Autonomous Lantern Illumination Control via Machine Learning
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Solution Overview
Problem
Current lantern control systems require active monitoring, either through closed-circuit television systems or centralized management, to achieve optimal illumination levels, which is inefficient and resource-intensive.
Innovation Solution
A sensor device equipped with machine learning and computer vision algorithms that autonomously detects surrounding conditions and adjusts lantern illumination levels, using a low-cost microcontroller and optical sensors, allowing for near-autonomous operation and integration into existing lanterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If active monitoring systems (CCTV or centralized management) are used to control lantern illumination, then optimal illumination levels can be achieved, but system complexity and resource consumption increase significantly
Solution Approach 1:
The lantern control system performs self-service by autonomously determining optimal illumination levels through embedded machine learning algorithms that process sensor data locally. The system independently detects environmental conditions, identifies objects of interest, and adjusts lighting without requiring external monitoring or centralized control, thereby achieving optimal illumination while eliminating complex infrastructure.
Solution Approach 2:
The invention extracts the intelligence and decision-making capability from centralized systems and embeds it directly within each lantern unit. By placing machine learning models and sensor processing locally at the lantern, the system removes the need for external CCTV or centralized management infrastructure, achieving autonomy while simplifying overall system architecture.
2Device complexity
If simple motion sensors are used to control lanterns, then system complexity is reduced, but the system triggers on irrelevant objects like debris and animals, reducing reliability
Solution Approach 1:
The system changes the detection parameter from simple motion detection to sophisticated object classification using machine learning algorithms. By analyzing multiple parameters including object shape, size, texture, and movement patterns, the system distinguishes between relevant targets (people, vehicles) and irrelevant objects (debris, animals), achieving high detection accuracy while maintaining manageable system complexity through embedded processing.
Solution Approach 2:
The invention replaces simple mechanical motion sensors with intelligent computer vision systems that use machine learning algorithms for object recognition. This substitution enables the system to understand the semantic meaning of detected objects rather than merely responding to motion, significantly improving reliability by filtering out false triggers from non-relevant objects while keeping the lantern unit itself relatively simple through integrated processing.
3Extent of automation
If autonomous machine learning algorithms are embedded in each lantern, then the need for centralized monitoring is eliminated, but device complexity and cost increase
Solution Approach 1:
The invention segments the automation function by distributing machine learning capabilities to individual lantern units rather than requiring centralized processing. Each lantern becomes an autonomous decision-making unit with embedded algorithms that process sensor data locally and control illumination independently, achieving high automation while keeping each individual device relatively simple and manageable.
Solution Approach 2:
The lantern control device performs multiple functions within a single integrated unit: environmental sensing, object detection, machine learning inference, and illumination control. By combining these functions into one universal device, the system achieves autonomous operation without requiring separate components for each function, thereby reducing overall device complexity while maintaining high automation capability.
Data Source
AI summary
An electronic device to control illumination levels of a light emitting device. The electronic device comprises a microcontroller that is adapted to: receive data relating to an object sensed by a sensor; provide the received data to a machine learning algorithm; and output a control signal to define an illumination pattern and an illumination level of the light emitting device based on analysis of the received data.