Noise Suppression of Artificial Intelligence Filter for Radio Frequency Interference

Lai, Wen‐Cheng; Ren, Jinchang; Xin, Xiaoning

doi:10.1109/icce-taiwan49838.2020.9258177

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…On the construction sites, we realized that, considering the low power of the percussive excitations and signal amplitude (roughly −70 dBV in Figure 8b), work will be required to set noise reduction at the input of the FFT analyzer. In this respect, we must say that, in addition to more "classical" techniques [23], a new generation of approaches to the problem of noise reduction, based on machine learning and artificial intelligence, are being pursued [24], and we are confident that specific noise filtering could be worked out for developments of our type of measurements.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Properties and Macroscopic Defects of Metallic Bars, Pipes, and Strands through the Spectrum of Low-Frequency Excitations

Mancini,

Turchetta,

Cirillo

2024

Materials

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It is demonstrated that the application of piezoelectric sensors to metallic bars and strands can enable determining the status of the integrity of these elements through the spectrum of their acoustic excitations. The voltage output of the piezo, secured to metal bars or strands, is fed to the input of a Fast Fourier Transform analyzer, which allows displaying the spectrum of the excitations from which information on the length, overall quality of the metal, and the presence of defects can be obtained. We show that the analysis, performed on several materials and strands of different lengths, could be useful for cases in which visible inspection and/or direct access to the entire body of the metallic elements is not possible. Applications of our study for testing metallic structures embedded in concrete foundations are reported for construction sites.

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Section: Discussionmentioning

confidence: 99%

Analysis of Properties and Macroscopic Defects of Metallic Bars, Pipes, and Strands through the Spectrum of Low-Frequency Excitations

Mancini,

Turchetta,

Cirillo

2024

Materials

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Artificial Intelligence Enabling Denoising in Passive Electronic Filtering Circuits for Industry 5.0 Machines

Massaro

2024

Machines

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The paper proposes an innovative model able to predict the output signals of resistance and capacitance (RC) low-pass filters for machine-controlled systems. Specifically, the work is focused on the analysis of the parametric responses in the time- and frequency-domain of the filter output signals, by considering a white generic noise superimposed onto an input sinusoidal signal. The goal is to predict the filter output using a black-box model to support the denoising process by means of a double-stage RC filter. Artificial neural networks (ANNs) and random forest (RF) algorithms are compared to predict the output of noisy signals. The work is concluded by defining guidelines to correct the voltage output by knowing the predictions and by adding further RC elements correcting the distorted signals. The model is suitable for the implementation of Industry 5.0 Digital Twin (DT) networks applied to manufacturing processes.

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Noise Suppression of Artificial Intelligence Filter for Radio Frequency Interference

Cited by 2 publications

References 17 publications

Analysis of Properties and Macroscopic Defects of Metallic Bars, Pipes, and Strands through the Spectrum of Low-Frequency Excitations

Analysis of Properties and Macroscopic Defects of Metallic Bars, Pipes, and Strands through the Spectrum of Low-Frequency Excitations

Artificial Intelligence Enabling Denoising in Passive Electronic Filtering Circuits for Industry 5.0 Machines

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