Automatic Filter Selection for Noisy Weight Measurement Signals
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Solution Overview
Problem
Existing signal processing methods for weight measuring units, such as those using computer software, face challenges in introducing site signals and assessing filtering performance, making them inconvenient for effective noise reduction.
Innovation Solution
An automatic filtering method that analyzes input signals to obtain frequency spectra, selects suitable filters based on these spectra, and fine-tunes filtering parameters to effectively filter out various noise types, ensuring reliable noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer software is used to analyze signals and select filters, then filtering performance can be improved, but the complexity of introducing site signals and assessing filtering performance increases
Solution Approach 1:
The weight measuring unit automatically performs frequency spectrum analysis and filter selection without requiring external computer software intervention. The system self-services by internally analyzing the frequency spectrum of input signals and autonomously selecting appropriate filters from stored filter groups, thereby improving filtering performance while avoiding the complexity of external computer integration.
2Measurement precision
If filter selection is based on user experience and experiments, then filtering effectiveness can be improved, but the time and effort required for filter selection increases
Solution Approach 1:
Multiple filter groups with different frequency characteristics are pre-stored in the system before actual use. When a signal needs filtering, the system quickly analyzes the frequency spectrum and selects from these pre-prepared filter groups, avoiding the need for real-time filter design or extensive experimentation, thus reducing the time and effort required for filter selection while maintaining effectiveness.
3Device complexity
If a single filter type is used for all signal conditions, then device complexity is reduced, but the adaptability to different noise types decreases
Solution Approach 1:
The system dynamically selects different filter groups based on the analyzed frequency spectrum characteristics of the input signal. Instead of using a fixed single filter type, the system adapts its filtering approach by choosing from multiple pre-stored filter groups with different frequency characteristics, allowing it to handle various noise types effectively while keeping the overall device structure relatively simple.
Data Source
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AI summary
An automatic filtering method and device includes a step of analyzing an input signal so as to obtain its frequency spectrum; and a step of selecting, on the basis of the frequency spectrum, at least one filter from among a plurality of preset filters and filtering the input signal. The method and device automatically use a suitable filter on the basis of the frequency spectrum and repeat simulation of automatic fine adjustment to ensure a filtering effect against noise.