An Optimization and Parametric Study of a Schlieren Motion Estimation Method

Wang, Q.; Mei, X.H.; Wu, Yue; Zhao, Chunyu

doi:10.1007/s10494-021-00246-1

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Tamimi et al presented the evaluation of feature values and parameter selection in classifiers using different optimization algorithms [7]. Wang investigated the parameter optimization based on the grain shadow motion estimation algorithm for the ignition process [8]. Tiryaki et al investigated performance evaluation in terms of predicting the mechanical properties of impregnated wood based on teaching-based optimization and traditional regression techniques [9].…”

Section: Related Workmentioning

confidence: 99%

Parameter Optimization and State Evaluation of Basketball Teaching Based on BPNN

Wang

2022

Mobile Information Systems

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BPNN is a multi-layer network with forward and backward error algorithms. It is currently the most widely used NN. Especially when it is applied to the basketball teaching scene, it can improve the research on the parameter optimization and state evaluation of the BP neural network in the teaching scene. This paper is mainly to study how to optimize the parameters of basketball teaching scene to improve the teaching effect and to evaluate the state. This paper improves the BPNN technology to study the basketball teaching scene and then proposes a genetic algorithm on the basis of the BPNN. It optimizes the parameters of basketball teaching scene through genetic algorithm and then analyzes the experimental data. The experimental results of this paper show that the BPNN is not as good as the BPNN based on the genetic algorithm in optimizing the parameters of the basketball teaching scene. The BPNN based on genetic algorithm has a successful recognition rate of more than 93% for basketball dribbling and shooting, which is 7-8% higher than that of the BPNN.

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Section: Related Workmentioning

confidence: 99%

Parameter Optimization and State Evaluation of Basketball Teaching Based on BPNN

Wang

2022

Mobile Information Systems

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Schlieren Image Velocimetry of Swirl Sprays

Machado¹,

Costa²,

Andrade³

et al. 2022

Flow Turbulence Combust

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Schlieren image velocimetry is based on light de ection through ow heterogeneities and image crosscorrelations. This is a low-cost and relatively low complexity technique that allows measurement of the droplet velocity eld in a large region of a spray. A Z-type Toepler schlieren system with a high-speed camera was used to determine mean vertical and horizontal droplet velocities, as well as the cone angles of sprays produced by a pressure swirl injector with characteristic geometric constant K = 2. Different LEDs and digital lters were evaluated for edge detection and improvement of image contrast. Open software was adopted for digital image processing and velocimetry. Interrogation windows and overlaps of different sizes were tested to obtain an appropriate correlation for determination of the velocity eld.The digital images were obtained with 5×10 3 fps and a resolution of 2.77 pixels/mm. Since the swirl sprays analysed presented instabilities, a number of 100 cross-correlations of images was required to reduce mean velocity uctuations. Injection pressures varied from 0.05 to 7 bar and mass ow rates varied from 1.389 to 13.89 g/s, using water as test uid. The wideband warm white LED with Laplacian or high-pass lters provided velocity data for a larger range of injection pressures. Mean axial velocities varied from 3.3 to 11.3 m/s, approximately, with mean horizontal velocities varying from around 0.17 to 3.3 m/s for pressures from 0.05 to 3.22 bar. The velocity data were compared to microscopic shadowgraphy results, showing a good agreement. Spray cone angles ranged from about 32.5 o to 69.5 o , for injection pressures from 0.05 to 7 bar, and results of triangulation with a blue LED were closer to semiempirical data.

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A physics-oriented shadowgraph motion estimation algorithm for turbulent flow structures

Mei,

Wang

2024

Aerospace Science and Technology

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An Optimization and Parametric Study of a Schlieren Motion Estimation Method

Cited by 4 publications

References 19 publications

Parameter Optimization and State Evaluation of Basketball Teaching Based on BPNN

Parameter Optimization and State Evaluation of Basketball Teaching Based on BPNN

Schlieren Image Velocimetry of Swirl Sprays

A physics-oriented shadowgraph motion estimation algorithm for turbulent flow structures

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