• Korean Journal of Food Science and Technology
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• v.53 no.3
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• pp.313-320
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• 2021

The present study aimed to investigate the effect of whole wheat cookie supplemented with Capsosiphon fulvescens (CF) extract on serum glucose homeostasis in C57BL/6 mice. This study examined whether the same effect was demonstrated for whole wheat cookie in comparison to previous research documenting the glucose-lowering effect of food products combined with CF extract. Mice were divided into three groups depending on the diet administered: normal cookie (NC), whole wheat cookie (WC), and WC blended with CF extract (WCFE). After 4 weeks of administering the experimental diet, the blood glucose level, serum insulin level, and homeostatic model assessment for insulin resistance index were found to be significantly lower in the WCFE group than in the NC and WC groups. These results suggest that whole wheat cookie containing CF extract is effective in preventing insulin resistance and maintaining blood glucose homeostasis.

• Journal of the Korean Geosynthetics Society
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• v.20 no.1
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• pp.45-55
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• 2021

In this study, the behavioral characteristics of the fill dam were analyzed during water level fluctuations through a numerical analysis model, and the reservoir safety management plan was prepared. The variation in plastic deviatoric strain, horizontal displacement, stress path, pore water pressure, etc., due to elevation of water level in the upper and lower sides of shell and core were analyzed using numerical analysis software, viz. GTS NX and LIQCA. The analysis results manifest that as the water level in the dam body increases rapidly, the pore water pressure and displacement also increase quickly. It was found that the elevation of the water level causes an increase in pore water pressure in the dam body as well as an increase in the saturation of the dam body and decreased effective stress. It is considered that this type of dam behavior can be the cause of the reduction of strength and stiffness of the dam. Also, it is assumed that the accumulated plastic deviatoric strain due to the deformation of the dam body caused by water infiltration causes an increase in displacement. Based on these experimental results and the results of analyses of the existing reservoir safety diagnosis techniques, an improvement plan for dam safety diagnosis and evaluation criteria was proposed, and these results can be used as primary data while revising dam safety diagnosis guidelines.

• Therapeutic Science for Rehabilitation
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• v.10 no.3
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• pp.7-22
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• 2021

Objective : The purpose of this study was to verify the effect of virtual reality interventions (VRIs) on cognitive function in individuals with stroke through a systematic literature review and meta-analysis. Methods : We reviewed randomized controlled trials (RCTs) the last 10 years using academic databases. PubMed, MEDLINE, and CINAHL were used for international studies, and DBpia, KISS, Kyoboscholar, and e-article were used for Korean studies. For the quantitative meta-analysis, subgroups of outcomes were classified into general cognitive function (G-CF), attention and memory (A&M), and executive function (EF). Results : Nine RCTs were analyzed. The total number of participants was 271 (140 in the experimental group). The effect size (Cohen's d) was estimated using a random effects model. The effect sizes of the outcome subgroups of were as follows: small to medium for G-CF (d=0.422; 95% CI: 0.101~0.742; p=0.010), small for A&M (d=0.249; 95% CI: -0.107~0.605; p=0.170), and medium for EF (d=0.666; 95% CI: 0.136~1.195; p=0.014). Conclusion : Considering the various stimuli provided by the virtual environment and the results from available research, virtual reality should be applied to interventions for integrated cognitive functions. In addition, it would be appropriate to be used as an additional intervention to traditional cognitive rehabilitation for stroke.

• Journal of Ginseng Research
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• v.46 no.3
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• pp.418-425
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• 2022

Background: Sorafenib is effective in treating hepatoma, but most patients develop resistance to it. STAT3 signaling has been implicated in sorafenib resistance. Artesunate (ART) and 20(R)-ginsenoside Rg3 (Rg3) have anti-hepatoma effects and can inhibit STAT3 signaling in cancer cells. This study aimed to evaluate the effects of Rg3 in combination with ART (Rg3-plus-ART) in overcoming sorafenib resistance, and to examine the involvement of STAT3 signaling in these effects. Methods: Sorafenib-resistant HepG2 cells (HepG2-SR) were used to evaluate the in vitro anti-hepatoma effects of Rg3-plus-ART. A HepG2-SR hepatoma-bearing BALB/c-nu/nu mouse model was used to assess the in vivo anti-hepatoma effects of Rg3-plus-ART. CCK-8 assays and Annexin V-FITC/PI double staining were used to examine cell proliferation and apoptosis, respectively. Immunoblotting was employed to examine protein levels. ROS generation was examined by measuring DCF-DA fluorescence. Results: Rg3-plus-ART synergistically reduced viability of, and evoked apoptosis in HepG2-SR cells, and suppressed HepG2-SR tumor growth in mice. Mechanistic studies revealed that Rg3-plus-ART inhibited activation/phosphorylation of Src and STAT3 in HepG2-SR cultures and tumors. The combination also decreased the STAT3 nuclear level and induced ROS production in HepG2-SR cultures. Furthermore, overactivation of STAT3 or removal of ROS diminished the anti-proliferative effects of Rg3-plus-ART, and removal of ROS diminished Rg3-plus-ART's inhibitory effects on STAT3 activation in HepG2-SR cells. Conclusions: Rg3-plus-ART overcomes sorafenib resistance in experimental models, and inhibition of Src/STAT3 signaling and modulation of ROS/STAT3 signaling contribute to the underlying mechanisms. This study provides a pharmacological basis for developing Rg3-plus-ART into a novel modality for treating sorafenib-resistant hepatoma.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.393-399
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• 2021

In this study, experiments were conducted to estimate the PM concentrations by learning the nighttime CCTV images of various PM concentrations environments. In the case of daytime images, there have been many related studies, and the various texture and brightness information of images is well expressed, so the information affecting learning is clear. However, nighttime images contain less information than daytime images, and studies using only nighttime images are rare. Therefore, we conducted an experiment combining nighttime images with non-uniform characteristics due to light sources such as vehicles and streetlights and building roofs, building walls, and streetlights with relatively constant light sources as an ROI (Region of Interest). After that, the correlation was analyzed compared to the daytime experiment to see if deep learning-based PM concentrations estimation was possible with nighttime images. As a result of the experiment, the result of roof ROI learning was the highest, and the combined learning model with the entire image showed more improved results. Overall, R² exceeded 0.9, indicating that PM estimation is possible from nighttime CCTV images, and it was calculated that additional combined learning of weather data did not significantly affect the experimental results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1215-1225
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• 2021

Although Artificial Intelligence (AI) techniques have shown impressive performance in various fields, they are vulnerable to adversarial examples which induce misclassification by adding human-imperceptible perturbations to the input. Previous studies to defend the adversarial examples can be classified into three categories: (1) model retraining methods; (2) input transformation methods; and (3) adversarial examples detection methods. However, even though the defense methods against adversarial examples have constantly been proposed, there is no research to classify the type of adversarial attack. In this paper, we proposed an adversarial attack family classification method based on dimensionality reduction and clustering. Specifically, after extracting adversarial perturbation from adversarial example, we performed Linear Discriminant Analysis (LDA) to reduce the dimensionality of adversarial perturbation and performed K-means algorithm to classify the type of adversarial attack family. From the experimental results using MNIST dataset and CIFAR-10 dataset, we show that the proposed method can efficiently classify five tyeps of adversarial attack(FGSM, BIM, PGD, DeepFool, C&W). We also show that the proposed method provides good classification performance even in a situation where the legitimate input to the adversarial example is unknown.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Bulletin of the Society of Naval Architects of Korea
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• v.26 no.4
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• pp.81-93
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• 1989

The present study attempts to develop the theoretical model for the damage estimation of offshore tubular members which are subjected to the accidental impact loads due to collision, falling objects and so on. For the reasons of the simplicity of the problem being considered, however, this paper postulates that the accidental load can be approximated to be the quasi-static one, in which dynamic effects are negelcted. Based upon the theoretical and experimental results which are obtained from the present study as well as the existing literature, the load-displacement relations taking the interaction effect between the local denting and the global bending deformation into account are presented in the explicit form when the concentrated lateral load acts on the tubular member whose end condition is supposed to be rotation ally free and axially restrained, in which membrane forces develop. Thus, the practical estimation of damage deformation for the local denting and the global bending damage of tubular members against the accidental loads is possible and also the collision absorption capability of the member can be calculated by performing the integration of the area below the given load-displacement curves, provided that all the energy is dissipated to the deforming the member itself.

• The Korea Journal of Herbology
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• v.38 no.5
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• pp.9-19
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• 2023

Objectives : Osteoporosis is a systemic skeletal disorder characterized by reduced bone mineral density and increased risk of fractures. Bisphosphonates and selective estrogen receptors, which are bone resorption inhibitors that are currently widely used as osteoporosis treatments, show serious side effects when administered for a long time. Research on bone resorption inhibitors that complement the problems of existing treatments is needed. The purpose of this study was to investigate the effect of inhibiting osteoclast differentiation and activity on the tuberous root of Ampelopsis japonica (Thunb.) Makino (AM). Methods : After extracting AM using distilled water and ethanol, the inhibitory effects of the two solvents on osteoclast differentiation were compared using the RANKL-induced in vitro experimental model and the TRAP assay kit. The impact of AM on bone resorption was investigated through the pit formation assay, and its effect on F-actin formation was assessed through fluorescent staining. Additionally, protein and mRNA expression levels of osteoclast differentiation markers (NFATc1, c-Fos, TRAP and ATP6v0d2) and resorption markers (MMP-9, CTK, and CA2) were analyzed via western blot and RT-PCR. Results : AM treatment significantly decreased the number of TRAP-positive cells and pit formation area. Furthermore, AM suppressed both the protein and mRNA expression of NFATc1 and c-Fos, key transcription factors involved in osteoclast differentiation and it downregulated the expression of osteoclast-associated genes such as TRAP, CTK, MMP-9, CA2, and ATP6v0d2. Conclusions : These results suggest that AM can inhibit bone resorption and osteoclast differentiation, indicating its potential for use in the treatment and prevention of osteoporosis.