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Journal articles on the topic 'Synchroextracting transform'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Yu, Gang, Mingjin Yu, and Chuanyan Xu. "Synchroextracting Transform." IEEE Transactions on Industrial Electronics 64, no. 10 (2017): 8042–54. http://dx.doi.org/10.1109/tie.2017.2696503.

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2

Lin, Ying, Siyuan Chen, Guangzhi Zhang, Minmin Huang, and Baoli Wang. "High-resolution time–frequency analysis based on a synchroextracting adaptive S-transform and its application." Journal of Geophysics and Engineering 19, no. 5 (2022): 1124–33. http://dx.doi.org/10.1093/jge/gxac068.

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Abstract We propose synchroextracting adaptive S-transform (SEAST) by combining the adaptivity provided by the recently introduced ‘Sparse Adaptive S-transform’ (SAST) with the high resolution of synchroextracting spectral decomposition method. Traditional synchroextracting transforms are based on short-time Fourier transforms (STFTs) and their application is limited by having a fixed analysis window size for high and low frequencies and no multi-resolution features. The arbitrary window functions used in the SAST vary with frequency and amplitude, making it more suitable for non-stationary seismic signal analysis. The SEAST retains the multi-resolution advantages of adaptive S-transforms, while providing the strong time–frequency focus associated with synchroextracting transforms. The method was used to calculate seismic dispersion attributes and the resulting field data indicates that its time–frequency resolution and joint P-wave dispersion attributes can help to fine-tune identification of the location of oil and gas reservoirs.

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3

Li, Bei, and Zhuosheng Zhang. "Synchroextracting Transform Based on the Novel Short-Time Fractional Fourier Transform." Fractal and Fractional 8, no. 12 (2024): 736. https://doi.org/10.3390/fractalfract8120736.

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As a generalization of the short-time Fourier transform (STFT), the novel short-time fractional Fourier transform (NSTFRFT) has been introduced recently. In order to improve the concentration of the time–frequency representation (TFR) generated by the NSTFRFT, two post-processing time–frequency analysis methods, two synchroextracting transforms based on the NSTFRFT with two different fractional Fourier transform (FRFT) angles, are proposed in this paper. One is achieved via an equation where the instantaneous frequency satisfies the condition where the FRFT angle takes π2, and the other one is obtained using the instantaneous frequency estimator in the case that the FRFT angle takes a value related to the chirp rate of the signal. Although the conditions of the two synchroextracting transforms are different, their implementation can be unified into the same algorithm. The proposed synchroextracting transforms supplement existing post-processing time–frequency analysis methods which are based on the NSTFRFT. Experiments are conducted to verify the performance and superiority of the proposed methods.

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4

Hu, Zhifeng, Zhinong Li, Liying Ge, Qinghua Mao, and Xuhui Zhang. "Diagnosis Method for Mechanical Faults Based on Rotation Synchroextracting Chirplet Transform." Applied Sciences 12, no. 24 (2022): 12972. http://dx.doi.org/10.3390/app122412972.

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The problems of the synchroextracting transform method being unable to handle FM signals and being prone to time–frequency feature discontinuity in a strong noise environment are addressed by the construction of a novel rotation synchroextracting chirplet transform under the framework of the synchroextracting transform. The method retains the advantage of the generalized linear chirplet transform that can fit the time–frequency characteristics of the original signal and retains the high precision time–frequency analysis ability of the synchroextracting transform. The simulation results show that the proposed method is obviously superior to the generalized chirplet transform and synchroextracting transform method. The method can obtain the time–frequency energy located at the time–frequency ridges of FM-AM signals and multicomponent signals with crossed-frequency components, and has high time–frequency analysis ability and anti-interference ability. Finally, the proposed method is applied to diagnose mechanical faults. The experimental results further verify the effectiveness of the proposed method, which can effectively extract the characteristic freque.ncy of fault signal.

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5

Zhang, Ran, Xingxing Liu, Yongjun Zheng, et al. "Time‐frequency synchroextracting transform." IET Signal Processing 16, no. 2 (2021): 117–31. http://dx.doi.org/10.1049/sil2.12073.

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6

Bao, Wenjie, Songyong Liu, Zhen Liu, and Fucai Li. "Generalized synchroextracting transform: Algorithm and applications." Mechanical Systems and Signal Processing 224 (February 2025): 112116. http://dx.doi.org/10.1016/j.ymssp.2024.112116.

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7

You, Guanghui, Yong Lv, Yefeng Jiang, and Cancan Yi. "A Novel Fault Diagnosis Scheme for Rolling Bearing Based on Convex Optimization in Synchroextracting Chirplet Transform." Sensors 20, no. 10 (2020): 2813. http://dx.doi.org/10.3390/s20102813.

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Synchroextracting transform (SET) developed from synchrosqueezing transform (SST) is a novel time-frequency (TF) analysis method. Its concentrated TF spectrum is obtained by applying a synchroextracting operator into TF transformation co-efficients on the TF plane. For this class of post-processing TF analysis methods, the main research focuses on the accurate estimation of instantaneous frequency (IF). However, the performance of TF analysis is greatly affected by the strong frequency modulation (FM) signal. In particular, the actual measured mechanical vibration signals always contain strong background noise, which decreases the resolution of TF representation, resulting in an inaccurate ridge extraction. To solve this problem, an improved penalty function based on the convex optimization scheme is firstly introduced for signal denoising. Based on the superiority of the linear chirplet transform (LCT) in dealing with modulated signals, the synchroextracting chirplet transform (SECT) is employed to sharpen the TF representation after the convex optimization denoising operation. To verify the effectiveness of the proposed method, the numerical simulation signals and the measured fault signals of rolling bearing are carried out, respectively. The results demonstrate that the proposed method leads to a better solution in rolling bearing fault feature extraction.

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8

Shahin, M. Abdulla, and Jayakumari J. "High clarity speech separation using synchroextracting transform." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2621–30. https://doi.org/10.11591/ijece.v11i3.pp2621-2630.

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Degenerate unmixing estimation technique (DUET) is the most ideal blind source separation (BSS) method for underdetermined conditions with number of sources exceeds number of mixtures. Estimation of mixing parameters which is the most critical step in the DUET algorithm is developed based on the characteristic feature of sparseness of speech signals in time frequency (TF) domain. Hence, DUET relies on the clarity of time frequency representation (TFR) and even the slightest interference in the TF plane will be detrimental to the unmixing performance. In conventional DUET algorithm, short time Fourier transform (STFT) is utilized for extracting the TFR of speech signals. However, STFT can provide on limited sharpness to the TFR due to its inherent conceptual limitations, which worsens under noise contamination. This paper presents the application of post-processing techniques like synchrosqueezed transform (SST) and synchroextracting transform (SET) to the DUET algorithm, to improve the TF resolution. The performance enhancement is evaluated both qualitatively and quantitatively by visual inspection, Renyi entropy of TFR and objective measures of speech signals. The results show enhancement in TF resolution and high clarity signal reconstruction. The method also provides adequate robustness to noise contamination.

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9

Ashtari Jafari, Mohammad. "Comparative Application of Time-Frequency Methods on Strong Motion Signals." Advances in Civil Engineering 2021 (July 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/9933078.

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Real-world physical signals are commonly nonstationary, and their frequency details change with time and do not remain constant. Fourier transform that uses infinite sine/cosine waves as basis functions represents frequency constituents of signals but does not show the variations of the signal frequency contents over time. Multiresolution demonstration of the time-frequency domain may be achieved by the techniques that can support adjustable resolution in time and frequency. Earthquake strong motion signals are nonstationary and indicate time-varying frequency content due to the scattering from the source to the site. In this paper, we applied short-time Fourier transform, S-transform, continuous wavelet transform, fast discrete wavelet transform, synchrosqueezing transform, synchroextracting transform, continuous wavelet synchrosqueezing, filter bank synchrosqueezing, empirical mode decomposition, and Fourier decomposition methods on the near-source strong motion signals from the 7 May 2020 Mosha-Iran earthquake to study and compare the frequency content of this event estimated by these methods. According to the results that are examined by Renyi entropy and relative error, synchroextracting performed better in terms of energy concentration, and the Fourier decomposition method revealed the lowest difference between the original and reconstructed records.

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10

Li, Zhen, Jinghuai Gao, Hui Li, Zhuosheng Zhang, Naihao Liu, and Xiangxiang Zhu. "Synchroextracting transform: The theory analysis and comparisons with the synchrosqueezing transform." Signal Processing 166 (January 2020): 107243. http://dx.doi.org/10.1016/j.sigpro.2019.107243.

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11

Jiang, Yun, Wanzhong Chen, Mingyang Li, Tao Zhang, and Yang You. "Synchroextracting chirplet transform-based epileptic seizures detection using EEG." Biomedical Signal Processing and Control 68 (July 2021): 102699. http://dx.doi.org/10.1016/j.bspc.2021.102699.

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12

Dong, Haoran, and Gang Yu. "Comments on “Synchroextracting transform: The theory analysis and comparisons with the synchrosqueezing transform”." Signal Processing 190 (January 2022): 108345. http://dx.doi.org/10.1016/j.sigpro.2021.108345.

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13

Xin, Yu, Hong Hao, and Jun Li. "Time-varying system identification by enhanced Empirical Wavelet Transform based on Synchroextracting Transform." Engineering Structures 196 (October 2019): 109313. http://dx.doi.org/10.1016/j.engstruct.2019.109313.

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14

Xu, Yonggang, Liang Wang, Gang Yu, and Yanxue Wang. "Generalized S-Synchroextracting Transform for Fault Diagnosis in Rolling Bearing." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–14. http://dx.doi.org/10.1109/tim.2021.3127305.

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15

Roshan, Kumar, Kumar Gaurav, Zhao Wei, et al. "Identification of earthquake induced structural damage based on synchroextracting transform." Earthquake Engineering and Engineering Vibration 23, no. 2 (2024): 475–87. http://dx.doi.org/10.1007/s11803-024-2249-5.

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16

Li, Zhen, Jinghuai Gao, Zhiguo Wang, Naihao Liu, and Yang Yang. "Time-Synchroextracting General Chirplet Transform for Seismic Time–Frequency Analysis." IEEE Transactions on Geoscience and Remote Sensing 58, no. 12 (2020): 8626–36. http://dx.doi.org/10.1109/tgrs.2020.2989403.

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17

Ma, Yubo, Yong Lv, Rui Yuan, and Gangbing Song. "Matching Synchroextracting Transform for Mechanical Fault Diagnosis Under Variable-Speed Conditions." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–12. http://dx.doi.org/10.1109/tim.2021.3134335.

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18

Chen, Peng, Kesheng Wang, Ming J. Zuo, and Dongdong Wei. "An ameliorated synchroextracting transform based on upgraded local instantaneous frequency approximation." Measurement 148 (December 2019): 106953. http://dx.doi.org/10.1016/j.measurement.2019.106953.

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19

Zhu, Xiangxiang, Zhuosheng Zhang, Jinghuai Gao, et al. "Synchroextracting chirplet transform for accurate IF estimate and perfect signal reconstruction." Digital Signal Processing 93 (October 2019): 172–86. http://dx.doi.org/10.1016/j.dsp.2019.07.015.

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20

Han, Bo, Yiqi Zhou, and Gang Yu. "Second-order synchroextracting wavelet transform for nonstationary signal analysis of rotating machinery." Signal Processing 186 (September 2021): 108123. http://dx.doi.org/10.1016/j.sigpro.2021.108123.

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21

Shi, Zhenjin, Xu Yang, Yueyang Li, and Gang Yu. "Wavelet-based Synchroextracting Transform: An effective TFA tool for machinery fault diagnosis." Control Engineering Practice 114 (September 2021): 104884. http://dx.doi.org/10.1016/j.conengprac.2021.104884.

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22

Li, Zhen, Jinghuai Gao, and Zhiguo Wang. "A Time-Synchroextracting Transform for the Time–Frequency Analysis of Seismic Data." IEEE Geoscience and Remote Sensing Letters 17, no. 5 (2020): 864–68. http://dx.doi.org/10.1109/lgrs.2019.2931138.

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23

Meng, Zong, Meng Lv, Zihan Liu, and Fengjie Fan. "General synchroextracting chirplet transform: Application to the rotor rub-impact fault diagnosis." Measurement 169 (February 2021): 108523. http://dx.doi.org/10.1016/j.measurement.2020.108523.

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24

Cui, Lingli, Jiahui Chen, Dongdong Liu, and Dong Zhen. "Fault diagnosis of offshore wind turbines based on component separable synchroextracting transform." Ocean Engineering 291 (January 2024): 116275. http://dx.doi.org/10.1016/j.oceaneng.2023.116275.

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25

Chen, Zhigang, Yunlong Jiang, Yingying Wang, Huaibin Zhang, and Qun He. "Fault Diagnosis of Fracturing Vehicle Based on Local Mean Decomposition and Synchroextracting Transform." Journal of Physics: Conference Series 2174, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1742-6596/2174/1/012003.

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Abstract The power end of a fracturing truck is a key component that provides kinetic energy during pressure operations. Its vibration signal is collected during operation because of the complex working conditions and heavy loads, resulting in the collected signal being filled with a large amount of noise, for which it is difficult to perform effective fault feature extraction. To address this problem, a new fault diagnosis method is proposed. This method combines local mean decomposition (LMD) and synchroextracting transform (SET) for signal processing. First, LMD processing is done on the acquired signal to obtain several product function (PF) components. The cross-correlation coefficient and kurtosis value are used as references to select the true PF components. After that, the SET method is used to process the real PF components, extract the energy that is most correlated with the time-varying features of the signal, remove the fuzzy energy, improve the time-frequency resolution, and enhance the fault features contained in the signal to facilitate accurate fault diagnosis. Finally, the vibration signals collected from the power end of the fracturing vehicle are experimentally verified. The results show that the method can accurately extract the fault characteristics of bearing failure in the power end, and provide some useful reference for the diagnosis method of fracturing vehicle power system.

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26

Pang, Feifei, Zhengfu Ren, Haiyan Wang, and Junqi Zhao. "Time-frequency analysis based on multi-resolution synchroextracting Chirplet transform in reverberant environments." Applied Acoustics 231 (March 2025): 110526. https://doi.org/10.1016/j.apacoust.2024.110526.

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27

Li, Jiantao, Xinqun Zhu, Siu-seong Law, and Bijan Samali. "Time-varying characteristics of bridges under the passage of vehicles using synchroextracting transform." Mechanical Systems and Signal Processing 140 (June 2020): 106727. http://dx.doi.org/10.1016/j.ymssp.2020.106727.

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28

Liu, Hui, and Jiawei Xiang. "Kernel regression residual decomposition-based synchroextracting transform to detect faults in mechanical systems." ISA Transactions 87 (April 2019): 251–63. http://dx.doi.org/10.1016/j.isatra.2018.12.004.

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29

Jiang, Chuandong, Yi Zhou, Yunzhi Wang, Qingming Duan, and Baofeng Tian. "Harmonic Noise-Elimination Method Based on the Synchroextracting Transform for Magnetic-Resonance Sounding Data." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–11. http://dx.doi.org/10.1109/tim.2021.3102689.

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30

Yan, Zhu, Jingpin Jiao, and Yonggang Xu. "Adaptive linear chirplet synchroextracting transform for time-frequency feature extraction of non-stationary signals." Mechanical Systems and Signal Processing 220 (November 2024): 111700. http://dx.doi.org/10.1016/j.ymssp.2024.111700.

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31

Li, Jiaxin, David Mba, Xiaochuan Li, Yajun Shang, Shuai He, and Tian Ran Lin. "An adaptive synchroextracting transform for the analysis of noise contaminated multi-component nonstationary signals." Applied Acoustics 202 (January 2023): 109169. http://dx.doi.org/10.1016/j.apacoust.2022.109169.

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32

Yuan, Ping-Ping, Jian Zhang, Jia-Qi Feng, Hang-Hang Wang, Wei-Xin Ren, and Chao Wang. "An improved time-frequency analysis method for structural instantaneous frequency identification based on generalized S-transform and synchroextracting transform." Engineering Structures 252 (February 2022): 113657. http://dx.doi.org/10.1016/j.engstruct.2021.113657.

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33

Li, Xuemei, Chunyang Wang, Xuelian Liu, Bo Xiao, and Zishuo Wang. "A Vibration Fault Signal Identification Method via SEST." Electronics 11, no. 9 (2022): 1300. http://dx.doi.org/10.3390/electronics11091300.

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(1) Background: with the development of intelligent transportation, effectively collecting and identifying the working state of vehicles is conducive to the analysis and processing of vehicle information by internet of vehicles, so as to reduce the occurrence of traffic accidents. Aiming at the problem of low identification accuracy of the mechanical vibration fault signal, a signal identification method based on time-frequency detection is introduced; (2) Methods: this paper constructs a parameter model of the synchroextracting S transform on the basis of the poor time-frequency concentration of the original S transform; (3) Results: in the case of SNR = −5~+30 dB, compared with other transformations, the Rényi entropy value of SEST is the smallest, and the Rényi entropy value is 0.5246 when SNR = +22 dB; (4) Conclusions: through simulation comparison and analysis, the excellent time-frequency concentration and anti-noise characteristics of the SEST are highlighted, and the rotor vibration fault signals such as rotor misalignment, unbalance and bearing wear are identified by SEST.

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34

Hua, Zehui, Juanjuan Shi, and Zhongkui Zhu. "Matching Linear Chirplet Strategy-Based Synchroextracting Transform and Its Application to Rotating Machinery Fault Diagnosis." IEEE Access 8 (2020): 185725–37. http://dx.doi.org/10.1109/access.2020.3027067.

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35

Yu, Kun, Tian Ran Lin, Hui Ma, Hongfei Li, and Jin Zeng. "A Combined Polynomial Chirplet Transform and Synchroextracting Technique for Analyzing Nonstationary Signals of Rotating Machinery." IEEE Transactions on Instrumentation and Measurement 69, no. 4 (2020): 1505–18. http://dx.doi.org/10.1109/tim.2019.2913058.

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36

Lv, Site, Hongan Wu, Shan Zeng, Chen Yu, and Ke Yang. "Multi-scale chirplet synchroextracting transform for accurate characterization of adjacent fault features in rotating machinery." Mechanical Systems and Signal Processing 234 (July 2025): 112826. https://doi.org/10.1016/j.ymssp.2025.112826.

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37

Song, Youshuo, Jun Cao, and Yujia Hu. "In-process feature extraction of milling chatter based on second-order synchroextracting transform and fast kutrogram." Mechanical Systems and Signal Processing 208 (February 2024): 111018. http://dx.doi.org/10.1016/j.ymssp.2023.111018.

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38

Sepehry, Naserodin, Mohammad Ehsani, Hamdireza Amindavar, Weidong Zhu, and Firooz Bakhtiari Nejad. "A novel enhanced Superlet Synchroextracting transform ensemble learning for structural health monitoring using nonlinear wave modulation." Engineering Applications of Artificial Intelligence 147 (May 2025): 110341. https://doi.org/10.1016/j.engappai.2025.110341.

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39

Hu, Ying, Hui Chen, Hongyan Qian, Xinyue Zhou, Yuanjun Wang, and Bin Lyu. "A high‐precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S‐transform." Geophysical Prospecting 68, no. 3 (2019): 941–54. http://dx.doi.org/10.1111/1365-2478.12888.

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40

Lv, Site, Yong Lv, Rui Yuan, and Yubo Ma. "High-Order Synchroextracting Chirplet Transform for Accurate Instantaneous Frequency Estimation and Its Application in Fault Diagnosis of Rotary Machinery." IEEE Sensors Journal 21, no. 24 (2021): 27827–39. http://dx.doi.org/10.1109/jsen.2021.3125357.

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41

Lv, Yong, Site Lv, Rui Yuan, and Hewenxuan Li. "Longitudinal synchroextracting transform: A useful tool for characterizing signals with strong frequency modulation and application to machine fault diagnosis." Measurement 182 (September 2021): 109750. http://dx.doi.org/10.1016/j.measurement.2021.109750.

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42

Lv, Site, Yong Lv, Rui Yuan, and Hewenxuan Li. "High-order synchroextracting transform for characterizing signals with strong AM-FM features and its application in mechanical fault diagnosis." Mechanical Systems and Signal Processing 172 (June 2022): 108959. http://dx.doi.org/10.1016/j.ymssp.2022.108959.

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43

Guo, Yong, and Li-Dong Yang. "Radar Moving Target Detection Method Based on SET2 and AlexNet." Mathematical Problems in Engineering 2022 (December 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/3359871.

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Aiming at the nonstationary characteristics of echo signal for a high-speed maneuvering target, a signal feature extraction method is proposed by combining the time-frequency analysis and convolution neural network, and then the automatic detection of radar moving target in a noisy environment is realized. Firstly, the echo signal is modelled as a more accurate Gaussian modulation-linear frequency modulation (GM-LFM) signal and converted into the time-frequency image by a second-order synchroextracting transform (SET2). Then, ridge extraction is applied to extract the maximum energy ridge from the time-frequency distribution, and the data set is constructed by the maximum energy ridge. Finally, the data set is input into AlexNet for training, and the deep-level features of echo signal are extracted to realize the automatic moving targets detection. Simulation results show that SET2 and RE can effectively enhance the time-frequency characteristics of echo signal under the noisy environment, and the detection accuracy and noise robustness of the proposed method are better than that of SET1 and smooth pseudo-Wigner–Ville distribution (SPWVD).

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44

Ma, Zengqiang, Wanying Ruan, Mingyi Chen, and Xiang Li. "An Improved Time-Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing." Shock and Vibration 2018 (September 18, 2018): 1–18. http://dx.doi.org/10.1155/2018/8710190.

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Instantaneous frequency estimation of rolling bearing is a key step in order tracking without tachometers, and time-frequency analysis method is an effective solution. In this paper, a new method applying the variational mode decomposition (VMD) in association with the synchroextracting transform (SET), named VMD-SET, is proposed as an improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing. The SET is a new time-frequency analysis method which belongs to a postprocessing procedure of the short-time Fourier transform (STFT) and has excellent performance in energy concentration. Considering nonstationary broadband fault vibration signals of rolling bearing under variable speed conditions, the time-frequency characteristics cannot be obtained accurately by SET alone. Thus, VMD-SET method is proposed. Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) with different center frequency by VMD. Then, effective IMFs are selected by mutual information and kurtosis criteria and are reconstructed. Next, the SET method is applied to the reconstructed signal to generate the time-frequency representation with high resolution. Finally, instantaneous frequency trajectory can be accurately extracted by peak search from the time-frequency representation. The proposed method is free from time-varying sidebands and is robust to noise interference. It is proved by numerical simulated signal analysis and is further validated by lab experimental rolling bearing vibration signal analysis. The results show this method can estimate the instantaneous frequency with high precision without noise interference.

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45

Zhou, Jing, Linsheng Huo, Chen Huang, Zhuodong Yang, and Hongnan Li. "Feasibility Study of Earthquake-Induced Damage Assessment for Structures by Utilizing Images from Surveillance Cameras." Structural Control and Health Monitoring 2024 (May 14, 2024): 1–19. http://dx.doi.org/10.1155/2024/4993972.

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Rapid and accurate structural damage assessment after an earthquake is important for efficient emergency management. The widespread application of surveillance cameras provides a new possibility for improving the efficiency of assessment. However, it is still challenging to directly assess the structural seismic damage based on videos captured by indoor surveillance cameras during earthquakes. In this study, we elaborate on the concept of estimating the structural natural frequency based on the relative pixel displacement of inter-stories. Furthermore, we propose a strategy for post-earthquake structural damage assessment that integrates the computer vision and time-frequency analysis. This approach aims to navigate the difficulties inherent in earthquake damage assessment and improve emergency responses. The relative pixel displacement between the camera and the fixed features on the floor is extracted from videos by using the Harris corner detection and Kanade–Lucas–Tomasi algorithms. The structural natural frequency is estimated using the synchroextracting transform-enhanced empirical wavelet transform. The natural frequency shift-related seismic damage index is defined and calculated for damage assessment. A shake table experiment of a small-scale steel model is conducted to verify the accuracy and feasibility of the approach, and the practicality of the proposed approach is further verified by utilizing the data from a full-scale reinforced concrete benchmark model experiment. The results demonstrate that the approach can accurately and efficiently evaluate the structural damage after an earthquake based on the video captured by surveillance cameras during the earthquake. The error of the acquired damage index is less than 0.1. We will apply more advanced algorithms in the future to alleviate this problem.

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46

Wu, Shiqian, Lifei Yang, and Liangliang Tao. "Synergistic WSET-CNN and Confidence-Driven Pseudo-Labeling for Few-Shot Aero-Engine Bearing Fault Diagnosis." Processes 13, no. 7 (2025): 1970. https://doi.org/10.3390/pr13071970.

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Reliable fault diagnosis in aero-engine bearing systems is essential for maintaining process stability and safety. However, acquiring fault samples in aerospace applications is costly and difficult, resulting in severely limited data for model training. Traditional methods often perform poorly under such constraints, lacking the ability to extract discriminative features or effectively correlate observed signal changes with underlying process faults. To address this challenge, this study presents a process-oriented framework—WSET-CNN-OOA-LSSVM—designed for effective fault recognition in small-sample scenarios. The framework begins with Wavelet Synchroextracting Transform (WSET), enhancing time–frequency resolution and capturing energy-concentrated fault signatures that reflect degradation along the process timeline. A tailored CNN with asymmetric pooling and progressive dropout preserves temporal dynamics while preventing overfitting. To compensate for limited labels, confidence-based pseudo-labeling is employed, guided by Mahalanobis distance and adaptive thresholds to ensure reliability. Classification is finalized using an Osprey Optimization Algorithm (OOA)-enhanced Least Squares SVM, which adapts decision boundaries to reflect subtle process state transitions. Validated on both test bench and real aero-engine data, the framework achieves 93.4% accuracy with only five fault samples per class and 100% in full-scale scenarios, outperforming eight existing methods. Therefore, the experimental results confirm that the proposed framework can effectively overcome the data scarcity challenge in aerospace bearing fault diagnosis, demonstrating its practical viability for few-shot learning applications in industrial condition monitoring.

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47

Ren, Ke, Lan Du, Xiaofei Lu, Zhenyu Zhuo, and Lu Li. "Instantaneous Frequency Estimation Based on Modified Kalman Filter for Cone-Shaped Target." Remote Sensing 12, no. 17 (2020): 2766. http://dx.doi.org/10.3390/rs12172766.

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The instantaneous frequency (IF) is a vital parameter for the analysis of non-stationary multicomponent signals, and plays an important role in space cone-shaped target recognition. For a cone-shaped target, IF estimation is not a trivial issue due to the proximity of the energy of the IF components, the intersections among different IF components, and the existence of noise. Compared with the general parameterized time-frequency (GPTF), the traditional Kalman filter can perform better when the energy of different signal components is close. Nevertheless, the traditional Kalman filter usually makes association mistakes at the intersections of IF components and is sensitive to the noise. In this paper, a novel IF estimation method based on modified Kalman filter (MKF) is proposed, in which the MKF is used to associate the intersecting IF trajectories obtained by the synchroextracting transform (SET). The core of MKF is the introduction of trajectory correction strategy in which a trajectory survival rate is defined to judge the occurrence of association mistakes. When the trajectory survival rate is below the predetermined threshold, it means that an association mistakes occurs, and then the new trajectories generated by the random sample consensus algorithm are used to correct the wrong associations timely. The trajectory correction strategy can effectively obviate the association mistakes caused by the intersections of IF components and the noise. The windowing technique is also used in the trajectory correction strategy to improve computational speed. The experimental results based on the electromagnetic computation data show that the proposed method is more robust and precise than the traditional Kalman filter. Moreover, the proposed method has great performance advantages compared with other methods (i.e., the multiridge detection, the ant colony optimization, and the GPTF methods) especially in the case of low signal noise ratio (SNR).

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Hansi Fu. "Artistic Creation and Animation Scene Designing using Hierarchical Spatio-Temporal Graph Convolutional Neural Network Optimized with Gazelle Optimization Algorithm." Journal of Electrical Systems 20, no. 3s (2024): 2321–34. http://dx.doi.org/10.52783/jes.3056.

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Creating moving visuals, usually in the form of 2D or 3D animations, is the focus of the graphic design field known as animation design. A sequence of consecutive images, or frames, is played quickly one after the other in animation design to provide the impression of motion. In this Manuscript, Artistic Creation and Animation Scene Designing using Hierarchical Spatio-temporal graph convolutional neural network Optimized with Gazelle Optimization (AC-ASD-HSTCNN-GO)is proposed. Initially, data is taken from LiDAR dataset then the data are fed to preprocessing segment. For pre-processing Orthogonal Master Slave Adaptive Notch Filter (OMSANF) is used to remove duplicate data and replacing missing data from collected data’s. Then the feature are extracted by using General Synchroextracting Chirplet Transform (GSCT) technique, which extracts features such as objects, faces and scenes. Finally HSTGCNN for creating 3D animation image design .In general the HSTGCNN does not express some adaption of optimization strategies for determining optimum parameters to assure the product recommendation. Hence, the GOA is proposed to enhance HSTGCNN.The proposed AC-ASD-HSTCNN-GO method is activated in python and the performance of proposed AC-ASD-HSTCNN-GO is estimated under performance metrics like accuracy, precision, F1-score, recall, specificity, computational time, error rate, RoC. Finally, performance of AC-ASD-HSTCNN-GO method provides 15.27%, 17.14%, 18.58% greater accuracy, 16.11%, 19.65%, 20.53% greater precision and 19.31%, 21.29%, 22.15% greater F1-Score while compared with existing techniques like Graphic Design of 3D Animation Scenes Depend on Deep Learning with Information Security Technology (GD-3DASDL-IST), GRADE: Generating Realistic Animated Dynamic Environments for Robotics Research (GRADE-RR)andApplication of Random Forest Algorithm in Natural Landscape Animation Design (ALN-RFA-NLAD) respectively.

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Geetha Paranjothi, Arunachalam A.S. "Lung Cancer Detection: Advancing CT Image Analysis Through Hybrid Bidirectional Long Short-Term Memory and Recurrent Neural Network." Journal of Information Systems Engineering and Management 10, no. 18s (2025): 29–46. https://doi.org/10.52783/jisem.v10i18s.2880.

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Globally, lung cancer (LC) is the leading cause of death from cancer. Medical image analysis based on deep learning (DL) has strong potential for detecting and diagnosing lung cancer by identifying early symptoms with image aid from positron emission tomography (PET) and computed tomography (CT). The majority of DL models created for LC detection are very resource-intensive, requiring a great deal of computational capacity; hence, they pose a challenge to deployment on a standard clinical system and are therefore significantly less accessible in resource-constrained settings. This additional computational load may delay diagnosis and treatment, thus affecting the outcome of the patients. Therein lies the critical need for developing more lightweight and efficient deep learning models that ensure high accuracy while reducing computational requirements. This manuscript presents a Lung Cancer Detection technique, LCD-CT-BiLSTM-RNN, based on advanced CT image analysis. First, noise reduction in the lung CT images by anisotropic guided filtering (AGF) is performed. Then, adaptive fuzzy K-means clustering (AFKMC) separates the affected areas of cancer, and Synchroextracting Transform (SET) adds the spectral features. Finally, a hybrid BiLSTM and RNN architecture runs the classification task with an improved overall accuracy. Hybrid optimization using Slime Mould Optimization (SMO) and Golden Eagle Optimization (GEO) fine-tunes the model. The performance of the methods is assessed using MATLAB's accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), and ROC to compare the acquired findings with the existing approaches. The performance of the proposed method provides 2.03%, 3.45%, and 2.36% higher accuracy compared with existing techniques like Fuzzy Particle Swarm Optimization with Convolutional Neural Network for Detection of LC (FPSO-CNN), Deep learning Instantaneously Accomplished Neural Network with Improved Profuse Clustering Technique for LC detection (DITNN-IPCT), and Residual Learning Denoising Model with Convolutional Neural Network (DR-Net-CNN) for the Detection of LC.

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Khan, Asif, Salman Khalid, Izaz Raouf, Jung-Woo Sohn, and Heung-Soo Kim. "Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning." Sensors 21, no. 18 (2021): 6239. http://dx.doi.org/10.3390/s21186239.

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Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data.

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