• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.17-23
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• 2020

In this paper, collecting real-time data frames that occur during RS232 communication between an HMI and PLC of automation equipment by sniffing real-time information data frames through MCU without modification of the HMI or PLC, a method is proposed that allows users to collect data without being dependent on the modification of PLC and HMI systems. The user collects necessary information from the sniffing data through the parsing operation, and the original communication interface is maintained by transmitting the corresponding sniffing frame to the destination. The MCU's UART communication interface circuit is physically designed according to the RS232 communication standard, and this additionally improves efficiency more so than an interrupt-based system by using the DMA device inside the MCU. In addition, the data frame IO operation is performed by logically separating the work of the DMA interrupt service routine from the work of the main thread using the circular queue. Through this method, the user receives the sniffing data frame between the HMI and PLC in RS232 format, and the frame transfer between PLC and HMI arrives normally at the original destination. By sniffing the data frame without further modification of the PLC and HMI, it can be confirmed that it arrives at the user system normally.

• Food Science and Preservation
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• v.25 no.1
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• pp.98-106
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• 2018

Portulaca oleracea L., a species of Portulacaceae, is ubiquitous. It is a well-known traditional Chinese medicine for removing heat, counteracting toxicity, cooling blood, and maintaining hemostasia; it is also used as antidysentery agent. This study investigated the anti-oxidative and anti-inflammatory activities of water and ethanol extracts from P. oleracea. The total polyphenol content ($21.08{\pm}0.03mg\;GAE/g$) and total flavonoid content ($5.45{\pm}0.76mg\;QE/g$) of the ethanolic extracts were higher than those of the water extracts. The antioxidative activities were determined by evaluating the 1,1-diphenyl-2-picrylhydrazyl (DPPH) and the 2,2'-azinobis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radical scavenging activity and by the ferric reducing antioxidant potential (FRAP) assay. The ABTS radical scavenging activity of the water extract (75.53%) was higher in those of the water extract (67.03%) at concentration of $1,000{\mu}g/mL$. The DPPH radical scavenging activity and FRAP of the ethanol extract were higher than those of the water extract. We also investigated the anti-inflammatory activity of the P. oleracea extracts in LPS-stimulated Raw 264.7 cells. The production levels of nitric oxide (NO) and reactive oxygen species (ROS) significantly decreased with an increasing concentration of the extract. The expression levels of pro-inflammatory cytokines (tumor necrosis faction (TNF)-${\alpha}$, interleukin (IL)-$1{\beta}$, and IL-6) were significantly lower in the ethanol extract than in the LPS alone treatment group. Based on these results, ethanolic extract from P. oleracea could be an effective antioxidant and anti-inflammatory agent.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.399-404
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• 2021

From 1 January 2020, the limit for Sulphur in fuel oil used on board ships operating outside designated emission control areas will be reduced to 0.5 %. This regulation by international maritime organization (IMO) is able to significantly reduce the amount of Sulphur oxides (SOx) discharging from ships and should have environmental advantages and health for all over the world. To meet the regulation, in these days, wet scrubber system is being actively developed. However, this process leads to make washing wastewater. In this study, we evaluated ion exchange resin system in accordance with scrubber wastewater discharge regulation by IMO. Theoretical wastewater used as feed solution of lab scale water treatment systems. The results revealed that nitrate ion was removed selectively in spite of high TDS wash wastewater solution depending on ion exchange resin property. Moreover, it was possible to improve efficiency of the system by optimizing operating conditions.

• Korean Journal of Food Science and Technology
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• v.53 no.6
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• pp.756-760
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• 2021

Frankincense has been used as a traditional medicine for treating rheumatoid arthritis, dermatitis, and muscle pain. In this study, the anti-tuberculosis effects of Frankincense were evaluated in immune responses of macrophages. Frankincense methanol extract was not cytotoxic to the host. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide reduction assay using human macrophage (THP-1) cells did not show cytotoxic effects or morphological changes with treatments of 31.3, 62.5, and 125 ㎍/mL Frankincense methanol extract (FRM). Inhibitory effects of Frankincense methanol extract on the growth of Mycobacterium tuberculosis in human macrophages were investigated. The immune response was measured by monitoring the levels of TNF-α and IL-1β in THP-1 cells with or without M. tuberculosis infection under Frankincense methanol extract treatment. Inflammatory cytokine levels and M. tuberculosis numbers were reduced in THP-1 cells treated with Frankincense methanol extract. Therefore, Frankincense methanol extract could be used as a potential anti-tuberculosis agent.

• Journal of Conservation Science
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• v.37 no.5
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• pp.426-439
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• 2021

In this study, we compare the properties and estimate the durability of five oil colors that are the most popularly used colors in the conservation of oil paintings. A set of these colors was obtained form four manufacturers each, and their properties were analyzed by conducting deterioration experiments. Subsequently, we observed the colors and performed X-ray fluorescence analysis. As a result of colour observation and XRF analysis, it was confirmed that there are the differences according to the pigment types, mixing rations and the manufacturers even for colors having the same product name. The deterioration test indicated differences in the appearance of the colors ; for instance, the color difference was above 12.0 in most of the samples, including restoration color and acrylic gouache. In the case of Lemon Yellow a lot of discoloration and cracking occurred, and difference in gloss was ob served in Viridian of manufacturer C. Most cracks were ob served in the restoration color obtained from manufacturer B and were assumed to be because of the resin used by the manufacturer. Nevertheless, additional research will have to be conducted by controlling variables in order to find out the cause. Through this study, we demonstrated that retouching materials for conservation of oil painting differed in their physical properties according to the color and manufacturer. Therefore a conservator should be mindful during the selection and use of materials for retouching oil paintings.

• 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 Remote Sensing
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• v.37 no.5_2
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• pp.1317-1328
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• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.

• Journal of the Korean Society of Radiology
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• v.15 no.2
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• pp.153-158
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• 2021

X-ray image analysis is a very important field to improve the early diagnosis rate and prediction accuracy of periodontal disease. Research on the development and application of artificial intelligence-based algorithms to improve the quality of such dental X-ray images is being widely conducted worldwide. Thus, the aim of this study was to design a super-resolution algorithm for predicting periodontal disease and to evaluate its applicability in dental X-ray images. The super-resolution algorithm was constructed based on the convolution layer and ReLU, and an image obtained by up-sampling a low-resolution image by 2 times was used as an input data. Also, 1,500 dental X-ray data used for deep learning training were used. Quantitative evaluation of images used root mean square error and structural similarity, which are factors that can measure similarity through comparison of two images. In addition, the recently developed no-reference based natural image quality evaluator and blind/referenceless image spatial quality evaluator were additionally analyzed. According to the results, we confirmed that the average similarity and no-reference-based evaluation values were improved by 1.86 and 2.14 times, respectively, compared to the existing bicubic-based upsampling method when the proposed method was used. In conclusion, the super-resolution algorithm for predicting periodontal disease proved useful in dental X-ray images, and it is expected to be highly applicable in various fields in the future.

• Korean Journal of Food Science and Technology
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• v.53 no.6
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• pp.715-721
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• 2021

Cognitive impairment and Alzheimer's disease are serious social problems associated with the rising elderly population in Korea. 1,2,3,4,6-Penta-O-galloyl-β-ᴅ-glucopyranose (PGG) is a gallotannin isolated from medicinal plants such as Rhus chinensis. This study was performed to evaluate the effect of PGG on biomarkers related to cognitive function in human neuroblastoma SK-N-SH cells. Inhibition of acetylcholinesterase (AChE) activity is considered to be one of the main therapeutic strategies. PGG inhibited AChE activity in the test tube as well as in SK-N-SH cells. In addition, PGG induced protein and mRNA expression of brain-derived neurotrophic factor (BDNF), which is a mammalian neurotrophin that plays major roles in the development, maintenance, repair, and survival of neuronal populations. As one of the underlying molecular mechanisms that induce BDNF expression, PGG induced the activation of Ca²⁺/calmodulin (CaM)-dependent protein kinase II (CaMKII)-cAMP response element binding protein (CREB) pathway. In conclusion, PGG may be an useful material for improving cognitive function.