• Proceedings of the Korea Water Resources Association Conference
• /
• 2004.05b
• /
• pp.1285-1289
• /
• 2004

Elman Discrete Recurrent Neural Networks Model(EDRNNM) was used to be a suitable short-term hydrological forecasting tool yielding a very high degree of flood stage forecasting accuracy at Musung station of Wi-stream one of IHP representative basins in South Korea. A relative new approach method has recurrent feedback nodes and virtual small memory in the structure. EDRNNM was trained by using two algorithms, namely, LMBP and RBP The model parameters, optimal connection weights and biases, were estimated during training procedure. They were applied to evaluate model validation. Sensitivity analysis test was also performed to account for the uncertainty of input nodes information. The sensitivity analysis approach could suggest a reduction of one from five initially chosen input nodes. Because the uncertainty of input nodes information always result in uncertainty in model results, it can help to reduce the uncertainty of EDRNNM application and management in small catchment.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.24 no.10
• /
• pp.1388-1398
• /
• 2000

A pipe prover is a flowmeter calibrator used in flow measurement field. Gravimetric and volumetric methods were applied to determine the basic volume of the pipe prover. Uncertainty of its basic volume measurement was evaluated in accordance with the procedure recommended by International Organization for Standardization. The combined standard uncertainty of determining the basic volume was estimated from the sensitivity coefficient and the standard uncertainty of independent variables. It was found that the uncertainties of the weighing and volume measurements have dominant influence on that of the basic volume determination. With the quantitative analysis of the sensitivity coefficient, the contribution of the each variable uncertainty to the combined standard uncertainty of the basic volume is shown clearly.

• Structural Monitoring and Maintenance
• /
• v.9 no.1
• /
• pp.59-80
• /
• 2022

In the present study, the objective is to detect the structural damages using the responses obtained from the sensors at the optimal location under uncertainty conditions. Reducing the error rate in damage detection process due to responses' noise is an important goal in this study. In the proposed algorithm for optimal sensor placement, the noise of responses recorded from the sensors is initially reduced using the principal component analysis. Afterward, the optimal sensor placement is obtained by the damage detection equation based sensitivity analysis. The sensors are placed on degrees of freedom corresponding to the minimum error rate in structural damage detection through this procedure. The efficiency of the proposed method is studied on a truss bridge, a space dome, a double-layer grid as well as a three-story experimental frame structure and the results are compared. Moreover, the performance of the suggested method is compared with three other algorithms of Average Driving Point Residue (ADPR), Effective Independence (EI) method, and a mass weighting version of EI. In the examples, young's modulus, density, and cross-sectional areas of the elements are considered as uncertainty parameters. Ultimately, the results have demonstrated that the presented algorithm under uncertainty conditions represents a high accuracy to obtain the optimal sensor placement in the structures.

• Fire Science and Engineering
• /
• v.26 no.2
• /
• pp.40-52
• /
• 2012

This paper presents the uncertainty analysis results of fire modeling input parameters for motor control center in switchgear room of nuclear power plants. FDS (Fire Dynamics simulator) 5.5 was used to simulate the fire scenario and Latin Hyper Cube Monte Carlo simulations were employed to generate random samples for FDS input parameters. The uncertainty analysis results of input parameters are compared with those of the model uncertainty analysis and sensitivity analysis approaches of NUREG-1934. The study results show that the input parameter uncertainty analysis approach may lead to more conservative results than the uncertainty analysis and sensitivity analysis methods of NUREG-1934.

• International journal of steel structures
• /
• v.18 no.5
• /
• pp.1607-1616
• /
• 2018

Quantitatively modeling and propagating all sources of uncertainty stand at the core of seismic fragility assessment of structures. This paper investigates the effects of various sources of uncertainty on seismic responses and seismic fragility estimates of single-layer reticulated domes. Sensitivity analyses are performed to examine the sensitivity of typical seismic responses to uncertainties in structural modeling parameters, and the results suggest that the variability in structural damping, yielding strength, steel ultimate strain, dead load and snow load has significant effects on the seismic responses, and these five parameters should be taken as random variables in the seismic fragility assessment. Based on this, fragility estimates and fragility curves incorporating different levels of uncertainty are obtained on the basis of the results of incremental dynamic analyses on the corresponding set of 40 sample models generated by Latin Hypercube Sampling method. The comparisons of these fragility curves illustrate that, the inclusion of only ground motion uncertainty is inappropriate and inadequate, and the appropriate way is incorporating the variability in the five identified structural modeling parameters as well into the seismic fragility assessment of single-layer reticulated domes.

• The Research Journal of the Costume Culture
• /
• v.26 no.1
• /
• pp.1-18
• /
• 2018

In this study, the effects of anticipated regret and perceived uncertainty on price sensitivity or purchase hesitation in overseas purchasing are analyzed along with the effects of price sensitivity on purchase hesitation. The survey was conducted among internet fashion consumers with experience in overseas purchasing and 480 responses were used in the data analysis. The results showed the psychosocial anticipated regret positively influenced the price importance, and the service, product and psychosocial anticipated regret positively influenced the price search. The preference and psychology uncertainty positively influenced the price importance, and the information and psychology uncertainty positively influenced the price search. The price importance positively influenced payment stage hesitation and shopping cart abandonment, and the price search positively influenced purchase hesitation in overseas purchasing. The functional, service and psychosocial anticipated regret positively influenced payment stage hesitation, and the service and psychosocial anticipated regret positively influenced shopping cart abandonment and overall purchase hesitation. In addition, the perceived uncertainty positively influenced the payment stage hesitation, and the information and psychology uncertainty positively influenced the shopping cart abandonment and overall purchase hesitation. The results of this study will be helpful for developing the marketing strategy for customer relationship management for overseas internet shopping web-sites.

• Nuclear Engineering and Technology
• /
• v.56 no.9
• /
• pp.3686-3699
• /
• 2024

This paper explores the alternating conditional expectation (ACE) algorithm-based surrogate model to advance the state-of-practice in uncertainty and sensitivity analysis methodologies for severe accident codes. For engineering purposes, the ACE algorithm has been used as an alternative means to find the optimal functional forms of the multiple input variables and response variables of interest. Analysis results here demonstrate that compared with the reference cases the proposed surrogate model provides much higher performance in terms of the coefficient of determination (R²) and normalized root mean square error (NRMSE), thus giving more robust insights into the relationship and correlation between the input parameters and figures of merit (FOMs) of interest. Relevant results and insights are summarized in terms of points of interest.

• Proceedings of the Korean Nuclear Society Conference
• /
• 1996.11a
• /
• pp.415-420
• /
• 1996

The main objective of the present study is to propose a new measure of uncertainty importance based on distributional sensitivity analysis. The new measure is developed to utilize a metric distance obtained from cumulative distribution functions (cdfs). The measure is evaluated for two cases: one is a cdf given by a known analytical distribution and the other given by an empirical distribution generated by a crude Monte Carlo simulation. To study its applicability, the present measure has been applied to two different cases. The results are compared with those of existing three methods. The present approach is a useful measure of uncertainty importance which is based on cdfs. This method is simple and easy to calculate uncertainty importance without any complex process. On the basis of the results obtained in the present work, the present method is recommended to be used as a tool for the analysis of uncertainty importance.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.23 no.4
• /
• pp.361-370
• /
• 2015

There will inevitably be errors and uncertainties in tire yaw mark related critical speed formula, which is derived merely from the relationship between the centrifugal force and the friction force acting on the point-mass vehicle. Constructing and measuring yaw marks through appropriate simulation works have made it possible to perform uncertainty analysis in calculation of critical speeds under variation of variety of conditions and parameters while existing yaw mark experimental tests have not performed properly. This paper does not present only the critical speed analysis results for parametric sensitivity and uncertainty of chord and middle ordinate, coefficient of friction and road grade, but also modeling uncertainty such as variation of braking level during turning and vehicle size. The yaw mark analysis methods and results may be now applied in practice of traffic accident investigation.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1990.04a
• /
• pp.272-288
• /
• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.