scholarly journals Development of a Shoreline Detection Method Using an Artificial Neural Network Based on Satellite SAR Imagery

2021 ◽  
Vol 13 (12) ◽  
pp. 2254
Author(s):  
Yoshimitsu Tajima ◽  
Lianhui Wu ◽  
Kunihiro Watanabe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Present Method ◽  
Detection Method ◽  
Computational Cost ◽  
Shoreline Change ◽  
Sar Images ◽  
Artificial Neural ◽  
Sar Imagery ◽  
Mean Square Errors

Monitoring shoreline change is one of the essential tasks for sustainable coastal zone management. Due to its wide coverage and relatively high spatiotemporal monitoring resolutions, satellite imagery based on synthetic aperture radar (SAR) is considered a promising data source for shoreline monitoring. In this study, we developed a robust shoreline detection method based on satellite SAR imagery using an artificial neural network (NN). The method uses the feedforward NN to classify the pixels of SAR imagery into two categories, land and sea. The shoreline location is then determined as a boundary of these two groups of classified pixels. To enhance the performance of the present NN for land–sea classification, we introduced two different approaches in the settings of the input layer that account not only for the local characteristics of pixels but also for the spatial pixel patterns with a certain distance from the target pixel. Two different approaches were tested against SAR images, which were not used for model training, and the results showed classification accuracies higher than 95% in most SAR images. The extracted shorelines were compared with those obtained from eye detection. We found that the root mean square errors of the shoreline position were generally less than around 15 m. The developed method was further applied to two long coasts. The relatively high accuracy and low computational cost support the advantages of the present method for shoreline detection and monitoring. It should also be highlighted that the present method is calibration-free, and has robust applicability to the shoreline with arbitrary angles and profiles.

Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

2020 ◽  
pp. 489-510
Author(s):  
Khwairakpam Amitab ◽  
Debdatta Kandar ◽  
Arnab K. Maji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Synthetic Aperture Radar ◽  
Back Propagation ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Multi Layer Perceptron ◽  
Sar Images ◽  
Artificial Neural

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

scholarly journals An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 48428-48437 ◽  
Author(s):  
Fatemeh Safara ◽  
Amin Salih Mohammed ◽  
Moayad Yousif Potrus ◽  
Saqib Ali ◽  
Quan Thanh Tho ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Algorithm ◽  
Detection Method ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
Artificial Neural ◽  
Gender Detection

scholarly journals PREDICTION FOR TRAFFIC ACCIDENT SEVERITY: COMPARING THE ARTIFICIAL NEURAL NETWORK, GENETIC ALGORITHM, COMBINED GENETIC ALGORITHM AND PATTERN SEARCH METHODS

Transport ◽  
2012 ◽  
Vol 26 (4) ◽  
pp. 353-366 ◽  
Author(s):  
Mehmet Metin Kunt ◽  
Iman Aghayan ◽  
Nima Noii
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Seat Belt ◽  
Pattern Search ◽  
Speed Ratio ◽  
Feed Forward Network ◽  
R Value ◽  
Artificial Neural ◽  
Mean Square Errors

This paper focuses on predicting the severity of freeway traffic accidents by employing twelve accident-related parameters in a genetic algorithm (GA), pattern search and artificial neural network (ANN) modelling methods. The models were developed using the input parameters of driver's age and gender, the use of a seat belt, the type and safety of a vehicle, weather conditions, road surface, speed ratio, crash time, crash type, collision type and traffic flow. The models were constructed based on 1000 of crashes in total that occurred during 2007 on the Tehran–Ghom Freeway due to the fact that the remaining records were not suitable for this study. The GA evaluated eleven equations to obtain the best one. Then, GA and PS methods were combined using the best GA equation. The neural network used multi-layer perceptron (MLP) architecture that consisted of a multi-layer feed-forward network with hidden sigmoid and linear output neurons that could also fit multi-dimensional mapping problems arbitrarily well. The ANN was applied during training, testing and validation and had 12 inputs, 25 neurons in the hidden layers and 3 neurons in the output layer. The best-fit model was selected according to the R-value, root mean square errors (RMSE), mean absolute errors (MAE) and the sum of square error (SSE). The highest R-value was obtained for the ANN around 0.87, demonstrating that the ANN provided the best prediction. The combination of GA and PS methods allowed for various prediction rankings ranging from linear relationships to complex equations. The advantage of these models is improving themselves adding new data.

The use of genetic algorithm and self-updating artificial neural network for the inverse design of cabin environment

2016 ◽  
Vol 26 (3) ◽  
pp. 347-354 ◽  
Author(s):  
Tian-hu Zhang ◽  
Xue-yi You
Keyword(s):  
Neural Network ◽  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Computational Cost ◽  
The Self ◽  
Inverse Design ◽  
Artificial Neural ◽  
The Impact

The inverse process of computational fluid dynamics was used to explore the expected indoor environment with the preset objectives. An inverse design method integrating genetic algorithm and self-updating artificial neural network is presented. To reduce the computational cost and eliminate the impact of prediction error of artificial neural network, a self-updating artificial neural network is proposed to realize the self-adaption of computational fluid dynamics database, where all the design objectives of solutions are obtained by computational fluid dynamics instead of artificial neural network. The proposed method was applied to the inverse design of an MD-82 aircraft cabin. The result shows that the performance of artificial neural network is improved with the increase of computational fluid dynamics database. When the number of computational fluid dynamics cases is more than 80, the success rate of artificial neural network increases to more than 40%. Comparing to genetic algorithm and computational fluid dynamics, the proposed hybrid method reduces about 53% of the computational cost. The pseudo solutions are avoided when the self-updating artificial neural network is adopted. In addition, the number of computational fluid dynamics cases is determined automatically, and the requirement of human adjustment is avoided.

scholarly journals Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1367
Author(s):  
Xiangyu Han ◽  
Dingkang Li ◽  
Lizong Huang ◽  
Hanqing Huang ◽  
Jin Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Detection Method ◽  
Recognition Rate ◽  
Local Optimum ◽  
Line Current ◽  
The Neural Network ◽  
Artificial Neural ◽  
Simple Neural Network

The influence of a series arc on line current is different with different loads, which makes it difficult to accurately extract arc fault characteristics suitable for all loads according to line current signal. To improve the accuracy of arc fault detection, a series arc fault detection method based on category recognition and an artificial neural network is proposed on the basis of analyzing the current characteristics of arc faults under different loads. According to the waveform of current and voltage, the load is divided into three types: Resistive category (Re), resistive-inductive category (RI), and rectifying circuit with a capacitive filter category (RCCF). Based on the wavelet transform, the characteristics of line current in the time domain and frequency domain when the series arc occurs under different types of loads are analyzed, and then the time and frequency indicators are taken as the inputs of the artificial neural network to establish three-layer neural networks corresponding to three types of loads to realize the detection of the series arc fault of lines under different categories of loads. To avoid the neural network falling into a local optimum, the initial weight and threshold of the neural network are optimized by a genetic algorithm, which further improves the accuracy of the neural network in arc identification. The experimental results show that the proposed arc detection method has the advantages of high recognition rate and a simple neural network model.

scholarly journals Intelligent Wind Retrieval from Chinese Gaofen-3 SAR Imagery in Quad Polarization

2019 ◽  
Vol 36 (11) ◽  
pp. 2121-2138 ◽  
Author(s):  
Weizeng Shao ◽  
Shuai Zhu ◽  
Xiaopeng Zhang ◽  
Shuiping Gou ◽  
Changzhe Jiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Retrieval Algorithm ◽  
Neural Network Method ◽  
Wind Retrieval ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Sar Imagery

AbstractThis study proposes the use of the artificial neural network for wind retrieval with Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) data. More than 10 000 images acquired in wave mode and quad-polarization strip map were collected over global seas throughout the 2-yr mission. The GF-3 operated in a quad-polarization channel—vertical–vertical (VV), vertical–horizontal (VH), horizontal–horizontal (HH), and horizontal–vertical (HV). These images were collocated with winds from the European Centre for Medium-Range Weather Forecasts at a 0.125° grid. The newly released wind retrieval algorithm for copolarization (VV and HH) SAR included CMOD7 and C-SARMOD2. We developed an algorithm based on an artificial neural network method using the SAR-measured normalized radar cross section at quad-polarization channels, herein named QPWIND_GF. Simulations using the QPWIND_GF showed that the correlation coefficient of wind speed was 0.94. We then validated the retrieval wind speeds against the measurements at a 0.25° grid from the Advanced Scatterometer. A comparison showed that the root-mean-square error (RMSE) of wind speed was 0.74 m s−1, which was better than the wind speed obtained using state-of-the-art methods—including, for example, CMOD7 (RMSE 0.88 m s−1) and C-SARMOD2 (RMSE 1.98 m s−1). The finding indicated that the accuracy of wind retrieval from GF-3 SAR images was significantly improved. Our work demonstrates the advanced feasibility of an artificial neural network method for SAR marine applications.

Artificial neural network–based internal leakage fault detection for hydraulic actuators: An experimental investigation

2017 ◽  
Vol 232 (4) ◽  
pp. 369-382 ◽  
Author(s):  
Zhikai Yao ◽  
Yongping Yu ◽  
Jianyong Yao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Experimental Investigation ◽  
Signal Processing ◽  
Deep Neural Network ◽  
Detection Method ◽  
The Other ◽  
Detection Accuracy ◽  
Artificial Neural

Internal leakage is a typical fault in the hydraulic systems, which may be caused by seal damage, and result in deteriorated performance of the system. To study this issue, this article carries out an experimental investigation of artificial neural network–based detection method for internal leakage fault. A period of pressure signal at one chamber of the actuator was taken in response to sinusoidal-like inputs for the closed-loop controlled system as a basic signal unit, and totally, 1000 periodic signal units are obtained from the experiments. The above experimental measurements are repetitively implemented with 11 different active exerted internal leakage levels, that is, totally 11,000 basic signal units are obtained. For signal processing, the pressure signal in the operation condition without active exerted leakage is chosen to generate a baseline with suitable pre-proceed, and the relative values of the other basic signal units (D-value between the baseline and other original signals) act as the global samples of the following artificial neural networks, traditional back propagation neural network, deep neural network, convolution neural network and auto-encoder neural network, separately; 8800 samples by random extraction as train samples to train the above neural networks and the other samples different from the train samples act as test samples to examine the detection accuracy of the proposed method. It is shown that the deep neural network with five layers can obtain a best detection accuracy (92.23%) of the above-mentioned neural networks. In addition, the methods based on wavelet transform and Hilbert–Huang transform are also applied, and a comparison of these methods is provided at last. From the comparison, it is shown that the proposed detection method obtains a good result without a need to model the internal leakage or a complicated signal processing.

scholarly journals A New Loss Generation Body Force Model for Fan/Compressor Blade Rows: An Artificial-Neural-Network Based Methodology

2021 ◽  
Vol 6 (1) ◽  
pp. 5
Author(s):  
Syamak Pazireh ◽  
Jeffrey J. Defoe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Body Force ◽  
Computational Cost ◽  
Force Model ◽  
Flow Conditions ◽  
Local Flow ◽  
Blade Section ◽  
Artificial Neural ◽  
Profile Loss

Body force models of fans and compressors are widely employed for predicting performance due to the reduction in computational cost associated with their use, particularly in nonuniform inflows. Such models are generally divided into a portion responsible for flow turning and another for loss generation. Recently, accurate, uncalibrated turning force models have been developed, but accurate loss generation models have typically required calibration against higher fidelity computations (especially when flow separation occurs). In this paper, a blade profile loss model is introduced which requires the trailing edge boundary layer momentum thicknesses. To estimate the momentum thickness for a given blade section, an artificial neural network is trained using over 400,000 combinations of blade section shape and flow conditions. A blade-to-blade flow field solver is used to generate the training data. The model obtained depends only on blade geometry information and the local flow conditions, making its implementation in a typical computational fluid dynamics framework straightforward. We show good agreement in the prediction of profile loss for 2D cascades both on and off design in the defined ranges for the neural network training.

scholarly journals Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nesrine Amor ◽  
Muhammad Tayyab Noman ◽  
Michal Petru
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Titanium Dioxide ◽  
Functional Properties ◽  
Cleaning Efficiency ◽  
Protection Factor ◽  
Artificial Neural ◽  
Mlp Model ◽  
Mean Square Errors ◽  
Coated Cotton

AbstractThis paper represents the efficiency of machine learning tool, i.e., artificial neural network (ANN), for the prediction of functional properties of nano titanium dioxide coated cotton composites. A comparative analysis was performed between the predicted results of ANN, multiple linear regression (MLR) and experimental results. ANN was applied to map out the complex input-output conditions to predict the optimal results. A backpropagation ANN model called a multilayer perceptron (MLP), trained with Bayesian regularization were used in this study. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor were analysed as output results. The accuracy of the proposed algorithm was evaluated and compared with MLR results. The obtained results reveal that MLP provides efficient results that are statistically significant in the prediction of functional properties ($$p<0.1 e^{-10} $$ p < 0.1 e - 10 ) compared to MLR. The correlation coefficient of MLP model ($$>95\%$$ > 95 % ) indicates that there is a strong correlation between the measured and predicted functional properties with a trivial mean absolute error and root mean square errors values. MLP model is suitable for the functional properties and can be used for the investigation of other properties of nano coated fabrics.

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