• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.3
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• pp.112-121
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• 2020

In suspension bridges, hanger cables are the main load-supporting members. The tension of the hanger cables of a suspension bridge is a very important parameter for assessing the integrity and safety of the bridge. In general, indirect methods are used to measure the tension of the hanger cables of a suspension bridge in traffic use. A representative indirect method is the vibration method, which extracts modal frequencies from the cables' responses and then measures the cable tension using the cables' geometric conditions and the modal frequencies. In this study, the image processing technique is applied to facilitate the estimation of the dynamic responses of the cables using the image signal, for which a portable digital camcorder was used due to its convenience and cost-efficiency. Ambient vibration tests were conducted on a suspension bridge in traffic use to verify the validity of the back analysis method, which can estimate the tension of remote hanger cables using the modal frequencies as a parameter. In addition, the tension estimated through back analysis method, which was conducted to minimize the difference between the modal frequencies calculated using finite element analysis of the hanger cables and the measured modal frequencies, was compared with that measured using the vibration method.

• Polymer(Korea)
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• v.24 no.3
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• pp.389-398
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• 2000

Transparent, abrasion resistant coatings with 4~13 ${\mu}{\textrm}{m}$ thickness were prepared by spin-coating on polycarbonates with organic/inorganic hybrid solutions, followed by UV curing and heat treatment at 12$0^{\circ}C$ for 12 hours. The coating solutions were composed of inorganic phase and organic phase in 0:100, 20:80, 30:70, 50:50, 80:20 wt% ratios, respectively, mixed with photoinitiator, senaitizer and surfactant. The inorganic phase was formed by sol-gel reaction of TEOS and silane coupling agent MPTMS in 1 : 2 or 2 : 1 molar ratios, the organic phase consisted of difunctional urethane acrylate oligomeric resin, multifunctional acrylate TMPTA and HDDA in 4 : 3 : 3 wt% ratio. The coating systems were investigated by FT-IR, $^{29}$ Si-NMR spectra. In addition, TGA/DSC for thermal analysis and SEM, AFM observation for coated surface were examined. Gererally, the homogeneity of phases, the surface smoothness of coating and abrasion resistance were improved with the higher content of inorganic component. Namely, coating system with below 10 $\AA$ surface roughness and T$_{g}$ of 15$0^{\circ}C$ showed only 10% decrease in light transmittance after abrasion test, whereas uncoated polycarbonate substrate exhibited 46% decrease..

• Journal of the Korean Vacuum Society
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• v.20 no.3
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• pp.225-232
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• 2011

Reactive ion etching (RIE) technique for maskless surface texturing of mc-silicon solar wafers has been applied and succeed in fabricating a grass-like black-silicon with an average reflectance of $4{\pm}1%$ in a wavelength range of 300~1,200 nm. In order to investigate the optimized texturing conditions for mass production of high quantum efficiency solar cell Surface characteristics such as the spatial distribution of average reflectance, micrscopic surface morphology and minority carrier lifetime were monitored for samples from saw-damaged $15.6{\times}15.6\;cm^2$ bare wafer to key-processed wafers as well as the mc-Si solar cells. We observed that RIE textured wafers reveal lower average reflectance along from center to edges by 1% and referred the origin to the non-uniform surface structures with a depth of 2 times deeper and half-maximum width of 3 times. Samples with anti-reflection coating after forming emitter layer also revealed longer minority carrier lifetime by 40% for the edge compared to wafer center due to size effects. As results, mc-Si solar cells with RIE-textured surface also revealed higher efficiency by 2% and better external quantum efficiency by 15% for edge positions with higher height.

• Korean Journal of Materials Research
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• v.4 no.5
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• pp.600-607
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• 1994

Optimum exper~mental conditions were established for the growth of heteroepitaxial GaAs layers on InP using liquid phase epitaxy (LPE) technique. Results showed that the optimum growth temperature was $720^{\circ}C$ at a cooling rate of $0.5^{\circ}C$/min. Surface morphology of the grown layers significantly improved by addition of about 0.005wt% Se to the Ga growth melt, which effectively suppressed melt-back of InP substrates into the melt during the initial stage of growth. It was observed that the quality of GaAs layers also improved substantially when the substrates patterned with grating structure were used, as determined by the (400) double crystal X-ray diffraction. The transmission electron microscopy observation indicated t.hat the misfit dislocations interact with each other at the grating region, resulting in a lower dislocation density in the upper GaAs layer.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Journal of Life Science
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• v.29 no.6
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• pp.705-711
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• 2019

Benign prostatic hyperplasia (BPH) is the most common urogenital disorder in men, benign tumor and is a typical disease deteriorating the quality of old men's lives, and its prevalence increases with age. Though the molecular pathogenesis of BPH has not yet been clearly revealed, it is known that the variation and aging of the endocrine including sex hormone may cause BPH. Especially the hypertrophy of the prostate cell by the formation of the excessive dihydrotestosterone (DHT) is estimated to cause BPH. If testosterone exists excessively in blood, a lot of DHT is produced in prostate by $5{\alpha}-reductase$. Thus, in this study we tried to analyze haematological change and histopathological change by using the model rat with BPH caused by hypodermic injection of testosterone to prove the effect of Houttuynia cordata extracts on BPH. Rats were divided into four experimental groups: no treatment group (N), the testosterone injection and D.W treatment group (DO), the testosterone injection and Houttuynia cordata treatment group (HO) and testosterone injection and finasteride treatment group (FO). Prostate weight, volume and weight ratio in the HO and FO groups were significantly lower than the DO group. Testosterone and DHT levels in the HO group were significantly lower than the DO group. The HO and FO groups showed trophic symptoms and were lined by flattened epithelial cells, thus, the stromal proliferation is relatively low as compared to the DO group. These results suggest that Houttuynia cordata may control benign prostatic hyperplasia.

• Tunnel and Underground Space
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• v.29 no.3
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• pp.184-196
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• 2019

This study aims to extract a 3D image of micro-cracks generated by hydraulic fracturing tests, using the deep learning method and X-ray computed tomography images. The pixel-level cracks are difficult to be detected via conventional image processing methods, such as global thresholding, canny edge detection, and the region growing method. Thus, the convolutional neural network-based encoder-decoder network is adapted to extract and analyze the micro-crack quantitatively. The number of training data can be acquired by dividing, rotating, and flipping images and the optimum combination for the image augmentation method is verified. Application of the optimal image augmentation method shows enhanced performance for not only the validation dataset but also the test dataset. In addition, the influence of the original number of training data to the performance of the deep learning-based neural network is confirmed, and it leads to succeed the pixel-level crack detection.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.87-94
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• 2019

In this paper, the method of analyzing the unformatted data of consumers accumulated on social networks in the era of the Fourth Industrial Revolution by utilizing data from the service design and social psychology aspects was proposed. First, the fandom phenomenon, which shows subjective and collective behavior in a space on a social network rather than physical space, was defined from a data service perspective. The fandom model has been transformed into a collective level of customer Persona that has been analyzed at a personal level in traditional service design, and social network analysis that analyzes consumers' big data has been presented as an efficient way to pattern and visually analyze it. Consumer data collected through social leasing were pre-processed by column based on correlation, stability, missing, and ID-ness. Based on the above data, the company's brand strategy was divided into active and passive interventions and the effect of this strategic attitude on the growth direction of the consumer's fandom community was analyzed. To this end, the fandom model of consumers was proposed by dividing it into four strategies that the brand strategy had: stand-alone, decentralized, integrated and centralized, and the fandom shape of consumers was proposed as a growth model analysis technique that analyzes changes over time.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.21-26
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• 2020

An autonomous driving system is based on the deep learning system built by big data which are obtained by various IoT sensors. The miniaturization and high performance of the IoT sensors are needed for diverse devices including the autonomous driving system. Specially, the miniaturization of the sensors leads to compel the miniaturization of the fixer structures. In the viewpoint of the miniaturization, metallic structure is a best solution to attach the small IoT sensors to the main body. However, it is hard to manufacture the small metallic structure with a conventional machining process or manufacturing cost greatly increases. As one of solutions for the problems, in this work, metallic FDM (Fused depositon modeling) based on metallic filament was proposed and the FDM process was investigated to fabricate the small metallic structure. Final part was obtained by the post-process that consists of debinding and sintering. In this work, the relationship between infill rate and the density of the part after the post-process was investigated. The investigation of the relationship is based on the fact that the infill rate and the density obtained from the post-processing is not same. It can be said that this work is a fundamental research to obtain the higher density of the printed part.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.