Seabed classification from ship-radiated noise using an ensemble deep learning

2021 ◽  
Vol 149 (4) ◽  
pp. A113-A113
Author(s):  
Christian D. Escobar-Amado ◽  
Mohsen Badiey ◽  
Tracianne B. Neilsen ◽  
Jhon A. Castro-Correa ◽  
David P. Knobles
Keyword(s):  
Deep Learning ◽  
Radiated Noise ◽  
Seabed Classification

Seabed classification from merchant ship-radiated noise using a physics-based ensemble of deep learning algorithms

2021 ◽  
Vol 150 (2) ◽  
pp. 1434-1447
Author(s):  
Christian D. Escobar-Amado ◽  
Tracianne B. Neilsen ◽  
Jhon A. Castro-Correa ◽  
David F. Van Komen ◽  
Mohsen Badiey ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Radiated Noise ◽  
Seabed Classification ◽  
Merchant Ship

Seabed classification and source localization from ship spectrograms using deep learning models

2021 ◽  
Vol 150 (4) ◽  
pp. A315-A315
Author(s):  
Jhon A. Castro-Correa ◽  
Christian D. Escobar-Amado ◽  
Mohsen Badiey ◽  
Tracianne B. Neilsen ◽  
David P. Knobles
Keyword(s):  
Deep Learning ◽  
Source Localization ◽  
Learning Models ◽  
Seabed Classification

scholarly journals Validating deep learning seabed classification via acoustic similarity

2021 ◽  
Vol 1 (4) ◽  
pp. 040802
Author(s):  
David J. Forman ◽  
Tracianne B. Neilsen ◽  
David F. Van Komen ◽  
David P. Knobles
Keyword(s):  
Deep Learning ◽  
Acoustic Similarity ◽  
Seabed Classification

Seabed classification using localized forward modeling and deep learning

2020 ◽  
Vol 148 (4) ◽  
pp. 2730-2730
Author(s):  
Christina Frederick ◽  
Soledad Villar ◽  
Zoi-Heleni Michalopoulou
Keyword(s):  
Deep Learning ◽  
Forward Modeling ◽  
Seabed Classification

scholarly journals Joint Representation and Recognition for Ship-Radiated Noise Based on Multimodal Deep Learning

2019 ◽  
Vol 7 (11) ◽  
pp. 380
Author(s):  
Fei Yuan ◽  
Xiaoquan Ke ◽  
En Cheng
Keyword(s):  
Deep Learning ◽  
Recognition Performance ◽  
Visual Observation ◽  
Experimental Results ◽  
Visual Modality ◽  
Traditional Methods ◽  
Radiated Noise ◽  
Noise Data ◽  
Joint Representation ◽  
Single Modality

Ship recognition based on ship-radiated noise is one of the most important and challenging subjects in underwater acoustic signal processing. The recognition methods for ship-radiated noise recognition include traditional methods and deep learning (DL) methods. Developing from the DL methods and inspired by audio–video speech recognition (AVSR), the paper further introduces multimodal deep learning (multimodal-DL) methods for the recognition of ship-radiated noise. In this paper, ship-radiated noise (acoustics modality) and visual observation of the ships (visual modality) are two different modalities that the multimodal-DL methods model on. The paper specially designs a multimodal-DL framework, the multimodal convolutional neural networks (multimodal-CNNs) for the recognition of ship-radiated noise. Then the paper proposes a strategy based on canonical correlation analysis (CCA-based strategy) to build a joint representation and recognition on the two different single-modality (acoustics modality and visual modality). The multimodal-CNNs and the CCA-based strategy are tested on real ship-radiated noise data recorded. Experimental results show that, using the CCA-based strategy, strong-discriminative information can be built from weak-discriminative information provided from a single-modality. Experimental results also show that as long as any one of the single-modalities can provide information for the recognition, the multimodal-DL methods can have a much better multiclass recognition performance than the DL methods. The paper also discusses the advantages and superiorities of the multimodal-Dl methods over the traditional methods for ship-radiated noise recognition.

scholarly journals Ship Radiated Noise Recognition Technology Based on ML-DS Decision Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Biao Wang ◽  
Chengxi Wu ◽  
Yunan Zhu ◽  
Mingliang Zhang ◽  
Hanqiong Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Information Source ◽  
Recognition Rate ◽  
Heterogeneous Data ◽  
Decision Fusion ◽  
Radiated Noise

Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster–Shafer (ML-DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence-based decision-making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two-dimensional spectrogram features and one-dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision-making of D-S evidence theory of different times, the decision-making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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