Adaptive Baseline Estimator for Load Status Detection
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Solution Overview
Problem
Accurately detecting the load status of a moveable platform, such as a container, is challenging due to varying characteristics over time, including changes in the platform and sensor device hardware, as well as differences in manufacturing and environmental conditions, leading to inaccurate measurement data.
Innovation Solution
An adaptive baseline estimator refines a baseline to determine whether a moveable platform is empty or loaded, using a Time-of-Flight sensor to measure distances and adjust the baseline based on new measurement data, allowing for iterative refinement and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sensor device is used to detect load status of a moveable platform, then measurement capability is provided, but measurement precision deteriorates due to varying characteristics of the platform and sensor hardware over time
Solution Approach 1:
The system performs preliminary calibration by establishing a baseline measurement when the container is known to be empty. This baseline is stored and used as a reference for subsequent load status determinations, allowing the system to compensate for hardware variations before actual measurements are taken.
Solution Approach 2:
The system continuously compares current sensor measurements against the stored baseline and uses this feedback to determine load status. The feedback mechanism allows the system to adapt to gradual changes in sensor and platform characteristics by referencing the original baseline condition.
2Manufacturing precision
If traditional load detection methods are used, then device complexity is reduced, but manufacturing precision variations cause inaccurate measurements
Solution Approach 1:
The system performs self-calibration by automatically establishing its own baseline during an initial empty state. This self-service approach eliminates the need for manual calibration procedures or external reference standards, allowing the system to compensate for manufacturing variations autonomously.
Solution Approach 2:
The system changes the reference parameter from fixed manufacturer specifications to a dynamically established baseline measured during operation. This parameter change allows the system to adapt to actual hardware characteristics rather than relying on idealized manufacturing tolerances.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The adaptive baseline estimator effectively improves the accuracy of load status determination by accounting for changes in the platform and sensor characteristics, reducing errors and enhancing asset tracking capabilities.
Implementation Method 1
using a Time-of-Flight sensor to measure distances
Data Source
Figure 1A~1B
Figure 2~3
Figure 4~5
AI summary
Based on a relationship between measurement data from at least one sensor and a baseline, a load status of a platform is determined. In response to the determined load status, such as determining that the platform is empty, the baseline is refined using a baseline estimator to produce a refined baseline that can be used to determine a further load status of the platform.