Frequency Spectrum Method Research Articles

Doppler Radar Sensor-Based Fall Detection Using a Convolutional Bidirectional Long Short-Term Memory Model.

Falls among the elderly are a common and serious health risk that can lead to physical injuries and other complications. To promptly detect and respond to fall events, radar-based fall detection systems have gained widespread attention. In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term memory (CB-LSTM) model. The introduction of the CB-LSTM model enables the fall detection system to capture both temporal sequential and spatial features simultaneously, thereby enhancing the accuracy and reliability of the detection. Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods currently available. In summary, this study provides effective technical support using the frequency spectrum and deep learning methods to monitor falls among the elderly through the design and experimental validation of a radar-based fall detection system, which has great potential for improving quality of life for the elderly and providing timely rescue measures.

Nonlinear spectrum feature fusion diagnosis method for RV reducer of industrial robots

RV reducers of industrial robots may be possibly subjected to wear and vibration impact and invalidated for keeping the repeatability due to long-time dynamic load in their life cycle. Any failure of RV reducers may decrease the reliability and lead to incredible loss due to the unexpectedly shutdown of the manufacturing system. Failures can cause nonlinear interference when occurring in RV reducers and pose a challenge to RV reducer fault diagnosis. Therefore, it is important to take proper measures to diagnose the failure in RV reducers and reduce the influence of nonlinear interference. Nonlinear spectrum features fusion method is proposed for diagnosing the faults of RV reducers in industrial robots. For the purpose of extracting fault features more effectively, nonlinear output frequency response function is employed to obtain nonlinear frequency spectrum for exactly describing the nonlinear mechanism of faulty phenomenon. Moreover, enhanced evidence theory with similarity measure is proposed to compute the basic probability assignment functions of evidences according to the similarity between different modes for improving the identification accuracy of different faults. RV reducer experiments with different faults are performed to simulate robot operating conditions for obtaining normal and fault data under a variable speed working condition. Among the 180 sets of test data including normal conditions, the diagnostic accuracy of the proposed nonlinear feature fusion method is 92.22% and greatly preferable to that of the ordinary frequency spectrum method. Experimental results demonstrated that the nonlinear spectrum method can effectively extract nonlinear features and the introduction of similarity can effectively improve the diagnostic accuracy in the proposed feature fusion method.

Design and Test of Cleaning Loss Kernel Recognition System for Corn Combine Harvester

Cleaning loss is an important parameter to judge the performance of a corn combine harvester. At present, there exists the phenomenon that corn cleaning loss rate detection devices have a long signal processing time and low recognition accuracy. To solve this problem, based on the principle of the impacting piezoelectric effect, the impacting signals of corn kernels and impurities are analyzed by means of the frequency spectrum method to obtain the characteristic frequency for effectively distinguishing corn kernels and impurities, which is determined as 8.7 kHz. Based on this characteristic frequency, a corn cleaning loss kernel recognition system is designed, which can realize the function of corn kernel recognition and cleaning loss rate recording. In this system, signal processing circuits which mainly include two-order high-pass filtration, envelope wave detection and voltage comparison are designed. On the basis of the signal processing circuit, adding the judgment program for the holding time of the output square wave signal improves the system’s recognition accuracy for kernels impacting the sensitive plate simultaneously. The system was tested in indoor conditions. The results show that 20–30 corn kernels could be accurately recognized per minute on a single sensitive plate, and the recognition accuracy rate of this system could reach 85% when three corn kernels impacted simultaneously. The results serve as a theoretical basis and represent a new method for the design of a cleaning loss kernel recognition system.

A new algorithm for precise location of power cable fault based on zero phase filter

In order to improve the fault location accuracy of power cables, the two-side method is used for fault location. The main factors affecting the cable fault accuracy of the double-ended fault location method are the wave speed of traveling waves transmitted in the cable and the time difference between the traveling waves arriving at the monitoring equipment. In fact, traveling wave signals with different frequencies propagate at different wave speeds, and the use of fixed wave speeds for positioning calculations will inevitably cause positioning errors. First, the center frequency spectrum method is used to calculate the wave speed, and different wave speeds are used for different types of cables, so as to reduce the positioning error caused by the wave speed. Then, the devices on both sides use high-precision GPS to synchronize the time to reduce the natural error. For the time difference between the detected traveling waves reaching the devices on both sides, the triangle algorithm can be used to find the starting point for time difference calculation, thereby constructing a new cable fault location algorithm. Finally, a large number of PSCAD simulations and field discharge tests of cable grounding were carried out. The simulation results and test results verified the feasibility and superiority of this algorithm.

Evaluating the Properties of Railway Ballast Using Spectral Analysis of Ground Penetrating Radar Signal Based on Optimized Variational Mode Decomposition

Ground penetrating radar (GPR) has been widely applied in the assessment of railway ballast conditions (fouling, moisture) by using the spectrum of the tested GPR signal. However, the drawbacks of the low time‐frequency resolution and mode mixing prevent the traditional spectrum methods from a wide application. This paper uses the advanced time‐frequency analysis of GPR signal based on optimized variational mode decomposition to extract the features of ballast. The new approach overperforms the conventional frequency spectrum methods of GPR signal processing by giving a clear and quantitative assessment of ballast signals. Experimental results of GPR with dry and wet fouled ballasts demonstrate that, by comparison with the feature extraction method of conventional spectrum methods such as spectrogram and wavelet, the feature extraction method based on the optimized VMD has much better separability and quantitative identification capability.

An empirical examination of sample size effects on population demographic estimates in birds using single nucleotide polymorphism (SNP) data.

Sample size is a critical aspect of study design in population genomics research, yet few empirical studies have examined the impacts of small sample sizes. We used datasets from eight diverging bird lineages to make pairwise comparisons at different levels of taxonomic divergence (populations, subspecies, and species). Our data are from loci linked to ultraconserved elements and our analyses used one single nucleotide polymorphism per locus. All individuals were genotyped at all loci, effectively doubling sample size for coalescent analyses. We estimated population demographic parameters (effective population size, migration rate, and time since divergence) in a coalescent framework using Diffusion Approximation for Demographic Inference, an allele frequency spectrum method. Using divergence-with-gene-flow models optimized with full datasets, we subsampled at sequentially smaller sample sizes from full datasets of 6–8 diploid individuals per population (with both alleles called) down to 1:1, and then we compared estimates and their changes in accuracy. Accuracy was strongly affected by sample size, with considerable differences among estimated parameters and among lineages. Effective population size parameters (ν) tended to be underestimated at low sample sizes (fewer than three diploid individuals per population, or 6:6 haplotypes in coalescent terms). Migration (m) was fairly consistently estimated until <2 individuals per population, and no consistent trend of over-or underestimation was found in either time since divergence (T) or theta (Θ = 4Nrefμ). Lineages that were taxonomically recognized above the population level (subspecies and species pairs; that is, deeper divergences) tended to have lower variation in scaled root mean square error of parameter estimation at smaller sample sizes than population-level divergences, and many parameters were estimated accurately down to three diploid individuals per population. Shallower divergence levels (i.e., populations) often required at least five individuals per population for reliable demographic inferences using this approach. Although divergence levels might be unknown at the outset of study design, our results provide a framework for planning appropriate sampling and for interpreting results if smaller sample sizes must be used.

Nonlinear feature extraction and chaos analysis of flow field

A novel signal processing method named adaptive variational mode decomposition with the fractal (AFVMD), which is based on variational mode decomposition and fractal theory, is proposed in this paper for solving a problem that it is easy to misjudge the working conditions of the centrifugal compressor. The measured signal of a compressor is unstable, so a traditional method is used to analyze the nonlinear phenomenon of the stall flutter. Owing to the fact that the robustness of VMD method is strong and its combination with the fractal dimension can accurately describe self-similarity and fractal characteristics of a measured signal, the proposed AFVMD method can not only achieve noise reduction, but also extract nonlinear feature from a complex signal. Taking the dynamic pressure data of the impeller during the instability of a centrifugal compressor as an object to verify the effectiveness and superiority of the proposed AFVMD method, the results are obtained as follows. Firstly, compared with the wavelet noise reduction method, the proposed AFVMD method has both noise reduction and feature extraction functions, and the compressor pressure pulsation spectrum has more significant stall characteristics. Secondly, none of the traditional nonlinear analysis methods can reflect the stall process, so the chaotic phase space attractor is used to visualize the flow field changes. Due to the reasonable choice of the delay time and the embedding dimension, the physical information originally mixed in the signal is separated, so that the attractor phase diagram method has a better process of judging the flow stall than the frequency spectrum method. The results show that the proposed AFVMD method can judge the compressor about to enter into the deep surge earlier. Thirdly, In order to quantify the superiority of the proposed method, if the process of surging and the occurrence of deep wheezing can be predicted in advance, the largest Lyapunov exponent is used as an evaluation index. The above results show that the largest Lyapunov exponent of the proposed AFVMD is smallest for illustrating that the signal has more accurate flow field nonlinear information, which improves the predictability of the signal.

Applications of Fractional Lower Order Frequency Spectrum Technologies to Bearing Fault Analysis

The traditional spectral analysis method is used to study the characteristics of bearing fault signals in frequency domain, which is reasonable and effective in general cases. However, it is proved that the fault signals have heavy tails in this paper, which are α stable distribution, and 1 &lt; α &lt; 2, and even the noises belong to α stable distribution. Then the conventional spectral analysis methods degenerate and even fail under α stable distribution environment. Several improved frequency spectral analysis methods are proposed employing fractional lower order covariation or fractional lower order covariance in this paper, including fractional lower order Blackman‐Tukey covariation spectrum (FLOBTCS), fractional lower order periodogram covariation spectrum (FLOPCS), and fractional lower order welch covariation spectrum (FLOWCS). In order to suppress side lobe and improve resolution, we present novel fractional lower order autoregression (FLO‐AR) and fractional lower order autoregressive moving average (FLO‐ARMA) parameter model frequency spectrum methods, and the calculation steps are summarized. The proposed spectrum methods are compared with the existing methods based on second‐order statistics under Gaussian and SαS distribution environments, and the results show that the new algorithms have better performance than the traditional methods. Finally, the improved methods are applied to estimate frequency spectrums of the normal and outer race fault signals, and it is demonstrated that they are effective for fault diagnosis.

McSwan: A joint site frequency spectrum method to detect and date selective sweeps across multiple population genomes.

Inferring the mode and tempo of natural selection helps further our understanding of adaptation to past environmental changes. Here, we introduce McSwan, a method to detect and date past and recent natural selection events in the case of a hard sweep. The method is based on the comparison of site frequency spectra obtained under various demographic models that include selection. McSwan demonstrated high power (high sensitivity and specificity) in capturing hard selective sweep events without requiring haplotype phasing. It performed slightly better than SweeD when the recent effective population size was low and the genomic region was small. We then applied our method to a European (CEU) and an African (LWK) human re-sequencing data set. Most hard sweeps were detected in the CEU population (96%). Moreover, hard sweeps in the African population were estimated to have occurred further back in time (mode: 43,625years BP) compared to those of Europeans (mode: 24,850years BP). Most of the estimated ages of hard sweeps in Europeans were associated with the Last Glacial Maximum and were enriched in immunity-associated genes.

The influence of catalytic additives on electrochemical properties of bipolar membranes

The influence of organic, inorganic, and hybrid catalytic additives on the current–voltage characteristics of the bipolar region of industrial MB-1 and MB-3 bipolar membranes and analogues of an MB-2 bipolar membrane is investigated by the method of frequency spectrum of electrochemical impedance. It is shown that the introduction of compounds accelerating water dissociation reaction into the bipolar region makes it possible to obtain bipolar membranes with lower operating voltage when compared to industrial bipolar membranes.

Discrimination of brownheart of Korla pear using vibration frequency spectrum technique

The purpose of this work was to use a nondestructive method for detecting the brownheart of Korla pear to reduce the chance of infection among pears without brownheart. A mechanical impulse method based on vibration testing system was used to excite the fruits. The consistent acquisition signal indicated that the test is repeatable at the same positions of fruit equator (cheek). A remarkable frequency signal was excited at 9-12 N force by a rubber tipped hammer. The dominant frequency was identified at the maximum response magnitude to assess the internal defect, and the result was not influenced by the distances between the defect borders and the excitation points. The sharp increase of defect mass could significantly affect the dominant frequency. Relationship between the dominant response frequency (fd) and the defect mass percentage (ω) was characterized by an equation fd =410.649e-0.0833ω+261.947 with a good correlation coefficient (R2=0.925). A defect mass of 2.281% was determined as a discrimination threshold. Once the threshold exceeded 2.281%, the defective pear could be classified with a high accuracy rate of 96.7%. This finding would provide guidance for determining the optimal detecting time to the brownheart of Korla pears, according to the specific storage conditions when the vibration frequency spectrum method is deployed. Keywords: nondestructive detection, vibration frequency spectrum, brownheart of Korla pear, internal defect, fruit quality DOI: 10.3965/j.ijabe.20171002.1910 Citation: Xu H B, Wu J, Wang Z P, Gao Y M, Wang Z P, Zhao Z Q. Discrimination of brownheart of Korla pear using vibration frequency spectrum technique. Int J Agric & Biol Eng, 2017; 10(2): 259–266.

Detection the nonlinear ultrasonic signals based on modified Duffing equations

The nonlinear ultrasonic signals, like second harmonic generation (SHG) signals, could reflect the nonlinearity of material induced by fatigue damage in nonlinear ultrasonic technique which are weak nonlinear signals and usually submerged by strong background noise. In this paper the modified Duffing equations are applied to detect the SHG signals relating to the fatigue damage of material. Due to the Duffing equation could only detect the signal with specific frequency and initial phase, firstly the frequency transformation is carried on the Duffing equation which could detect the signal with any frequency. Then the influence of initial phases of to-be-detected signal and reference signal on the detection result is studied in detail, four modified Duffing equations are proposed to detect actual engineering signals with any initial phase. The relationship between the response amplitude and the total driving force is applied to estimate the amplitude of weak periodic signal. The detection results show the modified Duffing equations could effectively detect the second harmonic in SHG signals. When the SHG signals include strong background noise, the noise doesn’t change the motion state of Duffing equation and the second harmonic signal could be detected until the SNR of noisy SHG signals are −26.3, yet the frequency spectrum method could only identify when the SNR is greater than 0.5. When estimation the amplitude of second harmonic signal, the estimation error of Duffing equation is obviously less than the frequency spectrum analysis method under the same noise level, which illustrates the Duffing equation has the noise immune capacity. The presence of the second harmonic signal in nonlinear ultrasonic experiments could provide an insight about the early fatigue damage of engineering components.

Near‐bed shear stress, turbulence production and dissipation in a shallow and narrow tidal channel

AbstractNear‐bed, highly resolved velocity profiles were measured in the lower 0.03 m of the water column using acoustic Doppler profiling velocimeters in narrow tidal channels in a salt marsh. The bed shear stress was estimated from the velocity profiles using three methods: the log‐law, Reynolds stress, and shear stress derived from the turbulent kinetic energy (TKE). Bed shear stresses were largest during ebbing tide, while near‐bed velocities were larger during flooding tide. The Reynolds stress and TKE method gave similar results, while the log‐law method resulted in smaller bed shear stress values during ebbing tide. Shear stresses and turbulent kinetic energy followed a similar trend with the largest peaks during ebbing tide. The maximum turbulent kinetic energy was on the order of 1 × 10− 2 m2/s2. The fluid shear stress during flooding tide was approximately 30% of the fluid shear stress during ebbing tide. The maximum TKE‐derived shear stress was 0.7 N/m2 and 2.7 N/m2 during flooding and ebbing tide, respectively, and occurred around 0.02 m above the bed. Turbulence dissipation was estimated using the frequency spectrum and structure function methods. Turbulence dissipation estimates from both methods were maximum near the bed (~0.01 m). Both the structure function and the frequency spectrum methods resulted in maximum dissipation estimates on the order of 4 × 10− 3 m2/s3. Turbulence production exceeded turbulence dissipation at every phase of the tide, suggesting that advection and vertical diffusion are not negligible. However, turbulence production and dissipation were within a factor of 2 for 77% of the estimates. The turbulence production and dissipation decreased quickly away from the bed, suggesting that measurements higher in the water column cannot be translated directly to turbulence production and dissipation estimates near the bed. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Digital image frequency spectrum method for analyzing speckle displacement in frequency domain.

A technique called the digital image frequency spectrum (DIFS) method, used for analyzing digital speckle patterns with deformations in the frequency domain, is proposed. Two subimages in the same position as two digital speckle patterns before and after deformation are used to form a Young's fringe. The parallel Young's fringe pattern is automatically analyzed in the frequency domain. One finds that the displacement between the two subimages can be directly obtained, and a matching procedure used in digital image correlation (DIC) is unnecessary when DIFS is applied. Experimental results show that the relative error of this method is less than 3%, and it takes less time in computing integer-pixel displacement procedure.

Design and Implementation of a Multichannel Pulse Compression System Based on FPGA

The implementation of digital signal process system based on FPGA is an important method for embedded system. Pulse compression can be implemented in digital method. The realization of a multichannel pulse compression system by using all digital method has the characteristics of high reliability, strong anti-disturbance, good flexibility and convenient for application. In essence, pulse compress is a method of frequency spectrum expanding, and is used in matched filtering. It incarnates the matching level of filter and the expectant phase of received signal. A multichannel pulse compression system is designed using FFT IP core which can be reused in different periods of digital pulse compression, respectively performing FFT and IFFT calculation, so that the hardware consumption is saved significantly. This paper presents the logic programming of Multichannel Pulse Compression System based on Xilinx FPGA, and introduces the high- speed transmission module, the digital signal process module and the data buffer module in detail.

Lathing Processing Dosage Trends Identifying Based on Current Signal

It is extremely meaningful to identify the processing dosage which directly influence the processing time, quality and other processing factors. In order to obtain the trend of lathing processing dosage, ordinary horizontal lathe was researched using the power signal frequency spectrum method, through the frequency distribution and the differences of amplitude, the trends of lathing processing dosage was identified. The collected current signal was turned into power signal first, when the cut depth is large, it is able to identify the trends of processing dosage through the fluctuations of the power signal, but when the cutting depth is small, compared to the power signal of idling state, there is not obvious difference to distinguish them. In this case, the frequency distribution and the differences of amplitude of the power signal were introduced.

Defect classification in two-layer complex structures based on spectrum analysis of pulsed eddy current

Pulsed eddy current (PEC) testing is an increasingly emerging non-destructive testing and evaluation (NDT&E) technique. Defect classification is one of the most important steps in PEC defect characterisation. It is difficult to classify defects in two-layer complex structures of ageing aircraft or other complex structures. As a result of taking a square-wave signal as excitation, the PEC response signals contain rich information in the frequency domain. In this paper, two defect classification methods based on spectrum analysis are proposed to classify the defects in two-layer complex structures. Method 1 is based on direct frequency spectrum analysis, while method 2 is by means of differential frequency spectrum analysis. Experimental tests and blind tests are carried out and the results show that the different classes of defect can be identified and classified effectively. The lift-off test results show that the lift-off effect can be restrained by the proposed method. The frequency spectrum method has a good prospect in the field of PEC non-destructive testing and evaluation.

Envelope demodulation analysis of bearing based on order tracking and empirical mode decomposition

In order to process the non-stationary vibration signals such as speed up or speed down signals effectively,a new method combined computed order tracking analysis with empirical mode decomposition technique to analyze the instan- taneously signals in the rotate mechanism is explored.Firstly, the vibration sisals at start-up in the gearbox are sampled at constant time increments in time-domain and then the data are resampled with software at constant angle increments in an- gle-domain.Therefore,the time domain instantaneously signals is changed into angle domain stationary ones.Then,many IMF components are obtained to decompose the signals in angle domain with empirical mode decomposition(EMD).At last,the high frequency IMF component,which contains the fault in- formation of bearing,is analyzed with order envelope spectrum analysis.The outcome shows it can avoid effectively thefre- quency smearphenomenon with order tracking technology, which cannot be solved with the traditional frequency spectrum method and the non-stationary signals are translated into sta- tionary signals.The IMF component,which contains the fault information,can be extracted with EMD.It is trenchancy sup- plement for traditional spectrum analysis combined the order envelope spectrum analysis with EMD,and has more amplitude appliance foreground.

Statistic analysis of influence of phase distortion on diffractive optical element for beam smoothing

In this paper, the diffractive optical element (DOE) is used to realize beam smoothing with the smoothing by spectral dispersion (SSD) technique. The influences of the high and low frequency phase distortions on the DOE for beam smoothing are statistically analyzed based on the spatial frequency spectrum method. The amplitude and initial phase spectra of the far field intensity distribution are modulated by the characteristic of the phase distortion. The relationship between the performance parameters of the beam smoothing and the characteristic of the phase distortion is obtained. This can afford a theoretical tool to determine whether the characteristic of the phase distortion and the performance of the designed DOE for beam smoothing are able to satisfy the requirement of the light efficiency and the non-uniformity simultaneously or not.

Application of spatial-frequency spectrum method on diffractive optical element for beam smoothing

Summary Diffractive optical element (DOE) is a promising technique to realize beam smoothing. To calculate the true performance of the DOE for beam smoothing, the spatial-frequency spectrum method has been used to re-define the performance parameters of the DOE for beam smoothing. In this paper, the spatial-frequency spectrum method is used to basically determine the performance limitation of the DOE for beam smoothing. Considering the low-pass filtering effect will be induced by physical process in the practical applications, the DOE is designed with the low-pass filtering effect in the optimization. Furthermore, the performance realized by utilizing the DOE and the time average technique is theoretically analyzed with the spatial frequency spectrum method.