18675806. TRAINING METHOD, TRAINING SYSTEM, AND NON-TRANSITORY COMPUTER READABLE RECORDING MEDIUM STORING TRAINING PROGRAM simplified abstract (Panasonic Intellectual Property Corporation of America)
TRAINING METHOD, TRAINING SYSTEM, AND NON-TRANSITORY COMPUTER READABLE RECORDING MEDIUM STORING TRAINING PROGRAM
Organization Name
Panasonic Intellectual Property Corporation of America
Inventor(s)
TRAINING METHOD, TRAINING SYSTEM, AND NON-TRANSITORY COMPUTER READABLE RECORDING MEDIUM STORING TRAINING PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18675806 titled 'TRAINING METHOD, TRAINING SYSTEM, AND NON-TRANSITORY COMPUTER READABLE RECORDING MEDIUM STORING TRAINING PROGRAM
The abstract describes a training system that selects the best sensor parameter candidate and trained neural network model candidate for optimal sensor operation.
- The system determines sensor parameter candidates for sensor operation.
- It generates sensor data sets and correct answer identification information for each parameter candidate.
- Multiple trained neural network model candidates are generated based on the sensor parameter candidates.
- Identification performance of the trained models is calculated.
- The system selects the best performing pair of trained model and sensor parameter candidate.
- The selected pair is output for use in sensor operation.
Potential Applications: - Enhancing sensor performance in various industries such as automotive, healthcare, and manufacturing. - Improving accuracy and efficiency of sensor data analysis and interpretation.
Problems Solved: - Selecting the most suitable sensor parameters for optimal performance. - Enhancing the accuracy of sensor data analysis through trained neural network models.
Benefits: - Increased efficiency and accuracy in sensor operations. - Improved decision-making based on sensor data analysis.
Commercial Applications: Title: "Optimized Sensor Parameter Selection System" This technology can be applied in industries such as: - Automotive for autonomous driving systems. - Healthcare for patient monitoring devices. - Manufacturing for quality control processes.
Questions about the technology: 1. How does this system improve sensor performance compared to traditional methods? 2. What are the key factors considered in selecting the best sensor parameter candidate and trained neural network model candidate?
Original Abstract Submitted
The training system determines a plurality of sensor parameter candidates to be used for an operation of a sensor, generates a plurality of sensor data sets corresponding to each of the plurality of sensor parameter candidates and including sensor data to be obtained by the operation of the sensor and a plurality of pieces of correct answer identification information corresponding to each of the sensor data, generates a plurality of trained neural network model candidates corresponding to the plurality of sensor parameter candidates, calculates identification performance of the plurality of trained neural network model candidates, selects a pair of the trained neural network model candidate with the highest identification performance and the sensor parameter candidate corresponding to the trained neural network model candidate with the highest identification performance, and outputs the selected pair of the sensor parameter candidate and the trained neural network model candidate.
