Tunable hyperbolic Cohen-class kernel for cross-term diminishing in time–frequency distributions

Martinez-Herrera, Ana L.; Ledesma-Carrillo, Luis M.; Ferrucho-Alvarez, Edna R.; Lopez–Ramirez, Misael; Rodriguez-Donate, Carlos; Cabal-Yépez, Eduardo

doi:10.1016/j.ymssp.2022.109850

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…By incorporating the idea of multiplication, the improved algorithm has various parameters of the multi-component chirp signal that can be effectively estimated, thereby improving the estimation performance of the chirp signal. More up-to-date research can be found in Zhang (2020) , Martinez-Herrera et al (2023) .…”

Section: Introductionmentioning

confidence: 99%

Algorithm design of a combinatorial mathematical model for computer random signals

Yao,

Qiu

2024

PeerJ Computer Science

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To improve the processing effect of computer random signals, the manuscript employs the intelligent signal recognition algorithm to design a combinatorial mathematical model for computer random signals, and studies the parameter estimation of conventional frequency hopping signal (FHS) based on optimizing kernel function (KF). First, the mathematical form and graphical representation of the ambiguity function of the conventional FHS are explored. Furthermore, a new KF is presented according to its fuzzy function (FF) and the parameters of conventional FHSs are estimated according to the time-frequency distribution corresponding to the KF. Then, simulation experiments are carried out in different types of interference noise environments. The proposed combinatorial mathematical model for computer random signals shows a practical impact, and can effectively improve the effect of random signal combination.

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Section: Introductionmentioning

confidence: 99%

Algorithm design of a combinatorial mathematical model for computer random signals

Yao,

Qiu

2024

PeerJ Computer Science

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“…Time-frequency analysis is aimed at extracting information from vibration signals functioning as both time and frequency, which raises the efficiency in accomplishing fault diagnosis in non-stationary conditions [13]. Traditional time-frequency analysis includes short-time Fourier transform (STFT) [14], Wigner Ville distribution (WVD) [15] and directional Choi Williams distribution [16], empirical mode decomposition (EMD) technology [17], Hilbert-Huang transform (HHT) [18] and wavelet transform (WT) [19]. However, considering the weak correlation between frequency characteristic and time, integrating time domain analysis and frequency domain analysis makes it possible to enrich the features extracted from the vibration signals to identify the failure or success of the stirred reactor and the kind of fault without the need of complex time-frequency analysis.…”

Section: Introductionmentioning

confidence: 99%

Application of the CatBoost Model for Stirred Reactor State Monitoring Based on Vibration Signals

Ren,

Yu,

Chen

et al. 2024

CMES

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Stirred reactors are key equipment in production, and unpredictable failures will result in significant economic losses and safety issues. Therefore, it is necessary to monitor its health state. To achieve this goal, in this study, five states of the stirred reactor were firstly preset: normal, shaft bending, blade eccentricity, bearing wear, and bolt looseness. Vibration signals along x, y and z axes were collected and analyzed in both the time domain and frequency domain. Secondly, 93 statistical features were extracted and evaluated by ReliefF, Maximal Information Coefficient (MIC) and XGBoost. The above evaluation results were then fused by D-S evidence theory to extract the final 16 features that are most relevant to the state of the stirred reactor. Finally, the CatBoost algorithm was introduced to establish the stirred reactor health monitoring model. The validation results showed that the model achieves 100% accuracy in detecting the fault/normal state of the stirred reactor and 98% accuracy in diagnosing the type of fault.

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The parameter estimation of LFM signal using generalized Versoria Wigner-Ville distribution in impulsive noise

Dou,

Li,

Lin

2023

2023 IEEE/CIC International Conference on Communications in China (ICCC)

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Tunable hyperbolic Cohen-class kernel for cross-term diminishing in time–frequency distributions

Cited by 9 publications

References 23 publications

Algorithm design of a combinatorial mathematical model for computer random signals

Algorithm design of a combinatorial mathematical model for computer random signals

Application of the CatBoost Model for Stirred Reactor State Monitoring Based on Vibration Signals

The parameter estimation of LFM signal using generalized Versoria Wigner-Ville distribution in impulsive noise

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