• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.287-292
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• 2005

This paper presents a rational polynomial approximation method to estimate modal parameters of wind excited structures using incomplete noisy measurements of structural responses and partial measurements of wind velocities only. A stochastic model of the excitation wind force acting on the structure is estimated from partial measurements of wind velocities. Then the transfer functions of the structure are approximated as rational polynomial functions. From the poles and zeros of the estimated rational polynomial functions, the modal parameters, such as natural frequencies, damping ratios, and mode shapes are extracted. Since the frequency characteristics of wind forces acting on structures can be assumed as a smooth Gaussian process especially around the natural frequencies of the structures according to the central limit theorem (Brillinger, 1969; Yaglom, 1987), the estimated modal parameters are robust and reliable with respect to the assumed stochastic input models. To verify the proposed method, the modal parameters of a TV transmission tower excited by gust wind are estimated. Comparison study with the results of other researchers shows the efficacy of the suggested method.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.1-19
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• 2023

The purpose of this study was to find out what attributes are valid for the edge between students through longitudinal network analysis, and the results of TERGM (temporal exponential random graph model) and SAOM (stochastic actor-oriented model) statistical models were compared. The TERGM model interprets the research results based on the edge formation of the entire network, and the SAOM model interprets the research results on the surrounding networks formed by specific actors. The TERGM model expressed the influence of a previous time through a time term, and the SAOM model considered temporal dependence by implementing a network that evolves by an actor's opportunity as a ratio function.

• Journal of the Society of Naval Architects of Korea
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• v.28 no.2
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• pp.195-206
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• 1991

This paper is concerned with comparison of the first-order reliability methods applied to the assessment of structural safety. For convenience the reliability methods are divided into two categories : the One can explicitly consider the effects of uncertainties in material and geometric variables on those of load effects, say stresses and displacement in the structural analysis procedure and the other one does not. The first method is commonly termed as the stochastic finite element method(SFEM) or probabilistic finite element method(PFEM) and the second method is termed heroin as the ordinary reliability method to distinct it from the stochastic finite element method in which the structural analysis is carried out just once and the load effects are directly input into the reliability analysis procedure. This is based on the reasonable assumption that the level of uncertainties of load effects is the same as those of load itself. In this paper the above two different reliability method have been applied to the safety assessment of plane frame structures and compared thier results from the view point of their efficiency and usefulness. As lear as results of the present structure models are concerned, it can be said that the ordinary reliability method can give reasonable results when the uncertainties of material and geometric variables are comparatively small, say when less than about 15% and the stochastic finite element method is desired to be applied to the structure in which the COV's are comparatively great, say when greater than about 15%.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.195-209
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• 2003

Spatial-Stochastic Neural Networks Model(SSNNM) is used to estimate long-term streamflow in the parallel reservoir groups. SSNNM employs two kinds of backpropagation algorithms, based on LMBP and BFGS-QNBP separately. SSNNM has three layers, input, hidden, and output layer, in the structure and network configuration consists of 8-8-2 nodes one by one. Nodes in input layer are composed of streamflow, precipitation, pan evaporation, and temperature with the monthly average values collected from Andong and Imha reservoir. But some temporal differences apparently exist in their time series. For the SSNNM training procedure, the training sets in input layer are generated by the PARMA(1,1) stochastic model and they covers insufficient time series. Generated data series are used to train SSNNM and the model parameters, optimal connection weights and biases, are estimated during training procedure. They are applied to evaluate model validation using observed data sets. In this study, the new approaches give outstanding results by the comparison of statistical analysis and hydrographs in the model validation. SSNNM will help to manage and control water distribution and give basic data to develop long-term coupled operation system in parallel reservoir groups of the Upper Nakdong River.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.401-409
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• 2023

This study compared and analyzed the performance of a distributed area search algorithm and a distributed probability search algorithm based on information exchange between ships. The distributed algorithm is a method that can search for an optimal avoidance route based on information exchange between ships. In the distributed area search algorithm, only a ship with the maximum cost reduction among neighboring ships has priority, so the next expected location can be changed. The distributed stochastic search algorithm allows a non-optimal value to be searched with a certain probability so that a new value can be searched. A circular-based ship collision avoidance model was used for the ship-to-ship collision avoidance experiment. The experimental method simulated the distributed area search algorithm and the distributed stochastic search algorithm while increasing the number of ships from 2 to 50 that were the same distance from the center of the circle. The calculation time required for each algorithm, sailing distance, and number of message exchanges were compared and analyzed. As a result of the experiment, the DSSA(Distributed Stochastic Search Algorithm) recorded a 25%calculation time, 88% navigation distance, and 84% of number of message exchange rate compared to DLSA.

• Korean Journal of Clinical Pharmacy
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• v.23 no.2
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• pp.137-141
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• 2013

Purpose: This study compared the performance of new NONMEM estimation methods using a population analysis dataset collected from a clinical study that consisted of 40 individuals and 567 observations after a single oral dose of glimepiride. Method: The NONMEM 7.2 estimation methods tested were first-order conditional estimation with interaction (FOCEI), importance sampling (IMP), importance sampling assisted by mode a posteriori (IMPMAP), iterative two stage (ITS), stochastic approximation expectation-maximization (SAEM), and Markov chain Monte Carlo Bayesian (BAYES) using a two-compartment open model. Results: The parameters estimated by IMP, IMPMAP, ITS, SAEM, and BAYES were similar to those estimated using FOCEI, and the objective function value (OFV) for diagnosing the model criteria was significantly decreased in FOCEI, IMPMAP, SAEM, and BAYES in comparison with IMP. Parameter precision in terms of the estimated standard error was estimated precisely with FOCEI, IMP, IMPMAP, and BAYES. The run time for the model analysis was shortest with BAYES. Conclusion: In conclusion, the new estimation methods in NONMEM 7.2 performed similarly in terms of parameter estimation, but the results in terms of parameter precision and model run times using BAYES were most suitable for analyzing this dataset.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.2
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• pp.37-45
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• 2022

A simplified model using the lifetime distribution has been presented to estimate the Mean Residual Life (MRL) of rubble-mound breakwaters, which is not like a stochastic process model based on time-dependent history data to the cumulative damage progress of rubble-mound breakwaters. The parameters involved in the lifetime distribution can be easily estimated by using the upper and lower limits of lifetime and their likelihood that made a judgement by several experts taking account of the initial design lifetime, the past sequences of loads, and others. The simplified model presented in this paper has been applied to the rubble-mound breakwater with TTP armor layer. Wiener Process (WP)-based stochastic model also has been applied together with Monte-Carlo Simulation (MCS) technique to the breakwater of the same condition having time-dependent cumulative damage to TTP armor layer. From the comparison of lifetime distribution obtained from each models including Mean Time To Failure (MTTF), it has found that the lifetime distributions of rubble-mound breakwater can be very satisfactorily fitted by log-normal distribution for all types of cumulative damage progresses, such as exponential, linear, and logarithmic deterioration which are feasible in the real situations. Finally, the MRL of rubble-mound breakwaters estimated by the simplified model presented in this paper have been compared with those by WP stochastic process. It can be shown that results of the presented simplified model have been identical with those of WP stochastic process until any ages in the range of MTT F regardless of the deterioration types. However, a little of differences have been seen at the ages in the neighborhood of MTTF, specially, for the linear and logarithmic deterioration of cumulative damages. For the accurate estimation of MRL of harbor structures, it may be desirable that the stochastic processes should be used to consider properly time-dependent uncertainties of damage deterioration. Nevertheless, the simplified model presented in this paper can be useful in the building of the MRL-based preventive maintenance planning for several kinds of harbor structures, because of which is not needed time-dependent history data about the damage deterioration of structures as mentioned above.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.189-194
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• 2020

This paper analyzes the gradient descent method, which is the one most used for learning neural networks. Learning means updating a parameter so the loss function is at its minimum. The loss function quantifies the difference between actual and predicted values. The gradient descent method uses the slope of the loss function to update the parameter to minimize error, and is currently used in libraries that provide the best deep learning algorithms. However, these algorithms are provided in the form of a black box, making it difficult to identify the advantages and disadvantages of various gradient descent methods. This paper analyzes the characteristics of the stochastic gradient descent method, the momentum method, the AdaGrad method, and the Adadelta method, which are currently used gradient descent methods. The experimental data used a modified National Institute of Standards and Technology (MNIST) data set that is widely used to verify neural networks. The hidden layer consists of two layers: the first with 500 neurons, and the second with 300. The activation function of the output layer is the softmax function, and the rectified linear unit function is used for the remaining input and hidden layers. The loss function uses cross-entropy error.

• Smart Structures and Systems
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• v.20 no.2
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• pp.231-246
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• 2017

A recent work by the authors has demonstrated the feasibility of scour evaluation for Kao-Ping-Hsi Cable-Stayed Bridge simply based on ambient vibration measurements. To further attain the goal of scour monitoring, a key challenge comes from the interference of several environmental factors that may also significantly alter the pier frequencies without the change of scour depth. Consequently, this study attempts to investigate the variation in certain modal frequencies of this bridge induced by several environmental factors. Four sets of pier vibration measurements were taken either during the season of plum rains, under regular summer days without rain, or in a period of typhoon. These signals are analyzed with the stochastic subspace identification and empirical mode decomposition techniques. The variations of the identified modal frequencies are then compared with those of the corresponding traffic load, air temperature, and water level. Comparison of the analyzed results elucidates that both the traffic load and the environmental temperature are negatively correlated with the bridge frequencies. However, the traffic load is clearly a more dominant factor to alternate the identified bridge deck frequency than the environmental temperature. The pier modes are also influenced by the passing traffic on the bridge deck, even though with a weaker correlation. In addition, the variation of air temperature follows a similar tendency as that of the passing traffic, but its effect on changing the bridge frequencies is obviously not as significant. As for the effect from the alternation of water level, it is observed that the frequency baselines of the pier modes may positively correlate with the water level during the seasons of plum rains and typhoon.

• Journal of Korea Port Economic Association
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• v.31 no.3
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• pp.1-14
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• 2015

This study utilizes Monte Carlo simulation to forecast the time charter rate of vessels, the three-month Libor interest rate, and the ship demolition price, to mitigate future uncertainties involving these factors. The simulation was performed 10,000 times to obtain an exact result. For the empirical analysis - based on considerations in ordering ships in 2010-a comparison between the Monte Carlo simulation-based stochastic discounted cash flow (DCF) method and traditional DCF methods was made. The analysis revealed that the net present value obtained through Monte Carlo simulation was lower than that obtained via regular DCF methods, alerting the owners to risks and preventing them from placing injudicious orders for ships. This research has implications in reducing the uncertainties that future shipping markets face, through the use of a stochastic DCF approach with relevant variables and probability methods.