Recognition of an obstacle in a flow using artificial neural networks

Carrillo, M.; Que, Ulices; González, José Antonio García-Cruces; López, Carlos

doi:10.1103/physreve.96.023306

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…To overcome these limitations, data processing approaches using feedforward neural network types of artificial neural networks (ANNs) have recently been proposed [22][23][24][25][26][27][28] for object detection. For example, Boulogne et al [24] applied an ANN to the flow velocity data acquired by numerical simulations of potential flow frameworks to determine the dipole positions, while Abdulsadda et al [25] and Zheng et al [26] used ANNs with flow field data obtained experimentally from LLS-inspired sensor arrays in three-dimensional water tanks to determine the dipole positions.…”

Section: Introductionmentioning

confidence: 99%

“…To acquire the sensory data, Abdulsadda et al [25] used a sensor array consisting of ionic polymer-metal composite flow sensors, whereas Zheng et al [26] used a cross-shaped sensor array consisting of underwater pressure sensors. Furthermore, Carrillo et al [27] estimated the location and size of obstacles in a pipe flow using an ANN, and Lakkam et al [28] employed an ANN to determine the shape parameters of hydrofoils with more complex shapes located in a uniform flow.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning model inspired by lateral line system for underwater object detection

Jeong¹,

Yoo²,

Kim³

2022

Bioinspir. Biomim.

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Inspired by the lateral line systems of various aquatic organisms that are capable of hydrodynamic imaging using ambient flow information, this study develops a deep learning-based object localization model that can detect the location of objects using flow information measured from a moving sensor array. In numerical simulations with the assumption of a potential flow, a two-dimensional hydrofoil navigates around four stationary cylinders in a uniform flow and obtains two types of sensory data during a simulation, namely flow velocity and pressure, from an array of sensors located on the surface of the hydrofoil. Several neural network models are constructed using the flow velocity and pressure data, and these are used to detect the positions of the hydrofoil and surrounding objects. The model based on a long short-term memory network, which is capable of learning order dependence in sequence prediction problems, outperforms the other models. The number of sensors is then optimized using feature selection techniques. This sensor optimization leads to a new object localization model that achieves impressive accuracy in predicting the locations of the hydrofoil and objects with only 40$\%$ of the sensors used in the original model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning model inspired by lateral line system for underwater object detection

Jeong¹,

Yoo²,

Kim³

2022

Bioinspir. Biomim.

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“…Deep neural networks have enabled a novel analysis of turbulent flow fields by banking on the higher dimensional data associated with rotational and intermittent turbulent eddies 19 , thereby revealing that ANN are significantly more accurate than conventional Reynolds-averaged Navier-Stokes models. Very recently, ANN have also been used for solving an engineering problem of obstruction detection in flow pipes 20 .…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamic object identification with artificial neural models

Lakkam

Balamurali

Bouffanais

2019

Sci Rep

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The lateral-line system that has evolved in many aquatic animals enables them to navigate murky fluid environments, locate and discriminate obstacles. Here, we present a data-driven model that uses artificial neural networks to process flow data originating from a stationary sensor array located away from an obstacle placed in a potential flow. The ability of neural networks to estimate complex underlying relationships between parameters, in the absence of any explicit mathematical description, is first assessed with two basic potential flow problems: single source/sink identification and doublet detection. Subsequently, we address the inverse problem of identifying an obstacle shape from distant measures of the pressure or velocity field. Using the analytical solution to the forward problem, very large training data sets are generated, allowing us to obtain the synaptic weights by means of a gradient-descent based optimization. The resulting neural network exhibits remarkable effectiveness in predicting unknown obstacle shapes, especially at relatively large distances for which classical linear regression models are completely ineffectual. These results have far-reaching implications for the design and development of artificial passive hydrodynamic sensing technology.

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“…These scenarios correspond to inverse problems focused on the reconstruction of the initial conditions and environmental variables. Inverse problems related to determine parameters and initial conditions associated to partial differential equations using Artificial Neural Networks happen in different areas, for instance in fluid dynamics [14, 15]. The aim of this paper is to present a strategy to solve this inverse problem by combining the numerical solution of the IVP (1) and the use of SVMs to classify and bound the initial conditions and environmental variables.…”

Section: Introductionmentioning

confidence: 99%

Location of sources in reaction-diffusion equations using support vector machines

2019

Self Cite

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The reaction-diffusion equation serves to model systems in the diffusion regime with sources. Specific applications include diffusion processes in chemical reactions, as well as the propagation of species, diseases, and populations in general. In some of these applications the location of an outbreak, for instance, the source point of a disease or the nest of a vector spreading a virus is important. Also important are the environmental parameters of the domain where the process diffuses, namely the space-dependent diffusion coefficient and the proliferation parameter of the process. Determining both, the location of a source and the environmental parameters, define an inverse problem that in turn, involves a partial differential equation. In this paper we classify the values of these parameters using Support Vector Machines (SVM) trained with numerical solutions of the reaction-diffusion problem. Our set up has accuracy of classifying the outbreak location above 90% and 77% of classifying both, the location and the environmental parameters. The approach presented in our analysis can be directly implemented by measuring the population under study at specific locations in the spatial domain as function of time.

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Recognition of an obstacle in a flow using artificial neural networks

Cited by 11 publications

References 40 publications

Deep learning model inspired by lateral line system for underwater object detection

Deep learning model inspired by lateral line system for underwater object detection

Hydrodynamic object identification with artificial neural models

Location of sources in reaction-diffusion equations using support vector machines

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