Wyniki wyszukiwania - BazTech

1

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

|

2024

|

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.

2

The role of green energy in sustainable development: evidence from the Monte-Carlo simulation algorithm using bayes approach

Van Le Thi Thuy

Polish Journal of Management Studies

|

2023

|

Vol. 28, No. 2

406--422

EN

This research investigates the impact of green energy - power consumption from wind, solar, hydroelectric and other renewable sources (including hydro, wind, solar, geothermal, wave and tidal, and bioenergy) on sustainable development in 77 countries worldwide during the period 2005-2022. The Bayesian approach assumes that the observed data sample is fixed and the model parameters are random. The posterior distribution of the parameters will be estimated based on the observed sample and the prior distribution of those parameters, using it to interpret the results. The Bayes result shows that the average per capita energy consumption from wind, solar, hydro, and renewable energy positively affects sustainable development. The probability of a positive impact from wind, solar, and renewable energy on sustainable development is nearly 100%, while this value is relatively low at 58.35% for hydro energy, indicating that the use of hydro energy has a positive impact on sustainable development but has not fully realized its potential. Based on these findings, the author proposes appropriate policy implications for the countries and Vietnam.

PL

Niniejsze badanie analizuje wpływ zielonej energii - zużycia energii wiatrowej, słonecznej, wodnej i innych źródeł odnawialnych (w tym energii wodnej, wiatrowej, słonecznej, geotermalnej, fal i pływów oraz bioenergii) na zrównoważony rozwój w 77 krajach na całym świecie w latach 2005-2022. Podejście bayesowskie zakłada, że obserwowana próbka danych jest stała, a parametry modelu są losowe. Późniejszy rozkład parametrów zostanie oszacowany na podstawie obserwowanej próbki i wcześniejszego rozkładu tych parametrów, wykorzystując go do interpretacji wyników. Wynik Bayesa pokazuje, że średnie zużycie energii na mieszkańca z energii wiatrowej, słonecznej, wodnej i odnawialnej pozytywnie wpływa na zrównoważony rozwój. Prawdopodobieństwo pozytywnego wpływu energii wiatrowej, słonecznej i odnawialnej na zrównoważony rozwój wynosi prawie 100%, podczas gdy wartość ta jest stosunkowo niska i wynosi 58,35% dla energii wodnej, co wskazuje, że wykorzystanie energii wodnej ma pozytywny wpływ na zrównoważony rozwój, ale nie w pełni wykorzystało swój potencjał. Na podstawie tych ustaleń autor proponuje odpowiednie implikacje polityczne dla krajów i Wietnamu.

3

Bayesian seismic AVO inversion with a group optimization strategy

Li Chuanhui, Liu Xuewei

Acta Geophysica

|

2023

|

Vol. 71, no. 6

2733--2746

EN

The seismic amplitude-versus-offset (AVO) inversion based on the Bayesian framework is effective for obtaining the elastic parameters of the stratum from observed seismic data. Usually, this algorithm is used to obtain sparse reflectivity levels of elastic parameters through a proper long-tailed prior probability distribution. Considering the high correlation between underground parameters, a group optimization strategy was used for Bayesian AVO inversion, where the model parameters were inverted in groups at reflection interfaces. A group Cauchy constraint consisting of Cauchy constraint and l2-norm was designed. In the constraint, the Cauchy constraint is executed between groups to promote the sparsity of model parameters, and l2 regularization is executed within each group for constraining uniformity. The new Bayesian AVO inversion algorithm based on the group optimization strategy can help estimate the reflectivity levels of elastic parameters with desired sparsity. Additionally, even in the absence of the covariance matrix, the algorithm can still ensure the correlation of resulting solutions. It has been demonstrated that good results have been obtained from both numerical and field examples.

4

Armia Polska we Francji : organizacja, wyszkolenie, walki (1917–1919)

Radziwiłłowicz D.

Kwartalnik Bellona

|

2017

|

Nr 2

107--131

PL

Armia Polska we Francji, nazywana Błękitną Armią, reprezentowała wysiłek wojenny i wolę walki z Niemcami Polaków wywodzących się ze wszystkich trzech zaborów. Oprócz 27 tys. ochotników z Ameryki w jej szeregach znalazło się w około 20 tys. Polaków z zaboru pruskiego. Przeszli oni przez obozy jenieckie we Francji, w Belgii i Wielkiej Brytanii. W latach 1917–1918 w Londynie, w Feltham, znajdował się obóz jeńców, w którym przebywało 3330 Wielkopolan, Pomorzan i Ślązaków – ochotników do Błękitnej Armii. Z ziemi włoskiej do polskiej przybyło 37 tys. żołnierzy i oficerów zorganizowanych w 18 pułków i oddziałów wojskowych noszących jako patronów imiona polskich i włoskich bohaterów narodowych, ze sztandarami ofiarowanymi przez włoskie miasta: Mediolan, Turyn, Bergamo i Chivasso,. Do Błękitnej Armii należały także formacje polskie utworzone na terenie Rosji: 4 i 5 Dywizja Strzelców Polskich oraz Oddział Wojsk Polskich w Północnej Rosji nazwany od miejsca formowania murmańczykami. Powstała 4 czerwca 1917 roku Armia Polska we Francji, była istotną częścią sił zbrojnych II Rzeczypospolitej. Tradycje tej armii odżyły w Wojsku Polskim III Rzeczypospolitej.

EN

The Polish Army in France, called the Blue Army, represented the war effort of Poles coming from all three areas of occupation who wanted to fight the Germans. Apart from 27,000 volunteers from America, the army included about 20,000 Poles from the Prussian partition. They went through POW camps in France, Belgium and Great Britain. Between 1917-1918, in Feltham, London, there was a POW camp with 3,330 Blue Army volunteers from Wielkopolska, Pomorze and lzisk. 37,000 troops and officers organized into 18 regiments and military units came from Italy to Poland. They held banners, given to them in Italian cities - Milan, Turin, Bergamo and Chivasso, with names of Polish and Italian national heroes as patrons. The Blue Army also included Polish formations created on the territory of Russia: the 4th and the 5th Polish Rifle Division, and a formation of Polish troops in northern Russia called Murmanians. The Polish Army in France, created on June 4, 1917, was an important part of the military forces of the Second Polish Republic. The traditions of this army were revived in the Armed Forces of the Republic of Poland.

5

A 3D Bayesian Computed Tomography Reconstruction Algorithm with Gauss-Markov-Potts Prior Model and its Application to Real Data

Chapdelaine C., Mohammad-Djafari A., Gac N., Parra E.

Fundamenta Informaticae

|

2017

|

Vol. 155, nr 4

373--405

EN

Iterative reconstruction methods in Computed Tomography (CT) are known to provide better image quality than analytical methods but they are not still applied in many fields because of their computational cost. In the last years, Graphical Processor Units (GPU) have emerged as powerful devices in order to parallelize calculations, but the efficiency of their use is conditionned on applying algorithms that can be massively parallelizable. Moreover, in non-destructive testing (NDT) applications, a segmentation of the reconstructed volume is often needed in order to have an accurate diagnosis on the material health, but performing a segmentation after the reconstruction introduces uncertainties in the diagnosis from both the reconstruction and the segmentation algorithms. In this paper, we propose an iterative reconstruction method for 3D CT that performs a joint reconstruction and segmentation of the controlled object in NDT for industrial applications. The method is based on a 3D Gauss-Markov-Potts prior model in Bayesian framework, which has shown its effective use in many image restoration and super-resolution problems. First, we briefly describe this model, before deriving the expression of the joint posterior distribution of all the unknowns. Next, an effective maximization of this distribution is presented. We use a ray-driven projector and a voxel-driven backprojector implemented on GPU. The algorithm is developed so it can be massively parallelized. Finally, we present our results on simulated and real phantoms. In addition, we investigate further reconstruction quality indicators in order to compare our results with other methods.