Log in

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

To see the other types of publications on this topic, follow the link: Neural networks (Computer science) Sonar.

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

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Neural networks (Computer science) Sonar.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Khishe, Mohammad, and Abbas Safari. "Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm." Wireless Personal Communications 108, no. 4 (2019): 2241–60. http://dx.doi.org/10.1007/s11277-019-06520-w.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Ge, Shengguo, Kuo Li, and Siti Nurulain Binti Mohd Rum. "Deep Learning Approach in DOA Estimation: A Systematic Literature Review." Mobile Information Systems 2021 (September 16, 2021): 1–14. http://dx.doi.org/10.1155/2021/6392875.

Full text

Abstract:

In array signal processing, the direction of arrival (DOA) of the signal source has drawn broad research interests with its wide applications in fields such as sonar, radar, communications, medical detection, and electronic countermeasures. In recent years, the application of deep learning (DL) to DOA estimation has achieved great success. This study provides a systematic review of research on DOA estimation using deep neural network methods. We manually selected twenty-five papers related to this research from five prominent databases (SpringerLink, IEEE Xplore, ScienceDirect, Scopus, and Goo

APA, Harvard, Vancouver, ISO, and other styles

13

Thanh Le, Hoang, Son Lam Phung, Philip B. Chapple, Abdesselam Bouzerdoum, Christian H. Ritz, and Le Chung Tran. "Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery." IEEE Access 8 (2020): 94126–39. http://dx.doi.org/10.1109/access.2020.2995390.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Zakharia, Manell E., and Patrick Chevret. "Neural network approach for inverting velocity dispersion; application to sediment and to sonar target characterization." Inverse Problems 16, no. 6 (2000): 1693–708. http://dx.doi.org/10.1088/0266-5611/16/6/307.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Suresh, Vishnu, Przemyslaw Janik, Jacek Rezmer, and Zbigniew Leonowicz. "Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm." Energies 13, no. 3 (2020): 723. http://dx.doi.org/10.3390/en13030723.

Full text

Abstract:

The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural Network (CNN) approach consisting of different architectures, such as the regular CNN, multi-headed CNN, and CNN-LSTM (CNN-Long Short-Term Memory), which utilizes a sliding window data-level approach and other data pre-processing techniques to make accurate forecasts. The output of the solar panels is linked to

APA, Harvard, Vancouver, ISO, and other styles

16

Mosavi, M. R., M. Khishe, and M. Akbarisani. "Neural Network Trained by Biogeography-Based Optimizer with Chaos for Sonar Data Set Classification." Wireless Personal Communications 95, no. 4 (2017): 4623–42. http://dx.doi.org/10.1007/s11277-017-4110-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

ERKMEN, B., and T. YILDIRIM. "Improving classification performance of sonar targets by applying general regression neural network with PCA." Expert Systems with Applications 35, no. 1-2 (2008): 472–75. http://dx.doi.org/10.1016/j.eswa.2007.07.021.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Ghimire, Deo, Raj, and Mi. "Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction." Energies 12, no. 12 (2019): 2407. http://dx.doi.org/10.3390/en12122407.

Full text

Abstract:

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and de

APA, Harvard, Vancouver, ISO, and other styles

19

Lo Sciuto, Grazia. "Application of Artificial Intelligence for Optimization of Organic Solar Cells Production Process." Photonics Letters of Poland 12, no. 2 (2020): 34. http://dx.doi.org/10.4302/plp.v12i2.993.

Full text

Abstract:

The study of organic solar cells (OSCs) has been rapidly developed in recent years. Organic solar cell technology is sought after mainly due to the ease of manufacture and their exclusive properties such as mechanical flexibility, light-weight, and transparency. These properties of OSCs are well-suited for unconventional applications with power conversion efficiencies more high than 10%. The flexibility of the used substrates and the thinness of the devices make OSCs ideal for roll-to-roll production. However the organic solar cells still have very low conversion efficiencies due to degradatio

APA, Harvard, Vancouver, ISO, and other styles

20

Mendonça de Paiva, Gabriel, Sergio Pires Pimentel, Bernardo Pinheiro Alvarenga, Enes Gonçalves Marra, Marco Mussetta, and Sonia Leva. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks." Energies 13, no. 11 (2020): 3005. http://dx.doi.org/10.3390/en13113005.

Full text

Abstract:

The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, i

APA, Harvard, Vancouver, ISO, and other styles

21

Lee, Woonghee, Keonwoo Kim, Junsep Park, Jinhee Kim, and Younghoon Kim. "Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks." IEEE Access 6 (2018): 73068–80. http://dx.doi.org/10.1109/access.2018.2883330.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Liu, Chun-Hung, Jyh-Cherng Gu, and Ming-Ta Yang. "A Simplified LSTM Neural Networks for One Day-Ahead Solar Power Forecasting." IEEE Access 9 (2021): 17174–95. http://dx.doi.org/10.1109/access.2021.3053638.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Boubaker, Sahbi, Mohamed Benghanem, Adel Mellit, Ayoub Lefza, Omar Kahouli, and Lioua Kolsi. "Deep Neural Networks for Predicting Solar Radiation at Hail Region, Saudi Arabia." IEEE Access 9 (2021): 36719–29. http://dx.doi.org/10.1109/access.2021.3062205.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Crisosto, Cristian, Martin Hofmann, Riyad Mubarak, and Gunther Seckmeyer. "One-Hour Prediction of the Global Solar Irradiance from All-Sky Images Using Artificial Neural Networks." Energies 11, no. 11 (2018): 2906. http://dx.doi.org/10.3390/en11112906.

Full text

Abstract:

We present a method to predict the global horizontal irradiance (GHI) one hour ahead in one-minute resolution using Artificial Neural Networks (ANNs). A feed-forward neural network with Levenberg–Marquardt Backpropagation (LM-BP) was used and was trained with four years of data from all-sky images and measured global irradiance as input. The pictures were recorded by a hemispheric sky imager at the Institute of Meteorology and Climatology (IMuK) of the Leibniz Universität Hannover, Hannover, Germany (52.23° N, 09.42° E, and 50 m above sea level). The time series of the global horizontal irradi

APA, Harvard, Vancouver, ISO, and other styles

25

Riesgo, Francisco García, Sergio Luis Suárez Gómez, Enrique Díez Alonso, Carlos González-Gutiérrez, and Jesús Daniel Santos. "Fully Convolutional Approaches for Numerical Approximation of Turbulent Phases in Solar Adaptive Optics." Mathematics 9, no. 14 (2021): 1630. http://dx.doi.org/10.3390/math9141630.

Full text

Abstract:

Information on the correlations from solar Shack–Hartmann wavefront sensors is usually used for reconstruction algorithms. However, modern applications of artificial neural networks as adaptive optics reconstruction algorithms allow the use of the full image as an input to the system intended for estimating a correction, avoiding approximations and a loss of information, and obtaining numerical values of those correlations. Although studied for night-time adaptive optics, the solar scenario implies more complexity due to the resolution of the solar images potentially taken. Fully convolutional

APA, Harvard, Vancouver, ISO, and other styles

26

Hwang, Hye-Rin, Berm-Soo Kim, Tae-Hyun Cho, and In-Soo Lee. "Implementation of a Fault Diagnosis System Using Neural Networks for Solar Panel." International Journal of Control, Automation and Systems 17, no. 4 (2019): 1050–58. http://dx.doi.org/10.1007/s12555-018-0153-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Farrokhrooz, Mehdi, and Mahmood Karimi. "Marine Vessels Acoustic Radiated Noise Classification in Passive Sonar Using Probabilistic Neural Network and Spectral Features." Intelligent Automation & Soft Computing 17, no. 3 (2011): 369–83. http://dx.doi.org/10.1080/10798587.2011.10643155.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

CAI, CONG-ZHONG, WAN-LU WANG, and YU-ZONG CHEN. "SUPPORT VECTOR MACHINE CLASSIFICATION OF PHYSICAL AND BIOLOGICAL DATASETS." International Journal of Modern Physics C 14, no. 05 (2003): 575–85. http://dx.doi.org/10.1142/s0129183103004759.

Full text

Abstract:

The support vector machine (SVM) is used in the classification of sonar signals and DNA-binding proteins. Our study on the classification of sonar signals shows that SVM produces a result better than that obtained from other classification methods, which is consistent from the findings of other studies. The testing accuracy of classification is 95.19% as compared with that of 90.4% from multilayered neural network and that of 82.7% from nearest neighbor classifier. From our results on the classification of DNA-binding proteins, one finds that SVM gives a testing accuracy of 82.32%, which is sl

APA, Harvard, Vancouver, ISO, and other styles

29

García Riesgo, Francisco, Sergio Luis Suárez Gómez, Jesús Daniel Santos, Enrique Díez Alonso, and Fernando Sánchez Lasheras. "Overview and Choice of Artificial Intelligence Approaches for Night-Time Adaptive Optics Reconstruction." Mathematics 9, no. 11 (2021): 1220. http://dx.doi.org/10.3390/math9111220.

Full text

Abstract:

Adaptive optics (AO) is one of the most relevant systems for ground-based telescopes image correction. AO is characterized by demanding computational systems that must be able to quickly manage large amounts of data, trying to make all the calculations needed the closest to real-time. Furthermore, next generations of telescopes that are already being constructed will demand higher computational requirements. For these reasons, artificial neural networks (ANNs) have recently become one alternative to commonly used tomographic reconstructions based on several algorithms as the least-squares meth

APA, Harvard, Vancouver, ISO, and other styles

30

Dhillon, Sukham, Charu Madhu, Daljeet Kaur, and Sarvjit Singh. "A Solar Energy Forecast Model Using Neural Networks: Application for Prediction of Power for Wireless Sensor Networks in Precision Agriculture." Wireless Personal Communications 112, no. 4 (2020): 2741–60. http://dx.doi.org/10.1007/s11277-020-07173-w.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Yun, Won Soo, Dong Woo Cho, and Yoon Su Baek. "Dynamic Path Planning for Robot Navigation Using Sonor Mapping and Neural Networks." Journal of Dynamic Systems, Measurement, and Control 119, no. 1 (1997): 19–26. http://dx.doi.org/10.1115/1.2801208.

Full text

Abstract:

This paper presents a new path planning algorithm for safe navigation of a mobile robot in dynamic as well as static environments. The certainty grid concept is adopted to represent the robot’s surroundings and a simple sensor model is developed for fast acquisition of environmental information. The proposed system integrates global and local path planning and has been implemented in a partially known structured environment without loss of generality for an indoor mobile robot. The global planner finds the initial path based on Dijkstra’s algorithm, while the local planning scheme uses three n

APA, Harvard, Vancouver, ISO, and other styles

32

Aler, Ricardo, Javier Huertas-Tato, José M. Valls, and Inés M. Galván. "Improving Prediction Intervals Using Measured Solar Power with a Multi-Objective Approach." Energies 12, no. 24 (2019): 4713. http://dx.doi.org/10.3390/en12244713.

Full text

Abstract:

Prediction Intervals are pairs of lower and upper bounds on point forecasts and are useful to take into account the uncertainty on predictions. This article studies the influence of using measured solar power, available at prediction time, on the quality of prediction intervals. While previous studies have suggested that using measured variables can improve point forecasts, not much research has been done on the usefulness of that additional information, so that prediction intervals with less uncertainty can be obtained. With this aim, a multi-objective particle swarm optimization method was u

APA, Harvard, Vancouver, ISO, and other styles

33

Galván, Inés M., José M. Valls, Alejandro Cervantes, and Ricardo Aler. "Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks." Information Sciences 418-419 (December 2017): 363–82. http://dx.doi.org/10.1016/j.ins.2017.08.039.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Ameen, Bikhtiyar, Heiko Balzter, Claire Jarvis, and James Wheeler. "Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks." Energies 12, no. 1 (2019): 148. http://dx.doi.org/10.3390/en12010148.

Full text

Abstract:

More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificia

APA, Harvard, Vancouver, ISO, and other styles

35

Sivhugwana, K. S., and E. Ranganai. "Intelligent techniques, harmonically coupled and SARIMA models in forecasting solar radiation data: A hybridization approach." Journal of Energy in Southern Africa 31, no. 3 (2020): 14–37. http://dx.doi.org/10.17159/2413-3051/2020/v31i3a7754.

Full text

Abstract:

The unsteady and intermittent feature (mainly due to atmospheric mechanisms and diurnal cycles) of solar energy resource is often a stumbling block, due to its unpredictable nature, to receiving high-intensity levels of solar radiation at ground level. Hence, there has been a growing demand for accurate solar irradiance forecasts that properly explain the mixture of deterministic and stochastic characteristic (which may be linear or nonlinear) in which solar radiation presents itself on the earth’s surface. The seasonal autoregressive integrated moving average (SARIMA) models are popular for a

APA, Harvard, Vancouver, ISO, and other styles

36

Ahmed, Shahzad, Hafiz Mian Muhammad Adil, Iftikhar Ahmad, Muhammad Kashif Azeem, Zil e Huma, and Safdar Abbas Khan. "Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation." Energies 13, no. 14 (2020): 3695. http://dx.doi.org/10.3390/en13143695.

Full text

Abstract:

The problem of extracting maximum power from a photovoltaic (PV) system with negligible power loss is concerned with the power generating capability of the PV array and nature of the output load. Changing weather conditions and nonlinear behavior of PV systems pose a challenge in tracking of varying maximum power point. A robust nonlinear controller is required to ensure maximum power point tracking (MPPT) by handling nonlinearities of a system and making it robust against changing environmental conditions. Sliding mode controller is robust against disturbances, model uncertainties and paramet

APA, Harvard, Vancouver, ISO, and other styles

37

Ratshilengo, Mamphaga, Caston Sigauke, and Alphonce Bere. "Short-Term Solar Power Forecasting Using Genetic Algorithms: An Application Using South African Data." Applied Sciences 11, no. 9 (2021): 4214. http://dx.doi.org/10.3390/app11094214.

Full text

Abstract:

Renewable energy forecasts are critical to renewable energy grids and backup plans, operational plans, and short-term power purchases. This paper focused on short-term forecasting of high-frequency global horizontal irradiance data from one of South Africa’s radiometric stations. The aim of the study was to compare the predictive performance of the genetic algorithm and recurrent neural network models with the K-nearest neighbour model, which was used as the benchmark model. Empirical results from the study showed that the genetic algorithm model has the best conditional predictive ability com

APA, Harvard, Vancouver, ISO, and other styles

38

Ku, Meng-Lin, and Ting-Jui Lin. "Neural-Network-Based Power Control Prediction for Solar-Powered Energy Harvesting Communications." IEEE Internet of Things Journal 8, no. 16 (2021): 12983–98. http://dx.doi.org/10.1109/jiot.2021.3064150.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Wang, Maofa, Baochun Qiu, Zeifei Zhu, Huanhuan Xue, and Chuanping Zhou. "Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network." Applied Sciences 11, no. 16 (2021): 7530. http://dx.doi.org/10.3390/app11167530.

Full text

Abstract:

The active tracking technology of underwater acoustic targets is an important research direction in the field of underwater acoustic signal processing and sonar, and it has always been issued that draws researchers’ attention. The commonly used Kalman filter active tracking (KFAT) method is an effective tracking method, however, it is difficult to detect weak SNR signals, and it is easy to lose the target after the azimuth of different targets overlaps. This paper proposes a KFAT based on deep convolutional neural network (DCNN) method, which can effectively solve the problem of target loss. F

APA, Harvard, Vancouver, ISO, and other styles

40

Cheng, Hsu-Yung, Chih-Chang Yu, Kuo-Chang Hsu, Chi-Chang Chan, Mei-Hui Tseng, and Chih-Lung Lin. "Estimating Solar Irradiance on Tilted Surface with Arbitrary Orientations and Tilt Angles." Energies 12, no. 8 (2019): 1427. http://dx.doi.org/10.3390/en12081427.

Full text

Abstract:

Photovoltaics modules are usually installed with a tilt angle to improve performance and to avoid water or dust accumulation. However, measured irradiance data on inclined surfaces are rarely available, since installing pyranometers with various tilt angles induces high costs. Estimating inclined irradiance of arbitrary orientations and tilt angles is important because the installation orientations and tilt angles might be different at different sites. The goal of this work is to propose a unified transfer model to obtain inclined solar irradiance of arbitrary tilt angles and orientations. Art

APA, Harvard, Vancouver, ISO, and other styles

41

Papageorgiou, Konstantinos, Gustavo Carvalho, Elpiniki I. Papageorgiou, Dionysis Bochtis, and George Stamoulis. "Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique." Energies 13, no. 6 (2020): 1427. http://dx.doi.org/10.3390/en13061427.

Full text

Abstract:

Photovoltaic Solar Energy (PSE) sector has sparked great interest from governments over the last decade towards diminution of world’s dependency to fossil fuels, greenhouse gas emissions reduction and global warming mitigation. Photovoltaic solar energy also holds a significant role in the transition to sustainable energy systems. These systems and their optimal exploitation require an effective supply chain management system, such as design of the network, collection, storage, or transportation of this energy resource, without disregarding a country’s certain socio-economic and political cond

APA, Harvard, Vancouver, ISO, and other styles

42

Chen, Feiyan, Zhigao Zhou, Aiwen Lin, Jiqiang Niu, Wenmin Qin, and Zhong Yang. "Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods." Energies 12, no. 1 (2019): 150. http://dx.doi.org/10.3390/en12010150.

Full text

Abstract:

Accurate estimation of direct horizontal irradiance (DHI) is a prerequisite for the design and location of concentrated solar power thermal systems. Previous studies have shown that DHI observation stations are too sparsely distributed to meet requirements, as a result of the high construction and maintenance costs of observation platforms. Satellite retrieval and reanalysis have been widely used for estimating DHI, but their accuracy needs to be further improved. In addition, numerous modelling techniques have been used for this purpose worldwide. In this study, we apply five machine learning

APA, Harvard, Vancouver, ISO, and other styles

43

Wang, Xiaoyan, and Gaokui Xu. "Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter." Complexity 2021 (July 14, 2021): 1–10. http://dx.doi.org/10.1155/2021/5561975.

Full text

Abstract:

Traditional energy sources have become one of the most serious causes of environmental pollution because of the growing demand for energy. Because of the carbon emissions that have recently increased greatly, we had to search for a safe, cheap, and environmentally friendly energy source. Many photovoltaic (PV) solar panels are used as an energy source because of free and environmental friendliness. However, this technology has become a source of inspiration for many researchers. The proposed method suggests to extract useful features from PV and wind generators and then train the system to con

APA, Harvard, Vancouver, ISO, and other styles

44

Wang, Linhua, and Jiarong Shi. "A Comprehensive Application of Machine Learning Techniques for Short-Term Solar Radiation Prediction." Applied Sciences 11, no. 13 (2021): 5808. http://dx.doi.org/10.3390/app11135808.

Full text

Abstract:

Forecasting the output power of solar PV systems is required for the good operation of the power grid and the optimal management of energy fluxes occurring in the solar system. Before forecasting the solar system’s output, it is essential to focus on the prediction of solar irradiance. In this paper, the solar radiation data collected for two years in a certain place in Jiangsu in China are investigated. The objective of this paper is to improve the ability of short-term solar radiation prediction. Firstly, missing data are recovered through the means of matrix completion. Then the completed d

APA, Harvard, Vancouver, ISO, and other styles

45

Cifuentes, Jenny, Geovanny Marulanda, Antonio Bello, and Javier Reneses. "Air Temperature Forecasting Using Machine Learning Techniques: A Review." Energies 13, no. 16 (2020): 4215. http://dx.doi.org/10.3390/en13164215.

Full text

Abstract:

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, a

APA, Harvard, Vancouver, ISO, and other styles

46

Calabrese, Bernardo, Ramiro Velázquez, Carolina Del-Valle-Soto, Roberto de Fazio, Nicola Ivan Giannoccaro, and Paolo Visconti. "Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired." Energies 13, no. 22 (2020): 6104. http://dx.doi.org/10.3390/en13226104.

Full text

Abstract:

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming

APA, Harvard, Vancouver, ISO, and other styles

47

Truong, Ngoc-Son, Ngoc-Tri Ngo, and Anh-Duc Pham. "Forecasting Time-Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency." Computational Intelligence and Neuroscience 2021 (July 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/6028573.

Full text

Abstract:

Building energy efficiency is important because buildings consume a significant energy amount. The study proposed additive artificial neural networks (AANNs) for predicting energy use in residential buildings. A dataset in hourly resolution was used to evaluate the AANNs model, which was collected from a residential building with a solar photovoltaic system. The proposed AANNs model achieved good predictive accuracy with 14.04% in mean absolute percentage error (MAPE) and 111.98 Watt-hour in the mean absolute error (MAE). Compared to the support vector regression (SVR), the AANNs model can sig

APA, Harvard, Vancouver, ISO, and other styles

48

Álvarez-Alvarado, José Manuel, José Gabriel Ríos-Moreno, Saul Antonio Obregón-Biosca, Guillermo Ronquillo-Lomelí, Eusebio Ventura-Ramos, and Mario Trejo-Perea. "Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review." Applied Sciences 11, no. 3 (2021): 1044. http://dx.doi.org/10.3390/app11031044.

Full text

Abstract:

The use of intelligent algorithms for global solar prediction is an ideal tool for research focused on the use of solar energy. Forecasting solar radiation supports different applications focused on the generation and transport of energy in places where there are no meteorological stations. Different solar radiation prediction techniques have been applied in different time horizons, such as neural networks (ANN) or machine learning (ML), with the latter being the most important. The support vector machine (SVM) is a classification method of the ML that is used to predict solar radiation. To ob

APA, Harvard, Vancouver, ISO, and other styles

49

K. Tiwari, Pawan, Mugdha Sahu, Gagan Kumar, and Mohsen Ashourian. "Pivotal Role of Quantum Dots in the Advancement of Healthcare Research." Computational Intelligence and Neuroscience 2021 (August 6, 2021): 1–9. http://dx.doi.org/10.1155/2021/2096208.

Full text

Abstract:

The quantum dot is a kind of nanoparticle whose dimension is smaller than the size of a typical nanoparticle ranging from tens of nanometers to a few hundredths of nanometers. The quantum mechanical behavior associated with the quantum dot displays different optical and electronic properties, enabling the quantum dot to find potential applications in a multitude of areas such as solar cells, light-emitting diodes, lasers, and biomedical applications. The objective of this investigation is to explore its fundamentals, synthesis, and applications, especially in the healthcare domain. We have dis

APA, Harvard, Vancouver, ISO, and other styles

50

Ge, Qiang, Fengxue Ruan, Baojun Qiao, Qian Zhang, Xianyu Zuo, and Lanxue Dang. "Side-Scan Sonar Image Classification Based on Style Transfer and Pre-Trained Convolutional Neural Networks." Electronics 10, no. 15 (2021): 1823. http://dx.doi.org/10.3390/electronics10151823.

Full text

Abstract:

Side-scan sonar is widely used in underwater rescue and the detection of undersea targets, such as shipwrecks, aircraft crashes, etc. Automatic object classification plays an important role in the rescue process to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in side-scan sonar images is still lacking, which is due to a lack of datasets and the small number of image samples containing specific target objects. Secondly, the real data of side-scan sonar images are unbalanced. Therefore, a side-scan sonar

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!