Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and a...Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.展开更多
In this paper, a linear moving average recursive filtering technique is proposed to reduce the peak-to-average power ratio (PAR) of orthogonal frequency division multiplexing (OFDM) signals. The proposed low complexit...In this paper, a linear moving average recursive filtering technique is proposed to reduce the peak-to-average power ratio (PAR) of orthogonal frequency division multiplexing (OFDM) signals. The proposed low complexity technique is analyzed in an oversampled OFDM system and a simple distribution approximation of the oversampled and linearly filtered OFDM signals is also proposed. Corresponding time domain linear equalizers are developed to recover originally transmitted data symbols. Through extensive computer simulations, effects of the new filtering technique on the oversampled OFDM peak-to-average power ratio (PAR), power spectral density (PSD) and corresponding linear equalizers on the frequency selective Rayleigh fading channel transmission symbol-error-rate (SER) performance are investigated. The newly proposed recursive filtering scheme results in attractive PAR reduction, requires no extra fast Fourier transform/inverse fast Fourier transform (FFT/IFFT) operations, refrains from transmitting any side information, and reduces out-of-band radiation. Also, corresponding linear receivers are shown to perform very close to their frequency domain counterparts.展开更多
Analysis of long-term EEG signals needs that it be segmented into pseudo stationary epochs. That work is done by regarding to statistical characteristics of a signal such as amplitude and frequency. Time series measur...Analysis of long-term EEG signals needs that it be segmented into pseudo stationary epochs. That work is done by regarding to statistical characteristics of a signal such as amplitude and frequency. Time series measured in real world is frequently non-stationary and to extract important information from the measured time series it is significant to utilize a filter or smoother as a pre-processing step. In the proposed approach, the signal is initially filtered by Moving Average (MA) or Savitzky-Golay filter to attenuate its short-term variations. Then, changes of the amplitude or frequency of the signal is calculated by Modified Varri method which is an acceptable algorithm for segmenting a signal. By using synthetic and real EEG data, the proposed methods are compared with original approach (simple Modified Varri). The simulation results indicate the absolute advantage of the proposed methods.展开更多
由于复杂海况随机海浪对船舶航行及人命安全造成威胁,通过构建海浪波高预测模型实现高海况海浪预警对提升航行安全具有重要意义。针对海浪波高预测问题,本文提出一种MAF-GWO-LSTM预测模型。首先利用滑动平均滤波器(Moving Average Filte...由于复杂海况随机海浪对船舶航行及人命安全造成威胁,通过构建海浪波高预测模型实现高海况海浪预警对提升航行安全具有重要意义。针对海浪波高预测问题,本文提出一种MAF-GWO-LSTM预测模型。首先利用滑动平均滤波器(Moving Average Filter,MAF)对实测海浪数据进行处理得到有效波高的光滑趋势序列,作为预测模型的输入训练集;再选用长短时记忆神经网络LSTM作为预测浪模型,依据灰狼优化算法(Grey Wolf Optimization,GWO)对滑动窗口MA及神经网络训练过程中的参数进行自适应寻优,并以南海实测有效波高数据进行验证。研究结果表明,采用MAF滤波有利于提取海浪有效波高特征,再通过GWO-LSTM预测模型优化神经网络参数,最优参数下波高预报精度达到R^(2)=0.991 0。论文研究可为高海况下海浪有效波高预报预警提供一种有效手段。展开更多
有源电力滤波器(Active Power Filter,APF)补偿电流检测环节对其补偿性能有着重要的影响。三相电网电压不对称且畸变时,传统的p-q法和i_p-i_q法无法准确实现对负荷综合补偿。为此文中引入基于滑动平均滤波器-自适应陷波滤波器(MAF-ANF)...有源电力滤波器(Active Power Filter,APF)补偿电流检测环节对其补偿性能有着重要的影响。三相电网电压不对称且畸变时,传统的p-q法和i_p-i_q法无法准确实现对负荷综合补偿。为此文中引入基于滑动平均滤波器-自适应陷波滤波器(MAF-ANF)的方法实现对非理想电网电压条件下网侧电压的处理,得到平衡无畸变的电网电压,并对负载电流处理,得到准确的基波电流正序分量。然后对传统p-q法进行改进,得到了基于MAF-ANF的谐波检测方法,该方法较传统方法谐波检测精度提高,取得了更好的补偿效果。最后通过Simulink仿真证明了在非理想电网电压条件下所提检测算法的正确性和有效性。展开更多
Neutrons have been extensively used in many fields,such as nuclear physics,biology,geology,medical science,and national defense,owing to their unique penetration characteristics.Gamma rays are usually accompanied by t...Neutrons have been extensively used in many fields,such as nuclear physics,biology,geology,medical science,and national defense,owing to their unique penetration characteristics.Gamma rays are usually accompanied by the detection of neutrons.The capability to discriminate neutrons from gamma rays is important for evaluating plastic scintillator neutron detectors because similar pulse shapes are generated from both forms of radiation in the detection system.The pulse signals measured by plastic scintillators contain noise,which decreases the accuracy of n-y discrimination.To improve the performance of n-y discrimination,the noise of the pulse signals should be filtered before the n-y discrimination process.In this study,the influences of the Fourier transform,wavelet transform,moving-average filter,and Kalman algorithm on the charge comparison method,fractal spectrum method,and back-propagation neural network methods were studied.It was found that the Fourier transform filtering algorithm exhibits better adaptability to the charge comparison method than others,with an increasing accuracy of 6.87%compared to that without the filtering process.Meanwhile,the Kalman filter offers an improvement of 3.04%over the fractal spectrum method,and the adaptability of the moving-average filter in backpropagation neural network discrimination is better than that in other methods,with an increase in 8.48%.The Kalman filtering algorithm has a significant impact on the peak value of the pulse,reaching 4.49%,and it has an insignificant impact on the energy resolution of the spectrum measurement after discrimination.展开更多
为减小光散射法的矿井岩尘颗粒物测量质量浓度误差,仿真模拟光子在含尘空间内的随机蒙特卡洛过程,并根据煤、岩尘颗粒物在不同散射面下的捕获光子数以及不同质量浓度范围下动态时间弯曲(dynamic time warping,DTW)距离的差异进行尘源区...为减小光散射法的矿井岩尘颗粒物测量质量浓度误差,仿真模拟光子在含尘空间内的随机蒙特卡洛过程,并根据煤、岩尘颗粒物在不同散射面下的捕获光子数以及不同质量浓度范围下动态时间弯曲(dynamic time warping,DTW)距离的差异进行尘源区分,针对浓度补偿实验获取的岩尘颗粒物测量质量浓度波动较大的问题,使用移动平均和卡尔曼滤波算法进行测量质量浓度的平滑处理。研究结果表明:岩尘颗粒物捕获光子数在90°散射面下有较大差异,在3~555 mg/m^(3)范围内区分煤、岩尘的模数转换差值的DTW判断阈值为23854.06,卡尔曼滤波算法在减小相对测量误差方面比移动平均更好。1#光电传感器在194~555 mg/m^(3)范围内平均相对测量最小误差为-1.34%,2#光电传感器在3~191 mg/m^(3)范围内平均相对测量最小误差为6.06%。研究结果可为矿井粉尘光学测量装置提供数据参考。展开更多
基金The National Key Research and Development Program of China under contract No.2017YFC1404000the Basic Scientific Fund for National Public Research Institutes of China under contract No.2018S03the National Natural Science Foundation of China under contract Nos 41776038 and 41821004
文摘Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.
文摘In this paper, a linear moving average recursive filtering technique is proposed to reduce the peak-to-average power ratio (PAR) of orthogonal frequency division multiplexing (OFDM) signals. The proposed low complexity technique is analyzed in an oversampled OFDM system and a simple distribution approximation of the oversampled and linearly filtered OFDM signals is also proposed. Corresponding time domain linear equalizers are developed to recover originally transmitted data symbols. Through extensive computer simulations, effects of the new filtering technique on the oversampled OFDM peak-to-average power ratio (PAR), power spectral density (PSD) and corresponding linear equalizers on the frequency selective Rayleigh fading channel transmission symbol-error-rate (SER) performance are investigated. The newly proposed recursive filtering scheme results in attractive PAR reduction, requires no extra fast Fourier transform/inverse fast Fourier transform (FFT/IFFT) operations, refrains from transmitting any side information, and reduces out-of-band radiation. Also, corresponding linear receivers are shown to perform very close to their frequency domain counterparts.
文摘Analysis of long-term EEG signals needs that it be segmented into pseudo stationary epochs. That work is done by regarding to statistical characteristics of a signal such as amplitude and frequency. Time series measured in real world is frequently non-stationary and to extract important information from the measured time series it is significant to utilize a filter or smoother as a pre-processing step. In the proposed approach, the signal is initially filtered by Moving Average (MA) or Savitzky-Golay filter to attenuate its short-term variations. Then, changes of the amplitude or frequency of the signal is calculated by Modified Varri method which is an acceptable algorithm for segmenting a signal. By using synthetic and real EEG data, the proposed methods are compared with original approach (simple Modified Varri). The simulation results indicate the absolute advantage of the proposed methods.
文摘由于复杂海况随机海浪对船舶航行及人命安全造成威胁,通过构建海浪波高预测模型实现高海况海浪预警对提升航行安全具有重要意义。针对海浪波高预测问题,本文提出一种MAF-GWO-LSTM预测模型。首先利用滑动平均滤波器(Moving Average Filter,MAF)对实测海浪数据进行处理得到有效波高的光滑趋势序列,作为预测模型的输入训练集;再选用长短时记忆神经网络LSTM作为预测浪模型,依据灰狼优化算法(Grey Wolf Optimization,GWO)对滑动窗口MA及神经网络训练过程中的参数进行自适应寻优,并以南海实测有效波高数据进行验证。研究结果表明,采用MAF滤波有利于提取海浪有效波高特征,再通过GWO-LSTM预测模型优化神经网络参数,最优参数下波高预报精度达到R^(2)=0.991 0。论文研究可为高海况下海浪有效波高预报预警提供一种有效手段。
文摘有源电力滤波器(Active Power Filter,APF)补偿电流检测环节对其补偿性能有着重要的影响。三相电网电压不对称且畸变时,传统的p-q法和i_p-i_q法无法准确实现对负荷综合补偿。为此文中引入基于滑动平均滤波器-自适应陷波滤波器(MAF-ANF)的方法实现对非理想电网电压条件下网侧电压的处理,得到平衡无畸变的电网电压,并对负载电流处理,得到准确的基波电流正序分量。然后对传统p-q法进行改进,得到了基于MAF-ANF的谐波检测方法,该方法较传统方法谐波检测精度提高,取得了更好的补偿效果。最后通过Simulink仿真证明了在非理想电网电压条件下所提检测算法的正确性和有效性。
基金supported by the Key Natural Science Projects of the Sichuan Education Department(No.18ZA0067)the Key Science and Technology Projects of Leshan(No.19SZD117)。
文摘Neutrons have been extensively used in many fields,such as nuclear physics,biology,geology,medical science,and national defense,owing to their unique penetration characteristics.Gamma rays are usually accompanied by the detection of neutrons.The capability to discriminate neutrons from gamma rays is important for evaluating plastic scintillator neutron detectors because similar pulse shapes are generated from both forms of radiation in the detection system.The pulse signals measured by plastic scintillators contain noise,which decreases the accuracy of n-y discrimination.To improve the performance of n-y discrimination,the noise of the pulse signals should be filtered before the n-y discrimination process.In this study,the influences of the Fourier transform,wavelet transform,moving-average filter,and Kalman algorithm on the charge comparison method,fractal spectrum method,and back-propagation neural network methods were studied.It was found that the Fourier transform filtering algorithm exhibits better adaptability to the charge comparison method than others,with an increasing accuracy of 6.87%compared to that without the filtering process.Meanwhile,the Kalman filter offers an improvement of 3.04%over the fractal spectrum method,and the adaptability of the moving-average filter in backpropagation neural network discrimination is better than that in other methods,with an increase in 8.48%.The Kalman filtering algorithm has a significant impact on the peak value of the pulse,reaching 4.49%,and it has an insignificant impact on the energy resolution of the spectrum measurement after discrimination.
