• Journal of Positioning, Navigation, and Timing
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• v.8 no.4
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• pp.209-214
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• 2019

Precise point positioning (PPP) requires precise orbit and clock products. International GNSS service (IGS) real-time service (RTS) data can be used in real-time for PPP, but it may not be possible to receive these corrections for a short time due to internet or hardware failure. In addition, the time required for IGS to combine RTS data from each analysis center results in a delay of about 30 seconds for the RTS data. Short-term orbit prediction can be possible because it includes the rate of correction, but the clock correction only provides bias. Thus, a short-term prediction model is needed to preidict RTS clock corrections. In this paper, we used a long short-term memory (LSTM) network to predict RTS clock correction for three minutes. The prediction accuracy of the LSTM was compared with that of the polynomial model. After applying the predicted clock corrections to the broadcast ephemeris, we performed PPP and analyzed the positioning accuracy. The LSTM network predicted the clock correction within 2 cm error, and the PPP accuracy is almost the same as received RTS data.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4014-4021
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• 2021

If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails. Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation is necessary to prevent this progression. In this study, the corresponding time was defined as the golden time. To achieve the objective of accurately predicting the golden time, the prediction was performed using the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has an architecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in which the fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affect inference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regression model. By using the prediction result through the proposed DFNN with rule-dropout, it is expected to prevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery, which failed in the LOCA situation.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.10a
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• pp.183-190
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• 2008

• Journal of the Korean Society of Safety
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• v.21 no.6 s.78
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• pp.74-81
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• 2006

Probabilistic Risk Assessment considering statistically random variables is performed for the preliminary design of a Arch Bridge. Component reliabilities of girders have been evaluated using the response surfaces of the design variables at the selected critical sections based on the maximum shear and negative moment locations. Response Surface Method(RSM) is successfully applied for reliability analyses for this relatively small probability of failure of the complex structure, which is hard to be obtained by Monte-Carlo Simulations or by First Order Second Moment Method that can not easily calculate the derivative terms of implicit limit state functions. For the analysis of system reliability, parallel resistance system composed of girders is changed into parallel series connection system. The upper and lower probabilities of failure for the structural system have been evaluated and compared with the suggested prediction method for the combination of failure modes. The suggested prediction method for the combination of failure modes reveals the unexpected combinations of element failures in significantly reduced time and efforts compared with the previous permutation method or system reliability analysis method.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.5
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• pp.101-108
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• 2017

Recently, the occurrence of landslides has been increasing over the years due to the extreme weather event. Developments of landslides monitoring technology that reduce damage caused by landslide are urgently needed. Therefore, in this study, a strain ratio sensor was developed to predict the ground behavior during the slope failure, and the change in surface ground displacement was observed as slope failed on the field model experiment. As a result, in the slope failure, the ground displacement process increases the risk of collapse as the inverse displacement approaches zero. It is closely related to the prediction of precursor. In all cases, increase in displacement and reverse speed of inverse displacement with time was observed during the slope failure, and it is very important event for monitoring collapse phenomenon of risky slopes. In the future, it can be used as disaster prevention technology to contribute in reduction of landslide damage and activation of measurement industry.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• Journal of the Korea Concrete Institute
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• v.18 no.2 s.92
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• pp.233-238
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• 2006

In lifetime estimation, the FEM analysis method is proposed for predicting corrosion failure time of concrete structures exposed to sea-water. This study shows that the corrosion rate of rebar in artificial pore solution can be transferred to the corrosion rate of rebar in concrete using the relationship between pore volume and concrete volume by Jennings' model. And this study considered the pitting corrosion effects of reinforcement bar on corrosion failure analysis, rebar size to cover depth and nonlinear crack analysis. These analysis results have good accordance with the experimental results of Williamson's work. This methodology can be applied to lifetime prediction procedure of reinforced concrete structures and also gives more reasonable results of concrete cover failure time estimation of reinforced concrete structures exposed to sea-water.

• Tunnel and Underground Space
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• v.19 no.6
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• pp.558-566
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• 2009

Time-dependent behavior is a basic mechanical property of rocks. Predicting the failure time of rock structures by analyzing the time-dependent characteristic is important and problematic. It is tried to predict the failure time of tunnel, slope & laboratory creep test specimen from measured displacement(or strain) and rate with relationship suggested by Voight($\ddot{\Omega}=A\dot{\Omega}^\alpha$, where $\Omega$ is a measurable quantity such as strain & displacement and A & $\alpha$ are constants). A & $\alpha$ are estimated through applying the nonlinear least square method to the single and double integrated Voight's equations and utilized to predict the failure time. Predicted failure time is in accordance with real one except minor error. Linear inverse rate method applied to creep strain and rate yields a poor linear correlation of data and precision of predicted failure time is not better than methods using strain and rate.

• The Journal of Engineering Geology
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• v.20 no.3
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• pp.243-255
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• 2010

The slope design of an open-pit mine must consider economical efficiency and stability. Thus, the overall slope angle is the principal factor because of limited support or reinforcement options available in such a setting. In this study, slope displacement, as monitored by a GPS system, was analyzed for a coal mine at Pasir, Indonesia. Predictions of failure time by inverse velocity analysis showed good agreement with field observations. Therefore, the failure time of an unstable slope can be roughly estimated prior to failure. A GIS model that combines fuzzy theory and the analytical hierarchy process (AHP) was developed to assess slope instability in open-pit coal mines. This model simultaneously considers seven factors that influence the instability of open-pit slopes (i.e., overall slope gradient, slope height, surface flows, excavation plan, tension cracks, faults, and water body). Application of the proposed method to an open-pit coal mine revealed an enhanced prediction accuracy of failure time and failure site compared with existing methods.

• Journal of the Korean Society for Railway
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• v.9 no.6 s.37
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• pp.732-738
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• 2006

This research provides a scheme for Highly Accelerated Stress Test that is necessary to demonstrate reliability prediction of Korean Rapid Transit Railway Train Control System sub-equipment, which is calculated by a relevant standard for failure rate prediction of electronic products. Although determining failure information generated in the process of trial running by statistic analysis is widely accepted as a measure of confirmation for reliability prediction, this research suggests the modeling for System Life Test determined by accelerating stress factors as a measure of confirmation for reliability prediction of sub-equipment unit that is generated ahead of a trial running in System Life Cycle. Consequently, the research demonstrates sub-equipment unit reliability test, which is based on the model derived from Accelerated Stress Test, according to accuracy level and the number of samples, and conducts an official experiment by making out a reliability test procedure sheet based on test time as well.