In recent years, collaborative research between academia and industry has intensified in finding a successful approach to take the information from a computer generated drawings of products such as casting dies, and produce optimal manufacturing process plans. Core to this process is feature recognition. Artificial neural networks have a proven track record in pattern recognition and there abil...

This paper deals with the problem of combination of Neural Networks (NN) and traditional statistical pattern classiiers. It is shown that a Neural Network can be used to replace the vector quantizer (VQ) and some feature extraction and feature reduction modules in a discrete pattern recognition system. A criterion for training the NN-weights and the classiier jointly is derived, leading to the ...

neural networks because of their abilities are used to patterns recognition. in statistical process control charts, a common cause variation distort expected form of unnatural patterns and so detection of assignable causes efficiently and precisely in a real-time is difficult. therefore it would be logical to propose models based neural networks for recognition and analysis of patterns in proce...

This paper compares the emotional pattern recognition method between standard BP neural network classifier and BP neural network classifier improved by the L-M algorithm. Then we compare the method Support Vector Machine (SVM) to them. Experiment analyzes wavelet transform of surface Electromyography (EMG) to extract the maximum and minimum wavelet coefficients of multi-scale firstly. We then i...

Because of the difficulty of modeling the traffic conditions on a roadway network, little has been achieved to date in area control using dynamic traffic volume. The most commonly practiced method for timing control of area signals that takes into account traffic volume changes is time-interval-dependent control. This type of control strategy assumes that the traffic volume on each roadway of a...

Classification learning systems are useful in many domain areas. One problem with the development of these systems is feature noise. Learning from examples classification methods from statistical pattern recognition, machine learning, and connectionist theory are applied to synthetic data sets possessing a known percentage of feature noise. Linear discriminant analysis, the C5.0 tree classifica...

Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchial feed-forward models (e.g., convolutional neural networks) showed promise in this direction, they are still difficult to train especially when few training examples are available. In this paper, we present a framework for tra...

The application of statistical methods to monitor a process is critical ensure its stability. Statistical control aims detect and identify abnormal patterns that disrupt the natural behaviour process. Most studies in literature are focused on recognising single patterns. However, many industrial processes, more than one unusual chart pattern may appear simultaneously, i.e., concurrent (CCP). Th...

Neural networks because of their abilities are used to patterns recognition. In statistical process control charts, a common cause variation distort expected form of unnatural patterns and so detection of assignable causes efficiently and precisely in a real-time is difficult. Therefore it would be logical to propose models based neural networks for recognition and analysis of patterns in proce...