Self-learning lighting system with iterative parameter adjustment
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Solution Overview
Problem
Multi-variate control of lighting systems, including luminance, chrominance, and color-balance, is complex and often requires expertise, with pre-programmed settings failing to accommodate individual preferences and environments, leading to a time-consuming and frustrating process for users to achieve a desired ambiance.
Innovation Solution
A self-learning system that introduces minor changes to lighting parameters and collects user feedback to iteratively approach optimal settings, using a controller with a modification module, learning module, and control module to adjust parameters based on user response, with options for non-obtrusive and rapid-learning modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-programmed lighting parameters are used, then the system is easy to operate, but it cannot accommodate individual preferences or specific environments effectively
Solution Approach 1:
The system implements feedback by collecting user responses to lighting parameter changes. The controller receives feedback signals from the user indicating whether each parameter change is desirable or not, and uses this feedback to learn and adjust future parameter selections, thereby adapting to individual preferences while maintaining ease of operation.
Solution Approach 2:
The lighting system performs self-learning and self-optimization automatically without requiring user expertise. The controller autonomously analyzes feedback, determines optimal parameter combinations, and adjusts lighting settings independently, freeing the user from complex manual adjustments while achieving personalized ambiance.
2Adaptability or versatility
If users manually adjust lighting parameters to achieve desired ambiance, then adaptability is improved, but the process becomes time-consuming and complex
Solution Approach 1:
The system performs preliminary learning during initial use by collecting feedback on various parameter changes. This preliminary action builds a knowledge base of user preferences that enables rapid automatic adjustment later, eliminating the need for time-consuming manual tweaking while maintaining high adaptability.
Solution Approach 2:
The system uses feedback from user responses to automatically learn which parameter combinations are desirable. By processing this feedback and storing optimal settings, the system eliminates repeated manual adjustment time while maintaining adaptability to user preferences and environmental conditions.
3Measurement precision
If the system collects detailed user feedback on parameter changes, then learning accuracy is improved, but user burden increases
Solution Approach 1:
The system collects feedback selectively rather than requiring comprehensive user input for every parameter. By focusing on key feedback signals and using probabilistic learning methods, the system achieves sufficient learning accuracy with minimal user burden, avoiding excessive feedback collection while maintaining effective adaptation.
Data Source
AI summary
A system (120) and/or corresponding method introduces a minor change to a given set of parameters (110) that affect an ambiance (130) associated with an environment, and collects the user's response to the change. Based on the user's response, the system learns which changes to which parameters lead to an improved effect. By repeating the change-feedback sessions, the system approaches an optimal setting for achieving the desired ambiance in the given environment. Preferably, the change-feedback session is non-obtrusive, and occurs, for example, each time a light is turned on, and the feedback is collected when the light is turned off, using a multiple switch arrangement. If the light is turned off using one switch, the feedback is positive; if the light is turned off using an alternative switch, the feedback is negative. Alternatively, the system can be placed in a rapid-learning mode, wherein the change-feedback cycles occur more frequently.