By means of an arificial neural network (ANN) model, higher measurement accuracy of integer harmonics can be obtained. Combining the windowed fast Fourier transform (FFT) algorithm with the improved ANN model, we pres...By means of an arificial neural network (ANN) model, higher measurement accuracy of integer harmonics can be obtained. Combining the windowed fast Fourier transform (FFT) algorithm with the improved ANN model, we present a new precise algorithm for non-integer harmonics analysis. According to the result obtained from the Hanning-windowed FFT algorithm, we choose the initial values of orders of harmonics for the neural network. Through such processing, the time of iterations is shortened and the convergence rate of neural network is raised thereby. The simulation results show that close non-integer harmonics can be separated from a signal with higher accuracy and better real-time by using the algorithm presented in the paper. Key words fast Fourier transform (FFT) - artificial neural network (ANN) - Hanning-window - harmonics analysis CLC number TM 935 Foundation item: Supported by the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of China (2001-182) and the Science Foundation of Naval University of Engineering(HGDJJ03001).Biography: WANG Gong-bao (1962-), male, Professor, research direction: artificial neural network, wavelet analysis and their applications to signal processing in electric power systems.展开更多
Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,t...Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the customers.One of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the PCC.The proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic distortion.The input parameter used in the models is easily measured with basic power meters.The model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing plants.Eight(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden layer.The results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.展开更多
To eliminate harmonic pollution incurred from the static synchronous compensator(STATCOM), a method of applying artificial neural network is presented. When PWM wave is formed based on the harmonic suppression theory,...To eliminate harmonic pollution incurred from the static synchronous compensator(STATCOM), a method of applying artificial neural network is presented. When PWM wave is formed based on the harmonic suppression theory, a concave is set on certain angle of the square wave to suppress unnecessary harmonics, by timely and on-line determining the chopping angle corresponding to respective harmonics through artificial neural network, i.e. by setting the position of concave to eliminate corresponding harmonics, the harmonic component on output voltage of the inverter can be improved. To conclude through computer simulation test, the perfect control effect has been proved.展开更多
Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in hi...Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.展开更多
The method for harmonic cancellation based on artificial neural network (ANN)is proposed. The task is accomplished by generating reference signal with frequency that should beeliminated from the output. The reference ...The method for harmonic cancellation based on artificial neural network (ANN)is proposed. The task is accomplished by generating reference signal with frequency that should beeliminated from the output. The reference input is weighted by the ANN in such a way that it closelymatches the harmonic. The weighted reference signal is added to the fundamental signal such thatthe output harmonic is cancelled leaving the desired signal alone. The weights of ANN are adjustedby output harmonic, which is isolated by a bandpass filter. The above concept is used as a basis forthe development of adaptive harmonic cancellation (AHC) algorithm. Simulation results performedwith a hydraulic system demonstrate the efficiency and validity of the proposed AHC control scheme.展开更多
提出了一种保持生理特征的交互式人脸编辑方法。采用控制点分层策略,即以用户直接操作的控制点对(称为主控制点对)为输入层,其他控制点对(称为次控制点对)为输出层,建立人工神经网络;然后采用误差反向传播法(Error Back Propagation)学...提出了一种保持生理特征的交互式人脸编辑方法。采用控制点分层策略,即以用户直接操作的控制点对(称为主控制点对)为输入层,其他控制点对(称为次控制点对)为输出层,建立人工神经网络;然后采用误差反向传播法(Error Back Propagation)学习,从而建立主、次控制点之间的约束关系;最后通过输出层将编辑信息在模型中进行插值。该编辑结果可以应用到具有相同拓扑的任意人脸模型上。实验结果表明,采用分层控制的方法不仅保持了编辑操作的方便性、精确性,同时还保持了人脸生理特征的真实性。展开更多
文摘By means of an arificial neural network (ANN) model, higher measurement accuracy of integer harmonics can be obtained. Combining the windowed fast Fourier transform (FFT) algorithm with the improved ANN model, we present a new precise algorithm for non-integer harmonics analysis. According to the result obtained from the Hanning-windowed FFT algorithm, we choose the initial values of orders of harmonics for the neural network. Through such processing, the time of iterations is shortened and the convergence rate of neural network is raised thereby. The simulation results show that close non-integer harmonics can be separated from a signal with higher accuracy and better real-time by using the algorithm presented in the paper. Key words fast Fourier transform (FFT) - artificial neural network (ANN) - Hanning-window - harmonics analysis CLC number TM 935 Foundation item: Supported by the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of China (2001-182) and the Science Foundation of Naval University of Engineering(HGDJJ03001).Biography: WANG Gong-bao (1962-), male, Professor, research direction: artificial neural network, wavelet analysis and their applications to signal processing in electric power systems.
文摘Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the customers.One of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the PCC.The proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic distortion.The input parameter used in the models is easily measured with basic power meters.The model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing plants.Eight(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden layer.The results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.
文摘To eliminate harmonic pollution incurred from the static synchronous compensator(STATCOM), a method of applying artificial neural network is presented. When PWM wave is formed based on the harmonic suppression theory, a concave is set on certain angle of the square wave to suppress unnecessary harmonics, by timely and on-line determining the chopping angle corresponding to respective harmonics through artificial neural network, i.e. by setting the position of concave to eliminate corresponding harmonics, the harmonic component on output voltage of the inverter can be improved. To conclude through computer simulation test, the perfect control effect has been proved.
基金supported by the ‘‘Detection of very low-flux background neutrons in China Jinping Underground Laboratory’’ project of the National Natural Science Foundation of China(No.11275134)
文摘Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.
文摘The method for harmonic cancellation based on artificial neural network (ANN)is proposed. The task is accomplished by generating reference signal with frequency that should beeliminated from the output. The reference input is weighted by the ANN in such a way that it closelymatches the harmonic. The weighted reference signal is added to the fundamental signal such thatthe output harmonic is cancelled leaving the desired signal alone. The weights of ANN are adjustedby output harmonic, which is isolated by a bandpass filter. The above concept is used as a basis forthe development of adaptive harmonic cancellation (AHC) algorithm. Simulation results performedwith a hydraulic system demonstrate the efficiency and validity of the proposed AHC control scheme.
文摘提出了一种保持生理特征的交互式人脸编辑方法。采用控制点分层策略,即以用户直接操作的控制点对(称为主控制点对)为输入层,其他控制点对(称为次控制点对)为输出层,建立人工神经网络;然后采用误差反向传播法(Error Back Propagation)学习,从而建立主、次控制点之间的约束关系;最后通过输出层将编辑信息在模型中进行插值。该编辑结果可以应用到具有相同拓扑的任意人脸模型上。实验结果表明,采用分层控制的方法不仅保持了编辑操作的方便性、精确性,同时还保持了人脸生理特征的真实性。
