Odor Sensor Sampling Length Optimization via Prediction Equations
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Solution Overview
Problem
Current techniques for optimizing the operation of odor sensors to enhance sensing accuracy for target odor components are inadequate.
Innovation Solution
An information processing apparatus that acquires sensor output data for varying sampling lengths, generates a prediction equation using machine learning, and determines optimal sampling lengths for operating the odor sensor to improve sensing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sampling length of the odor sensor is extended to capture more odor component information, then the sensing accuracy is improved, but the response time and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by acquiring sensor output data for multiple different sampling lengths in advance, generating prediction equations beforehand that correlate sampling length with odor component prediction accuracy. This allows the optimal sampling length to be determined without real-time trial and error, thus improving response time while maintaining sensing accuracy.
Solution Approach 2:
The system changes the sampling length parameter to find the optimal value that balances sensing accuracy and response time. By using prediction equations generated from data collected at various sampling lengths, the system can select the sampling length that provides sufficient odor component information while minimizing the sampling duration, thereby resolving the contradiction between measurement precision and time loss.
2Reliability
If the sampling length is increased to improve odor component detection accuracy, then the prediction reliability is improved, but the operational complexity increases
Solution Approach 1:
The system performs self-service by automatically determining the optimal sampling length through the prediction equation without requiring manual intervention or complex operational procedures. The prediction equation generation unit and operation setting unit work autonomously to select the sampling length that ensures reliable odor component predictions, simplifying the operational process while maintaining high prediction reliability.
Solution Approach 2:
The system uses feedback mechanisms where sensor output data collected at different sampling lengths is analyzed to generate prediction equations. These equations provide feedback on the relationship between sampling length and prediction accuracy, enabling the system to automatically adjust and select the optimal sampling length, thereby improving prediction reliability without increasing operational complexity.
3Measurement precision
If multiple sampling lengths are tested to determine the optimal sampling length, then the sensing accuracy is improved, but the measurement time and energy consumption increase
Solution Approach 1:
The system performs preliminary measurements at multiple sampling lengths during an initial calibration phase to generate prediction equations. Once these equations are established, the optimal sampling length can be determined without repeating all the preliminary tests, thereby reducing subsequent energy consumption while maintaining the ability to achieve high sensing accuracy when needed.
Data Source
AI summary
An information processing apparatus (20) includes a sensor output data acquisition unit (210), a prediction equation generation unit (220), and an operation setting unit (230). The sensor output data acquisition unit (210) acquires sensor output data for each sampling length of an odor sensor with respect to a target gas. The prediction equation generation unit (220) generates, by using the sensor output data for each sampling length, a prediction equation for making a prediction for an odor component of the target gas. The operation setting unit (230) determines, by using the prediction equation, a sampling length for operating the odor sensor.