• Convergence Security Journal
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• v.21 no.1
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• pp.45-53
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• 2021

When the security monitoring system is performed in a separation network, there is little normal anomaly detection in internal networks or high-risk sections. Therefore, after the establishment of the security network, a model is needed to evaluate state-of-the-art cyber threat anomalies for internal network in separation network to complete the optimized security structure. In this study, We evaluate it by generating datasets of cyber vulnerabilities and malicious code arising from general and separation networks, It prepare for the latest cyber vulnerabilities in internal network cyber attacks to analyze threats, and established a cyber security test evaluation system that fits the characteristics. The study designed an evaluation model that can be applied to actual separation network institutions, and constructed a test data set for each situation and applied a real-time security assessment model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.47-60
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• 2011

With the increase in the use of mobile banking service, mobile banking has become an attractive target to attackers. Even though many security measures are applied to the current mobile banking service, some threats such as physical theft or penetration to a mobile device from remote side are still remained as unsolved. With aiming to fill this void, we propose a novel approach to prevent e-financial incidents by analyzing mobile device user's input patterns. This approach helps us to distinguish between original user's usage and attacker's usage through analyzing personal input patterns such as input time-interval, finger pressure level on the touch screen. Our proposed method shows high accuracy, and is effective to prevent the e-finance incidents proactively.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.525-539
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• 2020

A site containing buried solid waste and treated water and oil storage containers from a leather manufacturing plant was studied through soil and groundwater pollution and electrical resistivity surveys with the aim of identifying areas polluted by leachate generated by landfilling with leather waste and leakage wastewater. It was found that TPH and Zn exceeded environmental standards for soil pollution and, for leachate and groundwater, Cr(VI) concentrations exceeded standard levels for groundwater quality. An electrical resistivity survey was used to elucidate soil and groundwater pollution characteristics and diffusion pathways. Ten survey lines were set up with an electrode spacing of 5 m in a dipole-dipole array. The hydraulic characteristics of soil determined by groundwater contamination surveys matched well the low-resistivity-anomaly zones. Electrical resistivity surveys of areas containing contaminated soil and groundwater that have irregular strata due to waste reclamation are thus useful in highlighting vertical and horizontal pollutant diffusion pathways and in monitoring contaminated and potentially contaminated areas.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.91-97
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• 2020

Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017-2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Journal of the Korean Geosynthetics Society
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• v.22 no.1
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• pp.67-74
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• 2023

In order to monitor the stability of slopes, research on data-based slope failure prediction and early warning is increasing. However, most papers overlook the quality of data. Poor data quality can cause problems such as false alarms. Therefore, this paper proposes a two-step hybrid approach consisting of rules and machine learning models for quality control of data collected from slopes. The rule-based has the advantage of high accuracy and intuitive interpretation, and the machine learning model has the advantage of being able to derive patterns that cannot be explicitly expressed. The hybrid approach was able to take both of these advantages. Through a case study, the performance of using the two methods alone and the case of using the hybrid approach was compared, and the hybrid method was judged to have high performance. Therefore, it is judged that using a hybrid method is more appropriate than using the two methods alone for data quality control.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of the Korean Geotechnical Society
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• v.28 no.7
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• pp.41-53
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• 2012

During rainfall period, to identify the characteristics of the infiltration of moisture, electrical resistivity monitering survey was carried out to weathered zone. Four regions of geophysical exploration areas with different rock types, four regions were selected. An area consists of mafic granite and three areas are composed of sedimentary rocks (Sandstone, Shale, Unconsolidated Mudstone). Survey was conducted from June (rainy season) to November (dry season), and during the period the change in resistivity was observed. According to the result of monitoring exploration on Geumjeong and Jinju areas, for the estimation of the standard rainfall, it is necessary to estimate the effects of the antecedent rainfall during the rainy season based on the overall rainfall from June till October and also necessary to consider this for the estimation of the half period. Also, the vertical distribution of the low resistivity anomaly zone does not show that the infiltration of moisture does not occur uniformly from the surface of the ground to the lower ground but shows that it occurs along the relaxed gap of the crack or soil stratum of the weathering zone. In Pohang area, the type of moisture infiltration is different from that of the granite or sedimentary rock. Since, after the rainfall, the rate of infiltration to the lower ground is high and the period of cultivation to the lower bedrock aquifer is short, it has similar effect to that of the antecedent rainfall applied for the estimation of the standard rainfall being presently used. In Danyang, due to the degree of water content of the ground, the duration period of the low resistivity anomaly zone observed in the lower ground of the place where clastic sedimentary rock is distributed is similar to that in Pohang area. The degree of lateral water diffusion at the time of localized heavy rain is the same as that of the sedimentary rock in Jinju. According to the above analysis results, in Danyang area, the period when the antecedent rainfall has its influence is estimated as three weeks or so.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Journal of Advanced Navigation Technology
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• v.22 no.2
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• pp.49-56
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• 2018

As the energy generated by earthquake, tsunami, etc. propagates through the air and disturbs the electron density in the ionosphere, the perturbation can be detected by analyzing the ionospheric delay in satellite signal. The electron density in the ionosphere is affected by various factors such as solar activity, latitude, season, and local time. To distinguish from the anomaly, therefore, it is required to inspect the normal trend of the ionosphere. Also, as the perturbation magnitude diminishes by distance it is necessary to develop an appropriate algorithm to detect long-distance disturbances. In this paper, normal condition ionosphere trend is analyzed via IONEX data. We selected monitoring value that has no tendency and developed an algorithm to effectively detect the long-distance ionospheric disturbances by using the lasting characteristics of the disturbances. In the end, we concluded the $2^{nd}$ derivative of ionospheric delay would be proper monitoring value, and the false alarm with the developed algorithm turned out to be 1.4e-6 level. It was applied to 2011 Tohoku earthquake case and the ionospheric disturbance was successfully detected.