18375666. ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM simplified abstract (NEC Corporation)
Contents
- 1 ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM
Organization Name
Inventor(s)
Tsubasa Nakamura of Tokyo (JP)
ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18375666 titled 'ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM
Simplified Explanation
The abstract describes a system where an updating unit adjusts a spatial estimation model based on differences between teaching signals and estimated signals from sensors observing a spatial structure.
- The system updates a spatial estimation model using difference amounts between teaching signals and estimated signals.
- The first difference amount is between a teaching signal observed by a first sensor and an estimated signal.
- The second difference amount is between a teaching signal observed by a second sensor and an estimated signal of similar form.
Potential Applications
This technology could be applied in:
- Environmental monitoring systems
- Robotics for navigation and mapping
- Medical imaging for tracking and localization
Problems Solved
This technology addresses issues such as:
- Improving accuracy of spatial estimation models
- Enhancing sensor data fusion capabilities
- Optimizing performance of sensor networks
Benefits
The benefits of this technology include:
- Increased precision in spatial estimation
- Enhanced real-time monitoring and tracking capabilities
- Improved overall system efficiency
Potential Commercial Applications
This technology could be commercially applied in:
- Autonomous vehicles for navigation
- Smart home systems for environmental control
- Industrial automation for process optimization
Possible Prior Art
One possible prior art for this technology could be:
- Sensor fusion algorithms used in robotics and autonomous systems
What are the specific types of sensors used in this system?
The abstract mentions the use of a first sensor and a second sensor, but does not specify the exact types of sensors employed in the system. It would be beneficial to know if these sensors are optical, acoustic, electromagnetic, or any other specific type.
How does the updating unit determine the adjustments to the spatial estimation model?
While the abstract describes the updating unit making adjustments based on difference amounts between teaching signals and estimated signals, it does not detail the specific algorithm or method used for this determination. Understanding this process would provide insight into the system's functionality.
Original Abstract Submitted
In an arithmetic operation system, an updating unit updates a spatial estimation model, based on a first difference amount and a second difference amount. The first difference amount is a difference amount between a first teaching signal and a first estimated signal. The first teaching signal is a spatial distribution signal observed by a first sensor with respect to a spatial structure on a path of an emission wave in a target space (i.e., a teaching space) by using the emission wave. The second difference amount is a difference amount between a second teaching signal and a second estimated signal. The second teaching signal is an observed signal observed by a second sensor different in type from the first sensor. The second estimated signal is a signal for comparing with the second teaching signal, and is a signal of a form similar to that of the second teaching signal.
