Wyniki wyszukiwania - BazTech

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Reliability Estimation of Retraction Mechanism Kinematic Accuracy under Small Sample

Yan Yumeng, Zhou Jiyuan, Yin Yin, Nie Hong, Wei Xiaohui, Liang Taotao

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 1

art. no. 174777

EN

Due to intricate operating conditions, including structural clearances and assembly deviations, the acquisition of test data for the landing gear retraction mechanism is limited, posing challenges for reliability analysis. To solve the problem, a Bayesian-based reliability analysis methodfusing prior and test data is proposed, focusing on the mechanism kinematic accuracy under small-sample conditions. Firstly, a dynamic simulation model is established to collect prior data, and retraction tests are conducted to obtain test data. Then, based on Bayesian theory, the motion accuracy parameter estimation model integrating prior and test samples is established. To obtain accurate hyper parameters, the prior samples are expanded using the neural network. Finally, taking the retraction mechanism as the research object, the kinematic accuracy reliability is quantified, and the impact of uncertainty factors is analysed in depth. The results show that the proposed method is superior to the classical interval estimation method in stability and effectively mitigates the impact of uncertainty factors.

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Revisiting the optimal probability estimator from small samples for data mining

Cestnik Bojan

International Journal of Applied Mathematics and Computer Science

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2019

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Vol. 29, no. 4

783--796

EN

Estimation of probabilities from empirical data samples has drawn close attention in the scientific community and has been identified as a crucial phase in many machine learning and knowledge discovery research projects and applications. In addition to trivial and straightforward estimation with relative frequency, more elaborated probability estimation methods from small samples were proposed and applied in practice (e.g., Laplace’s rule, the m-estimate). Piegat and Landowski (2012) proposed a novel probability estimation method from small samples Eph√2 that is optimal according to the mean absolute error of the estimation result. In this paper we show that, even though the articulation of Piegat’s formula seems different, it is in fact a special case of the m-estimate, where pa = 1/2 and m = √2. In the context of an experimental framework, we present an in-depth analysis of several probability estimation methods with respect to their mean absolute errors and demonstrate their potential advantages and disadvantages. We extend the analysis from single instance samples to samples with a moderate number of instances. We define small samples for the purpose of estimating probabilities as samples containing either less than four successes or less than four failures and justify the definition by analysing probability estimation errors on various sample sizes.

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The mechanical properties at room and low temperature of P110 steel characterised by means of small punch test

Romelczyk-Baishya B., Lumelskyj D., Stępniewska M., Giżyński M., Pakieła Z.

Archives of Metallurgy and Materials

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2019

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Vol. 64, iss. 1

159--165

EN

In this paper small punch test (SPT) which is one of miniaturized samples technique, was employed to characterize the mechanical properties of carbon steel P110. The tests were carried out in the range of -175°C to RT. Results obtained for SPT were compared to those calculated for tensile and Charpy impact test. Based on tensile and SPT parameters numerical model was prepared. 8 mm in diameter and 0.8 mm in height (t) discs with and without notch were employed in this research. The specimens had different depth notch (a) in the range of 0.1 to 0.4 mm. It was estimated that α factor for comparison of Tsp and DBTT for carbon steel P110 is 0.55 and the linear relation is DBTT = 0.55TSPT. The numerical model fit with force – deflection curve of SPT. If the factor of notch depth and samples thickness is higher than 0.3 the fracture mode is transformed from ductile to brittle at -150°C.

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The new EMEA-CHMP guideline on clinical trials in small populations - methodological and statistical considerations with published examples

Gerß J., Köpcke W.

Biocybernetics and Biomedical Engineering

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2007

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Vol. 27, no. 3

59-66

EN

The EMEA-CHMP guideline on clinical trials in small populations considers problems associated with clinical trials, when there are very few patients available to study. In such conditions, less conventional and/or less common methodological approaches may be acceptable. In the present report, selected strategies for approaches to trials in small populations are briefly outlined. It focuses on Chapter 6 of the guideline – Methodological and statistical considerations. Methods are addressed, that may increase the efficiency of the design or analysis of a trial where large studies are not feasible. In addition to general principles, specific examples of published studies are presented.