Neural Network Ambient Temperature Estimation Under Dynamic Fan Control
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Solution Overview
Problem
Existing ambient temperature estimation technologies require significant man-hours for sensor placement and parameter adjustment, and their accuracy is compromised when the cooling fan's control value changes dynamically.
Innovation Solution
An ambient temperature estimating device employing a neural network that uses temperature values and heat source control values to estimate ambient temperature, reducing reliance on sensor positions and maintaining accuracy even with dynamic cooling fan control values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature sensors are manually positioned and parameters are manually adjusted to increase estimation accuracy, then measurement precision improves, but loss of time increases due to enormous man-hours required
Solution Approach 1:
The system performs self-learning through machine learning to automatically determine the correlation between temperature sensor readings and ambient temperature. The ambient temperature estimating device autonomously acquires temperature values from multiple sensors, inputs them into the neural network, and estimates ambient temperature without requiring manual parameter adjustment or sensor repositioning, thereby eliminating enormous man-hours while maintaining high estimation accuracy.
Solution Approach 2:
The patent replaces manual mechanical adjustment of sensor positions and parameters with an automated neural network system. Instead of manually positioning temperature sensors and adjusting parameters, the system uses machine learning algorithms to automatically process temperature data from sensors and estimate ambient temperature, substituting mechanical human operations with computational intelligence.
2Adaptability or versatility
If the control value of the cooling fan is dynamically changed, then adaptability improves, but measurement precision deteriorates because it becomes extremely difficult to mathematically express the correlation between temperatures
Solution Approach 1:
The system dynamically adapts to changing cooling fan control values by using a neural network that can process variable operating conditions. Instead of relying on fixed mathematical correlations that break down when the cooling fan control changes, the neural network learns to handle dynamic variations in temperature patterns caused by dynamic cooling fan operation, maintaining estimation accuracy under varying conditions.
Solution Approach 2:
The patent changes the approach from using fixed mathematical correlations to using a neural network that can accommodate parameter variations. The neural network processes temperature values from multiple sensors and automatically adjusts its internal parameters through machine learning to maintain accurate ambient temperature estimation even when cooling fan control values change dynamically, thereby handling parameter changes robustly.
Data Source
AI summary
Provided is an ambient temperature estimating device, ambient temperature estimating method, program, and system that are able to realize both high robustness and high ambient temperature estimation accuracy. An ambient temperature estimating device includes a neural network, a temperature acquisition unit configured to acquire one or more temperature values inside the ambient temperature estimating device, and a neural network calculator configured to estimate an ambient temperature around the ambient temperature estimating device using the neural network. Input values inputted to the neural network by the neural network calculator include the temperature values acquired by the temperature acquisition unit and a heat source control value for controlling a heat source inside the ambient temperature estimating device.