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A few attempts have been done to reduce ambiguous factors in the analysis of complex x-ray spectra. Uncertainty in peak identification and separation due to overlapping of x-ray peaks can be reduced by taking into account the response function of x-ray peaks and by using peak functions including all the x-ray sub-lines in a x-ray series. Ambiguous factors due to discontinuity of backgrounds which is sometimes followed by huge peaks can be reduced by considering contributions from the pile-up, escape and tail functions. The problem of uncertainty in the background fitting in a region of low x-ray energy, where many peaks overlap each other and determination of the background function contains much difficulties and ambiguities, can be solved by using peak functions involving tails in place of Gaussians.

A semi-empirical approach of elemental response functions for linear associated neural network analysis is proposed. The proposed method is a combined one of both the pure experimental and model based ones: spectra of standard single elemental samples of selected elements are measured, and model parameters related to the analyzing system are deduced: elemental responses are calculated by a suitable peak shape model and corrected with respect to the efficiency: channel-wise response data are generated using the Monte-Carlo simulation: finally, the response data are adjusted to the corresponding experimental data for some multi-elemental standard samples. This method is tested except for the last adjustment process.

Diagnosis by radial arterial pulse is very important in Traditional Chinese Medicine (TCM). The objective of this study is to evaluate the effects of detection time and position on the parameters of time- and frequency-domain of radial pulse wave and to differentiate between Dyspepsia and Rhinitis by the statistical analysis of two signal types. A sphygmograph was developed to record radial pressure pulse for spectral analysis. The measurements were expressed as the pressure wave and its frequency. In this study, 135 subjects including the controls, Dyspepsias and Rhinitis were enrolled in this study. The signals were taken from three diagnosis positions of both wrists. Seven parameters of pressure wave and two parameters of spectrum analysis were evaluated by ANOVA test and Tukey's test. The results showed that the effects of detection time and position on the parameters taken from pulse wave were inconsistent. No regular rules or relationship could be established. The power spectra of 10–50 Hz and 13–50 Hz from sphygmography revealed a significant effect of health status, position and their interaction. There was a significant difference in the power spectra of 13–50 Hz between the control and Rhinitis, as well as between the control and Dyspepsia at right Guan position. The results of this study strongly suggest that the spectrum of sphygmogram might be more helpful than the pressure wave signals for detection of Dyspepsia and Rhinitis.

This study is to investigate the influences of acupuncture for dyspepsia on the Radial Pressure Pulse (RPP) between the Chun, Guan and Chy positions of the right/left wrist. Two series of experiments were designed; for the first series, 30 patients with dyspepsia (Group P) and 30 normal subjects (Group N) were seated to undergo the measurements of pulse pressure waveforms from radial artery by sphygmograph. The parameters of RPP included the spectral energy of 0–10 Hz (SE_0–10Hz), 10–50 Hz (SE_10–50Hz) and 13–50 Hz (SE_13–50Hz). For the second series, acupuncture was administered at the right and left Tsu San Li (St-36) points for the same 30 dyspepsia patients, and then their pulse pressure waveforms were re-examined. The results showed that the SE_0–10Hz at Right Guan (RB) (p < 0.05), the SE_10–50Hz at RB (p < 0.01), and the SE_13–50Hz at RB (p < 0.01) and Left Guan (LB) (p < 0.05) of Group P were significantly greater than that of Group N. After the acupuncture, there were significant decreases in the SE_0–10Hz only at RB (p < 0.01), in the SE_10–50Hz at RB (p < 0.01), Right Chy (RC) (p < 0.05) and LB (p < 0.05), and in the SE_13–50Hz RB (p < 0.01), RC (p < 0.05) and LB (p < 0.01). We concluded that the pulse-frequency spectrum at RB was a more effective characteristic for dyspepsia patients, and the acupuncture had an effect on SE_10–50Hz and SE_13–50Hz more obviously than that on SE_0–10Hz.

Using an analytical approach, the sensitivity to sin² 2θ₁₃ with infinite statistics in a spectral analysis is investigated in reactor neutrino oscillation experiments with one reactor and two identical detectors. We derive an useful formula for the sensitivity which depends only on two parameters (the uncorrelated bin-to-bin systematic error over the square-root of the number of bins) and σ_dB (the bin-to-bin correlated detector specific systematic error) if they are smaller than other errors. It is pointed out that the spectrum analysis is advantageous over the rate analysis in search of neutrino oscillations for .

The spectrum and electronic properties of W_nCO(n = 1~6) clusters have been studied by using density functional theory (DFT) at the B3LYP/LANL2DZ level. It is found that the vibrational frequencies of the strongest infrared intensity are in a range of 1674.3–1846.4 cm^-1. For each cluster, the vibration modes at the strongest peak are both IR and Raman active, and be assigned to CO stretching modes. The polarizability analyses indicate that the mean dipole polarizabilities increase monotonically with the increase of cluster size, except W₆CO cluster. In addition, the ionization energies and electronegativities analyses manifest that the W₂CO cluster has well attracted electronic ability; W₆CO cluster is the easiest to lose electrons in all W_nCO clusters.

This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.

In this study, the failure of a certain type of gearbox of a high-speed locomotive group with cracks is examined, and a gearbox failure assessment method that considers the coupled vibration is established and combined with mechanical signal and frequency signal to determine the basis for judging the failure of a faulty gearbox. First, according to the mechanical model of the finite element calculation, we determined the stress weak links and then the layout response stress measurement points and acceleration measurement points. We then calculated the gearbox ratio, meshing frequency, vibration frequency, mechanical response, modal response, and other frequency characteristics using the Hilbert–Huang transform (HHT) method and the fast Fourier transform (FFT) algorithm to analyze the vibration signals generated by different speeds and wheel out-of-roundness conditions. These were used to calculate the frequency of the different vibration sources of the mechanical response on the weak areas. The frequency correlations of the different vibration sources on the mechanical response in the weak areas were then analyzed, and the vibration transmission law of the gearbox case was obtained. The fault determination criterion was then determined, and the final cause of the fault was obtained.

Switching signal is always composed of a series of fast voltage and current transient impulsive waves, which are created during turn-on and turn-off operation of the switching elements. This paper presents a scalogram and phase spectrum analyzing approach for the high-frequency switching noise detection. A complex Gaussian wavelet is used to extract the oscillation feature of the noise signal. The proposed scheme is more suitable in discriminating the type of switching noise than conventional wavelet transform approaches. Simulation results show the effectiveness and advantages of the method.

Self-organization of hexagonal distributions of cities of various sizes in Southern Germany is envisaged in central place theory. Yet scientific verification of this theory has been lacking over the years. To scientifically support this theory, we propose a group-theoretic double Fourier spectrum analysis procedure that can detect self-organizing patterns bifurcating from a uniform state. This procedure is reinforced by the optimization of the choice of an analysis domain and the spatial eigen-decomposition to capture agglomeration effects. Strong power spectra for hexagonal networks of cities were detected by the procedure for population data in Southern Germany. Moreover, a gigantic hexagonal distribution of cities in Eastern USA was found as an assemblage of a series of hexagonal networks. The amazing geometrical regularity observed in this distribution manifests the existence of such distribution in the real world, thereby underpinning central place theory.

The present study applies spectrum analysis to examine the impact of price limits on stock price movements. Based on spectrum analysis, the authors find that the imposition of price limits does alter the pattern of stock price movements in Taiwan. Yet, its efficacy erodes over time.

In this work, we show the spectrum structure of the linear Fokker–Planck equation by using the semigroup theory and the linear operator perturbation theory. As an application, we show the large time behavior of the solutions to the linear Fokker–Planck equation.

Facial expression recognition is one of the hotspots in the fields of computer vision and deep learning. It has very important applications in the domains of learning service recommendation, human–computer interaction and medical industry. Aiming at the problem that the traditional expression recognition method is not accurate, this paper proposes a method combining Gabor wavelet transform and convolutional neural network. Firstly, face positioning, cropping, histogram equalization and other preprocessing are performed on the expression image. Then we extract key frames of expression sequences. After that the Gabor wavelet transform is performed on the expression image to obtain magnitude and phase characteristics. Finally, we design a 2-channel CNN for training and classification. The experiment achieves an accuracy of 96.81% on the CK+ database and it has a certain improvement compared with the Gabor wavelet transform and the traditional CNN alone.

Cavitation phenomena are accompanied by harsh cavitation noise. Studying cavitation noise is an effective method to monitor cavitation conditions. In order to study the characteristics of cavitation noise, a Venturi test bench experiment platform is built, the cavitation flow field and cavitation noise of Venturi test bench are simulated. The frequency domain analysis of cavitation noise is carried out, and the relationship between cavitation region, flow rate and sound pressure level of cavitation noise is verified by comparison between simulation and experiment. Because the cavitation noise is easily polluted by ambient noise, wavelet transform is used to analyze the cavitation noise and background noise, and the characteristics of cavitation noise are studied by comparing the difference between them.

In order to analyze the effect of engine vibration on cab noise of construction machinery in multi-frequency bands, a new method based on ensemble empirical mode decomposition (EEMD) and spectral correlation analysis is proposed. Firstly, the intrinsic mode functions (IMFs) of vibration and noise signals were obtained by EEMD method, and then the IMFs which have the same frequency bands were selected. Secondly, we calculated the spectral correlation coefficients between the selected IMFs, getting the main frequency bands in which engine vibration has significant impact on cab noise. Thirdly, the dominated frequencies were picked out and analyzed by spectral analysis method. The study result shows that the main frequency bands and dominated frequencies in which engine vibration have serious impact on cab noise can be identified effectively by the proposed method, which provides effective guidance to noise reduction of construction machinery.