• Wind and Structures
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• v.17 no.2
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• pp.227-244
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• 2013

Non-stationary extreme winds such as thunderstorm downbursts are responsible for many structural damages. This research presents a time domain approach for estimating along-wind load effects on tall buildings using multiple wind speed time history samples, which are simulated from evolutionary power spectra density (EPSD) functions of non-stationary wind fluctuations using the method developed by the authors' earlier research. The influence of transient wind loads on various responses including time-varying mean, root-mean-square value and peak factor is also studied. Furthermore, a simplified model is proposed to describe the non-stationary wind fluctuation as a uniformly modulated process with a modulation function following the time-varying mean. Finally, the probabilistic extreme response and peak factor are quantified based on the up-crossing theory of non-stationary process. As compared to the time domain response analysis using limited samples of wind record, usually one sample, the analysis using multiple samples presented in this study will provide more statistical information of responses. The time domain simulation also facilitates consideration of nonlinearities of structural and wind load characteristics over previous frequency domain analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1491-1499
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• 2008

This paper presents a study on reliability evaluation of a power system considering wind turbine generators (WTG) with multi-state. Renewable energy resources such as wind, wave, solar, micro hydro, tidal and biomass etc. are becoming importance stage by stage because of considering effect of the environment. Wind energy is one of the most successful sources of renewable energy for the production of electrical energy. But, reliability evaluation of generating system with wind energy resources is a complex process. While the wind turbine generators can not modelled as two-state model as like as conventional generators, they should be modelled as multi-state model due to wind speed random variation. The methodology for obtaining reliability evaluation index of wind turbine generators is different from it of the conventional generators. A method for making outage capacity probability table of WTG for reliability is proposed in this paper. The detail process is presented using case study of simple system.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Nuclear Engineering and Technology
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• v.17 no.4
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• pp.302-316
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• 1985

Small break LOCA caused by a stuck-open PORV is one of the important contributors to nuclear power plant risk. This paper deals with the design of a pressurizer surveillance system using microcomputer to prevent the malfunction of system and has assessed the effect of this improvement through Probabilistic Risk Assessment (PRA) method. Micro-computer diagnoses the malfunction of system by a process checking method and performs automatically backup action related to each malfunction. Owing to this improvement, we can correctly diagnose “Spurious Opening”, “Fail to Reclose” and “Small break LOCA” which are difficult for operator to diagnose quickly and correctly and reduce the probability of a human error by an automatic backup action.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.239-241
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• 1998

The probabilistic production simulation of power system generally has been used to formulate a reasonable power production plan or generation planning. It integrates the convolution process of a generating unit's random outage(FOR) with equivalent load duration curve(ELDC), and provides the reliability indices of power system. This paper presents the reliability characteristics of power system reflected on demand side management and proposes the modified ELDC representation technique due to the high-efficient end-use diffusion among the customers. Load reductions are simulated from the multi-state deconvolution process with the saved capacity of end-use. Case study shows the computed reliability from the power system production simulation incorporated with DSM planning scheme.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.368-375
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• 2021

The performance time of human operators has been recognized as a key aspect of human reliability in socio-complex systems, including nuclear industries. Because of the importance of the time factor, most existing human reliability assessment methods provide ways to quantify human error probabilities (HEPs) that are associated with the performance time. To quantify such kinds of HEPs, it is crucial to rationally predict the length of time required and time available and compare them. However, there have not been detailed guidelines that identify the critical cue presentation time or initial time of human performance, which is important to calculate the time information. In this paper, we introduce a time-related HEP calculation technique with a decision algorithm that determines the critical cue and its timing. The calculation process is presented with the application examples. It is expected that the proposed algorithm will reduce the variability in the time-related reliability assessment and strengthen the scientific evidence of the assessment process. The detailed description is provided in the technical report KAERI/TR-7607/2019.

• Journal of the Korean Geotechnical Society
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• v.23 no.3
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• pp.111-121
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• 2007

The paper presents a reliability-based method that can capture the impact of uncertainty of seismic loadings. The proposed method incorporates probabilistic concepts into the classical limit equilibrium and the Newmark-type deformation techniques. The risk of damage is then computed by Monte Carlo simulation. Random process and RMS hazard method are introduced to produce seismic motions and also to use them in the seismic slope analyses. The geotechnical variability and sampling errors are also considered. The results of reliability analyses indicate that in a highly seismically active region, characterization of earthquake hazard is the more critical factor, and characterization of soil properties has a relatively small effect on the computed risk of slope failure and excessive slope deformations. The results can be applicable to both circular and non-circular slip surface failure modes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.1
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• pp.1-9
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• 2022

The PoN (Proof of Nonce) distributed consensus algorithm basically uses a non-competitive consensus method that can guarantee an equal opportunity for all nodes to participate in the block generation process, and this method was expected to resolve the first trilemma of the blockchain, called the decentralization problem. However, the decentralization performance of the PoN distributed consensus algorithm can be greatly affected by the network transaction transmission delay characteristics of the nodes composing the block chain system. In particular, in the consensus process, differences in network node performance may significantly affect the composition of the congress and committee on a first-come, first-served basis. Therefore, in this paper, we presented a problem by analyzing the decentralization performance of the PoN distributed consensus algorithm, and suggested a fairness control algorithm using a learning-based probabilistic acceptance rule to improve it. In addition, we verified the superiority of the proposed algorithm by conducting a numerical experiment, while considering the block chain systems composed of various heterogeneous characteristic systems with different network transmission delay.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.673-676
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• 2003

This paper deals with the problem of moving object detection and location in computer vision. We describe a new object-dependent motion analysis method for tracking target in an image sequence taken from a moving platform. We tackle these tasks with three steps. First, we make an active contour model of a target in order to build some of low-energy points, which are called kernels. Then we detect interest points in two windows called tracking windows around a kernel respectively. At the third step, we decide the correspondence of those detected interest points between tracking windows by the probabilistic relaxation method In this algorithm, the detecting process is iterative and begins with the detection of all potential correspondence pair in consecutive image. Each pair of corresponding points is then iteratively recomputed to get a globally optimum set of pairwise correspondences.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.168-173
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• 2000

Design of experiments is utilized for exploring the design space and for building response surface models in order to facilitate the effective solution of multi-objective optimization problems. Response surface models provide an efficient means to rapidly model the trade-off among many conflicting goals. In robust design, it is important not only to achieve robust design objectives but also to maintain the robustness of design feasibility under the effects of variations, called uncertainties. However, the evaluation of feasibility robustness often needs a computationally intensive process. To reduce the computational burden associated with the probabilistic feasibility evaluation, the first-order Taylor series expansions are used to derive individual mean and variance of constraints. For robust design applications, these constraint response surface models are used efficiently and effectively to calculate variances of constraints due to uncertainties. Robust optimization of automotive seat is used to illustrate the approach.