• Journal of Welding and Joining
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• v.31 no.6
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• pp.90-95
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• 2013

Pipe inside clad welding is mainly used to the flow pipe of sub-sea or chemical plant. For the inside clad welding to the medium pipe with the diameter of about 12", TIG welding is frequently applied with filler metal. In this case, the clad welding has the very broad weld area over $10m^2$. And, the non-destructive test (NDT) such as ultrasonic test (UT) or radiographic testing (RT) should be conducted on the broad weld area, and it costs very high due to the time-consuming work. Therefore, the present study investigated the variation of arc voltage to develop the in-line quality monitoring system for the pipe inside TIG cladding. The 4 experimental parameters (current, arc length, wire feed position, and shield gas flow rate) varied to observe the change of arc voltage and to establish the model for the monitoring. The arc voltage was decreased when the wire was fed to the backward eccentric position(over 2mm), and the shield gas flow rate was insufficient under 10L/min. In the case of the backward eccentric position over 2mm, the bead appearance was not good and the dilution ratio was increased due to deep penetration. When the shield gas flow rate was lower than 10L/min, the bead surface was oxidized.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.511-520
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• 2000

Ultrasonic pulse echo method comes to be difficult to apply to the multi-layered structure with very thin layer, because the echoes from the top and the bottom of the layer are superimposed. We can easily meet this problem when the silicon chip layer in the semiconductor is inspected by a SAM equipment using fairly low frequency lower than 20MHz by which severe attenuation in the epoxy mold compound of packaging material can be overcome. Conventionally, deconvolution technique has been used for the decomposition of superimposed UT signals, however it has disabilities when the waveform of the transmitted signal is distorted according to the propagation. In this paper, the wavelet transform based deconvolution(WTBD) technique is proposed as a new signal processing method that can decompose the superimposed echo signals with superior performances compared to the conventional deconvolution technique. WTBD method uses the wavelet transform in the pre-stage of deconvolution to extract out the common waveform from the transmitted and received signal with distortion. Performances of the proposed method we shown by through computer simulations using model signal with noise and we demonstrated by through experiments for the fabricated semiconductor sample with partial delamination at the top of silicon chip layer.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3003-3011
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• 2021

A FAC (flow-accelerated corrosion) test was performed for a straight pipe composed of the SA335 Gr P22 and SA106 Gr B (SA106-SA335-SA106) types of steel with welds as a function of the flow rate in the range of 7-12 m/s at 150 ℃ and with DO < 5 ppb at pH levels ranging from 7 to 9.5 up to a cumulative test time of 7200 h using the FAC demonstration test facility. Afterward, the experimental pipe was examined destructively to investigate opposite effects as well as entrance effects. In addition, the FAC rate obtained using a pipe specimen with a 50 mm inner diameter was compared with the rate obtained from a rotating cylindrical electrode. The effects of the complicated fluid flows at the elbow and orifice of the pipeline were also evaluated using another test section designed to examine the independent effects of the orifice and the elbow depending on the distance and the combined effects on orifice and elbow. The tests were performed under the following conditions: 130-150 ℃, DO < 5 ppb, pH 7 and a flow rate of 3 m/s. The FAC rate was determined using the thickness change obtained from commercial room-temperature ultrasonic testing (UT).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.45.2-46
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• 2021

The Hayabusa 2 mission target asteroid (162173) Ryugu is a near-Earth, carbonaceous (C-type) asteroid. Before the arrival, this asteroid is expected to be covered with mm- to cm- sized grains through the thermal infrared observations [1]. These grains are widely understood to be formed by past impacts with other celestial bodies and fractures induced by thermal fatigue [2]. However, the close-up images by the MASCOT lander showed lumpy boulders but no abundant fine grains [3]. Morota et al. suggested that there would be submillimeter particles on the top of these boulders but not resolved by Hayabusa 2's onboard instruments [4]. Hence, we conducted polarimetry of Ryugu to investigate microscopic grain sizes on its surface. Polarimetry is a powerful tool to estimate physical properties such as albedo and grain size. Especially, it is known that the maximum polarization degree (Pmax) and the geometric albedo (pV) show an empirical relationship depending on surface grain sizes [5]. We observed Ryugu from UT 2020 November 30 to December 10 at large phase angles (ranging from 78.5 to 89.7 degrees) to derive Pmax. We modified TRIPOL (Triple Range Imager and POLarimeter, [6]) to attach to the 1.8-m telescope at the Bohyunsan Optical Astronomy Observatory (BOAO). With this instrument, we observed the asteroid and determined linear polarization degrees at the Rc-band filter. We obtained sufficient data sets from 7 nights at this observatory to determine the Pmax value, and collaborated with other observatories in Japan (i.e., Hokkaido University, Higashi-Hiroshima, and Nishi-Harima) to acquire linear polarization degrees of the asteroid from total 24 nights observations with large phase angle coverage (From 28 to 104 degrees). The observational results have been published in Kuroda et al. (2021) [7]. We thus found the dominance of submillimeter particles on the surface of Ryugu from the comparison with other meteorite samples from the campaign observation. In this presentation, we report our activity to modify the TRIPOL for the 1.8-m telescope and the polarimetric performance. We also examine the rotational variability of the polarization degree using the TRIPOL data.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.60.2-60.2
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• 2021

In this study, we determinate the differential emission measure(DEM) of solar corona using three SDO/AIA EUV channel images and three AI-generated ones. To generate the AI-generated images, we apply a deep learning model based on multi-layer perceptrons by assuming that all pixels in solar EUV images are independent of one another. For the input data, we use three SDO/AIA EUV channels (171, 193, and 211). For the target data, we use other three SDO/AIA EUV channels (94, 131, and 335). We train the model using 358 pairs of SDO/AIA EUV images at every 00:00 UT in 2011. We use SDO/AIA pixels within 1.2 solar radii to consider not only the solar disk but also above the limb. We apply our model to several brightening patches and loops in SDO/AIA images for the determination of DEMs. Our main results from this study are as follows. First, our model successfully generates three solar EUV channel images using the other three channel images. Second, the noises in the AI-generated EUV channel images are greatly reduced compared to the original target ones. Third, the estimated DEMs using three SDO/AIA images and three AI-generated ones are similar to those using three SDO/AIA images and three stacked (50 frames) ones. These results imply that our deep learning model is able to analyze temperature response functions of SDO/AIA channel images, showing a sufficient possibility that AI-generated data can be used for multi-wavelength studies of various scientific fields. SDO: Solar Dynamics Observatory AIA: Atmospheric Imaging Assembly EUV: Extreme Ultra Violet DEM: Diffrential Emission Measure

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.41.3-41.3
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• 2019

To find out the origin and formation mechanism of LASCO-C2 post-CME blobs, we investigate 2 LASCO-C2 blobs and 35 low corona blobs observed by K-Cor on 2017 September 10 from 17:11 to 18:58 UT. By visual inspection of a post-CME ray and the locations of low corona blobs in K-Cor and LASCO-C2 images with examining the time-height data of all blobs, we find the following results: (1) The post-CME ray structure is well identified in the K-Cor images than LASCO-C2 ones. (2) Low corona blobs can be classified into two groups according to their formation mechanisms: 27 blobs belong to Group 1, generated by the tearing mode instability near the middles of current sheets as described by Furth et al., 1963; Shibata & Tanuma, 2001; Shen et al., 2011, the others belong to Group 2, formed by the tearing mode instability near the tips of current sheets as shown in Figure 5 of Sitnov et al., 2002. (3) Group 1 has low initial appearance heights <1.30 Rs>, broad speed range (38 ~ 945 km/s), and high accelerations <4,272 m/s² > than Group 2, which has initial appearance heights <1.72 Rs>, speed range (579 ~ 843 km/s), and accelerations <1,413 m/s² >. (4) among 8 blobs for Group 2, only 2 blobs are temporally and spatially associated with 2 LASCO-C2 ones and their initial observation heights are 1.93 and 1.79 Rs, respectively. Our results firstly demonstrate that LASCO-C2 blobs form the heights from about 1.7 to 2.0 Rs and they are generated by the tearing mode instability near the tips of current sheets.

• Journal of Korean Biological Nursing Science
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• v.26 no.1
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• pp.41-48
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• 2024

Purpose: This study investigated hand-washing practice among community-dwelling older adults during the coronavirus disease 2019 (COVID-19) pandemic and aimed to identify the impact of COVID-19-related concerns and depression on hand-washing practice. Methods: This was a secondary analysis of data extracted from the 2020 Community Health Survey. The primary data were collected through self-reporting from August 10 to September 8, 2020 in a cross-sectional study. The subjects comprised of 1,350 adults aged 65 or older living in Jeju Province who participated in the 2020 Community Health Survey. Results: The factors affecting hand-washing practice among older adults were male older adults (β = -.18, p < .001), age (β = -.07, p = .001), no education (β = -.20, p < .001) and elementary, middle, and high school graduation (β = -.15, p < .001) compared to a college or higher education, poor health perception (β = -.13, p < .001), COVID-19-related concerns (β = .08, p = .005), and depression (β = -.07, p = .001). To summarize, the factors negatively affecting hand-washing practice included male gender, lower education level, poor health perception, and depression. In contrast, factors positively associated with hand-washing practice included COVID-19-related concerns. Conclusion: These findings show the importance of considering these multifaceted determinants when designing targeted interventions and educational programs to promote hand-washing among older adults. Additionally, based on the relationship between hand-washing practice and COVID-19-related concerns and depression, interventions that can alleviate mental problems along with providing proper education are required.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.314-320
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• 2024

Objectives: This study aimed to identify the prevalence of falls in community-dwelling older adults and to identify associated factors using the 2019 Community Health Survey. Methods: The original data was from the 2019 Community Health Survey, and the study sample comprised 1,642 older adults aged 65 years and older in Jeju province. Data collection was conducted from August 16 to November 20, 2019, through an interview done by a trained investigator. Respondents were queried about demographic characteristics, riding bicycles, hospital treatment due to an accident or poisoning in the previous year, fall experiences in the past year, fear of falling, self-management status, and pain and discomfort. Multivariate logistic regression analysis was used to evaluate for associations between potential risk factors and falls. Results: The prevalence of falls in this community-dwelling older adults was 13.1%. Falls were associated with riding bicycles (odds ratio = 4.7; 95% confidence interval: 2.26-9.81), fear of falling (odds ratio = 0.3; 95% confidence interval: 0.24-0.49), hospital treatment due to an accident or poisoning in the previous year (odds ratio = 7.8; 95% confidence interval: 5.02-12.19), self-management status (odds ratio = 0.6; 95% confidence interval: 0.34-0.89), and pain and discomfort (odds ratio = 0.6; 95% confidence interval: 0.40-0.87). Conclusions: We found that the prevalence of approximately about 13% of older adults living in a community has experienced falls. Based on the results of the study, we provided primary data to develop the care management intervention program to prevent falls and avoid risk factors that cause falls in community-dwelling older adults.