion resistance is decreased in the order of AA5052-O, AA5083-H321 and AA6061-T6, with AA5052-O being the highest resistant. This is closely associated with the property of passive film formed on three tested Al alloys. Based on the slope of Mott-Schottky plots of an n-type semiconductor, the density of oxygen vacancies in the passive film formed on the alloys was determined. This revealed that the defect density is increased in the order of AA5052-O, AA5083-H321 and AA6061-T6. Considering these facts, it is implied that the addition of Mg, Si, and Cu to the Al alloys can degrade the passivity, which is characterized by a passive film structure containing more defect sites, contributing to the decrease in corrosion resistance in seawater.

• Progress in Superconductivity and Cryogenics
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• v.21 no.3
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• pp.43-46
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• 2019

A rare-earth barium copper oxide (REBCO) superconducting magnet was designed using no-insulation (NI) and multi-width (MW) winding techniques. The proposed magnet is comprised of 58 REBCO-wound single pancake coils with a bore size of 240 mm. When the magnet is operated at 20 K, the center magnetic flux density is designed to reach 3 T with an operational current of 169.55 A, 70 % of its critical current. The critical current was evaluated using experimental data of a short REBCO conductor sample. The designed magnet was then simulated using FEM software with uniform current density model. Magnetic field and mechanical properties of the magnet are evaluated using the simulated data. This magnet was designed as one of the base designs for the project "Tesla-Level Magnets with Large Bore Sizes for Industrial Applications" which was initiated in 2019, and will be wound using REBCO wires with the defect-irrelevant-winding (DIW) technique incorporated to reduce the overall manufacturing cost.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3943-3948
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• 2022

Single-photon emission computed tomography is one of the reliable pin-by-pin verification techniques for spent-fuel assemblies. One of the challenges with this technique is to increase the total fuel assembly verification speed while maintaining high verification accuracy. The aim of the present study, therefore, was to develop an artificial intelligence (AI) algorithm-based tomographic image analysis technique for partial-defect verification of fuel assemblies. With the Monte Carlo (MC) simulation technique, a tomographic image dataset consisting of 511 fuel-rod patterns of a 3 × 3 fuel assembly was generated, and with these images, the VGG16, GoogLeNet, and ResNet models were trained. According to an evaluation of these models for different training dataset sizes, the ResNet model showed 100% pattern estimation accuracy. And, based on the different tomographic image qualities, all of the models showed almost 100% pattern estimation accuracy, even for low-quality images with unrecognizable fuel patterns. This study verified that an AI model can be effectively employed for accurate and fast partial-defect verification of fuel assemblies.