Induction Cooktop Cookware Detection Using Iso-Level Curve Clustering
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Solution Overview
Problem
Conventional cookware item detection techniques on induction cooktops fail to accurately identify the position, size, shape, and orientation of cookware items, especially when items are close to each other, leading to incorrect estimates and confusion between single and multiple items.
Innovation Solution
A method involving the calculation of iso-level curves based on coverage factor information from induction coils, where the intercept level is adjusted to determine closed curves, and coil clustering is used to estimate the position, shape, and orientation of cookware items, allowing for precise identification and power control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cookware item detection techniques are used, then the system is simple to operate, but the measurement precision of cookware position, size, shape, and orientation is poor
Solution Approach 1:
The detection system segments the cooktop surface into multiple coils, each independently measuring coverage factor. This segmentation allows precise localization of cookware by analyzing which coils are affected and by how much, resolving the contradiction between simple operation and measurement precision.
Solution Approach 2:
The system transitions from 2D surface detection to 3D spatial reconstruction by using coverage factor variations across multiple coils at different positions. This dimensional approach enables accurate estimation of cookware height, shape, and orientation while maintaining system simplicity.
2Reliability
If conventional detection techniques are used, then the device complexity is low, but the reliability of distinguishing single vs. multiple cookware items is poor
Solution Approach 1:
The system uses feedback from coverage factor measurements across multiple coils to iteratively refine cookware item identification. By analyzing patterns of coil activation and coverage distribution, the system reliably distinguishes between single and multiple items while keeping the algorithm manageable through structured feedback loops.
Solution Approach 2:
The system changes parameters such as coverage factor thresholds and intercept levels to optimize the detection of multiple cookware items. By adjusting these parameters based on measurement patterns, the system achieves high reliability in item distinction without requiring overly complex algorithms.
3Measurement precision
If intercept level is kept high, then the calculation speed is fast, but the measurement precision of cookware boundaries is poor
Solution Approach 1:
The system performs preliminary calculations by pre-establishing the relationship between intercept levels and boundary detection accuracy. This allows the system to select optimal intercept levels in advance, achieving precise boundary detection without excessive computation time during actual cooking operations.
Solution Approach 2:
The system dynamically adjusts the intercept level parameter based on the detected cookware configuration. By changing this parameter adaptively, the system achieves accurate boundary detection while minimizing computation time through intelligent parameter selection rather than exhaustive calculation.
Data Source
AI summary
A method for identifying cookware on an induction cooktop having coils, includes the steps: (a) acquire a coverage factor matrix; (b) set a present level at a maximum value of the matrix; (c) count closed iso-level curves corresponding to the present level and save the result; (d) decrease the level by an amount; (e) count closed curves corresponding to the decreased level; (f) when the number of closed curves at the present level is not lower than that from the previous level, update the saved result with the present level; (g) when the number of closed curves at the present level is lower than that from the previous level, keep the previously saved result; (h) repeat steps (d) to (h), until the number decreases; (i) assign coils inside the curve to a distinct cluster; and (j) use the clustering to estimate a position, shape, size, and orientation of the cookware.