Wyniki wyszukiwania - BazTech

1

Inverse heat transfer problems: an application to bioheat transfer

Rojczyk M., Orlande H. R. B., Colaço M. J., Szczygieł I., Nowak A. J., Białecki R. A., Ostrowski Z.

Computer Assisted Methods in Engineering and Science

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2015

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Vol. 22, no. 4

365--383

EN

In this work, we applied the Markov chain Monte Carlo (MCMC) method for the estimation of parameters appearing in the Pennes’ formulation of the bioheat transfer equation. The inverse problem of parameter estimation was solved with the simulated transient temperature measurements. A one-dimensional (1D) test case was used to explore the capabilities of using the MCMC method in bioheat transfer problems, specifically for the detection of skin tumors by using surface temperature measurements. The analysis of the sensitivity coefficients was performed in order to examine linear dependence and low sensitivity of the model parameters. The solution of the direct problem was verified with a commercial code. The results obtained in this work show the ability of using inverse heat transfer analysis for the detection of skin tumors.

2

Comparison study of heavy haul locomotive wheels’ running surfaces wearing

Lin J., Asplund M.

Eksploatacja i Niezawodność

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2014

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Vol. 16, no. 2

276--287

PL

Resurs kół pociągu może być znacząco różny w zależności od ich miejsca zamontowania, warunków pracy, charakterystyk związanych z reprofilacją, itp. W artykule, porównano koła dwóch wybranych lokomotyw kursujących na Linii Rud Żelaza w północnej Szwecji, aby zbadać niektóre ze wspomnianych różnic. Zaproponowano możliwość łączenia danych pochodzących z oceny niezawodności z danymi degradacyjnymi oraz danymi z reprofilacji. Przeprowadzone badania pozwalają wyciągnąć następujące wnioski. Po pierwsze, krzywa wykładnicza degradacji oraz zadane warunki pracy można wykorzystać w celu przeprowadzenia badań niezawodności z użyciem modelu Weibulla z efektami losowymi (tzw. "frailty model"); po drugie, główną przyczyną zlecania reprofilacji kół jest zmęczenie toczne (RCF); po trzecie, analiza parametrów reprofilacji pozwala na monitorowanie i badanie zarówno szybkości zużycia kół, jak i ubytku materiału podczas reprofilacji, co może mieć zastosowanie w optymalizacji czynności obsługowych.

EN

The service life of railway wheels can differ significantly depending on their installed position, operating conditions, re-profiling characteristics, etc. This paper compares the wheels on two selected locomotives on the Iron Ore Line in northern Sweden to explore some of these differences. It proposes integrating reliability assessment data with both degradation data and re-profiling performance data. The following conclusions are drawn. First, by considering an exponential degradation path and given operation condition, the Weibull frailty model can be used to undertake reliability studies; second, among re-profiling work orders, rolling contact fatigue (RCF) is the principal reason; and third, by analysing re-profiling parameters, both the wear rate and the re-profiling loss can be monitored and investigated, a finding which could be applied in optimisation of maintenance activities.

3

Efficient Markov chain Monte Carlo sampling for electrical impedance tomography

Ma E.

Computer Assisted Methods in Engineering and Science

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2014

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Vol. 21, no. 3-4

223--232

EN

This paper studies electrical impedance tomography (EIT) using Bayesian inference [1]. The resulting posterior distribution is sampled by Markov chain Monte Carlo (MCMC) [2]. This paper studies a toy model of EIT as the one presented in [3], and focuses on efficient MCMC sampling for this model. First, this paper analyses the computation of forward map of EIT which is the bottleneck of each MCMC update. The forward map is computed by the finite element method [4]. Here its exact computation was conducted up to five times more efficient, by updating the Cholesky factor of the stiffness matrix [5]. Since the forward map computation takes up nearly all the CPU time in each MCMC update, the overall efficiency of MCMC algorithms can be improved almost to the same amount. The forward map can also be computed approximately by local linearisation, and this approximate computation is much more efficient than the exact one. Without loss of efficiency, this approximate computation is more accurate here, after a log transformation is introduced into the local linearisation process. Later on, this improvement of accuracy will play an important role when the approximate computation of forward map will be employed for devising efficient MCMC algorithms. Second, the paper presents two novel MCMC algorithms for sampling the posterior distribution in the toy model of EIT. The two algorithms are made within the ‘multiple prior update’ [6] and the ‘delayed-acceptance Metropolis-Hastings’ [7] schemes respectively. Both of them have MCMC proposals that are made of localized updates, so that the forward map computation in each MCMC update can be made efficient by updating the Cholesky factor of the stiffness matrix. Both algorithms’ performances are compared to that of the standard single-site Metropolis [8], which is considered hard to surpass [3]. The algorithm of ‘multiple prior update’ is found to be six times more efficient, while the delayed-acceptance Metropolis-Hastings with single-site update is at least twice more efficient.

4

Convergence diagnosis to stationary distribution in MCMC methods via atoms and renewal sets

Romaniuk M.

Control and Cybernetics

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2008

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Vol. 37, no 1

205-229

EN

MCMC setups are one of the best known methods for conducting computer simulations useful in such areas as statistics, physics, biology, etc. However, to obtain appropriate solutions, the additional convergence diagnosis must be applied for Markov Chain trajectory generated by the algorithm. We present the method for dealing with this problem based on features of so called "secondary" chain (the chain with specially selected state space). The secondary chain is created from the initial chain by picking only some observations connected with atoms or renewal sets. In this paper we focus on finding the moment when the simulated chain is close enough to the stationary distribution of the Markov chain. The discussed method has some appealing properties, like high degree of diagnosis automation. Apart from theoretical lemmas and a more heuristic approach, the examples of application are also provided.

5

On some method for diagnosing convergence in MCMC setups via atoms and renewal sets

Romaniuk M.

Control and Cybernetics

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2007

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Vol. 36, no 4

985-1008

EN

MCMC setups are among the best known methods for conducting computer simulations necessary in statistics, physics, biology, etc. However, to obtain appropriate solutions, additional convergence diagnosis must be applied for trajectory generated by Markov Chain. In the paper we present, the method for dealing with this problem, based on features of so called "secondary" chain (the chain with specially selected state space). The secondary chain is created from the initial chain by picking only some observations connected with atoms or renewal sets. The discussed method has some appealing properties, like high degree of diagnosis automation. Apart from theoretical lemmas, the example of application is also provided.

6

Modelowanie czasowe w systemach przepływu pneumatycznego z zastosowaniem metody Monte Carlo łańcuchów Markowa

Romanowski A., Grudzień K.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

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2004

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T. 8, z. 3

409-414

PL

W niniejszym artykule przedstawiono wyniki zastosowania teorii Bayesa i algorytmu Monte Carlo łańcuchów Markowa (MCMC) do modelowania części przemysłowego procesu transportu pneumatycznego materiałów stałych. Zaproponowane podejście pozwoliło na opracowanie czasowego modelu zjawisk zachodzących w trakcie formowania złogu (slug) podczas przepływu w sekcji poziomej instalacji transportowej. Efektywność zaimplementowanych metod statystycznych została zweryfikowana na bazie symulacji danych pomiarowych odpowiadających wycinkowi procesu wykonanych przy użyciu pojemnościowej tomografii procesowej (ECT).

EN

This paper presents effects of Bayes approach coupled with Markov chain Monte Carlo (MCMC) algorithm application to modeling of part of solids pneumatic conveying process. Proposed methods enabled formulation of temporal model of phenomena occurring during slug formation in the horizontal section of pneumatic conveyer. Implemented statistical methods are validated on the basis of electrical capacitance tomography (ECT) measurement data simulation of above-mentioned part of pneumatic conveying process.

7

Positron emission tomography by Markov chain Monte Carlo with auxiliary variables : a basic algorithm

Lasota S., Niemiro W., Koronacki J.

Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk

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1999

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nr 884

3-26

EN

In the report, an algorithm for positron emission tomography (PET) image reconstruction is proposed. The algorithm belongs to the family of Markov chain Monte Carlo methods with auxiliary variables. The well-known model of Vardi et al. (1985) is used for PET. The fact that an image consists of finitely many, in fact relatively few, gray-levels of unknown values is explicity used to advantage: in the algorithm, the levels are represented by a fixed number of labels, so that at one step of the algorithm current approximation to the image is easily described by a configuration of finitely many labels and at another step real-valued intensities are assigned to each label. The algorithm decomposes naturally into the image restoration algorithm and the additional reconstruction (of generalized deconvolution) step. Simulation results are included which suggest that the method proposed is truly reliable and worth further study leading to practical implementation.

PL

W raporcie przedstawiony jest nowy algorytm rekonstrukcji obrazów uzyskiwanych w tomografii pozytronowej (positron emission tomography, PET). Algorytm składa się z części służącej do oczyszczania poissonowsko zaszumionych obrazów opisywanych znaną liczbą intensywności Poissona oraz z kroku służącego do rekonstrukcji (uogólnionej dekonwulcji) obrazu. Zaproponowany algorytm nalezy do rodziny metod Monte-Carlo typu łańcuchów Markowa (Markov chain Monte Carlo) ze zmiennymi pomocniczymi typu Swendsena-Wanga oraz "rozprzęganiem" podobnym do zaproponowanego przez Higdona. Zadanie PET rozwiązane jest dla znanego modelu Vardiego i in. (1985). Zawarte w raporcie wyniki badań symulacyjnych pozwalają algorytm uznać za zdecydowanie zasługujący na opracowanie jego praktycznej implementacji. (W obecnej postaci algorytm jest wiarygodny ale zbyt wolny; jego szybka wersja będzie przedmiotem oddzielnego opracowania).