• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.4
• /
• pp.194-199
• /
• 2022

Preprocessing for high-quality data is required for high accuracy and usability in various and complex image data-based industries. However, when a contaminated hostile example that combines noise with existing image or video data is introduced, which can pose a great risk to the company, it is necessary to restore the previous damage to ensure the company's reliability, security, and complete results. As a countermeasure for this, restoration was previously performed using Defense-GAN, but there were disadvantages such as long learning time and low quality of the restoration. In order to improve this, this paper proposes a method using adversarial examples created through FGSM according to image segmentation in addition to using the VQ-VAE model. First, the generated examples are classified as a general classifier. Next, the unsegmented data is put into the pre-trained VQ-VAE model, restored, and then classified with a classifier. Finally, the data divided into quadrants is put into the 4-split-VQ-VAE model, the reconstructed fragments are combined, and then put into the classifier. Finally, after comparing the restored results and accuracy, the performance is analyzed according to the order of combining the two models according to whether or not they are split.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.6D
• /
• pp.577-585
• /
• 2010

Traffic accidents are caused by several factors such as drivers, vehicles, and road environment. It is necessary to investigate and analyze them in advance to prevent similar and repetitive traffic accidents. Especially, the human factor is most significant element and traffic accidents by drinking-driving caused from human factor have become social problem to be paid attention to. The study analyzes traffic accidents resulting from drinking-driving and the effects of driver's attributes and environmental factors on them. The study is composed as two parts. First, the log-linear model is applied to analyze that accidents by drinking or non-drinking driving associate with road geometry, weather condition and personal characteristics. Probability is tested for drinking-driving accidents relative to non-drinking drive accidents. The study analyzes probability differences between genders, between ages, and between kinds of vehicles through odds multipliers. Second, traffic accidents related to drinking are classified into property damage, minor injury, heavy injury, and death according to their severity. Heavy injury is more serious than minor one and death is more serious than heavy injury. The ordinal regression models are established to find effecting factors on traffic accident severity.

• Smart Media Journal
• /
• v.12 no.10
• /
• pp.93-101
• /
• 2023

Climate change has emerged as a global problem, with frequent temperature increases, droughts, and floods, and it is predicted that it will have a great impact on the characteristics and productivity of crops. Cnidium officinale is used not only as traditionally used herbal medicines, but also as various industrial raw materials such as health functional foods, natural medicines, and living materials, but productivity is decreasing due to threats such as continuous crop damage and climate change. Therefore, this paper proposes a model that can predict the physiologically active ingredient index according to the climate change scenario of Cnidium officinale, a representative medicinal crop vulnerable to climate change. In this paper, data was first augmented using the CTGAN algorithm to solve the problem of data imbalance in the collection of environment information, physiological reactions, and physiological active ingredient information. Column Shape and Column Pair Trends were used to measure augmented data quality, and overall quality of 88% was achieved on average. In addition, five models RF, SVR, XGBoost, AdaBoost, and LightBGM were used to predict phenol and flavonoid content by dividing them into ground and underground using augmented data. As a result of model evaluation, the XGBoost model showed the best performance in predicting the physiological active ingredients of the sacrum, and it was confirmed to be about twice as accurate as the SVR model.

• Animal Bioscience
• /
• v.37 no.6
• /
• pp.1021-1030
• /
• 2024

Objective: R-loops are DNA:RNA triplex hybrids, and their metabolism is tightly regulated by transcriptional regulation, DNA damage response, and chromatin structure dynamics. R-loop homeostasis is dynamically regulated and closely associated with gene transcription in mouse zygotes. However, the factors responsible for regulating these dynamic changes in the R-loops of fertilized mouse eggs have not yet been investigated. This study examined the functions of candidate factors that interact with R-loops during zygotic gene activation. Methods: In this study, we used publicly available next-generation sequencing datasets, including low-input ribosome profiling analysis and polymerase II chromatin immunoprecipitation-sequencing (ChIP-seq), to identify potential regulators of R-loop dynamics in zygotes. These datasets were downloaded, reanalyzed, and compared with mass spectrometry data to identify candidate factors involved in regulating R-loop dynamics. To validate the functions of these candidate factors, we treated mouse zygotes with chemical inhibitors using in vitro fertilization. Immunofluorescence with an anti-R-loop antibody was then performed to quantify changes in R-loop metabolism. Results: We identified DEAD-box-5 (DDX5) and histone deacetylase-2 (HDAC2) as candidates that potentially regulate R-loop metabolism in oocytes, zygotes and two-cell embryos based on change of their gene translation. Our analysis revealed that the DDX5 inhibition of activity led to decreased R-loop accumulation in pronuclei, indicating its involvement in regulating R-loop dynamics. However, the inhibition of histone deacetylase-2 activity did not significantly affect R-loop levels in pronuclei. Conclusion: These findings suggest that dynamic changes in R-loops during mouse zygote development are likely regulated by RNA helicases, particularly DDX5, in conjunction with transcriptional processes. Our study provides compelling evidence for the involvement of these factors in regulating R-loop dynamics during early embryonic development.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.26 no.1
• /
• pp.63-74
• /
• 2024

In agriculture, frost can be devastating, which is why observation and forecasting are so important. According to a recent report analyzing frost observation data from the Korea Meteorological Administration, despite global warming due to climate change, the late frost date in spring has not been accelerated, and the frequency of frost has not decreased. Therefore, it is important to automate and continuously operate frost observation in risk areas to prevent agricultural frost damage. In the existing frost observation using leaf wetness sensors, there is a problem that the reference voltage value fluctuates over a long period of time due to contamination of the observation sensor or changes in the humidity of the surrounding environment. In this study, a datalogger program was implemented to automatically solve these problems. The established frost observation system can stably and automatically accumulate time-resolved observation data over a long period of time. This data can be utilized in the future for the development of frost diagnosis models using machine learning methods and the production of frost occurrence prediction information for surrounding areas.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.3
• /
• pp.163-171
• /
• 2024

Structural health monitoring for ships and offshore structures is important in various aspects. Ships and offshore structures are continuously exposed to various environmental conditions, such as waves, wind, and currents. In the event of an accident, immense economic losses, environmental pollution, and safety problems can occur, so it is necessary to detect structural damage or defects early. In this study, structural response data of multi-linked floating offshore structures under various wave load conditions was calculated by performing fluid-structure coupled analysis. Furthermore, the order reduction method with distortion base mode was applied to the structures for predicting the structural response by using the results of numerical analysis. The distortion base mode order reduction method can predict the structural response of a desired area with high accuracy, but prediction performance is affected by sensor arrangement. Optimization based on a genetic algorithm was performed to search for optimal sensor arrangement and improve the prediction performance of the distortion base mode-based reduced-order model. Consequently, a sensor arrangement that predicted the structural response with an error of about 84.0% less than the initial sensor arrangement was derived based on the root mean squared error, which is a prediction performance evaluation index. The computational cost was reduced by about 8 times compared to evaluating the prediction performance of reduced-order models for a total of 43,758 sensor arrangement combinations. and the expected performance was overturned to approximately 84.0% based on sensor placement, including the largest square root error.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.4
• /
• pp.178-185
• /
• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• Journal of Korea Water Resources Association
• /
• v.57 no.7
• /
• pp.471-479
• /
• 2024

Flood damage is becoming more serious due to the heavy rainfall caused by climate change. Physically based hydrological models have been utilized to predict stream water level variability and provide flood forecasting. Recently, hydrological simulations using machine learning and deep learning algorithms based on nonlinear relationships between hydrological data have been getting attention. In this study, the Long Short-Term Memory (LSTM) algorithm is used to predict the water level of the Seomjin River watershed. In addition, Climate Prediction Center morphing method (CMORPH)-based gridded precipitation data is applied as input data for the algorithm to overcome for the limitations of ground data. The water level prediction results of the LSTM algorithm coupling with the CMORPH data showed that the mean CC was 0.98, RMSE was 0.07 m, and NSE was 0.97. It is expected that deep learning and remote data can be used together to overcome for the shortcomings of ground observation data and to obtain reliable prediction results.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.4
• /
• pp.267-274
• /
• 2024

Hydrogen embrittlement fracture poses a challenge in ensuring the structural integrity of materials exposed to hydrogen-rich environments. This study advances our comprehension of hydrogen-induced fracture through an integrated numerical modeling approach. In addition, it employs a ductile fracture model named the Gurson-cohesive model (GCM) and hydrogen diffusion analysis. GCM is employed as a fracture model that combines the Gurson model to illustrate the continuum damage evolution and the cohesive zone model to describe crack surface discontinuity and softening behavior. Moreover, porosity and stress triaxiality are considered as crack initiation criteria . A hydrogen diffusion analysis is also integrated with the GCM to account for hydrogen enhanced decohesion (HEDE) mechanisms and their subsequent impacts on crack initiation and propagation. This framework considers the influence of hydrogen on the softening behavior of the traction-separation relationship on the discontinuous crack surface. Parametric studies explore the sensitivity to diffusion properties and hydrogen-induced fracture properties. By combining numerical models of hydrogen diffusion and the ductile fracture model, this study provides an understanding of hydrogen-induced fracture and thereby contributes significantly to the ongoing efforts to design materials that are resilient to hydrogen embrittlement in practical engineering applications.

• Clinical and Experimental Pediatrics
• /
• v.51 no.10
• /
• pp.1102-1111
• /
• 2008

Purpose : Resveratrol, extracted from red wine and grapes, has an anti-cancer effect, an antiinflammatory effect, and an antioxidative effect mainly in heart disease and also has neuroprotective effects in the adult animal model. No studies for neuroprotective effects during the neonatal periods have been reported. Therefore, we studied the neuroprotective effect of resveratrol on hypoxic-ischemic brain damage in neonatal rats via anti-apoptosis. Methods : Embryonic cortical neuronal cell culture of rat brain was performed using pregnant Sprague-Dawley (SD) rats at 18 days of gestation (E18) for the in vitro approach. We injured the cells with hypoxia and administered resveratrol (1, 10, and $30{\mu}g/mL$) to the cells at 30 minutes before hypoxic insults. In addition, unilateral carotid artery ligation with hypoxia was induced in 7-day-old neonatal rats for the in vivo approach. We injected resveratrol (30 mg/kg) intraperitoneally into animal models. Real-time PCR and Western blotting were performed to identify the neuroprotective effects of resveratrol through anti-apoptosis. Results : In the in vitro approach of hypoxia, the expression of Bax, caspase-3, and the ratio of Bax/Bcl-2, indicators of the level of apoptosis, were significantly increased in the hypoxia group compared to the normoxia group. In the case of the resveratrol-treated group, expression was significantly decreased compared to the hypoxia group. And the results in the in vivo approach were the same as in the in vitro approach. Conclusion : The present study demonstrates that resveratrol plays neuroprotective role in hypoxic-ischemic brain damage during neonatal periods through the mechanism of anti-apoptosis.