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The ability to provide accurate, rapid and dynamically-controlled impedance matching offers significant advantages to a wide range of present and emerging radio-frequency (RF) power applications. This work develops a new type of tunable impedance matching networks (TMN) that enables a combination of much faster and more accurate impedance matching than is available with conventional techniques. This...
We propose an application of specific machine learning techniques capable of evaluating systemic health of a Radio Frequency (RF) power generator. System signatures or fingerprints are collected from multivariate time-series data samples of sensor values under typical operational loads. These fingerprints are transformed into feature vectors using standard scaling/translation methods and the Fast...
In this paper, we will present an approach developing a linear model of a radio frequency (RF) power generator by using pseudo random binary signals (PRBS). We will compare two linear models obtained respectively by the PRBS approach and a traditional modeling approach. The result shows that both approaches achieve a very similar model of the RF power generator. Moreover, it can be shown that the...
In this paper we apply a specific machine learning technique for classification of normal and not-normal operation of RF (Radio Frequency) power generators. Pre-processing techniques using FFT and bandpower convert time-series system signatures into single feature vectors. These feature vectors are modeled using k-component Mixture of Gaussians (MoG) where components and corresponding parameters are...
In this paper we apply various machine learning techniques for fault detection of RF (Radio Frequency) Power Generators. Fast Fourier Transform features are used in our analysis for all experiments. Radial Basis Function Networks (RBF) is used to build a two class classifier to differentiate between normal and one fault condition. We apply three one class classifiers to model the normal operating...
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