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Relevant bibliographies by topics / Neural networks (Computer science) Sonar

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Journal articles

Academic literature on the topic 'Neural networks (Computer science) Sonar'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Journal articles on the topic "Neural networks (Computer science) Sonar"

1

Yanchen, Wu. "Sonar Image Target Detection and Recognition Based on Convolution Neural Network." Mobile Information Systems 2021 (March 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/5589154.

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Recent advancements in deep learning offer an effective approach for the study in machine vision using optical images. In this paper, a convolution neural network is used to deal with the target task of sonar detection, and the performance of each neural network model in the sonar image detection and recognition task of underwater box and tire is compared. The simulation results show that the neural network method proposed in this paper is better than the traditional machine learning methods and SSD network models. The average accuracy of the proposed method for sonar image target recognition

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2

Wang, Zhen, and Shanwen Zhang. "Sonar Image Detection Based on Multi-Scale Multi-Column Convolution Neural Networks." IEEE Access 7 (2019): 160755–67. http://dx.doi.org/10.1109/access.2019.2951443.

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3

Jin, Leilei, Hong Liang, and Changsheng Yang. "Accurate Underwater ATR in Forward-Looking Sonar Imagery Using Deep Convolutional Neural Networks." IEEE Access 7 (2019): 125522–31. http://dx.doi.org/10.1109/access.2019.2939005.

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4

Wang, Zhen, Buhong Wang, Jianxin Guo, and Shanwen Zhang. "Sonar Objective Detection Based on Dilated Separable Densely Connected CNNs and Quantum-Behaved PSO Algorithm." Computational Intelligence and Neuroscience 2021 (January 18, 2021): 1–19. http://dx.doi.org/10.1155/2021/6235319.

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Underwater sonar objective detection plays an important role in the field of ocean exploration. In order to solve the problem of sonar objective detection under the complex environment, a sonar objective detection method is proposed based on dilated separable densely connected convolutional neural networks (DS-CNNs) and quantum-behaved particle swarm optimization (QPSO) algorithm. Firstly, the dilated separable convolution kernel is proposed to extend the local receptive field and enhance the feature extraction ability of the convolution layers. Secondly, based on the linear interpolation algo

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5

Li, Wang, Zhang, Xin, and Liu. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach." Energies 12, no. 13 (2019): 2538. http://dx.doi.org/10.3390/en12132538.

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The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point predi

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6

Liu, Peng, and Yan Song. "Segmentation of sonar imagery using convolutional neural networks and Markov random field." Multidimensional Systems and Signal Processing 31, no. 1 (2019): 21–47. http://dx.doi.org/10.1007/s11045-019-00652-9.

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7

Guariso, Giorgio, Giuseppe Nunnari, and Matteo Sangiorgio. "Multi-Step Solar Irradiance Forecasting and Domain Adaptation of Deep Neural Networks." Energies 13, no. 15 (2020): 3987. http://dx.doi.org/10.3390/en13153987.

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The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by using three kinds of predictor structures. Two approaches are introduced: Multi-Model (MM) and Multi-Output (MO). Model parameters are identified for two kinds of neural networks, namely the traditional feed-forward (FF) and a class of recurrent networks, those with long short-term memory (LSTM) hidden neurons, which is relatively new for solar radiation forecasting. The performances of the considered approaches are rigorously assessed by appropriate indices and compared with standard benchmarks: the

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8

Ge, Yujia, Yurong Nan, and Lijun Bai. "A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks." Energies 12, no. 24 (2019): 4762. http://dx.doi.org/10.3390/en12244762.

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For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into

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9

Dror, Itiel E., Faith L. Florer, Damien Rios, and Mark Zagaeski. "Using artificial bat sonar neural networks for complex pattern recognition: Recognizing faces and the speed of a moving target." Biological Cybernetics 74, no. 4 (1996): 331–38. http://dx.doi.org/10.1007/bf00194925.

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10

Dror, Itiel E., Faith L. Florer, Damien Rios, and Mark Zagaeski. "Using artificial bat sonar neural networks for complex pattern recognition: Recognizing faces and the speed of a moving target." Biological Cybernetics 74, no. 4 (1996): 331–38. http://dx.doi.org/10.1007/s004220050244.

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