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Heterodyne phase measurement in low signal-to-noise ratios using genetic algorithm
摘要： The heterodyne phase measurement using radio frequency (RF) signal can only be performed in a sufficiently high signal-to-noise ratio (SNR). Because the strength of heterodyne signal is extremely sensitive to the phase front misalignment between the signal and the reference beams, the requirement of high SNR greatly limits its implementation. An innovative heterodyne phase measurement method using sequence shifting and genetic algorithm (GA) is reported in this paper. In the method, after being shifted with different steps, the digital sequences of the heterodyne signals containing the phases to be measured are added up. Taking the shifting steps of the sequences as variables, the GA searches the optimal shifting steps, with which the signal strength in the sum can reach the maximum. When the algorithm converges, the phase information can be derived by the optimal shifting steps. Numerical analysis indicates that the root mean square error (RMSE) of the phase measurement by our method can be less than ??/40 even when SNR is as low as ?10dB. With the excellent performance in low SNR, the new measurement method can be realized easily in practical applications.
关键词： Sequence shifting,Heterodyne phase measurement,Genetic algorithm,Low signal-to-noise ratio
更新于2025-09-04 15:30:14
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[IEEE 2018 19th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM) - Erlagol (Altai Republic), Russia (2018.6.29-2018.7.3)] 2018 19th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM) - Use of Genetic Algorithm and Evolution Strategy when Revealing the Worst Case Effects of Crosstalk Propagation in PCB Bus of Spacecraft Autonomous Navigation System
摘要： Importance of the genetic algorithm (GA) and evolution strategy (ES) usage in the investigation of an ultrashort pulse peak voltage in a printed circuit board (PCB) bus of autonomous navigation system (ANS) is highlighted. Trapezoidal ultrashort pulse propagation along the conductors of the PCB bus was optimized. The optimization was made by maximization criteria of maximum crosstalk amplitude at the preset point. The two methods were used for the optimization, which results are compared. The ES optimization was run 20 times when the initial solution was 300 ps. The GA optimization was run 10 times with the parameters: the number of chromosomes – 5, 10; the number of populations – 5, 7, 10, 15; mutation coefficient – 0.1; crossover coefficient – 0.5. After the ES and GA optimizations the crosstalk maximums of 32% and 78% of steady state level in the active conductors respectively were revealed and localized.
关键词： evolution strategy,printed circuit board,ultrashort pulse,genetic algorithm,optimization,peak voltage
更新于2025-09-04 15:30:14
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Rapid prediction of acid detergent fiber content in corn stover based on NIR-spectroscopy technology
摘要： Prediction of acid detergent fiber (ADF) content in corn stover depends on precise data and appropriate analytical methods. In this paper, the optimal PLSR-BPNN model was created for rapidly getting ADF content based on the optimal selection of crucial parameters and the combination of partial least squares regression (PLSR) and back propagation neural network (BPNN). Herein, Mahalanobis distance (MD) was proposed as a tool to recognize and remove outliers. Additionally, on the basis of the characteristic bands extracted by correlation coefficient method (CC), principal component analysis (PCA) was performed to select principal components (PCs) to further compress data of bands for obtaining few characteristic wavelengths. It turned out that the performance of PLSR calibration model based on the selected 10 wavelengths was best. The correlation coefficient (R2), root mean square error of prediction (RMSEP), residual predictive deviation (RPD) and relative standard deviation (RSD) of test set successively were 0.9936, 0.3765, 12.5869, and 0.0087. Besides, BPNN was proposed to cut down the nonlinear regression residual of PLSR model. Genetic algorithm (GA) was applied to avoid the problem of local minimum in network. When RMSEP decreased to the minimum value of 0.2181, PLSR-BPNN model was proven to further improve performance and reached for the best level. Finally, the result of external validation shown that the R2, RMSEP, RPD, RSD were 0.9856, 0.4590, 8.3264, 0.0110, respectively, the created model presented the best predictive performance. Hence, the proposed methods combining with NIR-spectroscopy technology can be used to determine ADF content in corn stover.
关键词： Principal component analysis,Corn stover,Acid detergent fiber,Back propagation neural network,Genetic algorithm,Partial least squares regression
更新于2025-09-04 15:30:14
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An enhanced Dynamic Modeling of PV Module Using Levenberg- Marquardt Algorithm
摘要： An improved dynamic modeling of PV cell/modules based on automatic parameters extraction is proposed in this paper. For the sake of clarity, three models are compared in this study including, Single Diode (SDM), Double Diode (DDM) and the empirical model developed by Sandia National Laboratory (SANDIA). The use of nominal parameters or the values given by manufacturer in both SDM and DDM diode saturation current I0 and photo-generation current Iph equations can engender a significant error depending on the operating conditions and the consumed lifetime. Hence, these values can be handled as model parameters, and can be adjusted using automatic parameters extraction algorithms. Moreover, parameters based on static extraction methods (with fixed irradiation and temperature) namely, Rs, Rsh and n do not give satisfactory results under variable irradiation and temperature, which involve the use of a dynamic adjustment method to improve these parameters. In this way, static parameters extraction using genetic algorithm (GA) is proposed as a first stage for both SDM and DDM. After that, a dynamic parameters extraction based on the Levenberg-Marquardt algorithm (LMA) has been employed in the purpose to adjust some nominal parameters provided by the literature and the manufacturer, and those given by the static method. The idea consists of considering the PV module and the MPPT as a single system with dynamic inputs (irradiation and temperature) and output (Impp, Vmpp and Pmpp) to minimize the error between the measured and the simulated outputs. The validity of the proposed approach is compared with dynamic LMA models, nominal parameters based models, and the models based on static GA extracted parameters under of different weather conditions and out-door measurements. The improved models show promising results in terms of agreement with real data.
关键词： Photovoltaic module,Genetic Algorithm (GA),Dynamic Parameters Extraction,Static Parameters Extraction,Levenberg- Marquardt (LM)
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Kota Kinabalu, Malaysia (2018.10.7-2018.10.10)] 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Comparison of Typical PV Module Performances Based on the Circuit Models
摘要： In PV systems, it is usually expected that power generation under varying conditions at any time should be maximum as much as possible. Therefore, it is essential to build a circuit model based on a widely used single or double diode model instantly varying conditions with minimum error under the estimation these approximate values through manufacturers' data sheet. It is fact that circuits models ease the performance analysis of PV systems if the unknown parameters are accurately estimated at various temperatures and unchanged irradiance levels or vice versa. This paper comparatively studies the performances of the two approximate circuit models for the four types of PV modules using the real-coded genetic algorithms. The results indicated that I-V characteristics obtained from the identified parameters exhibit almost similar trend with those given in manufacturers' data sheets under the same conditions. Besides, the estimated parameters obtained from using both models slightly show difference from each other and it can be said that the single diode model is solely satisfactory to model a typical PV model as long as they are accurately estimated.
关键词： single diode circuit model,PV-Battery system,double diode circuit model,genetic algorithm optimization
更新于2025-09-04 15:30:14