scholarly journals Underwater-Sonar-Image-Based 3D Point Cloud Reconstruction for High Data Utilization and Object Classification Using a Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1763
Author(s):  
Minsung Sung ◽  
Jason Kim ◽  
Hyeonwoo Cho ◽  
Meungsuk Lee ◽  
Son-Cheol Yu
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
Field Experiments ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Object Classification ◽  
High Data ◽  
3D Geometry ◽  
Underwater Object ◽  
3D Point Cloud Reconstruction

This paper proposes a sonar-based underwater object classification method for autonomous underwater vehicles (AUVs) by reconstructing an object’s three-dimensional (3D) geometry. The point cloud of underwater objects can be generated from sonar images captured while the AUV passes over the object. Then, a neural network can predict the class given the generated point cloud. By reconstructing the 3D shape of the object, the proposed method can classify the object accurately through a straightforward training process. We verified the proposed method by performing simulations and field experiments.

Ship Classification Method for Point Cloud Images Based on Three-Dimensional Convolutional Neural Network

2020 ◽  
Vol 57 (16) ◽  
pp. 161022
Author(s):  
任永梅 Ren Yongmei ◽  
杨杰 Yang Jie ◽  
郭志强 Guo Zhiqiang ◽  
陈奕蕾 Chen Yilei
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Three Dimensional ◽  
Classification Method ◽  
Ship Classification

A Transfer Learning-Based Multi-Fidelity Point-Cloud Neural Network Approach for Melt Pool Modeling in Additive Manufacturing

2021 ◽  
Author(s):  
Xufeng Huang ◽  
Tingli Xie ◽  
Zhuo Wang ◽  
Lei Chen ◽  
Qi Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Transfer Learning ◽  
Point Cloud ◽  
Fe Simulation ◽  
Three Dimensional ◽  
Analytical Models ◽  
Simulation Data ◽  
Neural Network Approach ◽  
Melt Pool

Abstract Melt pool modeling is critical for model-based uncertainty quantification (UQ) and quality control in metallic Additive Manufacturing (AM). Finite element (FE) simulation for thermal modeling in metal AM, however, is tedious and time-consuming. This paper presents a multi-fidelity point-cloud neural network method (MF-PointNN) for surrogate modeling of melt pool based on FE simulation data. It merges the feature representations of low-fidelity (LF) analytical model and high-fidelity (HF) FE simulation data through the theory of transfer learning (TL). A basic PointNN is firstly trained using LF data to construct correlation between the inputs and thermal field of analytical models. Then, the basic PointNN is updated and fine-tuned using the small size of HF data to build the MF-PointNN. The trained MF-PointNN allows for efficient mapping from input variables and spatial positions to thermal histories, and thereby efficiently predict the three-dimensional melt pool. Results of melt pool modeling of electron beam additive manufacturing (EBAM) of Ti-6Al-4V under uncertainty demonstrate the efficacy of the proposed approach.

scholarly journals Weakly-Supervised Single-view Dense 3D Point Cloud Reconstruction via Differentiable Renderer

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Peng Jin ◽  
Shaoli Liu ◽  
Jianhua Liu ◽  
Hao Huang ◽  
Linlin Yang ◽  
...  
Keyword(s):  
Point Cloud ◽  
3D Structure ◽  
Three Dimensional ◽  
Ground Truth ◽  
3D Point Cloud ◽  
Real World Data ◽  
Single View ◽  
Camera Pose ◽  
3D Point Cloud Reconstruction ◽  
The Individual

AbstractIn recent years, addressing ill-posed problems by leveraging prior knowledge contained in databases on learning techniques has gained much attention. In this paper, we focus on complete three-dimensional (3D) point cloud reconstruction based on a single red-green-blue (RGB) image, a task that cannot be approached using classical reconstruction techniques. For this purpose, we used an encoder-decoder framework to encode the RGB information in latent space, and to predict the 3D structure of the considered object from different viewpoints. The individual predictions are combined to yield a common representation that is used in a module combining camera pose estimation and rendering, thereby achieving differentiability with respect to imaging process and the camera pose, and optimization of the two-dimensional prediction error of novel viewpoints. Thus, our method allows end-to-end training and does not require supervision based on additional ground-truth (GT) mask annotations or ground-truth camera pose annotations. Our evaluation of synthetic and real-world data demonstrates the robustness of our approach to appearance changes and self-occlusions, through outperformance of current state-of-the-art methods in terms of accuracy, density, and model completeness.

Point-Cloud Neural Network Using Transfer Learning-Based Multi-Fidelity Method for Thermal Field Prediction in Additive Manufacturing

2021 ◽  
Author(s):  
Xufeng Huang ◽  
Zhen Hu ◽  
Tingli Xie ◽  
Zhuo Wang ◽  
Lei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Transfer Learning ◽  
Point Cloud ◽  
Thermal Field ◽  
Fe Simulation ◽  
Three Dimensional ◽  
Analytical Models ◽  
Simulation Data ◽  
Melt Pool

Abstract Melt pool modeling is critical for model-based uncertainty quantification (UQ) and quality control in metallic Additive Manufacturing (AM). Finite element (FE) simulation for thermal modeling in metal AM, however, is tedious and time-consuming. This paper presents a multi-fidelity point-cloud neural network method (MF-PointNN) for surrogate modeling of melt pool based on FE simulation data. It merges the feature representations of low-fidelity (LF) analytical model and high-fidelity (HF) FE simulation data through the theory of transfer learning (TL). A basic PointNN is firstly trained using LF data to construct correlation between the inputs and thermal field of analytical models. Then, the basic PointNN is updated and fine-tuned using the small size of HF data to build the MF-PointNN. The trained MF-PointNN allows for efficient mapping from input variables and spatial positions to thermal histories, and thereby efficiently predict the three-dimensional melt pool. Results of melt pool modeling of electron beam additive manufacturing (EBAM) of Ti-6Al-4V under uncertainty demonstrate the efficacy of the proposed approach.

scholarly journals Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Wenlong Tian ◽  
Zhaoyong Mao ◽  
Fuliang Zhao ◽  
Zhicao Zhao
Keyword(s):  
Neural Network ◽  
Autonomous Underwater Vehicles ◽  
Back Propagation ◽  
Three Dimensional ◽  
Optimization Method ◽  
Back Propagation Neural Network ◽  
Underwater Vehicles ◽  
Minimum Drag ◽  
Computational Fluid Dynamics Cfd ◽  
The Relationship

This paper presents an optimization method for the design of the layout of an autonomous underwater vehicles (AUV) fleet to minimize the drag force. The layout of the AUV fleet is defined by two nondimensional parameters. Firstly, three-dimensional computational fluid dynamics (CFD) simulations are performed on the fleets with different layout parameters and detailed information on the hydrodynamic forces and flow structures around the AUVs is obtained. Then, based on the CFD data, a back-propagation neural network (BPNN) method is used to describe the relationship between the layout parameters and the drag of the fleet. Finally, a genetic algorithm (GA) is chosen to obtain the optimal layout parameters which correspond to the minimum drag. The optimization results show that (1) the total drag of the AUV fleet can be reduced by 12% when the follower AUV is located directly behind the leader AUV and (2) the drag of the follower AUV can be reduced by 66% when it is by the side of the leader AUV.

Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace

2017 ◽  
Vol 70 (6) ◽  
pp. 1293-1311 ◽  
Author(s):  
Xiang Cao ◽  
A-long Yu
Keyword(s):  
Neural Network ◽  
Search Task ◽  
Search Algorithm ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Target Search ◽  
Team Members ◽  
Neural Network Algorithm ◽  
Som Neural Network ◽  
Self Organising Map

To improve the efficiency of multiple Autonomous Underwater Vehicles (multi-AUV) cooperative target search in a Three-Dimensional (3D) underwater workspace, an integrated algorithm is proposed by combining a Self-Organising Map (SOM), neural network and Glasius Bioinspired Neural Network (GBNN). With this integrated algorithm, the 3D underwater workspace is first divided into subspaces dependent on the abilities of the AUV team members. After that, tasks are allocated to each subspace for an AUV by SOM. Finally, AUVs move to the assigned subspace in the shortest way and start their search task by GBNN. This integrated algorithm, by avoiding overlapping search paths and raising the coverage rate, can reduce energy consumption of the whole multi-AUV system. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve a multiple target search task with higher efficiency and adaptability compared with a more traditional bioinspired neural network algorithm.

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