超声波局放仪性能测评研究

分析超声波局放仪的工作原理和特性指标。提出一种声发射信号模拟测评法,详细分析了超声波局放仪高级功能测评主谐振频率、灵敏度、线性度和方向性4个性能指标。通过对超声波局放仪现场测评对比,验证了笔者所提测评方法的有效性。

Denoising Method for Shear Probe Signal Based on Wavelet Thresholding

Shear probe works under a tough environment where the turbulence signals to be measured are very weak. The measured turbulence signals often contain a large amount of noise. Due to wide frequency band, noise signals cannot be effectively removed by traditional methods based on Fourier transform. In this paper, a wavelet thresholding denoising method is proposed for turbulence signal processing in that wavelet analysis can be used for multi-resolution analysis and can extract local characteristics of the signals in both time and frequency domains. Turbulence signal denoising process is modeled based on the wavelet theory and characteristics of the turbulence signal. The threshold and decomposition level, as well as the procedure of the turbulence signal denoising, are determined using the wavelet thresholding method. The proposed wavelet thresholding method was validated by turbulence signal denoising of the Western Pacific Ocean trial data. The results show that the propsed method can reduce the noise in the measured signals by shear probes, and the frequency spectrums of the denoised signal correspond well to the Nasmyth spectrum.

An underwater acoustic data compression method based on compressed sensing

The use of underwater acoustic data has rapidly expanded with the application of multichannel, large-aperture underwater detection arrays. This study presents an underwater acoustic data compression method that is based on compressed sensing. Underwater acoustic signals are transformed into the sparse domain for data storage at a receiving terminal, and the improved orthogonal matching pursuit(IOMP) algorithm is used to reconstruct the original underwater acoustic signals at a data processing terminal. When an increase in sidelobe level occasionally causes a direction of arrival estimation error, the proposed compression method can achieve a 10 times stronger compression for narrowband signals and a 5 times stronger compression for wideband signals than the orthogonal matching pursuit(OMP) algorithm. The IOMP algorithm also reduces the computing time by about 20% more than the original OMP algorithm. The simulation and experimental results are discussed.

Interactive Genetic Algorithms with Fitness Adjustment

Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user’s preference mechanisms. Characteristics of the two noises are presented aiming at the application of interac- tive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual’s fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm’s performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.

Suppressing random noise to broaden the useful bandwidth of seismic reflection data

The signal-to-noise ratio （SNR） of seismic reflection data in many areas is rather poor and conventional two-dimensional filters designed to suppress noise with different moveout from the signal tend to generate artifacts. We have extended a method of multichannel filtering, based on the hypothesis that signals on adjacent channels are similar, for enhancing the SNR on stacked sections. Using only the mid-range frequencies where the SNR is highest, the event trend is found for overlapping windows on the section and the average signal vector is calculated. Then the data from the full bandwidth section are projected onto the spatially varying unit similarity vectors and the results are merged for the overlapping windows. Application of the method to synthetic data containing steeply dipping events and to a stacked section for a marine 2D line has produced good results. The modifications we have introduced carry a small overhead in computing time but they should enable the method to be used effectively even on sections containing steep dips.

A fast acquisition method of DSSS signals using differential decoding and fast Fourier transform

In low earth orbit (LEO) satellite or missile communication scenarios, signals may experience extremely large Doppler shifts and have short visual time. Thus, direct sequence spread spectrum (DSSS) systems should be able to achieve acquisition in a very short time in spite of large Doppler frequencies. However, the traditional methods cannot solve it well. This work describes a new method that uses a differential decoding technique for Doppler mitigation and a batch process of FFT (fast Fourier transform) and IFFT (invert FFT) for the purpose of parallel code phase search by frequency domain correlation. After the code phase is estimated, another FFT process is carried out to search the Doppler frequency. Since both code phase and Doppler frequency domains are searched in parallel, this architecture can provide acquisition fifty times faster than conventional FFT methods. The performance in terms of the probability of detection and false alarm are also analyzed and simulated, showing that a signal-to-noise ratio (SNR) loss of 3 dB is introduced by the differential decoding. The proposed method is an efficient way to shorten the acquisition time with slightly hardware increasing.

Extraction of Partial Discharge Acoustic Signal by Wavelet Transform with Teager′s Energy Operator

To develop a measurement system for monitoring partial discharge （PD） without the effect of external interferences,an algorithm of PD signal extraction based on wavelet transform with Teager＇s energy operators was presented. Acoustic signal generated by PD was selected to remove excessive interfering signals and electromagnetic interferences. Acoustic signals were collected and decomposed into I0 levels by wavelet transform into approximation and detail components. “Daubechies 25” was proved to be the most suitable mother wavelet for the extraction of PD acoustic signals. Compared with conventional wavelet denoising method, Teager＇s energy operators were adopted to the PD signal reconstruction and the signal to noise ratio was in creased by 20%-25% inthe experiment,without lost in energy and pulse amplitude.

Effect of Correlation of Two Dichotomous Noises on Stochastic Resonance

The effect of the correlation of two dichotomous noises on stochastic resonance is investigated for a linear stochastic system subject to a periodic oscillatory signal. It is found that, the correlation between the two dichotomous noises can not only affect the appearance of the stochastic resonance phenomenon, but also the distinctness of the stochastic resonance phenomenon. There is an optimal value of the correlation, at which the stochastic resonance phenomenon is most distinct. In addition, the correlation between the two dichotomous noises can also cause the movement of the peak of stochastic resonance. Finally, two stochastic resonances caused by two correlated multiplicative dichotomous noises can be found in this system.

Robust Digital Non-Linear Self-Interference Cancellation in Full Duplex Radios with Maximum Correntropy Criterion

Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α - stable distribution. Then we formulate a novel non-Gaussian SI problem. Under the maximum correntropy criterion （MCC）, a robust digital non-linear self-interference cancellation algorithm is proposed for the SI channel estimation. A gradient descent based algorithm is derived to search the optimal solution. Simulation results show that the proposed algorithm can achieve a smaller estimation error and a higher pseudo signal to interference plus noise ratio （PSINR） than the well-known least mean square （LMS） algorithm and least square （LS） algorithm.

Classification of Ultrasonic Signs Pre-processed by Fourier Transform through Artificial Neural Network Using the Echo Pulse Technique for the Identification of Defects in Welded Joints of Structural Steel

It is due to the need to ensure the security and integrity of equipment, that the non-destructive tests have been increasingly used in the industrial sector. Among these, the ultrasonic pulse echo technique is the most used in industry, mainly for its simplicity and efficiency. With one transducer only, it is possible to emit the ultrasonic and receive the echo pulse. The ANNs （artificial neural networks） are artificial intelligence techniques that, when properly trained, align themselves to inspection tests becoming a powerful tool in the detection and fault identification. In this work, the echo pulse technique was used to detect discontinuities in welds, where ANNs were fed from the information obtained by digital signal processing techniques （Fourier transform）, to identify and classify three distinct classes of defects. Results showed that with the combination of feature extraction by Fourier transformation and classification with neural networks, it is possible to obtain an automatic defect detection system in welded joints with average efficiency.

Single-Frequency Matching Pursuits Based Time-of-Flight Measurement in Viscoacoustic Medium

Broadband ultrasound signals will produce distortion in viscoacoustic medium, which may influence the accuracy of time-of-flight (TOF) measurement. Under the condition of single-frequency acoustic source, the wave propagation process in viscoacoustic medium was analyzed and an approximate solution of the wave propagation was given. Instances of broadband ultrasound were analyzed and simulated based on the single-frequency results. A single-frequency matching pursuits (SFMP) algorithm was then introduced to solve the waveform distortion problem. Time-frequency decomposition was applied to extracting the single-frequency compositions from broadband ultrasound signals, and then these compositions were sent to the matching pursuits (MP) algorithm for calculating the TOF parameters. Compared with the broadband signals, the shapes of extracted single-frequency signals change more slightly as distance and attenuation coefficient increase. The residuals of SFMP were far less than those of MP algorithm. Experimental results show that the SFMP algorithm is able to eliminate waveform distortion of broadband ultrasound in viscoacoustic medium, which helps improve the accuracy of TOF measurement.

A Novel Remote Sensing Signal De-noising Algorithm based on Neural Networks and Tensor Analysis

. This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet coefficients of the noisy signal to estimate the discontinuity of hard threshold function and soft threshold function, limiting its further application in order to overcome this shortcoming, this paper proposes a new threshold function, compared with the original threshold function, a new threshold function is simple and easy to calculate, not only with the soft threshold function is continuous. To deal with this drawback, we integrate the NN to enhance the model. Neural network belongs to the basic unsupervised learning of neural networks, the principle of competition based on the mechanism of learning and biological and the memory capacity can be increased as the number of learning patterns increases, not only offi ine learning can also be carried out on-line ＂learning while learning＂ type. The integrated algorithm can host better performance.

Neighbour Peer Selection Scheme Based on Effective Capacity for Mobile Peer-to-Peer Streaming

For Peer-to-Peer (P2P) streaming services in mobile networks, the selection of appropriate neighbour peers from candidate peers with demanding data is an important approach to improve Quality-of-Service (QoS). This paper proposes a novel Effective Capacity Peer Selection (ECPS) scheme based on effective capacity. In the ECPS scheme, the neighbour peer selection problem was modeled using the Multiple Attribute Decision Making (MADM) theory, which considered multiple factors of candidate peers, including Signal to Interference and Noise Ratio (SINR), residency time, power level, security, moving speed, and effective capacity. This model could increase the suitability of ECPS for wireless mobile environments. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to solve the MADM problem and identify the preferred neighbour peers. Simulation results show that the ECPS scheme can improve the network throughput, reduce packet delay by about 82%, and almost double the packet delivery ratio of the mobile P2P streaming service.

Adaptive Decoding Algorithm Based on Multiplicity of Candidate Sequences for Block Turbo Codes

It is known that Block Turbo Codes （BTC） can be nearly optimally decoded by Chase-II algorithm, in which the Least Reliable Bits （LRBs） are chosen empirically to keep the size of the test patterns （sequences） relatively small and to reduce the decoding complexity. While there are also other adaptive techniques, where the decoder＇s LRBs adapt to the external parameter of the decoder like SNR （Signal Noise Ratio） level, a novel adaptive algorithm for BTC based on the statistics of an internal variable of the decoder itself is proposed in this paper. Different from the previous reported results, it collects the statistics of the multiplicity of the candidate sequences, i.e., the number of the same candidate sequences with the same minimum squared Euclidean distance resulted from the decoding of test sequences. It is shown by Monte Carlo simulations that the proposed adaptive algorithm has only about 0.02dB coding loss but the average complexity of the proposed algorithm is about 42% less compared with Pyndiah＇s iterative decoding algorithm using the fixed LRBs parameter.

声波检测仪声时检定装置的设计与应用

本系统根据JJG990—2004《声波检测仪》检定规程要求，基于声信号法测声时的基本原理，设计出一套可用于检定声波检测仪声时的标准装置。实验数据表明，在设置各参数后，该系统可实现声波检测仪声时的精确检定。并通过实验分析了影响检测准确度的各种因素，为声波检测仪声时的计量检定及量值溯源提供可靠保障。

Photoacoustic Doppler flowmetry of carbon particles flow using an autocorrelation method

In order to measure the axial flowing velocity of carbon particle suspension with particle diameter of tens of micrometers, the photoacoustic Doppler(PAD) frequency shift is calculated based on a series of individual A scans using an autocorrelation method. A 532 nm pulsed laser with repetition rate of 20 Hz is used as a pumping source to generate photoacoustic signal. The photoacoustic signals are detected using a focused piezoelectric(PZT) ultrasound transducer with central frequency of 5 MHz. The suspension of carbon particles is driven by a syringe pump. The complex photoacoustic signal is calculated by the Hilbert transformation from time-domain photoacoustic signal, and then it is autocorrelated to calculate the Doppler frequency shift. The photoacoustic Doppler frequency shift is calculated by averaging the autocorrelation results of some individual A scans. The advantage of the autocorrelation method is that the time delay in autocorrelation can be defined by user, and the requirement of high pulse repetition rate is avoided. The feasibility of the proposed autocorrelation method is preliminarily demonstrated by quantifying the motion of a carbon particle suspension with flow velocity from 5 mm/s to 60 mm/s. The experimental results show that there is an approximately linear relation between the autocorrelation result and the setting velocity.

Study of TiO_2 thin films on Si substrate by the photoacoustic elastic bending method

The quantitative photoacoustic investigation of different TiO2 thin films on Si substrates is presented using the elastic bending method. Photoacoustic signal amplitude and phase spectra versus modulation frequency were measured and analyzed using different sample thicknesses. Within the proposed method particular attention is given to the analysis of optical, thermal, elastic and structural sample parameters. Considerable focus is devoted to the fitting procedure of experimental results using the two-layer sample theoretical model. Characteristics of previously developed photoacoustic apparatus are discussed, attempting to search the ideal experimental conditions which can provide a good signal-to-noise ratio and sensitivity.

Direction-of-arrival estimation for a uniform circular acoustic vector-sensor array mounted around a cylindrical baffle

This work investigates the direction-of-arrival(DOA) estimation for a uniform circular acoustic Vector-Sensor Array(UCAVSA) mounted around a cylindrical baffle.The total pressure field and the total particle velocity field near the surface of the cylindrical baffle are analyzed theoretically by applying the method of spatial Fourier transform.Then the so-called modal vector-sensor array signal processing algorithm,which is based on the decomposed wavefield representations,for the UCAVSA mounted around the cylindrical baffle is proposed.Simulation and experimental results show that the UCAVSA mounted around the cylindrical baffle has distinct advantages over the same manifold of traditional uniform circular pressure-sensor array(UCPSA).It is pointed out that the acoustic Vector-Sensor(AVS) could be used under the condition of the cylindrical baffle and that the UCAVSA mounted around the cylindrical baffle could also combine the anti-noise performance of the AVS with spatial resolution performance of array system by means of modal vector-sensor array signal processing algorithms.

Transverse flowmetry of carbon particles based on photoacoustic Doppler standard deviation using an auto-correlation method

In order to measure the flow velocity of carbon particle suspension perpendicular to the receiving axis of ultrasound transducer, the standard deviation of photoacoustic Doppler frequency spectrum is used to estimate the bandwidth broadening, and the spectrum standard deviation is calculated by an auto-correlation method. A 532 nm pulsed laser with the repetition rate of 20 Hz is used as a pumping source to generate photoacoustic signal. The photoacoustic signals are detected using a focused PZT ultrasound transducer with the central frequency of 10 MHz. The suspension of carbon particles is driven by a syringe pump. The complex photoacoustic signal is calculated by Hilbert transformation from time domain signal before auto-correlation. The standard deviation of the Doppler bandwidth broadening is calculated by averaging the auto-correlation results of several individual A scans. The feasibility of the proposed method is demonstrated by measuring the spectrum standard deviation of the transversal carbon particle flow from 5.0 mm/s to 8.4 mrn/s. The experimental results show that the auto-correlation result is approximately linearly distributed within the measuring range.