Proof-of-concept study of sparse processing particle image velocimetry for real time flow observation

Kanda, Naoki; Abe, Chihaya; Goto, Shintaro; Yamada, Keigo; Nakai, Kumi; Saito, Yuji; Asai, Keisuke; Nonomura, Taku

doi:10.1007/s00348-022-03471-0

Cited by 13 publications

(3 citation statements)

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“…OP n n c dj c j == (14) In the study of dynamic river surface velocity measurement techniques, prior information serves as an important reference, aiding in resolving the uncertainty between the target size in camera images and its actual physical dimensions. The prior size of river surface targets may be derived from historical measurement data, known geographical features, or information provided by specialized measurement equipment.…”

Section: Measuring Dynamic River Surface Flow Velocity Based On Optic...mentioning

confidence: 99%

“…However, existing image-based velocity measurement technologies still exhibit some flaws and shortcomings. For example, traditional image tracking algorithms have limited stability and accuracy under complex conditions such as reflections and ripples on the river surface [13][14][15][16]. Although optical flow methods can estimate velocity fields, their adaptability and precision in dynamic river environments need further improvement.…”

Section: Introductionmentioning

confidence: 99%

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Image Processing Techniques for Dynamic Surface Velocity Measurement in Rivers

Cheng,

Zhang

2024

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The measurement of dynamic surface velocities in rivers holds significant importance for hydrological studies, environmental monitoring, and water resource management. With the rapid evolution of image processing technologies, methods based on imagery for velocity measurement have garnered widespread attention due to their non-intrusive nature and operational simplicity. Addressing the invasive nature, limited measurement capabilities, and poor adaptability to complex environments inherent in traditional velocity measurement techniques, a novel technical scheme is proposed. Initially, an enhanced Mean shift algorithm is employed for effective tracking of river surface targets, overcoming stability and accuracy issues faced by conventional algorithms in complex aquatic environments. Subsequently, a new velocity measurement method, integrating optical flow techniques with calibration technology, is introduced to augment accuracy and mitigate environmental interferences. This research not only enhances the reliability of non-contact velocity measurement technologies but also offers new perspectives and tools for river monitoring, contributing significantly to the sustainable management and utilization of water resources.

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Section: Measuring Dynamic River Surface Flow Velocity Based On Optic...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Processing Techniques for Dynamic Surface Velocity Measurement in Rivers

Cheng,

Zhang

2024

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“…Collecting information from sensor measurements is often the only viable approach when estimating the internal state or hidden physical quantities. The optimization of sensor positions was intensively discussed in order to determine the most representative sensors and to reduce the resulting estimation error, such as when monitoring sensor networks [1][2][3][4], fluid flows around objects [5][6][7][8][9][10][11][12][13][14][15], plants and factories [16][17][18], infrastructures [19][20][21], circuits [22], and biological systems [23], estimating physical field [24][25][26][27], and localizing sources [28,29]. Recent advances in data science techniques have enabled us to extract reduced-order models from vastly large-scale measurements of complex phenomena [30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Efficient Sensor Node Selection for Observability Gramian Optimization

Yamada

Sasaki

Nagata

et al. 2023

Sensors

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Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approximate convex relaxation method with the Newton method and a gradient greedy method, and confirms the performance of the selection methods, including a convex relaxation method with semidefinite programming (SDP) and a pure greedy optimization method proposed in the previous studies. The matrix determinant of the observability Gramian was employed for the evaluations of the sensor subsets, while its gradient and Hessian were derived for the proposed methods. In the demonstration using numerical and real-world examples, the proposed approximate greedy method showed superiority in the run time when the sensor numbers were roughly the same as the dimensions of the latent system. The relaxation method with SDP is confirmed to be the most reasonable approach for a system with randomly generated matrices of higher dimensions. However, the degradation of the optimization results was also confirmed in the case of real-world datasets, while the pure greedy selection obtained the most stable optimization results.

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