• Smart Structures and Systems
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• v.24 no.5
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• pp.631-639
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• 2019

Finite element analysis is one of the important methods to study the structural performance. Due to the simplification, discretization and error of structural parameters, numerical model errors always exist. Besides, structural characteristics may also change because of material aging, structural damage, etc., making the initial finite element model cannot simulate the operational response of the structure accurately. Based on Bayesian methods, the initial model can be updated to obtain a more accurate numerical model. This paper presents the work on the field test, modal identification and model updating of a Chinese reinforced concrete pagoda. Based on the ambient vibration test, the acceleration response of the structure under operational environment was collected. The first six translational modes of the structure were identified by the enhanced frequency domain decomposition method. The initial finite element model of the pagoda was established, and the elastic modulus of columns, beams and slabs were selected as model parameters to be updated. Assuming the error between the measured mode and the calculated one follows a Gaussian distribution, the posterior probability density function (PDF) of the parameter to be updated is obtained and the uncertainty is quantitatively evaluated based on the Bayesian statistical theory and the Metropolis-Hastings algorithm, and then the optimal values of model parameters can be obtained. The results show that the difference between the calculated frequency of the finite element model and the measured one is reduced, and the modal correlation of the mode shape is improved. The updated numerical model can be used to evaluate the safety of the structure as a benchmark model for structural health monitoring (SHM).

• Journal of Magnetics
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• v.16 no.1
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• pp.36-41
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• 2011

If gas is leaking out of gas pipelines, it could cause a huge explosion. Accordingly, it is important to ensure the integrity of gas pipelines. Traditionally, over the years, gas-operating companies have used the ILI system, which is based on axial magnetic flux leakage (MFL), to inspect the gas pipelines. Relatively, there is a low probability of detection (POD) for the axial defects with the axial MFL-based ILI. To prevent the buried pipeline from corrosion, it requires a protective coating. In addition to the potential damage to the coating by environmental factors and external forces, there could be defects on the damaged coating area. Thus, it is essential that nondestructive evaluation methods for detecting axial defects (axial cracks, axial groove) and damaged coating be developed. In this study, an electromagnetic acoustic transducer (EMAT) sensor was designed and fabricated for detecting axial defects and coating disbondment. In order to validate the performances of the developed EMAT sensor, experiments were performed with specimens from axial cracks, axial grooves, and coating disbondment. The experimental results showed that the developed EMAT sensor could detect not only the axial cracks (minimum 5% depth of wall thickness) and axial grooves (minimum 10% depth of wall thickness), but also the coating disbondment.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.3
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• pp.189-197
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• 2021

Climate change effects are particularly apparent in many cool-season grasslands in South Korea. Moreover, the probability of climate extremes has intensified and is expected to increase further. In this study, we performed climate change vulnerability assessments in cool-season grasslands based on the analytic hierarchy process method to contribute toward effective decision-making to help reduce grassland damage caused by climate change and extreme weather conditions. In the analytic hierarchy process analysis, vulnerability was found to be influenced in the order of climate exposure (0.575), adaptive capacity (0.283), and sensitivity (0.141). The climate exposure rating value was low in Jeju-do Province and high in Daegu (0.36-0.39) and Incheon (0.33-0.5). The adaptive capacity index showed that grassland compatibility (0.616) is more important than other indicators. The adaptation index of Jeollanam-do Province was higher than that of other regions and relatively low in Gangwon-do Province. In terms of sensitivity, grassland area and unused grassland area were found to affect sensitivity the most with index values of 0.487 and 0.513, respectively. The grassland area rating value was low in Jeju-do and Gangwon-do Province, which had large grassland areas. In terms of vulnerability, that of Jeju-do Province was lower and of Gyeongsangbuk-do Province higher than of other regions. These results suggest that integrating the three aspects of vulnerability (climate exposure, sensitivity, and adaptive capacity) may offer comprehensive and spatially explicit adaptation plans to reduce the impacts of climate change on the cool-season grasslands of South Korea.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.587-593
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• 2021

In this thesis, we will break away from the classic DDoS response system for large-scale denial-of-service attacks that develop day by day, and effectively endure intelligent denial-of-service attacks by utilizing artificial intelligence-based technology, one of the core technologies of the 4th revolution. A possible service model development plan was proposed. That is, a method to detect denial of service attacks and minimize damage through machine learning artificial intelligence learning targeting a large amount of data collected from multiple security devices and web servers was proposed. In particular, the development of a model for using artificial intelligence technology is to detect a Western service attack by focusing on the fact that when a service denial attack occurs while repeating a certain traffic change and transmitting data in a stable flow, a different pattern of data flow is shown. Artificial intelligence technology was used. When a denial of service attack occurs, a deviation between the probability-based actual traffic and the predicted value occurs, so it is possible to respond by judging as aggressiveness data. In this paper, a service denial attack detection model was explained by analyzing data based on logs generated from security equipment or servers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.97-105
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• 2008

The various mutations of the malicious codes are fast generated on the network. Also the behaviors of them become intelligent and the damage becomes larger step by step. In this paper, we suggest the method to select the useful measures for the detection of the codes. The method has the advantage of shortening the detection time by using header data without payloads and uses connection data that are composed of TCP/IP packets, and much information of each connection makes use of the measures. A naive estimator is applied to the probability distribution that are calculated by the histogram estimator to select the specific measures among 80 measures for the useful detection. The useful measures are then selected by using relative entropy. This method solves the problem that is to misclassify the measure values. We present the usefulness of the proposed method through the result of the detection experiment using the detection patterns based on the selected measures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.209-217
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• 2021

Although forest fires are more often triggered by artificial causes than by natural causes, the combustion conditions that spread forest fire damage over a large area are affected by natural phenomena. Therefore, using partial least squares structural equation modeling (PLS-SEM), which can analyze the dependent and causal relationships between various factors, this study evaluated the causal relationships and relative influences between forest fire, weather, and drought, taking Gangwon Province as our sample region. The results indicated that the impact of drought on forest fires was 27 % and that of the weather was 38 %. In addition, forest fires in spring accounted for about 60 % of total forest fires. This indicatesthat along with meteorological factors, the autumn and winter droughts in the previous year affected forest fires. In assessing the risk of forest fires, if severe meteorological droughts occur in autumn and winter, the probability of forest fires may increase in the spring of the following year.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.110-116
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• 2022

Human failure event (HFE) dependency analysis is a part of human reliability analysis (HRA). For efficient HFE dependency analysis, a maximum number of minimal cut sets (MCSs) that have HFE combinations are generated from the fault trees for the probabilistic safety assessment (PSA) of nuclear power plants (NPPs). After collecting potential HFE combinations, dependency levels of subsequent HFEs on the preceding HFEs in each MCS are analyzed and assigned as conditional probabilities. Then, HFE recovery is performed to reflect these conditional probabilities in MCSs by modifying MCSs. Inappropriate HFE dependency analysis and HFE recovery might lead to an inaccurate core damage frequency (CDF). Using the above process, HFE recovery is performed on MCSs that are generated with a non-zero truncation limit, where many MCSs that have HFE combinations are truncated. As a result, the resultant CDF might be underestimated. In this paper, a new method is suggested to incorporate HFE recovery into the MCS generation stage. Compared to the current approach with a separate HFE recovery after MCS generation, this new method can (1) reduce the total time and burden for MCS generation and HFE recovery, (2) prevent the truncation of MCSs that have dependent HFEs, and (3) avoid CDF underestimation. This new method is a simple but very effective means of performing MCS generation and HFE recovery simultaneously and improving CDF accuracy. The effectiveness and strength of the new method are clearly demonstrated and discussed with fault trees and HFE combinations that have joint probabilities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.6
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• pp.793-802
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• 2022

Non-structural improvements to urban drainage systems are necessary to overcome the elevated levels of urban flood damage. This study proposed a type of automatic pump/gate operation technology for urban pump stations that takes reservoir inflows and river water levels into account and its performance is compared with the current operation using the concept of residual flow-based resilience. The proposed automatic operation relies on three pump operations and two gate operations. The water depth at the monitoring node was used for the pump operation, and the monitoring node was selected in consideration of the first overflow node and the maximum overflow node. The target area is the Daegu Bisan urban pump station, and the rainfall data consisted of probability rainfall sets with durations of 30 minutes, 60 minutes, 90 minutes and 120 minutes, and frequencies of 30, 50, and 70 years. As a result of the application of the proposed operation, differences in the resilience between the automatic operation and the current operation were at least 5.20E-05 with a maximum of 8.07E-04. The longer the duration is, the greater the difference in the resilience.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.35-41
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• 2022

Heavy snow damage can be prevented in advance with an appropriate security system. To develop the security system, we developed a model that predicts snow depth after a few hours when the snow depth is observed, and utilized it to calculate a failure probability with various types of greenhouses and observed snow depth data. We compared the Markov chain model and Bayesian long short-term memory models with varying input data. Markov chain model showed the worst performance, and the models that used only past snow depth data outperformed the models that used other weather data with snow depth (temperature, humidity, wind speed). Also, the models that utilized 1-hour past data outperformed the models that utilized 3-hour data and 6-hour data. Finally, the Bayesian LSTM model that uses 1-hour snow depth data was selected to predict snow depth. We compared the selected model and the shifting method, which uses present data as future data without prediction, and the model outperformed the shifting method when predicting data after 11-24 hours.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.