Adaptive Control Apparatus for Computing Systems
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Solution Overview
Problem
Computing systems face challenges in adapting to various user environments and situations, leading to unintended operations and user inconveniences, as existing solutions primarily focus on customizing user interfaces for specific users without effectively addressing dynamic environmental changes.
Innovation Solution
An apparatus and method that identify and adapt to the operating environment of a target system using sensors, estimating its state and controlling functions based on user feedback, incorporating modules for environment and user identification, state estimation, and control instruction generation to automatically adjust system settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and identification modules are added to identify operating environment and user, then environmental awareness and user identification accuracy are improved, but device complexity increases
Solution Approach 1:
The system employs a multi-functional architecture where a single integrated control apparatus performs multiple functions: environmental sensing, user identification, state estimation, and control function adjustment. Rather than separate dedicated systems for each function, the apparatus universally handles all these tasks through modular components that work together, reducing overall system complexity while maintaining high measurement precision.
Solution Approach 2:
The system implements a nested modular structure where identification modules (optical, sound, motion sensors) are integrated within the control apparatus, which in turn contains state estimation and machine learning sub-modules. This nested organization allows each component to be independently developed and optimized while fitting into a cohesive whole, managing complexity through hierarchical integration.
2Ease of operation
If the system automatically controls functions based on environment and user state, then user convenience and operational safety are improved, but the extent of automation increases system complexity
Solution Approach 1:
The system incorporates feedback loops where user interactions and environmental changes are continuously monitored, processed through machine learning modules, and used to adjust control functions in real-time. This feedback mechanism enables automatic control that adapts to user needs and environmental conditions, improving convenience while managing automation complexity through iterative learning and adjustment rather than rigid pre-programming.
3Adaptability or versatility
If control functions are adjusted based on user feedback and learned behavior, then system adaptability to user preferences is improved, but data processing requirements and computational load increase
Solution Approach 1:
The system applies partial learning and adaptation by focusing computational resources on the most significant user preferences and environmental factors rather than attempting to learn and adjust all possible parameters simultaneously. The machine learning modules process data selectively, making adjustments only when confidence thresholds are met or when changes are deemed necessary, thereby reducing overall computational energy consumption while maintaining effective user preference adaptation.
Data Source
AI summary
The present invention relates to an apparatus and a method for adaptively controlling functions of a target system by conditions or by users by controlling the target system based on operating environment of the target system. An adaptive system control apparatus includes an operating environment identification module configured to identify operating environment of a target system based on the sensing data detected by a sensor; a system state estimation module configured to estimate current state of the target system based on the identified operating environment information; a system control module configured to control the target system based on the estimated current state information; and a control result learning module configured to learn the control result of the target system by receiving feedback data from a user.