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A research approach of crack detection of rotating shafts based on acoustic emission (AE) signals and machine learning is proposed in this paper. The relationship between crack intensity and domain features are investigated, and the features which could well indicate the crack condition are selected for modelling and crack prediction. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN)...
Nanomanipulation under scanning electron microscopy (SEM) has been demonstrated as an enabling technique for the manipulation and characterization of nanomate-rials. We recently developed nanomanipulation techniques for the extraction and identification of DNA contained within sub-nuclear locations of a single cell nucleus. In nanomanipulation of DNA, a key step is target identification through SEM-fluorescence...
The growing interest in predictive maintenance makes industrials and researchers turning themselves to artificial intelligence methods for fulfilling the tasks of condition monitoring and prognostics. Within this frame, the general purpose of this paper is to investigate the capabilities of an Evolving extended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed...
An Enhanced Online Self-organizing Fuzzy Neural Network (EOS-FNN) is proposed in this paper. The proposed algorithm can improve computational efficiency while achieving comparable performance and accuracy compared to other methods. The proposed EOS-FNN starts with an empty rule set and automatically generates fuzzy rules according to the proposed criteria during the learning process. All the parameters...
The emergence of multi-electrode array enables the study of real-time neurophysiological activities across multiple regions of the brain. However, the real-time extracellular action potentials recorded on any electrode represent the simultaneous electrical activity of an unknown number of neurons which present a critical challenge to the accuracy of interpretation and identification of the neural...
In this paper, four data-driven classification approaches, that is, K-nearest neighbors (K-NN), self-organizing map (SOM), multi-layer perceptron (MLP), and Bayesian Network classifier (BNC), are applied to a health condition monitoring problem for the wearing cutter. The dataset is produced from a cutting machine using force sensing. A genetic algorithm (GA) based search is performed to select 3...
In automatic target recognition (ATR), correlation filters are widely used to detect target signature variations. In this paper we concentrate on a particular case: target pose angle. For the traditional maximum average correlation height (MACH) filter method, only a few special angles can be used due to the limitation of the training data and the requirement on efficiency for real-time applications...
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