Clock Resonator Temperature Compensation Using Multi-Sensor RNN Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Instruments such as gyroscopes and clock oscillators exhibit temperature-dependent behavior, leading to degraded performance due to unknown or uncontrolled temperature variations, which existing thermal look-up tables and frequency locking methods fail to adequately address.
Innovation Solution
A system incorporating a clock resonator with multiple temperature sensors and a recurrent neural network for temperature compensation, utilizing integrated temperature sensors on multiple layers around the mechanical resonator to detect temperature differences and generate compensation signals, coupled with temperature actuators to adjust the resonator's temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thermal look-up tables and frequency locking methods are used for temperature compensation, then some temperature stability is achieved, but they fail to adequately address temperature variations and thermal gradients
Solution Approach 1:
The patent divides the temperature sensing function into multiple independent temperature sensors positioned at different locations (different layers and azimuthal positions) around the mechanical resonator. This segmentation allows each sensor to measure local temperature conditions independently, enabling the system to capture temperature gradients and spatial variations that single-point measurements would miss, thereby resolving the contradiction between reliability and measurement precision.
Solution Approach 2:
The patent transitions from single-point temperature measurement to multi-dimensional temperature mapping by placing sensors at different vertical layers and azimuthal positions around the resonator. This dimensional expansion creates a comprehensive temperature field representation, allowing the system to accurately compensate for both uniform temperature changes and spatial gradients, thus improving both reliability and measurement precision simultaneously.
2Measurement precision
If multiple temperature sensors are integrated on multiple layers around the mechanical resonator, then temperature variations and thermal gradients are detected with higher precision, but device complexity increases
Solution Approach 1:
The patent makes each temperature sensor serve multiple functions: measuring local temperature, detecting thermal gradients, monitoring temperature variations over time, and providing data for both immediate compensation and long-term drift analysis. This multi-functionality justifies the increased number of sensors by extracting maximum value from each sensor element, balancing measurement precision gains against device complexity.
Solution Approach 2:
The patent implements a feedback control system where temperature sensor readings are continuously fed to a processor that calculates compensation values and applies them in real-time. This feedback mechanism transforms the complex multi-sensor data into actionable compensation signals, making the increased device complexity productive by systematically utilizing all sensor inputs to improve measurement precision and overall system performance.
3Reliability
If recurrent neural network with multiple layers is used for compensation, then temperature stability and accuracy are enhanced, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary processing of temperature sensor data through dimensionality reduction techniques before feeding it to the recurrent neural network. This pre-processing step compresses the high-dimensional sensor input into a lower-dimensional feature space that retains the essential temperature characteristics. By performing this preliminary action, the system reduces the computational burden on the neural network while preserving the information needed for accurate temperature compensation, thus enhancing reliability without excessive computational complexity.
Solution Approach 2:
The patent employs a dynamic recurrent neural network architecture with adjustable hyperparameters (number of layers, units per layer, learning rate) that can be optimized based on specific application requirements. This dynamic configuration allows the system to adapt the computational complexity to match the actual temperature stability needs, enabling flexible trade-offs between reliability enhancement and computational burden for different operational scenarios.
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
Enhances the stability and accuracy of gyroscopes and clock oscillators by effectively compensating for temperature variations, reducing bias and scale factor drifts, and maintaining precise timing synchronization even in GPS-denied environments.
Implementation Method 1
a first temperature sensor, on the structure; and a second temperature sensor, on the structure, the recurrent neural network being configured: to receive a signal from the first temperature sensor and from the second temperature sensor
Implementation Method 2
the recurrent neural network being configured: to receive a signal from the first temperature sensor and from the second temperature sensor, and to generate an output for compensating for temperature variations in the clock resonator
Implementation Method 3
coupled with temperature actuators to adjust the resonator's temperature
Data Source
AI summary
Systems and methods for temperature compensation. In some embodiments, the system includes: a clock resonator, the clock resonator including: a hermetic package; a mechanical resonator, in the hermetic package; a first temperature sensor, in the hermetic package; and a second temperature sensor, in the hermetic package.