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An electro-optic chaos generator is proposed based on phase modulation to intensity modulation conversion and an analog-digital hybrid feedback loop. The analog part takes the digital sequences from shift registers as input and converts them into analog noise like signal, from which new bits are determined. The effective bandwidth and complexity of the output analog signal can reach a high level with...
Bearing fault diagnosis under variable speed usually have confronted two obstacles: a) blurry time frequency representation (TFR) and thus unavailable instantaneous frequency (IF) for resampling, and b) errorprone resampling process. To address such problems, this paper proposes a method which consists of two main steps: a) a regional peak search algorithm which searches the frequency bins point by...
As a breakthrough in artificial intelligence, deep learning allows for the automatic extraction of features without considerable prior knowledge and the determination of the complex non-linear relationship of the input parameters. Owing to these advantages, deep neural networks (DNNs) are superior to traditional artificial neural networks with shallow architectures, and are thus becoming widely used...
Intelligent diagnosis method has been a hot topic in the prognostics and fault diagnosis of rotating machinery. A deep neural network (DNN) method for gear fault diagnosis based on stacked autoencode (SAE) and softmax regression is presented in this work. SAE is first utilized to extract features from the frequency spectra of vibration signal. And then the learned features are used to train softmax...
We present an optically coupled chaotic system involving three-phase modulated electro-optic nonlinear loops and an optical coupler. The dynamical properties and the time delay signature (TDS) suppressing performance of the system is analyzed in detail. Numerical results show that the TDS can be suppressed not only under statistical analysis of a single output, but also under mutual statistical analysis...
We experimentally demonstrate a reproducible broadband optical noise generation scheme. A flat spectrum and a symmetrical distribution can be obtained. The complexity of the analogue noise can be determined by the input binary sequence.
As a breakthrough in the field of machine fault diagnosis, deep learning has great potential to extract more abstract and discriminative features automatically without much prior knowledge compared with other methods, such as the signal processing and analysis-based methods and machine learning methods with shallow architectures. One of the most important aspects in measuring the extracted features...
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