Simultaneous measurement of pressure and temperature on the same surface by sensitive paints using the sensor selection method

Tiwari, Neetu; Uchida, Kazuki; Inoba, Ryoma; Saito, Yuji; Asai, Keisuke; Nonomura, Taku

doi:10.1007/s00348-022-03501-x

Cited by 11 publications

(1 citation statement)

References 47 publications

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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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Section: Introductionmentioning

confidence: 99%

Efficient Sensor Node Selection for Observability Gramian Optimization

Yamada

Sasaki

Nagata

et al. 2023

Sensors

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Development of the Senseiver for efficient field reconstruction from sparse observations

Santos,

Fox,

Mohan

et al. 2023

Nat Mach Intell

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The reconstruction of complex time-evolving fields from sensor observations is a grand challenge. Frequently, sensors have extremely sparse coverage and low-resource computing capacity for measuring highly nonlinear phenomena. While numerical simulations can model some of these phenomena using partial differential equations, the reconstruction problem is ill-posed. Data-driven-strategies provide crucial disambiguation, but these suffer in cases with small amounts of data, and struggle to handle large domains. Here we present the Senseiver, an attention-based framework that excels in reconstructing complex spatial fields from few observations with low overhead. The Senseiver reconstructs n-dimensional fields by encoding arbitrarily sized sparse sets of inputs into a latent space using cross-attention, producing uniform-sized outputs regardless of the number of observations. This allows efficient inference by decoding only a sparse set of output observations, while a dense set of observations is needed to train. This framework enables training of data with complex boundary conditions and extremely large fine-scale simulations. We build on the Perceiver IO by enabling training models with fewer parameters, which facilitates field deployment, and a training framework that allows a flexible number of sensors as input, which is critical for real-world applications. We show that the Senseiver advances the state-of-the-art of field reconstruction in many applications.

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