• Journal of the Korea Institute of Military Science and Technology
• /
• v.16 no.5
• /
• pp.631-638
• /
• 2013

The probabilistic fatigue life analysis is one of the common methods to account the uncertainty of parameters on the structural failure. Frequently, the Bayesian approach has been demonstrated as a proper method to show the uncertainty of parameters. In this work, the application of probabilistic fatigue life prediction method for the aircraft structure was studied. This effort was conducted by using the PoF(Probability of Failure) based on Bayesian approach. Furthermore, numerical example was carried out to confirm the validation of the suggested approach. In conclusion, it was shown that the Bayesian approach can calculate the probabilistic fatigue lives and the quantitative value of PoF effectively for the aircraft structural component. Moreover the calculated probabilistic fatigue lives can be utilized to determine the optimized inspection period of aircraft structures.

• Smart Structures and Systems
• /
• v.20 no.2
• /
• pp.207-217
• /
• 2017

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

• Journal of information and communication convergence engineering
• /
• v.10 no.4
• /
• pp.329-336
• /
• 2012

For designing broadcast protocols in mobile ad hoc networks (MANETs), one of the important goals is to reduce the rebroadcast packets redundancy while reaching all the nodes in network. In this paper, we propose a probabilistic broadcasting mechanism based on selfishness and additional coverage in MANETs. Our approach dynamically adjusts the rebroadcast probability according to the extra covered area and number of neighbor nodes. By these two factors, mobile hosts can be classified into three groups: normal, low selfishness, and high selfishness groups. The nodes in the normal group forward packets for other nodes with high probability, whereas the nodes in the low selfishness group rebroadcast packets with low probability and the nodes in the high selfishness group do not rebroadcast packets. We compared our approach with simple flooding and the fixed probabilistic approach. The simulation results show that the proposed schemes can significantly reduce the number of retransmissions by up to 40% compared simple flooding and fixed probabilistic scheme without significant reduction in the network reachability and end-to-end packet delay.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.54 no.11
• /
• pp.533-539
• /
• 2005

According to NERC definition, Available Transfer Capability (ATC) is a measure of the transfer capability remaining in the physical transmission network for the future commercial activity. To calculate Available Transfer Capability, accurate and defensible Total Transfer Capability, Capacity Benefit Margin and Transmission Reliability Margin should be calculated in advance. This paper proposes a method to quantify time varying Available Transfer Capability based on probabilistic approach. The uncertainties of power system and market are considered as complex random variables. Total Transfer Capability is determined by optimization technique such as SQP(Sequential Quadratic Programming). Transmission Reliability Margin with the desired probabilistic margin is calculated based on Probabilistic Load Flow analysis, and Capacity Benefit Margin is evaluated using LOLE of the system. Suggested Available Transfer Capability quantification method is verified using IEEE RTS with 72 bus. The proposed method shows efficiency and flexibility for the quantification of Available Transfer Capability.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2000.10a
• /
• pp.32-39
• /
• 2000

Probabilistic structural design optimization, which is characterized by the so-called probabilistic. constraints which introduce permissible probability of violation, is preferred to deterministic design optimization since unpredictable inherent uncertainties and randomness in structural and environmental properties are to be taken quantitatively into account by probabilistic design optimization. In this paper, the well-known reliability index based MPFP(Most Probable Failure Point) search approach and the newly introduced target performance measure based MPTP(Minimum Performance Target Point) search approach are summarized and compared. The present comparison focuses on the number of iterations required for the estimation of probabilistic constraints and a technique for improvement which removes exhaustive iterations is presented as well. A 10 bar truss problem is examined for this.

• Proceedings of the KSR Conference
• /
• 2011.05a
• /
• pp.988-992
• /
• 2011

The buckling characteristics of the continuous welded rail track(CWR) is uncertainly varied by many influence factors, such as rail temperature, operating conditions of a train and maintenance of the track etc. Therefore, applying the probabilistic approach method is essential to rationally consider uncertainty and randomness of the various parameters that affect the track buckling. In this study, the probabilistic approach analysis was carried out and the results were compared with the deterministic approach using the buckling probability evaluation system of CWR tracks developed by our research team. From the comparison, it was identified that a probabilistic approach can quantitatively assess the reliability of the CWR tracks based on failure probability and can be used as a tool for decision making in track design, maintenance and operating etc.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1989.10a
• /
• pp.40-45
• /
• 1989

In order to take account of the statistical properties of random variables used in the structural analysis, the conventional approach usually adopts the safety factor based on past experiences for the qualitative assessment of structural safety problem. Recently, new approach based on the probabilistic concept has been applied to the assessment of structural safety in order to circumvent the difficulties of the conventional approach in choosing the appropriate safety factor. Thus, computer program called "Probabilistic finite element method" is developed by incorporation the probabilistic concept into the conventional matrix method in order to investigate the effects of the random variables on the final output of the structural analysis. From the comparison of some examples, it can be concluded that the PFEM developed in this study deals with consistently with the uncertainty of random variables and provides the rational tool for the assessment of structural safety of plane frame.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.4
• /
• pp.782-804
• /
• 2011

In video-based recognition systems, stationary cameras are used to monitor an area of interest. These systems focus on a segmentation of the foreground in the video stream and the recognition of the events occurring in that area. The usual approach to discriminating the foreground from the video sequence is background subtraction. This paper presents a novel background subtraction method based on a probabilistic approach. We represent the posterior probability of the foreground based on the current image and all past images and derive an updated method. Furthermore, we present an efficient fusion method for the color and edge information in order to overcome the difficulties of existing background subtraction methods that use only color information. The suggested method is applied to synthetic data and real video streams, and its robust performance is demonstrated through experimentation.

• Communications for Statistical Applications and Methods
• /
• v.24 no.1
• /
• pp.67-80
• /
• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.19 no.5
• /
• pp.589-595
• /
• 2010

This paper presents a reliability-based shape optimization (RBSO) using the evolutionary structural optimization (ESO). An actual design involves uncertain conditions such as material property, operational load, poisson's ratio and dimensional variation. The deterministic optimization (DO) is obtained without considering of uncertainties related to the uncertainty parameters. However, the RBSO can consider the uncertainty variables because it has the probabilistic constraints. In order to determine whether the probabilistic constraint is satisfied or not, simulation techniques and approximation methods are developed. In this paper, the reliability-based shape design optimization method is proposed by utilization the reliability index approach (RIA), performance measure approach (PMA), single-loop single-vector (SLSV), adaptive-loop (ADL) are adopted to evaluate the probabilistic constraint. In order to apply the ESO method to the RBSO, a sensitivity number is defined as the change of strain energy in the displacement constraint. Numerical examples are presented to compare the DO with the RBSO. The results of design example show that the RBSO model is more reliable than deterministic optimization.