Hydromorphological Classification Using Synchronous Pressure and Inertial Sensing

Ristolainen, Asko; Kalev, Kaia; Tuhtan, Jeffrey A.; Kuusik, Alar; Kruusmaa, Maarja

doi:10.1109/tgrs.2018.2795641

Cited by 7 publications

(10 citation statements)

References 31 publications

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“…An MLP was used in all of the following three cases. In [101] the authors describe an experiment in which a sensor array is used to measure three distinct hydrodynamic environments. Via a set of features and a MLP classifier, they were able to discern these three locations based on the hydrodynamic environment.…”

Section: Neural Network For Hydrodynamic Imagingmentioning

confidence: 99%

“…Some ALL systems focus on estimating fluid flow parameters such as the flow speed, direction and vorticity, either in natural environments [101] or in confined flow tanks. In the latter case, an object is usually placed upstream with respect to a sensor array [82].…”

Section: Hydrodynamic Signaturesmentioning

confidence: 99%

“…river flow conditions [101]. We therefore select 16 frequency bands from the ANSI half-octave band definition [65], using 0.25 Hz as a reference frequency.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The second feature type, i.e. kurtosis and skewness, describes the distribution of data in the time windows, as used before in fluid flow classification [101]. Instead of averaging over the eight sensors, we take the minimal value, maximal value and standard deviation of both as separate features.…”

Section: Feature Extractionmentioning

confidence: 99%

“…These demonstrations often employ 1D-sensitive sensors or use alternating sensor placements to capture more detail of the local hydrodynamic environment. Closer to the topic of this thesis, some recent examples describe multi-layer perceptron neural networks that perform object localization [4,131], a two-class identification of objects [40], or a three-class identification of hydrodynamic environment [101]. The research featured in this thesis expanded on the current relevant literature in three main themes.…”

Section: Supplementary Informationmentioning

confidence: 99%

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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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Section: Neural Network For Hydrodynamic Imagingmentioning

confidence: 99%

Section: Hydrodynamic Signaturesmentioning

confidence: 99%

“…river flow conditions [101]. We therefore select 16 frequency bands from the ANSI half-octave band definition [65], using 0.25 Hz as a reference frequency.…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Supplementary Informationmentioning

confidence: 99%

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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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Biophysics of the Lateral Line and Applications

Wolf¹,

Netten²

2020

The Senses: A Comprehensive Reference

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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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Hydromorphological Classification Using Synchronous Pressure and Inertial Sensing

Cited by 7 publications

References 31 publications

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

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

Biophysics of the Lateral Line and Applications

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

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