Simulated Signal Data Research Articles

A novel unbinned model-independent method to measure the CKM angle γ in B± → DK± decays with optimised precision

We present a novel unbinned method to combine B± → DK± and charm threshold data for the amplitude-model unbiased measurement of the CKM angle γ in cases where the D meson decays to a three-body final state. The new unbinned approach avoids any kind of integration over the D Dalitz plot, to make optimal use the available information. We verify the method with simulated signal data where the D decays to KSπ+π−. Using realistic sample sizes, we find that the new method reaches the statistical precision on γ of an unbinned model-dependent fit, i.e. as good as possible and better than the widely used model-independent binned approach, without suffering from biases induced by a mis-modeled D decay amplitude.

DOA Estimation Based on LSTM Neural Network with Uniform Linear Antenna Array

Radio Direction Finding system is a system that determines the direction or coordinates of radio signal sources. The main function of this system is to determine the direction of arrival (DOA) of an incident radio wave. DOA information plays an important role in array signal processing and has many applications in communications, radar, seismic survey... In this paper, with the simulated signal data set obtained at the linear antenna array (ULA), the Long Short-Term Memory (LSTM) network model is used to estimate the DOA. The performance of the method is evaluated based on the RMSE parameter and compared with MUSIC, DNN algorithms in different cases such as: deviation of incident angle of radiation sources, signal-to-noise ratio (SNR).

Fault diagnosis of rotating mechanical bearing based on adaptive noise-complete ensemble empirical modal decomposition and time-reallocated multisynchronous compression transform

Due to the noise interference in the original vibration signals measured from vibration sensors, it is necessary to noise reduce the signals before extracting fault features from them. This paper proposes a fault feature extraction method combining the adaptive noise-complete ensemble empirical modal decomposition (CEEMDAN) and the time-reallocated multisynchronous compression transform (TMSST), which first decomposes the original signal, then combines the relevant index values of the decomposed signal to filter the optimal signal components, and finally uses the TMSST to extract the fault features from the reconstructed signal. In this paper, a set of simulated signal data and two sets of experimental data are used to evaluate the performance of the method, and the results show that the method works well for rolling bearing fault signal identification.

A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation.

The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system.

Anomaly detection for heavy power generation gas turbine considering the effect of output power variation

In the frequency modulation process of the heavy power generation gas turbine, the variation of output power will cause the fluctuation of the operating parameters. In order to detect the anomaly of the true performance deterioration accurately, a novel statistical anomaly detection model was developed. First, the mathematical description of the operating parameters under three different operating conditions—unsteady-state, steady-state and normal, steady-state and anomaly—was presented according to the characteristics of parameters and output power. Second, the new characteristic test statistic P-ratio based on the T-statistic was proposed for the anomaly detection under the steady-state condition. Then, the on-line steady-state detection algorithm based on the Gaussian mixture model was built for the unsteady-state identification. Finally, the efficacy of the model was examined on the synthetic deterioration data, which superimposes the anomaly simulation signal data on the real healthy data from a real power generation gas turbine. The testing result is shown to be satisfactory with respect to the false positive rate and the true positive rate. Future research is required to further improve the accuracy of the proposed model.

Design and Application of Software for Generating Simulated Signal Data

Simulated signal, produced by digital to analog converter, is important and necessary in many fields, such as electronic experiment, equipment debugging and maintenance, especially, in medical area. In this paper, the design and application of an effective software which may be used to generate simulated signal data by special wave picture obtained by scanner or drawing software are presented. First, we introduce how to realize producing needed data that meet the given conditions including amplitude, samples and resolution by this software. Second, the method and steps of getting the stimulated signal data from wave figure file is given. Third, an example of application about this software to generate simulated physiological signal data is shown. Then, the data are put into use and by digital to analog converter, an idea stimulated signal is produced. Therefore, with full confidence, we could draw a conclusion that it is very convenient and effective to get needed data for digital to analog conversion to generate all kinds simulated signal with a novel method.

Detection and Extraction of Waveform Shapes for Local Signals Using Characteristic Waveform Template

AbstractSince local signals appear time‐locally and their waveforms are steep, conventional signal processing methods are generally inadequate to detect them. In this paper, by focusing on amplitude distribution forms of an observed signal, a simple signal processing method is proposed to detect the local signals and extract their waveform shapes simultaneously. Concretely, a characteristic waveform template which consists of representative data series of a simplified target local signal is newly introduced, and a certain event regulated with it is adopted. The local signals can be detected by evaluating whether the amplitude distribution forms of observed signals are associated with the event, and substitution of conditional events for associated events improves detection performance. In addition, the proposed method has been extended to extract waveform shapes by introducing an enhanced waveform template, and automatic detection and extraction of waveform shapes can be performed simultaneously. The proposed method is applied to simulation signal data, and its effectiveness is confirmed by detection performance and extracted waveform shapes for the local signals. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 175(3): 37–47, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21074

脳ＣＴ画像における低コントラスト検出能の定量的評価：超急性期脳梗塞の識別に対するウィンドウ幅の影響に関する検討

In diagnostic brain computed tomography (CT) imaging of hyperacute ischemic stroke, the recognition of subtle lesions is difficult and may be missed. The aim of this study was to evaluate quantitatively the effect of the window width on low contrast detectability by using digital phantom images intended for detection of hyperacute ischemic stroke, and clinical CT images. We first derived the digital phantom images by subtracting the simulated signal data (intensity=1-3 hounsfield units (HU), diameter=10-30 mm) from the water phantom images at various mAs values. Observer studies were then performed under the various window widths at 20, 40, 60, and 80 HU by using the 30 digital phantom images and the 30 water phantom images. In addition, observer studies on brain CT images with 30 abnormal cases and 30 normal cases were performed under the window widths at 20 and 80 HU. Studies were also performed under the simultaneous display of the set of brain CT images on each window width. As a result of evaluation by receiver operating characteristic analysis, narrowing of the window width can improve the low contrast detectability in CT images with noticeable noise, and can decrease the variation in the interpretation skills between observers in clinical CT images. Moreover, the interpretation accuracy was improved by displaying simultaneously the clinical CT image set on window widths at 20 and 80 HU.

波形テンプレートを利用する局在信号の検出と波形形状推定

Since local signals appear time-locally and their waveforms are steep, conventional signal processing methods are generally inadequate to detect them. In this paper, by focusing on amplitude distribution forms of an observed signal, a simple signal processing method is proposed to detect the local signals and extract their waveform shapes simultaneously. Concretely, a characteristic waveform template which consists of representative data series of a simplified target local signal is newly introduced, and a certain event regulated with it is adopted. The local signals can be detected by evaluating whether the amplitude distribution forms of observed signals are associated with the event, and substitution of conditional events for associated events improves detection performance. In addition, the proposed method has been extended to extract waveform shapes by introducing an enhanced waveform template, and automatic detection and extraction of waveform shapes can be performed simultaneously. The proposed method is applied to simulation signal data, and its effectiveness is confirmed by detection performance and extracted waveform shapes for the local signals. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 175(3): 37–47, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21074