- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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FPGA-Based Implementation of an Artificial Neural Network for Measurement Acceleration in BOTDA Sensors
摘要： In recent years, using distributed fiber-optic sensors based on Brillouin scattering, for monitoring pipelines, tunnels, and other constructional structures have gained huge popularity. However, these sensors have a low signal-to-noise ratio (SNR), which usually increases their measurement error. To alleviate this issue, ensemble averaging is used which improves the SNR but in return increases the measurement time. Reducing the noise by averaging requires hundreds or thousands of scans of the optical fiber; hence averaging is usually responsible for a large percent of the entire system latency. In this paper, we propose a novel method based on artificial neural network for SNR enhancement and measurement acceleration in distributed fiber-optic sensors based on the Brillouin scattering. Our method takes the noisy Brillouin spectrums and improves their SNR by 20 dB, which reduces the measurement time significantly. It also improves the accuracy of the Brillouin frequency shift estimation process and its latency by more than 50% in comparison with the state-of-the-art software and hardware solutions.
关键词： Artificial neural network (ANN),digital signal processing,optical fibers,curve fitting,field-programmable gate arrays (FPGAs)
更新于2025-09-23 15:23:52
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[IEEE 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) - Sonderborg, Denmark (2019.4.23-2019.4.25)] 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) - An AN-GA Controlled SEPIC Converter for Photovoltaic Grid Integration
摘要： In this paper, Artificial Neural Network (ANN) optimization with Genetic Algorithm (GA) is implemented. The optimized training to ANN is provide using Bayesian regulation. For this study, a Photovoltaic (PV) system has considered and optimal power tracking been interpreted with proper adjustment of ANN weights using GA approach, which reduces the Root Mean Square Error (RMSE). In this work, the single-ended primary – inductor converter (SEPIC) has been utilized for better power tracking from PV modules. SEPIC Converter accomplish with impedance matching power device and provides utmost PV power tracking. Space vector pulse width modulation-dSPACE interface been utilized as an inverter control. Simulated responses show the potency of the proposed system under sag, swell and varying loading conditions.
关键词： Root Mean Square Error (RMSE),SEPIC.,Grid,Photovoltaic,Artificial neural network (ANN),Genetic Algorithm (GA)
更新于2025-09-19 17:13:59
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Free Space Optical Communication (System Design, Modeling, Characterization and Dealing with Turbulence) || 4. Mitigation of beam wandering due to atmospheric turbulence and prediction of control quality using intelligent decision making tools
摘要： In a Free Space Optical Link (FSOL), atmospheric turbulence causes fluctuations in both intensity and phase of the received beam and impairs link performance. Beam motion is one of the main causes for major power loss. This chapter presents an investigation of the performance of two types of controller designed for aiming a laser beam at a particular spot under dynamic disturbances. Multiple experiment observability nonlinear input-output data mapping is used as the principal component for the controllers’ design. The first design is based on the Taguchi method while the second is the Artificial Neural Network (ANN) method. These controllers process the beam location information from a static linear map of a 2D plane: Optoelectronic Position Detector (OPD) as observer, and then generate the necessary outputs to steer the beam with a micro-electromechanical mirror: Fast Steering Mirror (FSM). The beam centroid is computed using a Mono-Pulse Algorithm (MPA). Evidence of suitability and effectiveness of the proposed controllers are comprehensively assessed and quantitatively measured in terms of coefficients of correlation, correction speed, control exactness, centroid displacement and stability of the receiver signal through the experimental results from the FSO link setup established for the horizontal range of 0.5 km at an altitude of 15.25 m. The test field type is open flat terrain, grass and a few isolated obstacles.
关键词： atmospheric turbulence,Artificial Neural Network (ANN),Mono-Pulse Algorithm (MPA),Fast Steering Mirror (FSM),beam wandering,Taguchi method,Free Space Optical Link (FSOL),Optoelectronic Position Detector (OPD)
更新于2025-09-12 10:27:22
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Free Space Optical Communication (System Design, Modeling, Characterization and Dealing with Turbulence) || 5. Low power and compact RSM and neural-controller design for beam wandering mitigation with a horizontal-path propagating Gaussian-beam wave: focused beam case
摘要： Beam wander on the detector plane is one of the main causes of major power loss which severely degrades the performance of Free Space Optical (FSO) links. Confronted with this big problem, designing a suitable controller to compensate beam wandering at a fast rate so as to increase beam stability becomes significant. This chapter presents an investigation of the performance of two types of controller designed for increasing the stability of the beam on the detector plane under dynamic disturbances. The first design is based on Taguchi’s method: Response Surface Model (RSM) controller while the second is the Artificial Neural Network (ANN) method (neural-controller). These controllers process the beam spot information and generate the necessary outputs to mitigate beam wandering, so as to perfectly couple the Power In the Bucket (PIB): receiver aperture, into the detector. Pipelined-parallel architecture for both controllers are proposed and developed in a Field Programmable Gate Array (FPGA). The implementation of these two candidate controllers is described in detail as installed at the receiver station. Evidence of the suitability and the effectiveness of the proposed controllers in terms of prediction exactness, prediction error, reduction of beam wander, response to impulse and effective scintillation index are provided through experimental results from the FSO link established for the horizontal range of 0.5 km at an altitude of 15.25 m.
关键词： Artificial Neural Network (ANN),Field Programmable Gate Array (FPGA),beam wander,Free Space Optical (FSO) links,Response Surface Model (RSM)
更新于2025-09-12 10:27:22
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[IEEE 2019 54th International Universities Power Engineering Conference (UPEC) - Bucharest, Romania (2019.9.3-2019.9.6)] 2019 54th International Universities Power Engineering Conference (UPEC) - Design of an intelligent MPPT based on ANN using a real photovoltaic system data
摘要： Maximum power point tracking (MPPT) methods are a fundamental part in photovoltaic (PV) system design for increasing the generated power of a PV array. Whilst several methods have been introduced, the artificial neural network (ANN) is an attractive method for MPPT due to its less oscillation and fast response. However, accurate training data is a big challenge to design an optimized ANN-MPPT technique. In this paper, an ANN-MPPT technique based on a large experimental training data is proposed to avoid the system from having a high training error. Those data are collected during one year from experimental tests of a PV system installed at Brunel University, London, United Kingdom. The irradiation and temperature of weather conditions are selected as the input, and the available power at MPP from the PV system as the output of the ANN model. To assess the performance, the Perturb and Observe (P&O) and the proposed ANN-MPPT methods are simulated using a MATLAB/Simulink model for the PV system. The results show that the proposed ANN method accurately tracks the optimal maximum power point and avoids the phenomenon of drift problem, whilst achieving a higher output power when compared with P&O-MPPT method.
关键词： photovoltaic (PV),Perturb and Observe (P&O),Artificial Neural Network (ANN),Maximum power point tracking (MPPT)
更新于2025-09-11 14:15:04