Method of occupancy classification in a vehicle seat
First Claim
1. A method of recognizing and classifying a physical presence occupying a vehicle seat having a occupancy sensing system including an array of sensors having output, said method including the steps of:
- sensing the output of an array of sensors that detect a physical presence in a seat;
applying the sensor array output as a vector representation to a neural net that was trained using a learning vector quantization algorithm; and
recognizing the sensor array output as falling within one of a group of predetermined classification patterns that represent a physical presence in the seat defined by size, weight, and physical orientation.
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Accused Products
Abstract
A method of recognizing and classifying the occupancy in a vehicle seat having an occupancy sensing system, including the steps of sensing the output of an array of sensors that detect a physical presence in a seat and applying the sensor array output as a vector representation to a neural net that was trained using a learning vector quantization algorithm. The method also includes the step of recognizing the sensor array output as falling within one of a group of predetermined classification patterns that represent a physical presence in the seat defined by size, weight, and physical orientation.
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Citations
12 Claims
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1. A method of recognizing and classifying a physical presence occupying a vehicle seat having a occupancy sensing system including an array of sensors having output, said method including the steps of:
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sensing the output of an array of sensors that detect a physical presence in a seat; applying the sensor array output as a vector representation to a neural net that was trained using a learning vector quantization algorithm; and recognizing the sensor array output as falling within one of a group of predetermined classification patterns that represent a physical presence in the seat defined by size, weight, and physical orientation. - View Dependent Claims (2, 3, 4)
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5. A method of recognizing and classifying a physical presence occupying a vehicle seat having a occupancy sensing system by training a neural network, said method including the steps of:
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determining the number of times to process a training set of input values through a neural network for a first learning vector quantization algorithm; determining the number of times to process a training set of input values through a neural network for a second learning vector quantization algorithm; processing the training set of input values through the neural network the determined number of times using said first learning vector quantization algorithm so as to provide output units of the neural network; adjusting one of the output units of the neural net each time one of said training set of input values is processed using said first learning vector quantization algorithm; processing the training set of input values through the neural network the determined number of times using said second learning vector quantization algorithm; adjusting two of the output units of the neural net each time one of said training samples is processed using said second learning vector quantization algorithm; and storing the adjusted output units as the final trained values for the neural net. - View Dependent Claims (6, 7, 8, 9, 10, 11)
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12. A method of recognizing and classifying a physical presence occupying a vehicle seat having a occupancy sensing system including an array of sensors having analog output, said method including the steps of:
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sensing the analog output of an array of sensors that detect a physical presence in a seat; converting the analog output of the array of sensors into a digital vector expression; applying the digital vector expression to a trained neural net having a predetermined learning vector quantization algorithm; recognizing the output from the neural net as belonging to one of a variety of predetermined patterns representative of a physical presence in the seat; determining which one of a predetermined series of classifications defined by size, weight, and physical orientation that the recognized pattern belongs to; and continuously reinitiating the method steps to redetermine the classification.
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Specification