• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Clean Technology
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• v.28 no.1
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• pp.79-93
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• 2022

In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L^-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.

• Journal of the Korea Institute of Building Construction
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• v.24 no.2
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• pp.273-284
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• 2024

This study investigates the impact of international oil price fluctuations on overseas construction orders secured by domestic and foreign companies. The analysis employs statistical data spanning the past 20 years, encompassing international oil prices, overseas construction orders from domestic firms, and new overseas construction orders from the top 250 global construction companies. The correlation between these variables is assessed using correlation coefficients(R), determination coefficients(R²), and p-values. The results indicate a strong positive correlation between international oil prices and overseas construction orders. The correlation coefficient between domestic overseas construction orders and oil prices is found to be 0.8 or higher, signifying a significant influence. Similarly, a high correlation coefficient of 0.76 is observed between oil prices and new orders from leading global construction companies. Further analysis reveals a particularly strong correlation between oil prices and overseas construction orders in Asia and the Middle East, potentially due to the prevalence of oil-related projects in these regions. Additionally, a high correlation is observed between oil prices and orders for industrial facilities compared to architectural projects. This suggests an increase in plant construction volumes driven by fluctuations in oil prices. Based on these findings, the study proposes an entry strategy for navigating oil price volatility and maintaining competitiveness in the overseas construction market. Key recommendations include diversifying project locations and supplier bases; utilizing hedging techniques for exchange rate risk management, adapting to local infrastructure and market conditions, establishing local partnerships and securing skilled local labor, implementing technological innovations and digitization at construction sites to enhance productivity and cost reduction The insights gained from this study, coupled with the proposed overseas expansion strategies, offer valuable guidance for mitigating risks in the global construction market and fostering resilience in response to international oil price fluctuations. This approach is expected to strengthen the competitiveness of domestic and foreign construction firms seeking success in the international arena.

• Korean Journal of Poultry Science
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• v.51 no.2
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• pp.97-106
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• 2024

This study utilized the non-invasive MyotonPRO^® device to analyze the stiffness in breast muscles of commercial broilers (Ross 308 and Arbor Acres) and compared these findings with data reported for Ross 708, where Woody Breast (WB) symptoms had been previously documented. The research revealed that Ross 308 and Arbor Acres displayed relatively lower stiffness values compared to Ross 708, suggesting a lack of WB expression. These results indicate differentiation in breast muscle traits across strains and underscore the necessity for further research into factors influencing WB manifestation. The study also measured additional muscle tone characteristics such as Frequency, Decrement, Relaxation, and Creep across various growth stages (2, 4, 6, and 8 weeks), finding significant variations with pronounced severity at weeks 2 and 8. An increase in stiffness was observed as the broilers aged, pointing to potential growth-related or stress-induced changes affecting WB severity. A strong positive correlation was established between increased breast meat weight and WB severity, highlighting that heavier breast meat could exacerbate the condition. This correlation is vital for the poultry industry, suggesting that weight management could help mitigate WB effects. Moreover, the potential for genetic selection and breeding strategies to reduce WB occurrence was emphasized, which could aid in enhancing management practices in commercial poultry production. Collectively, these insights contribute to a deeper understanding of WB in broilers and propose avenues for future research and practical strategies to minimize its impact.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.24-35
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• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.45-68
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• 2022

The central and local governments of the Republic of Korea provided information necessary for disaster response through wireless emergency alerts (WEAs) in order to overcome the pandemic situation in which COVID-19 rapidly spreads. Among all channels for delivering disaster information, wireless emergency alert is the most efficient, and since it adopts the CBS(Cell Broadcast Service) method that broadcasts directly to the mobile phone, it has the advantage of being able to easily access disaster information through the mobile phone without the effort of searching. In this study, the characteristics of wireless emergency alerts sent to Seoul during the past year and one month (January 2020 to January 2021) were derived through various text mining methodologies, and various types of information contained in wireless emergency alerts were analyzed. In addition, it was confirmed through the population mobility by age in the districts of Seoul that what kind of influence it had on the movement behavior of people. After going through the process of classifying key words and information included in each character, text analysis was performed so that individual sent characters can be used as an analysis unit by applying a document cluster analysis technique based on the included words. The number of WEAs sent to the Seoul has grown dramatically since the spread of Covid-19. In January 2020, only 10 WEAs were sent to the Seoul, but the number of the WEAs increased 5 times in March, and 7.7 times over the previous months. Since the basic, regional local government were authorized to send wireless emergency alerts independently, the sending behavior of related to wireless emergency alerts are different for each local government. Although most of the basic local governments increased the transmission of WEAs as the number of confirmed cases of Covid-19 increases, the trend of the increase in WEAs according to the increase in the number of confirmed cases of Covid-19 was different by region. By using structured econometric model, the effect of disaster information included in wireless emergency alerts on population mobility was measured by dividing it into baseline effect and accumulating effect. Six types of disaster information, including date, order, online URL, symptom, location, normative guidance, were identified in WEAs and analyzed through econometric modelling. It was confirmed that the types of information that significantly change population mobility by age are different. Population mobility of people in their 60s and 70s decreased when wireless emergency alerts included information related to date and order. As date and order information is appeared in WEAs when they intend to give information about Covid-19 confirmed cases, these results show that the population mobility of higher ages decreased as they reacted to the messages reporting of confirmed cases of Covid-19. Online information (URL) decreased the population mobility of in their 20s, and information related to symptoms reduced the population mobility of people in their 30s. On the other hand, it was confirmed that normative words that including the meaning of encouraging compliance with quarantine policies did not cause significant changes in the population mobility of all ages. This means that only meaningful information which is useful for disaster response should be included in the wireless emergency alerts. Repeated sending of wireless emergency alerts reduces the magnitude of the impact of disaster information on population mobility. It proves indirectly that under the prolonged pandemic, people started to feel tired of getting repetitive WEAs with similar content and started to react less. In order to effectively use WEAs for quarantine and overcoming disaster situations, it is necessary to reduce the fatigue of the people who receive WEA by sending them only in necessary situations, and to raise awareness of WEAs.