Quantitative Evaluation of Algae Detection Based on Deep Neural Network Multi-Source Data Fusion

2021 ◽  
Author(s):  
Le Gao ◽  
Xiaofeng Li ◽  
Yuan Guo ◽  
Jifeng Qi ◽  
Bin Zhang
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Quantitative Evaluation ◽  
Deep Neural Network ◽  
Source Data

Rock hardness identification based on optimized PNN and multi-source data fusion

2021 ◽  
pp. 095440622110420
Author(s):  
Ying He ◽  
Muqin Tian ◽  
Jiancheng Song ◽  
Junling Feng
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Differential Evolution Algorithm ◽  
Vibration Signal ◽  
Optimization Method ◽  
Rock Wall ◽  
Rock Hardness ◽  
Source Data

To solve the problem that it is difficult to identify the cutting rock wall hardness of the roadheader in coal mine, a recognition method of cutting rock wall hardness is proposed based on multi-source data fusion and optimized probabilistic neural network. In this method, all kinds of cutting signals (the vibration signal of cutting arm, the pressure signal of hydraulic cylinders and current signal of cutting motor) are analyzed by wavelet packet to extract the feature vector, and the multi feature signal sample database of rock cutting with different hardness is established. To solve the problems of uncertain spread and complex network structure of probabilistic neural network (PNN), a PNN optimization method based on differential evolution algorithm (DE) and QR decomposition was proposed, and the rock hardness was identified based on multi-source data fusion by optimizing PNN. Then, based on the ground test monitoring data of a heavy longitudinal roadheader, the method is applied to recognize the cutting rock hardness, and compared with other common pattern recognition methods. The experimental results show that the cutting rock hardness recognition based on multi-source data fusion and optimized PNN has higher recognition accuracy, and the overall recognition error is reduced to 6.8%. The recognition of random cutting rock hardness is highly close to the actual. The method provides theoretical basis and technical premise for realizing automatic and intelligent cutting of heading face.

Road segmentation with image-LiDAR data fusion in deep neural network

2019 ◽  
Vol 79 (47-48) ◽  
pp. 35503-35518 ◽  
Author(s):  
Huafeng Liu ◽  
Yazhou Yao ◽  
Zeren Sun ◽  
Xiangrui Li ◽  
Ke Jia ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Deep Neural Network ◽  
Lidar Data ◽  
Road Segmentation

scholarly journals AutoRemover: Automatic Object Removal for Autonomous Driving Videos

2020 ◽  
Vol 34 (07) ◽  
pp. 12853-12861
Author(s):  
Rong Zhang ◽  
Wei Li ◽  
Peng Wang ◽  
Chenye Guan ◽  
Jin Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Moving Objects ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Object Removal ◽  
3D Data ◽  
Source Data ◽  
Tightly Coupled ◽  
Geometric Relationship

Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm AutoRemover, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unlabeled but tightly coupled with the moving objects. The second is the large ego-motion in the videos. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. To deal with large ego-motion, we take advantage of the multi-source data, in particular the 3D data, in autonomous driving. More specifically, the geometric relationship between frames is incorporated into an inpainting deep neural network to produce high-quality structurally consistent video output. Experiments show that our method outperforms other state-of-the-art (SOTA) object removal algorithms, reducing the RMSE by over 19%.

Data Fusion Based Hybrid Deep Neural Network Method for Solar PV Power Forecasting

2019 ◽  
Author(s):  
Dan A. Rosa De Jesus ◽  
Paras Mandal ◽  
Miguel Velez-Reyes ◽  
Shantanu Chakraborty ◽  
Tomonobu Senjyu
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Deep Neural Network ◽  
Solar Pv ◽  
Neural Network Method ◽  
Network Method ◽  
Power Forecasting

scholarly journals Multi-Fidelity Aerodynamic Data Fusion with a Deep Neural Network Modeling Method

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1022
Author(s):  
Lei He ◽  
Weiqi Qian ◽  
Tun Zhao ◽  
Qing Wang
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Deep Neural Network ◽  
Linear Part ◽  
Neural Network Modeling ◽  
Test Case ◽  
High Fidelity ◽  
Fusion Model ◽  
Nonlinear Part ◽  
True Value

To generate more high-quality aerodynamic data using the information provided by different fidelity data, where low-fidelity aerodynamic data provides the trend information and high-fidelity aerodynamic data provides value information, we applied a deep neural network (DNN) algorithm to fuse the information of multi-fidelity aerodynamic data. We discuss the relationships between the low-fidelity and high-fidelity data, and then we describe the proposed architecture for an aerodynamic data fusion model. The architecture consists of three fully-connected neural networks that are employed to approximate low-fidelity data, and the linear part and nonlinear part of correlation for the low- and high-fidelity data, respectively. To test the proposed multi-fidelity aerodynamic data fusion method, we calculated Euler and Navier–Stokes simulations for a typical airfoil at various Mach numbers and angles of attack to obtain the aerodynamic coefficients as low- and high-fidelity data. A fusion model of the longitudinal coefficients of lift CL and drag CD was constructed with the proposed method. For comparisons, variable complexity modeling and cokriging models were also built. The accuracy spread between the predicted value and true value was discussed for both the training and test data of the three different methods. We calculated the root mean square error and average relative deviation to demonstrate the performance of the three different methods. The fusion result of the proposed method was satisfactory on the test case, and showed a better performance compared with the other two traditional methods presented. The results provide evidence that the method proposed in this paper can be useful in dealing with the multi-fidelity aerodynamic data fusion problem.

scholarly journals Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184486-184496
Author(s):  
Damira Mussina ◽  
Aidana Irmanova ◽  
Prashant K. Jamwal ◽  
Mehdi Bagheri
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Condition Monitoring ◽  
High Voltage ◽  
Deep Neural Network ◽  
Modal Data ◽  
High Voltage Insulator ◽  
Voltage Insulator

Iterative deblending for simultaneous source data using the deep neural network

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. V131-V141 ◽  
Author(s):  
Shaohuan Zu ◽  
Junxing Cao ◽  
Shan Qu ◽  
Yangkang Chen
Keyword(s):  
Neural Network ◽  
Field Data ◽  
Deep Neural Network ◽  
Numerical Test ◽  
Synthetic Data ◽  
Data Set ◽  
Source Data ◽  
Simultaneous Source ◽  
Iterative Framework ◽  
Trained Network

Simultaneous source technology can accelerate data acquisition and improve subsurface illumination. But those advantages are compromised due to dense interference. To address the intense interference in simultaneous source data, we have investigated a method based on a deep neural network. The designed architecture consists of convolutional and deconvolutional networks. The convolutional network can learn the local features of the training data set, and the deconvolutional network constructs the output using the extracted features to match the ground truth. Because the main computational cost results from the optimization of the network parameters, the trained network can separate simultaneous source data efficiently. Besides, with the given dithering code, we embed the trained network into an iterative framework that can further improve the deblending. A numerical test on synthetic data demonstrates that the iterative framework with the trained network can obtain comparable performance with high efficiency compared to the conventional method. Next, we test our method with two different trained networks (one is from a synthetic data set, and the other is from a field data set) on field data. The test results confirm the performance of our method.

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