Optical Apparatus Reward Generation for Machine Learning Control
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Solution Overview
Problem
In image pickup apparatuses, such as cameras, it is challenging for users to properly set rewards for machine learning models to optimize the driving performance of optical elements like lenses, which requires balancing driving speed, position precision, power consumption, and quietness based on varying imaging conditions.
Innovation Solution
An optical apparatus with a setting unit for user input of requirements and a processor to generate reward information for a machine learning model, allowing users to adjust the level of requirements for driving optical elements, such as focus lenses, diaphragms, and zoom lenses, to optimize performance in terms of position precision, driving speed, power consumption, and quietness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to optimize driving performance of optical elements, then driving speed and position precision can be improved, but it becomes difficult for users to properly set rewards for the machine learning model
Solution Approach 1:
The patent introduces a reward information generation unit that acts as an intermediary between user requirements and the machine learning model. This unit automatically generates appropriate reward information based on user-specified requirements (such as priority levels for different optical elements), eliminating the need for users to directly configure complex reward parameters while still enabling precise control of optical element positioning.
2Speed
If driving speed is increased to improve responsiveness, then imaging speed is improved, but power consumption and noise increase
Solution Approach 1:
The patent implements dynamic driving control where the machine learning model adjusts driving parameters (speed, acceleration) in real-time based on current imaging conditions and user requirements. The system can optimize power consumption by selecting appropriate driving speeds for different scenarios - using higher speeds when responsiveness is prioritized and lower speeds when power saving is needed, rather than maintaining a fixed driving speed.
3Adaptability or versatility
If multiple optical elements are driven simultaneously to meet various imaging requirements, then imaging performance is improved, but control complexity increases
Solution Approach 1:
The patent creates a universal control framework where a single machine learning model handles the coordination of multiple optical elements (focus lens, zoom lens, diaphragm). The reward information generation unit provides a unified interface for users to specify requirements for all optical elements, and the model automatically determines optimal driving strategies for each element based on the overall imaging requirements, reducing the complexity of controlling multiple elements simultaneously.
Data Source
AI summary
An optical apparatus includes a setting unit configured for a user to set a requirement relating to driving of an optical element by an actuator, and a processor configured to generate reward information for generating a machine learning model for controlling the driving, based on a level of the requirement. The setting unit is configured for the user to input a change of the level of the requirement.