• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.968-978
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• 2017

Three small-break loss-of-coolant accident (SBLOCA) tests with safety injection pumps were carried out using the integral-effect test loop for SMART (System-integrated Modular Advanced ReacTor), i.e., the SMART-ITL facility. The types of break are a safety injection system line break, shutdown cooling system line break, and pressurizer safety valve line break. The thermal-hydraulic phenomena show a traditional behavior to decrease the temperature and pressure whereas the local phenomena are slightly different during the early stage of the transient after a break simulation. A safety injection using a high-pressure pump effectively cools down and recovers the inventory of a reactor coolant system. The global trends show reproducible results for an SBLOCA scenario with three different break locations. It was confirmed that the safety injection system is robustly safe enough to protect from a core uncovery.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.237-238
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• 2023

When primer is applied manually, adhesion performance decreases due to a decrease in primer penetration performance on the surface, and construction period delay may occur due to a long time due to a decrease in work efficiency when a large area is worked using a conventional roller. In addition, in the case of the roller method, precision work in corners and narrow spaces is not possible, so it is urgent to come up with measures to ensure uniform quality. In addition, secondary work occurs to remove fine powder from the surface before primer application, resulting in construction period delay due to the rise of the working stage. Therefore, in this study, equipment was used instead of manual work for primer work, and as a result, penetration performance and adhesion performance were improved about twice. From these results, it was confirmed that favorable results such as improving work speed, securing high quality, improving the working environment, and resolving the shortage of functional workers can be obtained.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.14-14
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• 2022

Greenhouse gas emissions are one of the critical factors that drive change in rice cropping systems. Within this changing system, less water irrigation and chemical fertilizer are seriously considered, as well combining precision farming technologies with irrigation control. Water and phosphorus (P) fertilizer are two of the most critical inputs in rice cultivation. Due to the lack of water availability in the system, P fertilizer is not available, especially in acidic soil conditions. Moreover, the various types of abiotic stresses, such as drought, high temperature, salinity, submergence, and limited fertilizer result in significant yield loss in the system. Even in the late stage of growth, the waves caused by diseases and insects make the field more unfruitful. Therefore, agronomists and breeders need to identify the secondary phenotypes to estimate the yield loss of when stress appears. The prediction will be clearer if we have a set of markers tagging the causal variation and the associated precise phenotype indices. Although there have been various studies for abiotic stress tolerance, we still lack functional molecular markers and phenotype indices. This is due to the underlying challenges caused by environmental factors in highly unpredictable regional and yearly environmental conditions in the field system. Pupl (phosphorus uptake 1) is still known as the first QTL associated with phosphorus uptake and have been validated in different field crops. Interestingly, some pyramiding lines of Pupl and other QTLs for other stress tolerances showed preferable phenotypes in the yield. Precise physiological studies with the help of genomics are on-going and some results will be discussed.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.2
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• pp.183-189
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• 2007

Ultra precise processed parts are required together with the development of optoelectronics industry. As important parts of optoelectronics industry, there are ferrule of optical connector and lens for optical devices. In particular, the lens requires high reliability with high precision without including flaws. These optical modules need ultra precise processing in order to reduce the loss of light sources and various nondestructive inspections are carried out in the finishing stage to separate good and bad quality products. Therefore, it was analyzed through the characteristics of response of amplitude and resonant frequency according to the mass variations of aspheric lens that is used currently in laser printers.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.369-378
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• 2006

For precision farming the influences of site-specific N application on rice grain yield and quality were investigated in 0.5 ha paddy field from 2001 to 2003. In pre-cultured soil, EC, O.M., total nitrogen, phosphate and potassium content showed high spatial variation, ranging from 11.63 to 52.03% of coefficient of variation, while that of pH was relatively low. In rice growth characteristics, tiller number at panicle formation stage was more than 10% in coefficient of variation, but plant height, SPAD figure at panicle formation stage and milled rice yield, protein content in brown rice showed less below 10%. Spatial dependence was over 0.60 in pH, total nitrogen, phosphate and potassium in pre-cultured soil and was over 0.50 in plant height, SPAD figure and protein content, while it was below 0.22 in tiller number at panicle formation. The range of spatial dependence was longer than 20m in all factors except for protein content in brown rice. Basal dressing nitrogen rate was positively correlated with PH, $SiO_{2}$, plant height and SPAD figure. Nitrogen fertilization rate at panicle formation stage was positively correlated with EC and O.M.. Protein content in brown rice was positively correlated with $SiO_{2}$ in pre-cultured soil. Milled rice yield was positively correlated with plant height, tiller number and SPAD figure at panicle formation stage.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2885-2891
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• 2015

Twitter, one of online social network services, is one of the most popular micro-blogs, which generates a large number of automated programs, known as tweet bots because of the open structure of Twitter. While these tweet bots are categorized to legitimate bots and malicious bots, it is important to detect tweet bots since malicious bots spread spam and malicious contents to human users. In the conventional work, temporal information was utilized for the classficiation of human and bot. In this paper, by utilizing geo-tagged tweets that provide high-precision location information of users, we first identify both Twitter users' exact location and the corresponding timestamp, and then propose an improved two-stage tweet bot detection algorithm by computing an entropy based on spatio-temporal information. As a main result, the proposed algorithm shows superior bot detection and false alarm probabilities over the conventional result which only uses temporal information.

• Explosives and Blasting
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• v.37 no.2
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• pp.14-21
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• 2019

Distinct element method is a powerful numerical tool for modelling the jointed rock masses. It is also a useful tool for modelling of later stage of blasting requiring large displacement. The distinct element method utilizes a rigid block idea in which the interacting force between distinct elements is calculated from contact displacement as elements penetrate slightly. The properties of joints defined as the boundaries of distinct elements are critical parameters to determine the block behavior, and affect the deformation and failure mode. However, regardless of real joint properties, joint stiffnesses have sometimes been selected without special concern just to prevent elements from penetrating too far into each other in some quasi-static problems. Depending on whether the main interest in the analysis is the prediction of the deformation with high precision, or the prediction of the block behaviour after failure, the input data such as joint stiffness may or may not have a significant effect on the results. The purpose of this study is to provide a sound understanding of the effect of the joint stiffness on the distinct element analysis results, and to help guide the selection of input data.