Arousal Level Control Apparatus Using Optimization Models
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Solution Overview
Problem
Existing arousal level control systems struggle to accurately predict the effect of environmental changes on arousal levels, leading to suboptimal control of arousal levels in environments such as offices and vehicles.
Innovation Solution
An arousal level control apparatus that uses an optimization model to calculate setting values for environmental control devices, incorporating physical quantity prediction and arousal level prediction models to maximize the objective function of arousal level variation, while ensuring the setting values are within a predetermined range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If environmental control devices are adjusted to improve arousal level, then work efficiency and alertness are improved, but comfort level and user acceptance may deteriorate
Solution Approach 1:
The system dynamically adjusts environmental parameters (temperature, lighting intensity, humidity) based on predicted arousal level changes. By optimizing these parameters within specific ranges, the system achieves both improved arousal control accuracy and maintained environmental comfort, resolving the contradiction between control effectiveness and user comfort.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor actual arousal level changes and compare them with predicted values. Based on this feedback, the system refines its control strategies, ensuring that environmental adjustments reliably improve arousal while maintaining comfort through iterative optimization.
2Productivity
If multiple environmental factors are controlled simultaneously to maximize arousal improvement, then control effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments the environmental control into independent controllable factors (temperature, lighting, humidity) and manages them separately through individual prediction models and control strategies. This segmentation allows the system to handle multiple factors simultaneously without proportionally increasing overall system complexity, as each factor can be optimized independently.
Solution Approach 2:
The system employs a unified optimization framework that can handle multiple environmental factors through a single integrated approach. The prediction model and control algorithm are designed to accommodate various environmental parameters universally, reducing the need for separate specialized systems for each factor and thereby limiting complexity growth.
3Measurement precision
If prediction models are made more accurate to better predict arousal changes, then control precision is improved, but computational requirements and system complexity increase
Solution Approach 1:
The system optimizes prediction model parameters and selects appropriate model complexities based on the specific task requirements and available data. By dynamically adjusting model parameters rather than using fixed complex models, the system achieves high prediction accuracy while keeping computational requirements manageable, resolving the contradiction between precision and complexity.
Data Source
AI summary
An arousal level control apparatus calculates a setting value of a control device under a constraint condition using an arousal level optimization model so that a value of an objective function is maximized. The control device affects a physical quantity of a surrounding environment that affects arousal level of a subject. The arousal level optimization model includes the constraint condition and the objective function. The constraint condition includes a physical quantity prediction model, an arousal level prediction model, and a setting value range condition that the setting value is within a predetermined range. The physical quantity prediction model is an explicit function that includes the physical quantity and the setting value as explanatory variables and has a predicted value of the physical quantity as an explained variable.