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Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Harmonic Parameter Estimation Based on Classical Cosine Window Three Spectral Line Interpolation FFT LI Minghao, WANG Xuming, ZHANG Zhiwei, TAN Yuhao, LEI Kejun Journal of Jishou University(Natural Sciences Edition)    2022, 43 (3): 38-43.   DOI: 10.13438/j.cnki.jdzk.2022.03.006 Abstract （456）      PDF（pc） （360KB）（134）       Knowledge map    Save Aiming at the problems of spectrum leakage and fence effect in signal processing by fast Fourier transform（FFT）,based on the analysis of the characteristics of main-lobe and side-lobe of several classical cosine windows,a harmonic analysis method based on cosine window three spectral line interpolation FFT is proposed by using the spectral lines with the largest amplitude near the real frequency point and the spectral lines on both sides. Combined with the least square and polynomial fitting methods,the correction formula of harmonic parameters is derived.The simulation results show that,compared with the traditional FFT method,the proposed method can better suppress the influence of spectrum leakage and fence effect,and the detection accuracy of harmonic parameters has been further improved. Reference | Related Articles | Metrics | Comments（0） Select FFT Dielectric Loss Angle Measurement of Improved Rife-Vincent(Ι) Window ZHANG Zhiwei, PENG hao, TAN Yuhao, LI Minghao, WANG Xuming Journal of Jishou University(Natural Sciences Edition)    2022, 43 (3): 44-47.   DOI: 10.13438/j.cnki.jdzk.2022.03.007 Abstract （450）      PDF（pc） （398KB）（86）       Knowledge map    Save Due to the asynchronous sampling of the signal,there will be spectrum leakage when using FFT to process the signal,resulting in the low measurement accuracy of FFT dielectric loss angle.In order to improve the measurement accuracy of dielectric loss angle,the side lobe characteristics of different window functions are analyzed.A new cosine window is constructed through the self-multiplication operation of Rife-Vincent (Ι) window,and an FFT dielectric loss angle measurement method based on improved Rife-Vincent (Ι) window is proposed.The new window function has better sidelobe characteristics than the traditional window function,and improves the suppression effect of spectrum leakage.The simulation results show that the measurement accuracy of using Rife-Vincent (Ι) self-multiplication window method is higher than that of using Hanning window and Blackman window method. Reference | Related Articles | Metrics | Comments（0） Select Design of Two-Dimensional Photonic Crystal Filter with Kerr Medium LI Zhixin, SUN Jing, WU Cong, SU Muyang Journal of Jishou University(Natural Sciences Edition)    2021, 42 (6): 23-29.   DOI: 10.13438/j.cnki.jdzk.2021.06.004 Abstract （343）      PDF（pc） （3676KB）（200）       Knowledge map    Save Making use of the feature that the permittivity of Kerr medium changes with the incident light, a new-type filter with Kerr medium is designed by controlling the side length of dielectric cylinder of coupled cavity to change the frequency. This filter consists of point defect, micro-cavity, download wave-guide, reflection heterojunction and main wave-guide, and the square GaAs dielectric cylinder is used in coupled cavity. The simulation calculation made through COMSOL Multiphysics shows that the filtering efficiency of this new filter can reach 99.7%. Reference | Related Articles | Metrics | Comments（0） Select Load Forecasting of Urban Residential District Based on LSTM-AdaBoost LI Longxiang, PENG Chen, LI Jun, WANG Yuyan, LU Rongbo Journal of Jishou University(Natural Sciences Edition)    2021, 42 (6): 30-35.   DOI: 10.13438/j.cnki.jdzk.2021.06.005 Abstract （534）      PDF（pc） （478KB）（251）       Knowledge map    Save In smart cities, accurate residential load forecasting is the key to achieving a balance between power supply and demand and reducing resource waste. In order to improve the accuracy of load forecasting of urban residential districts, a short-term load forecasting model LSTM-AdaBoost, which combines long and short-term memory network (LSTM) and ensemble learning, is proposed. The model uses dew point (the temperature at which water vapor in the air condenses into water droplets), historical load, week type and other characteristics as data input; then the LSTM network with timing memory function is used as the base learner for integrated learning; finally, the ensemble AdaBoost  algorithm performs a weighted combination of the base learner to obtain a strong learner. Experimental results show that the integrated LSTM-AdaBoost model has higher forecast accuracy than single forecasting methods such as LSTM network, support vector machine (SVM) and CART decision tree. Reference | Related Articles | Metrics | Comments（0）