• Earthquakes and Structures
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• v.8 no.6
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• pp.1463-1480
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• 2015

This research aims to develop an empirical model for simulation of time-varying frequency in earthquake ground motion so as to be used easily in engineering applications. Briefly, 10545 recordings of the Next Generation Attenuation (NGA) global database of accelerograms from shallow crustal earthquakes are selected and binned by magnitude, distance and site condition. Then the wavelet spectrum of each acceleration record is calculated by using one-dimensional continuous wavelet transform, and the frequencies corresponding to the maximum values of the wavelet spectrum at a series of sampling time, named predominant frequencies, are extracted to analyze the variation of frequency content of seismic ground motions in time. And the time-variation of the predominant frequencies of 178 magnitude-distance-site bins for different directions are obtained by calculating the mean square root of predominant frequencies within a bin. The exponential trigonometric function is then use to fit the data, which describes the predominant frequency of ground-motion as a function of time with model parameters given in tables for different magnitude, distance, site conditions and direction. Finally, a practical frequency-dependent amplitude envelope function is developed based on the time-varying frequency derived in this paper, which has clear statistical parameters and can emphasize the effect of low-frequency components on later seismic action. The results illustrate that the time-varying predominant frequency can preferably reflect the non-stationarity of the frequency content in earthquake ground motions and that empirical models given in this paper facilitates the simulation of ground motions.

• Atmosphere
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• v.29 no.5
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• pp.551-566
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• 2019

Quantitative understanding of a random error that is associated with Lagrangian particle dispersion modeling is a prerequisite for backward-in-time mode simulations. This study aims to quantify the random error of the WRF-FLEXPART model and suggest an optimum number of the Lagrangian particles for backward-in-time simulations over the Seoul metropolitan area. A series of backward-in-time simulations of the WRF-FLEXPART model has conducted at two receptor points by changing the number of Lagrangian particles and the relative error, as a quantitative indicator of random error, is analyzed to determine the optimum number of the release particles. The results show that in the Seoul metropolitan area a 1-day Lagrangian transport contributes 80~90% in residence time and ~100% in atmospheric enhancement of carbon monoxide. The relative errors in both the residence time and the atmospheric concentration enhancement are larger when the particles release in the daytime than in the nighttime, and in the inland area than in the coastal area. The sensitivity simulations reveal that the relative errors decrease with increasing the number of Lagrangian particles. The use of small number of Lagrangian particles caused significant random errors, which is attributed to the random number sampling process. For the particle number of 6000, the relative error in the atmospheric concentration enhancement is estimated as -6% ± 10% with reduction of computational time to 21% ± 7% on average. This study emphasizes the importance of quantitative analyses of the random errors in interpreting backward-in-time simulations of the WRF-FLEXPART model and in determining the number of Lagrangian particles as well.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.407-414
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• 2014

The Internet is a highly distributed and complex system consisting of billion devices and has become the field of various kinds of conflicts during the last two decades. As a matter of fact, various actors utilise the Internet for illicit purposes, such as for performing distributed denial of service attacks (DDoS) and for spreading various types of aggressive malware. Despite the fact that numerous services provide information regarding the threat level of the Internet, they are mostly based on information acquired by their sensors or on offline statistical sampling of various security applications (antivirus software, intrusion detection systems, etc.). This paper introduces proactive threat observatory system (PROTOS), an open-source early warning system that does not require a commercial license and is capable of estimating the threat level across the Internet. The proposed system utilises both a global and a local approach, and is thus able to determine whether a specific host is under an imminent threat, as well as to provide an estimation of the malicious activity across the Internet. Apart from these obvious advantages, PROTOS supports a large-scale installation and can be extended even further to improve the effectiveness by incorporating prediction and forecasting techniques.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.1
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• pp.83-91
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• 2014

This paper presents a capacitive readout circuit for tri-axes microaccelerometer with sub-fF offset calibration capability. A charge sensitive amplifier (CSA) with correlated double sampling (CDS) and digital to equivalent capacitance converter (DECC) is proposed. The DECC is implemented using 10-bit DAC, charge transfer switches, and a charge-storing capacitor. The DECC circuit can realize the equivalent capacitance of sub-fF range with a smaller area and higher accuracy than previous offset cancelling circuit using series-connected capacitor arrays. The readout circuit and MEMS sensing element are integrated in a single package. The supply voltage and the current consumption of analog blocks are 3.3 V and $230{\mu}A$, respectively. The sensitivities of tri-axes are measured to be 3.87 mg/LSB, 3.87 mg/LSB and 3.90 mg/LSB, respectively. The offset calibration which is controlled by 10-bit DECC has a resolution of 12.4 LSB per step with high linearity. The noise levels of tri-axes are $349{\mu}g$/${\sqrt}$Hz, $341{\mu}g$/${\sqrt}$Hz and $411{\mu}g$/${\sqrt}$Hz, respectively.

• Particle and aerosol research
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• v.15 no.3
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• pp.105-113
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• 2019

The exhaust emissions from coal-fired power plants have received much attention because coal-fired power plants are the one of the largest sources of particulate matter (PM) emissions in South Korea. To measure the PM10 and PM2.5, we developed the novel diluter which is comprised of ejector and porous tube in series. The dilution ratio must be defined to calculate particle concentrations of the sampled air as well as to probe match for the isokinetic sampling. For this reason, we verified the dilution ratio of the developed diluter by the flow rate, numerical solution, gas concentration and particle concentration. The ejector-supplied flow rates were 10-50 L/min and the porous tube-supplied flow rates were 30, 50 L/min in this study. All methods above showed similar dilution ratios to each other within 10 % error rate. The dilution ratio was confirmed by comparing mass concentrations before and after the dilution process.

• Smart Structures and Systems
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• v.23 no.3
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• pp.263-278
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• 2019

Moving train load parameters, including train speed, axle spacing, gross train weight and axle weights, are identified based on strain-monitoring data. In this paper, according to influence line theory, the classic moving force identification method is enhanced to handle time-varying velocity of the train. First, the moments that the axles move through a set of fixed points are identified from a series of pulses extracted from the second derivative of the structural strain response. Subsequently, the train speed and axle spacing are identified. In addition, based on the fact that the integral area of the structural strain response is a constant under a unit force at a unit speed, the gross train weight can be obtained from the integral area of the measured strain response. Meanwhile, the corrected second derivative peak values, in which the effect of time-varying velocity is eliminated, are selected to distribute the gross train weight. Hence the axle weights could be identified. Afterwards, numerical simulations are employed to verify the proposed method and investigate the effect of the sampling frequency on the identification accuracy. Eventually, the method is verified using the real-time strain data of a continuous steel truss railway bridge. Results show that train speed, axle spacing and gross train weight can be accurately identified in the time domain. However, only the approximate values of the axle weights could be obtained with the updated method. The identified results can provide reliable reference for determining fatigue deterioration and predicting the remaining service life of railway bridges.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.173-173
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• 2015

The flood water level in tidal river is determined by the joint effects of flood discharge and tidal water levels at downstream boundary. Due to the variable tidal boundary conditions, the evaluated design water levels associated with a certain flood event can be significantly different. To avoid determining of design water levels just by a certain tidal boundary condition and remove the influence of variability in boundary condition from the evaluation of design water levels, a probabilistic approach is considered in this study. This study focuses on the development of a method to evaluate the realistic design water levels in tidal river with taking into account the combined effects of river discharge and tidal level. The flood water levels are described by the joint probability of two driving forces, river discharge and tidal water levels. The developed method is applied to determine design water levels for the tidal reach of the Han River. An unsteady flow model is used to simulate the flow in the reach. To determine design water levels associated with a certain flood event, first, possible boundary conditions are obtained by sampling starting times of tidal level time series; then for each tidal boundary condition, corresponding peak water levels along the channel are computed; and finally, design water levels are determined by computing the expectations of the peak water levels. Two types of tides which are composed by different constituents are assumed (one is composed by $M_2$, and the other one is composed by $M_2$ and $M_2$) at downstream boundary, and two flood events with different maximum flood discharges are considered in this study. It is found that (a) the computed design water levels with two assumed tides have no significant difference for a certain flood event, though variability of peak water levels due to the tidal effect is considerably different; (b) tidal effect can reach to the Jamsil submerged weir and the effect is obvious in the downstream reach of the Singok submerged weir; (c) in the tidally affected reach, the variability of peak water levels due to the tidal effect is greater if the maximum flood discharge is smaller.

• Tunnel and Underground Space
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• v.31 no.5
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• pp.360-373
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• 2021

Many sources of uncertainty exist in geotechnical analysis ranging from the material parameters to the sampling and testing techniques. The conventional deterministic stability analysis of a plane failure in rock slope produce a safety factor but not a probability of failure or reliability index. In the conventional slope stability analysis by evaluating the ground uncertainty as an overall safety factor, it is difficult to evaluate the stability of the realistic rock slope in detail. This paper reviews some established probabilistic analysis techniques, such as the MCS, FOSM, PEM, Taylor Series as applied to plane failure of rock slopes in detail. While the Monte - Carlo methods leads to the most accurate calculation of the probability of safety, this method is too time consuming. Therefore, the simplified probability methods could be alternatives to the MCS. In this study, using these simple probability methods, the failure probability estimation of a plane failure in rock slope is presented.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.2
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• pp.137-145
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• 2022

Objectives: This study aimed to identify PM₁₀ mass concentration levels and conduct peak identification during five tasks in agricultural works. Methods: We investigated five agricultural tasks in 12 farms, which were harvesting, plowing, sowing, planting, and decapitation. All samples were measured by using the portable aerosol spectrometer(PAS 1.108) and the aerosol monitor(SidePak AM520). The collected data were compared with the national PM₁₀ concentrations. They were calculated to descriptive statistics, independent t-test, or ANOVA, and the peak identification on time series graph. Results: The ten investigated farms showed no significant difference with the national PM₁₀ concentrations, but the two greenhouses(AM, 143.31, 85.16 ㎍/m³) showed significant difference(p<0.05). As a result of the peak identification, the harvesting tasks showed repeated peak occurrence with the background concentration level of about 50 ㎍/m³. For plowing and sowing tasks, the peak occurred intermittently when the working was conducted near the sampling sites. Among the five tasks, the arithmetic mean of the harvesting task was 138.84±294.71 ㎍/m³, which was significantly higher than the other tasks(p<0.05). In addition, the case of using a tractor was higher than the case of not using the tractor(p<0.05), and the driver's seat showed the highest concentration(AM, 95.81 ㎍/m³). Conclusions: Works in greenhouses might have exposure to PM₁₀, while outdoor works is similar to general atmospheric PM₁₀ concentration levels. However, there is a possibility of intermittent exposure to high concentrations of PM₁₀ depending on the characteristics of agricultural tasks.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.