The Algorithms of Adaptive Active Noise Control Systems in a Duct

On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

Study on the Improvement of the Application of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise in Hydrology Based on RBFNN Data Extension Technology

The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult.Currently,some hydrologists employ the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method,a new time-frequency analysis method based on the empirical mode decomposition(EMD)algorithm,to decompose non-stationary raw data in order to obtain relatively stationary components for further study.However,the endpoint effect in CEEMDAN is often neglected,which can lead to decomposition errors that reduce the accuracy of the research results.In this study,we processed an original runoff sequence using the radial basis function neural network(RBFNN)technique to obtain the extension sequence before utilizing CEEMDAN decomposition.Then,we compared the decomposition results of the original sequence,RBFNN extension sequence,and standard sequence to investigate the influence of the endpoint effect and RBFNN extension on the CEEMDAN method.The results indicated that the RBFNN extension technique effectively reduced the error of medium and low frequency components caused by the endpoint effect.At both ends of the components,the extension sequence more accurately reflected the true fluctuation characteristics and variation trends.These advances are of great significance to the subsequent study of hydrology.Therefore,the CEEMDAN method,combined with an appropriate extension of the original runoff series,can more precisely determine multi-time scale characteristics,and provide a credible basis for the analysis of hydrologic time series and hydrological forecasting.

Novel Retinex algorithm by interpolation and adaptive noise suppression

In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination. After locating the points, the whole illumination image was computed by an interpolation technique. When attempting to recover the reflectance image, an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas. For color images, it was taken in the band of each channel separately. Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image entropy.

Multi-channel differencing adaptive noise cancellation with multi-kernel method

Although a various of existing techniques are able to improve the performance of detection of the weak interesting sig- nal, how to adaptively and efficiently attenuate the intricate noises especially in the case of no available reference noise signal is still the bottleneck to be overcome. According to the characteristics of sonar arrays, a multi-channel differencing method is presented to provide the prerequisite reference noise. However, the ingre- dient of obtained reference noise is too complicated to be used to effectively reduce the interference noise only using the clas- sical linear cancellation methods. Hence, a novel adaptive noise cancellation method based on the multi-kernel normalized least- mean-square algorithm consisting of weighted linear and Gaussian kernel functions is proposed, which allows to simultaneously con- sider the cancellation of linear and nonlinear components in the reference noise. The simulation results demonstrate that the out- put signal-to-noise ratio （SNR） of the novel multi-kernel adaptive filtering method outperforms the conventional linear normalized least-mean-square method and the mono-kernel normalized least- mean-square method using the realistic noise data measured in the lake experiment.

Least mean square error difference minimum criterion for adaptive chaotic noise canceller

The least mean square error difference （LMS-ED） minimum criterion for an adaptive chaotic noise canceller is proposed in this paper. Different from traditional least mean square error minimum criterion in which the error is uncorrelated with the input vector, the proposed LMS-ED minimum criterion tries to minimize the correlation between the error difference and input vector difference. The novel adaptive LMS-ED algorithm is then derived to update the weights of adaptive noise canceller. A comparison between cancelling performances of adaptive least mean square （LMS）, normalized LMS （NLMS） and proposed LMS-ED algorithms is simulated by using three kinds of chaotic noises. The simulation results clearly show that the proposed algorithm outperforms the LMS and NLMS algorithms in achieving small values of steady-state excess mean square error. Moreover, the computational complexity of the proposed LMS-ED algorithm is the same as that of the standard LMS algorithms.

Hardware implementation of adaptive filter for noise cancellation using TMS320C6713

Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi-tap adaptive noise cancellation （ANC） system by using the least mean square （LMS） algorithm on TMS320C6713 to remove undesired noise from a received signal for various audio related applications. Three different experiments are carried out by considering different audio inputs to test the efficiency of the designed ANC system. The ＇C＇ code implementation of LMS algorithm is introduced and simulated in code composer studio （CCS）, then realized on the digital signal processor （DSP） C6713. The 300 Hz, 500 Hz, 800 Hz, 1 kHz and 3 kHz of tone signals and male speech signal are used as the reference inputs to trace the noise of signal until it is eliminated. The performance of ANC system is studied in terms of convergence speed, order of the filter and signal-to-noise ratio （SNR）. The experimentam results demonstrate that the designed system shows a consider- able improvement in SNR.

A novel feature extraction method for ship-radiated noise

To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality.

Nonparametric VSS-APA based on precise background noise power estimate

The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean square(NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA(VSS-APA) provides a more reliable solution. A nonparametric VSS-APA(NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power(BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.

Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment

AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment.METHODS: In this study, we restrain high background noise using a new method-adaptive noise canceling based on independent component analysis (ANC-ICA), the method, by incorporating both second-order and higher-order statistics can remove noise components of the primary input signal based on statistical independence.RESULTS:The algorithm retained the local feature of lung sounds while eliminating high background noise, and performed more effectively than the conventional LMS algorithm.CONCLUSION:This method can cancel high battlefield noise of lung sounds effectively thus can help diagnose lung disease more accurately.

Frequency-Domain Wideband Acoustic Noise Cancellation System

When building an adaptive noise cancellation system for wideband acoustic signals, one can meet some difficulties in practical implementation of such a system. The major problem is related to the necessity of using real-time signal generation and processing. In this paper the active noise control system which utilizes adaptation in frequency domain is considered. It is shown that the proposed algorithms simplify practical implementation of a noise cancellation system. The results of computer simulations and prototype experiments show the effectiveness of the proposed methods. .

Network Sorting Algorithm of Multi-Frequency Signal with Adaptive SNR

An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.

基于CEEMDAN-HT的永磁同步电机匝间短路振动信号故障特征提取研究

由于长时间处于高负荷运行状态,永磁同步电机(permanent magnet synchronous motor, PMSM)定子绕组线圈匝与匝之间的绝缘性能容易降低,导致出现匝间短路,此时电机的振动强度会发生改变。针对此现象,提出将自适应噪声完备经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)与希尔伯特变换(Hilbert transform, HT)结合,构成一种CEEMDAN-HT非线性信号分析方法,并将其应用于提取振动信号故障特征。首先,利用CEEMDAN算法分解振动信号,得到一系列本征模态函数(intrinsic mode function, IMF),并将主元分析中的方差贡献率用于识别包含故障特征信息的IMF。其次,使用HT对方差贡献率较高的IMF进行分析,并以三维联合时频图呈现时间、瞬时频率与幅值,得到了主要故障特征。最后,使用ANSYS有限元软件建立了电机短路故障模型,并搭建了短路故障试验平台,通过对比有限元仿真结果与试验结果,对提出的方法进行了有效性和准确性验证。

Adaptive Noise Identification in Vision-assisted Motion Estimation for Unmanned Aerial Vehicles

Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles(UAVs).The noise of the vision location result is usually modeled as a white Gaussian noise so that this location result could be utilized as the observation vector in the Kalman filter to estimate the motion of the vehicle.Since the noise of the vision location result is affected by external environment,the variance of the noise is uncertain.However,in previous researches,the variance is usually set as a fixed empirical value,which will lower the accuracy of the motion estimation.The main contribution of this paper is that we proposed a novel adaptive noise variance identification(ANVI) method,which utilizes the special kinematic properties of the UAV for frequency analysis and then adaptively identifies the variance of the noise.The adaptively identified variance is used in the Kalman filter for more accurate motion estimation.The performance of the proposed method is assessed by simulations and field experiments on a quadrotor system.The results illustrate the effectiveness of the method.

Ultrasonic detection of interface in multilayers: Theory and adaptive noise canceling

The analytic expression of the received echo in multilayers NDT (Non-Destruction Evaluation) is derived. Then based on the analytic solution, interface signals are analyzed; and it is concluded that the received ultrasonic echo of mulilayers is composed of all the interface signals. By using the adaptive canceling of the signals from interfaces 0 and 1, the signals from interface 2 can be extracted. The method is applied to simulated and real echoes of multilayers,and the signals from interface 2 is separated successfully Based on the amplitude and arrival time of the signal from interface 2, the bond quality and depth of the interface can be evaluated

Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.

A hybrid approach based on complete ensemble empirical mode decomposition with adaptive noise for multi-step-ahead solar radiation forecasting

Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely.On the other hand,estimating it is extremely challenging due to the non-stationary behaviour and randomness of its components.In this research,a novel hybrid forecasting model,namely complete ensemble empirical mode decomposition with adaptive noise-Gaussian process regression(CEEMDAN-GPR),has been developed for daily global solar radiation prediction.The non-stationary global solar radiation series is transformed by CEEMDAN into regular subsets.After that,the GPR model uses these subsets as inputs to perform its prediction.According to the results of this research,the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting,namely wavelet-GPR and wavelet packet-GPR,in terms of mean square error,root mean square error,coefficient of determination and relative root mean square error values,which reached 3.23 MJ/m^(2)/day,1.80 MJ/m^(2)/day,95.56%,and 8.80%,respectively(for one-step forward forecasting).The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system.

Fast Affine Projection Algorithm for Adaptive Noise Canceling and Its Application on the Fetal Electrocardiogram Extraction

Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been researched.The simulations were made for the performance of these algorithms.The extraction of fetal electrocardiogram(FECG) is applied to compare the application effect of the above algorithms.The proposed FAP algorithm has obvious advantages in computational complexity,convergence speed and steadystate error.

A method of multi-channel reference signals acquiring in broadband ANC

In a flank array on an unmanned underwater vehicle （UUV）, self-generated noise which has broadband and colored spectrum property in frequency and spatial domain is the main factor affecting the performance of weak signal detection, so the technique of adaptive noise cancellation （ANC） as well as physical denoising and active noise cancellation are often used in practice. Because ANC is based on correlations, improvements in performance come from better correlation between reference signals and primary signals. Taking full advantage of the characteristics of flank arrays and the characteristics of information obtained from hydrophones, a new method for reference signal acquisition for adaptive noise cancellation is proposed, in which the multi-channel reference signals are obtained by accurate delaying for a given direction of arrival （DOA） and differencing between adjacent outputs of array elements. The validity of the proposed method was verified through system modeling simulations and lake experiments which showed good performance with little additional computational burden.

Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction:case study of the coastal waters of Beihai,China

Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.