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When facing radar target recognition, the main problems focus on the data representation capability and the robustness to cope with noise. The merits of deep learning such as automatic setting for training and hierarchical extraction of features. Most of existing deep networks are related to Restricted Boltzmann machine (RBM), which has played an important role in deep learning techniques. The models...
In this paper, two temporal models, Hidden Markov Model and Auto Regressive Moving Average model with exogenous inputs (ARMAX), are used for health condition monitoring of the cutter in a milling machine. Dataset is acquired through real time force signal sensing. A heuristic statistical approach is used to select dominant features, leading to the selection of 3 dominant features from the 16-dimensional...
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...
Partial discharge (PD) detection has been used in assessment of condition reliability of electrical insulation in high voltage equipment such as power station. Unfortunately, PD signals took during condition monitoring are often corrupted with excessive interference. The challenge to effectively and accurately determine and extract the pure PD signal from the large amount of noise still remains. The...
We introduce a direct model for speech recognition that assumes an unstructured, i.e., flat text output. The flat model allows us to model arbitrary attributes and dependences of the output. This is different from the HMMs typically used for speech recognition. This conventional modeling approach is based on sequential data and makes rigid assumptions on the dependences. HMMs have proven to be convenient...
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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