Hydrodynamic object identification with artificial neural models

Lakkam, Sreetej; Balamurali, B T; Bouffanais, Roland

doi:10.1038/s41598-019-47747-8

Cited by 15 publications

(9 citation statements)

References 35 publications

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“…More directly, zero crossings, maxima, and minima of the velocity profile can also be directly used to produce estimates for the distance (y-coordinate) and the lateral position (x-coordinate) [10]. In [30], [36], [37], it is shown that an ALL comprising of 2D-sensitive sensors makes localization more robust. Compared to the 1D sensitivity of fish and most other ALL implementations, this extension provides more and complementary spatial reference points [30], [36], which may also be helpful for shape recognition.…”

Section: ) Velocity Profilesmentioning

confidence: 99%

“…In the hydrodynamic near-field, the shape of the object affects the pressure gradient and thus also the principal shape of the measured velocity profile. This measured velocity profile shape can be modeled via a process called conformal mapping [34], [37], although these velocity profiles are expected to be less distinctive at distances further from the array [20] and in the limit resemble the signature of a sphere. So in order to preserve details and identify objects based on their measured velocity profiles, they need to be measured up close.…”

Section: ) Velocity Profilesmentioning

confidence: 99%

“…Recently [37], neural networks have been used in a simulation study to determine the shape parameters of a foil-shape object. Using a grid of sensors and a conformal mapping potential flow fluid model, the two perpendicular fluid flow components (x and y) were simulated, as well as the absolute fluid speed magnitude and the dynamic pressure at each of the sensors.…”

Section: ) State-of-the-art Flow-based Shape Classificationmentioning

confidence: 99%

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Shape Classification Using Hydrodynamic Detection via a Sparse Large-Scale 2D-Sensitive Artificial Lateral Line

et al. 2020

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Artificial lateral lines are fluid flow sensor arrays, bio-inspired by the fish lateral line organ, that measure a local hydrodynamic environment. These arrays are used to detect objects in water, without relying on light, sound, or on an active beacon. This passive sensing method, called hydrodynamic imaging, is complementary to sonar and vision systems and is suitable for collision avoidance and near-field covert sensing. This sensing method has so far been demonstrated on a biological scale from several to tens of centimeters. Here, we present measurements using a large-scale artificial lateral line of 3.5 meters, consisting of eight all-optical 2D-sensitive flow sensors. We measure the fluid flow as produced by the motion of five different objects, towed across a swimming pool. This results in repeatable stimuli, whose measurements demonstrate a complementary aspect of 2D-sensing. These measurements are both used for constructing temporal hydrodynamic signatures, which reflect the object's shape, and for flow-feature based near-field object classification. For the latter, we present a location-invariant feature extraction method which, using an Extreme Learning Machine neural network, results in a classification F1-score up to 98.6% with selected flow features. We find that, compared to the traditional sensing dimension parallel to the sensor array, the novel transverse fluid velocity component bears more information about the object shape. The classification of objects via hydrodynamic imaging thus benefits from 2D-sensing and can be scaled up to a supra biological scale of several meters. INDEX TERMS Hydrodynamic imaging, artificial lateral line, neural network, inverse problem, sensor array, feature selection.

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Section: ) Velocity Profilesmentioning

confidence: 99%

Section: ) Velocity Profilesmentioning

confidence: 99%

Section: ) State-of-the-art Flow-based Shape Classificationmentioning

confidence: 99%

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Shape Classification Using Hydrodynamic Detection via a Sparse Large-Scale 2D-Sensitive Artificial Lateral Line

et al. 2020

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“…More directly, zero crossings, maxima, and minima of the velocity profile also directly produce estimates for the distance (y-coordinate) and the lateral position (x-coordinate) [43]. In [9,77,122], it is shown that an ALL comprising of 2D-sensitive sensors makes localization more robust. This extension compared to the 1D sensitivity of fish and most other ALL implementations, provides more and complementary spatial reference points [9,122], which may also be helpful for shape recognition.…”

Section: Velocity Profilesmentioning

confidence: 99%

“…Recently [77], neural networks have been used in a simulation study to determine the shape parameters of a foil-shape object. Using a grid of sensors and a conformal mapping potential flow fluid model, they simulated the x and y fluid flow components, as well as the absolute fluid speed magnitude and the dynamic pressure at each of the sensors.…”

Section: Flow-based Shape Classificationmentioning

confidence: 99%

Hydrodynamic Imaging with Artificial Intelligence: detecting submerged objects at a distance using a 2D-sensitive flow sensor array and neural networks

Wolf

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Background 2 1.1.1 The biological lateral line 2 1.1.2 What is hydrodynamic imaging? 4 1.1.3 Artificial neural networks in practice 8 1.2 Research motivation 10 1.2.1 Scalability 10 1.2.2 Neural-network based signal processing 11 1.2.3 2D-sensitive sensing 11 1.3 Thesis outline 11 Graphical abstract 13 i sensing 2 design and validation of an all-optical 2d flow sensor 17 2.1 Introduction 18 2.2 Sensor and fluid model 20 2.2.1 Sensor design 20 2.2.2 FBG sensing 20 2.2.3 Isotropic sensor mechanics 21 2.2.4 Dynamic properties 24 2.2.5 Design optimization 25 2.3 Methods 26 2.4 Results 29 2.4.1 Linearity 29 2.4.2 Dynamic response 29 2.5 Discussion 31 2.5.1 Sensitivity and dynamic range 31 2.5.2 Two-dimensional fluid flow sensing 32 2.6 Conclusion 34 ii simulation studies 3 simulated 1d-sensitive localization 37 3.1 Introduction 38 3.2 Methods 40 v vi contents 3.2.1 Data generation 40 3.2.2 Neural network algorithms 44 3.3 Results 48 3.3.1 Parameter settings 48 3.3.2 Overall performance on high signal to noise input 49 3.3.3 MLP overfitting 50 3.3.4 Noise robustness 50 3.4 Discussion 53 3.4.1 Practical implementation neural networks 53 3.4.2 Overall performance on x and y coordinates 55 3.4.3 Noise robustness 55 3.5 Conclusion 56 3.5.1 Practical implementation neural networks 56 3.5.2 Further research on single source localization 56 4 simulated 1d-sensitive multi-source localization 59 4.1 Introduction 60 4.2 Background 61 4.2.1 Source location encoding by the lateral line organ 61 4.2.2 Object localization using artificial lateral lines 63 4.2.3 Convolutional neural networks 64 4.3 Methods 66 4.3.1 Location encoding in 3D 66 4.3.2 Data synthesis 69 4.3.3 CNN implementation 70 4.3.4 CNN optimization 73 4.3.5 Location decoding in 3D 75 4.4 Results 77 4.4.1 Probability grid reconstruction 77 4.4.2 3D position reconstruction 79 4.5 Discussion 79 4.5.1 The processing pipeline 80 4.5.2 CNN design variations 80 4.5.3 Iterative source detection 82 4.5.4 Detection limits and future research 83 4.6 Conclusion 84 Supplementary information 84 5 simulated 2d-sensitive localization 87 5.1 Introduction 88 5.1.1 State of the art 88 5.1.2 Aims of this study 89 contents vii 5.

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Autonomous vehicles decision-making enhancement using self-determination theory and mixed-precision neural networks

Ali¹,

Jaber

Daniel

et al. 2023

Multimed Tools Appl

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Hydrodynamic object identification with artificial neural models

Cited by 15 publications

References 35 publications

Shape Classification Using Hydrodynamic Detection via a Sparse Large-Scale 2D-Sensitive Artificial Lateral Line

Shape Classification Using Hydrodynamic Detection via a Sparse Large-Scale 2D-Sensitive Artificial Lateral Line

Hydrodynamic Imaging with Artificial Intelligence: detecting submerged objects at a distance using a 2D-sensitive flow sensor array and neural networks

Autonomous vehicles decision-making enhancement using self-determination theory and mixed-precision neural networks

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