scholarly journals Design and Implementation of Blind Source Separation Based on BP Neural Network in Space-Based AIS

2021 ◽  
Vol 2 ◽  
Author(s):  
Chengjie Li ◽  
Lidong Zhu ◽  
Zhongqiang Luo ◽  
Zhen Zhang ◽  
Yilun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Blind Source Separation ◽  
Bp Neural Network ◽  
Source Separation ◽  
Optimal Solution ◽  
Blind Signal Separation ◽  
Automatic Identification ◽  
Identification System ◽  
Data Set ◽  
Processing Power

In space-based AIS (Automatic Identification System), due to the high orbit and wide coverage of the satellite, there are many self-organizing communities within the observation range of the satellite, and the signals will inevitably conflict, which reduces the probability of ship detection. In this paper, to improve system processing power and security, according to the characteristics of neural network that can efficiently find the optimal solution of a problem, proposes a method that combines the problem of blind source separation with BP neural network, using the generated suitable data set to train the neural network, thereby automatically generating a traditional blind signal separation algorithm with a more stable separation effect. At last, through the simulation results of combining the blind source separation problem with BP neural network, the performance and stability of the space-based AIS can be effectively improved.

scholarly journals Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data

2020 ◽  
Vol 10 (11) ◽  
pp. 4010 ◽  
Author(s):  
Kwang-il Kim ◽  
Keon Myung Lee
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Automatic Identification ◽  
Identification System ◽  
Fishing Gear ◽  
Trajectory Data ◽  
Data Set ◽  
Prediction Module ◽  
Fishing Gears ◽  
Fully Connected

Marine resources are valuable assets to be protected from illegal, unreported, and unregulated (IUU) fishing and overfishing. IUU and overfishing detections require the identification of fishing gears for the fishing ships in operation. This paper is concerned with automatically identifying fishing gears from AIS (automatic identification system)-based trajectory data of fishing ships. It proposes a deep learning-based fishing gear-type identification method in which the six fishing gear type groups are identified from AIS-based ship movement data and environmental data. The proposed method conducts preprocessing to handle different lengths of messaging intervals, missing messages, and contaminated messages for the trajectory data. For capturing complicated dynamic patterns in trajectories of fishing gear types, a sliding window-based data slicing method is used to generate the training data set. The proposed method uses a CNN (convolutional neural network)-based deep neural network model which consists of the feature extraction module and the prediction module. The feature extraction module contains two CNN submodules followed by a fully connected network. The prediction module is a fully connected network which suggests a putative fishing gear type for the features extracted by the feature extraction module from input trajectory data. The proposed CNN-based model has been trained and tested with a real trajectory data set of 1380 fishing ships collected over a year. A new performance index, DPI (total performance of the day-wise performance index) is proposed to compare the performance of gear type identification techniques. To compare the performance of the proposed model, SVM (support vector machine)-based models have been also developed. In the experiments, the trained CNN-based model showed 0.963 DPI, while the SVM models showed 0.814 DPI on average for the 24-h window. The high value of the DPI index indicates that the trained model is good at identifying the types of fishing gears.

Nonlinear Blind Source Separation Using GA Optimized RBF-ICA and its Application to the Image Noise Removal

2011 ◽  
Vol 393-395 ◽  
pp. 205-208 ◽  
Author(s):  
Xue Mei Wang
Keyword(s):  
Neural Network ◽  
Blind Source Separation ◽  
Rbf Neural Network ◽  
Source Separation ◽  
Image Data ◽  
Noise Removal ◽  
Independent Component ◽  
Data Set ◽  
Mixing Matrix ◽  
Ica Algorithm

Today the blind source separation (BSS) algorithms are widely used to separate independent components in a data set based on its statistical properties. Especially in image data applications, the independent component analysis (ICA) based BSS procedure for image pre-processing has been successfully applied for independent component extraction in order to remove the noise signals mixed into the image data. The contribution of this paper refers to the development of a nonlinear BSS method using the radial basis function (RBF) neural network based ICA algorithm, which was built by adopted some modifications in the linear ICA model. Moreover, genetic algorithm (GA) was used to optimize the RBF neural network to obtain satisfactory nonlinear solve of the nonlinear mixing matrix. In the experiments of this work, the GA optimized nonlinear ICA method and other ICA models were applied for image de-noising. A comparative analysis has showed satisfactory and effective image de-noising results obtained by the presented method.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

A Note on Stone's Conjecture of Blind Signal Separation

2005 ◽  
Vol 17 (2) ◽  
pp. 321-330 ◽  
Author(s):  
Shengli Xie ◽  
Zhaoshui He ◽  
Yuli Fu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
The Novel ◽  
Blind Signal ◽  
Simple Simulation ◽  
Novel Approaches

Stone's method is one of the novel approaches to the blind source separation (BSS) problem and is based on Stone's conjecture. However, this conjecture has not been proved. We present a simple simulation to demonstrate that Stone's conjecture is incorrect. We then modify Stone's conjecture and prove this modified conjecture as a theorem, which can be used a basis for BSS algorithms.

Blind source separation based on self-organizing neural network

2006 ◽  
Vol 19 (3) ◽  
pp. 305-311 ◽  
Author(s):  
Anke Meyer-Bäse ◽  
Peter Gruber ◽  
Fabian Theis ◽  
Simon Foo
Keyword(s):  
Neural Network ◽  
Blind Source Separation ◽  
Source Separation ◽  
Self Organizing

An Optimized BP Neural Network Based on DS Evidential Reasoning on Heart Disease Prediction

2012 ◽  
Vol 263-266 ◽  
pp. 3342-3347
Author(s):  
Nan Nan Xie ◽  
Fei Yan Chen ◽  
Kuo Zhao ◽  
Liang Hu
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Evidential Reasoning ◽  
Self Organization ◽  
Uncertain Information ◽  
Data Set ◽  
Time Prediction ◽  
Minimum Points

BP neural network is a widely used neural network, with advantages as adaptability, fault tolerance and self-organization. However, BP neural network is difficult to determine the network structure, and easy to fall into local minimum points. In this paper, an optimized BP neural network was proposed based on DS, he advantages of DS Evidential Reasoning on uncertain information are used to improve the recognition rate and credibility of BP. Experiments on Heart Disease Data set shows the proposed method have good performance on run time, prediction accuracy and robustness.

Application of the collision mathematical model based on a BP neural network in railway vehicles

2020 ◽  
pp. 095440972095986
Author(s):  
Yu-Ru Li ◽  
Tao Zhu ◽  
Shou-Ne Xiao ◽  
Bing Yang ◽  
Guang-Wu Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Finite Element ◽  
Bp Neural Network ◽  
Learning Performance ◽  
Small Data ◽  
Data Set ◽  
Computation Efficiency ◽  
Simulation Results ◽  
Relative Errors

In order to enhance the learning performance of small-data-set models and improve the computation efficiency of finite element simulations of vehicle collision, the collision mathematical model (VCMM) based on the back-propagation (BP) neural network is established to predict the collision response data of a single car and marshalling cars at unknown velocities. The predicted results of VCMM were compared with the simulation results of the finite element method (FEM) to verify the model. The compared results show that the maximum relative errors of deformation, energy absorption and average interfacial force of a single vehicle are all below 8.5%, and the relative errors of the maximum compression of the C0 coupler and the internal energy of the A1 car among the marshalling cars are all less than 5%. In addition, the calculation time of the single car and marshalling cars collisions based on the VCMM are reduced by 24.36 and 61.8 times, respectively, compared with the FEM results, and the simulation calculation efficiency is greatly improved. The prediction result of VCMM will partially replace experimental and simulation results for crashworthiness and safety design of the vehicle structure in future studies.

scholarly journals Considerations on the Use of RIS Records

2015 ◽  
Vol 13 ◽  
pp. 263-266
Author(s):  
Mihai Ghiba
Keyword(s):  
Comparative Analysis ◽  
Risk Analysis ◽  
Information Services ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Insurance ◽  
Transport Activity ◽  
Data Set ◽  
Risk Functions ◽  
River Information Services

This paper makes a comparative analysis of the data recorded and used in inland transport activity utlizand information that is recorded in "electronic ship reporting" and "Automatic Identification System" in the standard River Information Services - Vessel Traking and Tracing combined with those additionally recorded in the logbook and in the seafarer’s service book. RIS recordings largely make the database necessary for keeping the Logbook, and differences can be evaluated through a matrix. The concentrated data set can be used to determine the working time of seafarers and establishing the compliance with promotion requirements based on the training fully recorded, to accumulate data necessary for shaping risk functions, to clarify the conditions under which shipping accidents occurred, with results in the recovery of losses by marine insurance, to a thorough and accurate risk analysis.

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