• 제목/요약/키워드: probabilistic models

검색결과 464건 처리시간 0.028초

Multi-level structural modeling of an offshore wind turbine

  • Petrini, Francesco;Gkoumas, Konstantinos;Zhou, Wensong;Li, Hui
    • Ocean Systems Engineering
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    • 제2권1호
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    • pp.1-16
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    • 2012
  • Offshore wind turbines are complex structural and mechanical systems located in a highly demanding environment. This paper proposes a multi-level system approach for studying the structural behavior of the support structure of an offshore wind turbine. In accordance with this approach, a proper numerical modeling requires the adoption of a suitable technique in order to organize the qualitative and quantitative assessment in various sub-problems, which can be solved by means of sub-models at different levels of detail, both for the structural behavior and for the simulation of loads. Consequently, in a first place, the effects on the structural response induced by the uncertainty of the parameters used to describe the environmental actions and the finite element model of the structure are inquired. After that, a meso-level FEM model of the blade is adopted in order to obtain the detailed load stress on the blade/hub connection.

Incorporating Station Related Aging Failures in Bulk System Reliability Analysis

  • Billinton Roy;Yang Hua
    • KIEE International Transactions on Power Engineering
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    • 제5A권4호
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    • pp.322-330
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    • 2005
  • This paper proposes methods to incorporate station related aging failures in composite system reliability assessment. Aging failures of station components, such as circuit breakers and bus bars, are a major concern in composite power system planning and operation as an increasing number of station components approach the wear-out phase. This paper presents probabilistic models for circuit breakers involving aging failures and relevant evaluation techniques to examine the effects of station related aging outages. The technique developed to incorporate station related aging failures are illustrated by application to a small composite test system. The paper illustrates the effects of circuit breaker aging outages on bulk system reliability evaluation and examines the relative effects of variations in component age. System sensitivity analysis is illustrated by varying selected component parameters. The results show the implications of including component aging failure considerations in the overall analysis of a composite system.

클러터 환경에서 다중 기동표적 추적트랙 초기화 (Track Initiation Algorithms for Multiple Maneuvering Target Tracking)

  • 배승한;송택렬
    • 제어로봇시스템학회논문지
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    • 제14권8호
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    • pp.733-739
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    • 2008
  • This article proposes algorithms for the automatic initiation of the tracks of maneuvering targets in cluttered environments. These track initiation algorithms consist of IPDA-AI(Integrated Probabilistic Data Association-Amplitude Information) and MPDA(Most Probable Data Association) in an Interacting Multiple Model(IMM) configuration, and they are referred to as the IMM-IPDAF-AI and IMM-MPDA respectively. The IMM portion consists of several filters based on different dynamical models to handle target maneuvers. Each of the filters utilizes an IPDA-AI(or MPDA) algorithm to deal with the problem of track existence in the presence of clutter. Although the primary purpose of this study is to deal with the track initiation problem, the IMM-IPDAF-AI and IMM-MPDA can also be used for the maintenance of existing tracks and the termination of tracks for targets when they disappear. For illustrative purposes, simulation is used to compare the performance of the algorithms proposed to other track formation algorithms.

Discriminative Training of Stochastic Segment Model Based on HMM Segmentation for Continuous Speech Recognition

  • Chung, Yong-Joo;Un, Chong-Kwan
    • The Journal of the Acoustical Society of Korea
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    • 제15권4E호
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    • pp.21-27
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    • 1996
  • In this paper, we propose a discriminative training algorithm for the stochastic segment model (SSM) in continuous speech recognition. As the SSM is usually trained by maximum likelihood estimation (MLE), a discriminative training algorithm is required to improve the recognition performance. Since the SSM does not assume the conditional independence of observation sequence as is done in hidden Markov models (HMMs), the search space for decoding an unknown input utterance is increased considerably. To reduce the computational complexity and starch space amount in an iterative training algorithm for discriminative SSMs, a hybrid architecture of SSMs and HMMs is programming using HMMs. Given the segment boundaries, the parameters of the SSM are discriminatively trained by the minimum error classification criterion based on a generalized probabilistic descent (GPD) method. With the discriminative training of the SSM, the word error rate is reduced by 17% compared with the MLE-trained SSM in speaker-independent continuous speech recognition.

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구조계의 신뢰도해석을 위한 개선된 기법 (Improved Methods for Reliability Evaluations of Structural Systems)

  • 류정수;윤정방
    • 한국전산구조공학회:학술대회논문집
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    • 한국전산구조공학회 1992년도 봄 학술발표회 논문집
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    • pp.51-57
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    • 1992
  • The primary objective of this study is the development of second moment methods for the efficient reliability evaluations of structural systems. Two methods are presented. One is the improved first order reliability method (IFORM), and the other is the modified probabilistic network evaluation technique (MPNET). For the purpose of verifying the proposed methods, example analyses are carried out on several cases with two failure modes, a plane frame structure involving three failure modes and simplified parallel member models for fatigue reliability evaluations of offshore structures. Numerical results indicate that the effectiveness of the proposed methods over the conventional ones (i.e., the FORM and the PNET) increases very significantly as the number of failure modes of the system increases.

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Stacked Autoencoder를 이용한 특징 추출 기반 Fuzzy k-Nearest Neighbors 패턴 분류기 설계 (Design of Fuzzy k-Nearest Neighbors Classifiers based on Feature Extraction by using Stacked Autoencoder)

  • 노석범;오성권
    • 전기학회논문지
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    • 제64권1호
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    • pp.113-120
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    • 2015
  • In this paper, we propose a feature extraction method using the stacked autoencoders which consist of restricted Boltzmann machines. The stacked autoencoders is a sort of deep networks. Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. In terms of pattern classification problem, the feature extraction is a key issue. We use the stacked autoencoders networks to extract new features which have a good influence on the improvement of the classification performance. After feature extraction, fuzzy k-nearest neighbors algorithm is used for a classifier which classifies the new extracted data set. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.

국내 가압경수형 원전에 대한 가압열충격 재평가 연구 (Pressurized Thermal Shock Re-Evaluation Studies for Korean PWR Plant)

  • 장성규;김현수;진태은;장창희
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2001년도 추계학술대회논문집A
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    • pp.16-21
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    • 2001
  • The PTS reference temperature of reactor pressure vessel for one of the Korean NPPs has been predicted to exceed the screening criteria before it reaches it's design life. To cope with this issue, a plant-specific PTS analysis had been performed in accordance with the Regulatory Guide 1.154 in 1999. As a result of that analysis, it was found that current methodology of RG. 1.154 was very conservative. The objective of this study is to examine the effects of changing various input parameters and to determine the amount of conservatism of the current PTS analysis method. To do this, based on the past PTS analysis experience, parametric study were performed for various models using modified VISA-II code. This paper discusses the analysis results and recommendations to reduce the conservatism of current analysis method.

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Effects of ground motion scaling on nonlinear higher mode building response

  • Wood, R.L.;Hutchinson, T.C.
    • Earthquakes and Structures
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    • 제3권6호
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    • pp.869-887
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    • 2012
  • Ground motion scaling techniques are actively debated in the earthquake engineering community. Considerations such as what amplitude, over what period range and to what target spectrum are amongst the questions of practical importance. In this paper, the effect of various ground motion scaling approaches are explored using three reinforced concrete prototypical building models of 8, 12 and 20 stories designed to respond nonlinearly under a design level earthquake event in the seismically active Southern California region. Twenty-one recorded earthquake motions are selected using a probabilistic seismic hazard analysis and subsequently scaled using four different strategies. These motions are subsequently compared to spectrally compatible motions. The nonlinear response of a planar frameidealized building is evaluated in terms of plasticity distribution, floor level acceleration and uncorrelated acceleration amplification ratio distributions; and interstory drift distributions. The most pronounced response variability observed in association with the scaling method is the extent of higher mode participation in the nonlinear demands.

Geostatistics for Bayesian interpretation of geophysical data

  • Oh Seokhoon;Lee Duk Kee;Yang Junmo;Youn Yong-Hoon
    • 한국지구물리탐사학회:학술대회논문집
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    • 한국지구물리탐사학회 2003년도 Proceedings of the international symposium on the fusion technology
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    • pp.340-343
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    • 2003
  • This study presents a practical procedure for the Bayesian inversion of geophysical data by Markov chain Monte Carlo (MCMC) sampling and geostatistics. We have applied geostatistical techniques for the acquisition of prior model information, and then the MCMC method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter. This approach provides an effective way to treat Bayesian inversion of geophysical data and reduce the non-uniqueness by incorporating various prior information.

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시뮬레이션과 최적화 모형을 혼합 적용한 구급차 위치선정 모형의 해법연구 (A Study of Ambulance Location Problem Applying the Iterative Procedure of Simulation and Optimization)

  • 임영선;김선훈;이영훈
    • 한국경영과학회지
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    • 제37권4호
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    • pp.197-209
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    • 2012
  • This paper studies an emergency service vehicle location problem, where minimum reliability level pre-specified at each demand point is assured. Several models are suggested depending on the busy fraction, which is the time proportion of unavailability for the ambulances. In this paper a new model on computing the busy fraction is suggested, where it varies depending on the distance between the demand point and ambulances, hence it may respond the more realistic situation. The busy fraction for the ambulance location determined by the optimization model is computed by the simulation, and updated through the iterative procedure. It has been shown that the performances of the solutions obtained by the algorithm suggested for the instances appeared in the literature.