• Applied Chemistry for Engineering
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• v.25 no.1
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• pp.41-46
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• 2014

The property improvement of polycarbonate coated with a multilayer film composed of an inorganic $SiO_2$ film and a photocatalytic $TiO_2$ film was studied. The $SiO_2$ film as a binder had an excellent light transmission characteristic. After the treatment with atmospheric pressure plasma, the surface of $SiO_2$ film showed the hydrophilicity, which increased the film coating uniformity with a $TiO_2$-containing aqueous solution. When $TiO_2$ film was over 200 nm thick, the absorption effect of UV rays in the range of 180~400 nm suppressed the yellowing phenomena of polycarbonate substrate. The inorganic film improved the heat resistance of polycarbonate substrates. $TiO_2$ film in the outmost under the exposure of UV rays promotes the catalytic oxidation characteristics and yields the capability to the decomposition of organic contaminants, and also increases the self-cleaning properties due to the increase of hydrophilicity. Structural stability of the polycarbonate substrate coated with inorganic $TiO_2$ and $SiO_2$ film was shown. The role of $SiO_2$ film between $TiO_2$ and polycarbonate substrate suppressed the peeling of $TiO_2$ film by inhibiting the photocatalytic oxidation effect of $TiO_2$ film on the polycarbonate substrate.

• Journal of the Korean Magnetics Society
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• v.8 no.6
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• pp.362-368
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• 1998

The magnetic properties, thermal stability and anti-corrosion properties of $Cr_86Al_14$ spin valves multilayers were studied. It was found that the magnetic properties of $Cr_86Al_14$ spin valves depend on the thickness of antiferromagnetic, ferromagnetic and non-ferromagnetic layers. Exchange coupled field ($H_{ex}$) and magnetoresistance ratio (%) showed the largest value of 20 Oe, 2 % in $glass/Cr_{86}Al_{14}(600 $\AA$)/Ni_{81}Fe_{19}(50$\AA$)/Cu(40 $\AA$)/Ni_{81}Fe_{19}(40 $\AA$)$ spin valves. The $H_{ex}$ MR ratios (%) of CrAl and FeMn spin valves were decreased with increasing annealing temperatures and were lost at 150 $^{\circ}C$, 250 $^{\circ}C$ respectively. Based on these result, it was elucidated that CrAl is more thermally stable than FeMn. It was also shown that there was no change of $H_{ex}$ MR ratios in CrAl, while FeMn was changed and lost 15 days later in corrosion resistance test under 35 $^{\circ}C$, 90 % humidity condition. FeMn was found to be pitted and peeled off 15 days later by SEM micrographic analysis.

• Journal of the Korean Magnetics Society
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• v.8 no.6
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• pp.380-387
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• 1998

In this paper, a sensor material with Fe/Zr multilayer thin film, in which the change in the magnetization and strain with hydrogenation is maximized, were developed. Compositionally modulated (CM) Fe/Zr multilayers with a $Fe_{80}Zr_{20}$ composition and modulation wavelengths ($\lambda$) $3~50{\AA}$ were deposited by sequentially sputtering (RF diode) elemental Fe and Zr targets. The films were electrolytically hydrogenated to select the optimum Fe/Zr multilayers that show the maximum increases in the magnetization and strain with hydrogenation. The changes in the magnetic properties of the thin films after hydrogenation, were measured using a hysteresis graph and a vibrating sample magnetometer (VSM), and the strains induced in the films by hydrogenation were also measured using a laser heterodyne interferometer (LHI). The optimum sensor material selected was incorporated in a fiber-optic hydrogen sensor (that can sense indirectly amount of hydrogen injected) by depositing it directly on the sensing arm of a single-mode fiber Michelson interferometer. The developed sensor holds significant promise for non-destructive test evaluation (NDE) applications because it is expected to be useful for detecting easily and accurately the subsurface corrosion in structural systems.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.25 no.1
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• pp.11-16
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• 2019

This study analyzed various packaging materials and types for frozen foods and the effects of packaging materials on temperature changes during frozen process. Pouches with different film thickness were prepared and placed in an IQF freezer, then the temperature inside pouches measured using a deep thermometer. The most common types of primary packaging for frozen foods in the market was plastic pouches with polyethylene or polyamide/polyethylene multilayer materials. The temperature change inside of packaging was delayed with film thickness increased. As the size of packaged food increased, the temperature change inside the food was slowed down. In addition, the pouches with air inside took more time to reach $-30^{\circ}C$ compared to pouches with less air during frozen process. This study provides information on packaging materials and types for frozen foods and preliminary data of temperature change by different types of packaging.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.1
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• pp.1-13
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• 2019

The accident at the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) resulted in the deposition of large quantities of radionuclides over parts of eastern Japan. Radioactive contaminants have been observed over a large area including forests, cities, rivers and lakes. Due to the strong adsorption of radioactive cesium by soil particles, radioactive cesium migrates with the eroded soil, follows the surface flow paths, and is delivered downstream of population-rich regions and eventually to coastal areas. In this study, we developed a model to simulate the transport of contaminated sediment in a watershed hydrological system and this model was compared with observation data from eroded soil observation instruments located at the Korea Atomic Energy Research Institute. Two methods were applied to analyze the soil particle size distribution of the collected soil samples, including standardized sieve analysis and image analysis methods. Numerical models were developed to simulate the movement of soil along with actual rainfall considering initial saturation, rainfall infiltration, multilayer and rain splash. In the 2019 study, a numerical model will be used to add rainfall shield effect by trees, evaporation effect and shield effects of surface water. An eroded soil observation instrument has been installed near the Wolsong nuclear power plant since 2018 and observation data are being continuously collected. Based on these observations data, we will develop the numerical model to analyze long-term behavior of radionuclides on land as they move from land to rivers, lakes and coastal areas.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.47 no.1
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• pp.49-56
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• 2021

In this study, a liquid crystal emulsion with a composition ratio similar to the skin lipid composition was manufactured using ceramide, glycolipids, cholesterol, and fatty acids, and a polarized microscope was used to identify liquid crystal particles in the formulation, and a cryso-SEM was used to confirm the formation of a multilayer structure. The above samples were stored at room temperature for one month and the liquid crystal structure was continuously maintained through microscopic observation. In addition, a cream composition containing liquid crystal emulsion was manufactured, and the efficiency of ceramide skin penetration was confirmed using three-dimensional artificial skin. Clinical trials were conducted using the cream composition and the results of clinical trials on skin barrier improvement were confirmed by measuring skin moisture retention (skin hydration) and transepidermal water loss (T EWL) of subjects compared to general emulsion.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.9-15
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• 2021

With the recent development of 5G and artificial intelligence technologies, it is interested in AIOT technology to collect, process, and analyze information in cloud edge environments. AIIoT technology is being applied to various smart environments, but research is needed to perform fast response processing through accurate analysis of collected information. In this paper, we propose a technique to minimize bandwidth and processing time by blocking the connection processing between AIOT information through fast processing and accurate analysis/forecasting of information collected in the smart environment. The proposed technique generates seeds for data indexes on AIOT devices by multipointing information collected by blockchain, and blocks them along with collection information to deliver them to the data center. At this time, we deploy Deep Neural Network (DNN) models between cloud and AIOT devices to reduce network overhead. Furthermore, server/data centers have improved the accuracy of inaccurate AIIoT information through the analysis and predicted results delivered to minimize latency. Furthermore, the proposed technique minimizes data latency by allowing it to be partitioned into a layered multilayer network because it groups it into blockchain by applying weights to AIOT information.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.1
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• pp.24-31
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• 2022

This study investigated microstructures, crystal structures, polarization, dielectric and electromechanical properties of 0.76Bi_1/2Na_1/2TiO₃-0.24SrTiO₃ (BNT-24ST)-based piezoceramcs by adding Li₂CO₃ and B₂O₃ (LB) as sintering aids for low-temperature sintering. All samples were successfully synthesized using conventional solid-state reaction method and sintered at 950, 1,000, 1,050, 1,100 and 1,175℃ for 2 hours. Without LB, specimens required sintering temperatures over 1,175℃ for sufficient densification, while the addition of 0.10-mol LB decreased the sintering temperatures down to 950℃. The average grain size and dielectric properties of BNT-24ST-10LB ceramics were enhanced with increasing sintering temperature. We found that the low-temperature sintered BNT-24ST piezoceramics by adding LB showed the d₃₃*value of 402 pm/V at 4 kV/mm after sintering at 1,050℃, which was better than that of high-temperature fired specimens sintered at 1,175℃ without LB (242 pm/V). We believe that the results of this study promise a candidate for low-cost multilayer ceramic actuator applications.