Signal Detection Using Deep Learning Reconstruction
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Solution Overview
Problem
Current abnormal signal marking detection methods require extensive manual operations and are cumbersome, leading to inaccurate feature pattern marking and detection.
Innovation Solution
A signal detection method using an optimized deep learning model to preprocess and reconstruct signals, comparing the original and reconstructed signals to determine abnormalities, with a larger difference indicating an abnormality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual operations are used to mark feature patterns of abnormal signals, then detection coverage can be comprehensive, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system uses unsupervised learning to enable the model to automatically learn and identify abnormal signal patterns without requiring manual marking of all feature patterns. The deep learning model self-trains on normal signals and automatically detects deviations, eliminating the need for extensive manual annotation while maintaining detection accuracy.
Solution Approach 2:
The patent replaces the manual mechanical marking process with an automated deep learning system. The unsupervised learning model automatically identifies abnormal patterns through mathematical optimization and feature extraction, substituting human operators with an intelligent algorithm that processes signals more efficiently.
2Reliability
If manual marking of feature patterns is performed, then all possible abnormal patterns can be covered, but the marking process is cumbersome and accuracy suffers
Solution Approach 1:
The deep learning model performs self-service by automatically learning the characteristics of normal signals and independently identifying abnormal patterns without human intervention in the marking process. The unsupervised learning algorithm autonomously extracts features and detects anomalies, improving both accuracy and operational ease.
Solution Approach 2:
The patent transforms the marking task from a manual parameter-setting process to an automated parameter-learning process. The model learns optimal detection parameters and feature weights automatically through training, changing the approach from fixed manual thresholds to adaptive learned parameters that improve accuracy and ease of use.
3Reliability
If extensive manual operations are used for signal marking, then comprehensive feature coverage is achieved, but detection efficiency decreases
Solution Approach 1:
The patent replaces manual marking operations with automated deep learning-based detection. The unsupervised learning model processes signals at machine speed, automatically identifying abnormal patterns without the limitations of human operators, thereby dramatically improving detection efficiency while maintaining comprehensive feature coverage through automated feature extraction.
Solution Approach 2:
The deep learning model enables continuous automated detection of abnormal signals without interruption for manual marking. The system processes signals continuously, with the model constantly monitoring and identifying anomalies, eliminating the stop-start nature of manual operations and improving overall detection productivity while maintaining completeness.
Data Source
AI summary
The disclosure provides a signal detection method. The signal detection method includes: collecting initial data; pre-processing the initial data to obtain an original signal; reconstructing the original signal by using an optimized deep learning model, to generate a reconstructed signal; and comparing the original signal with the reconstructed signal, to determine whether there is an abnormality in the original signal. The disclosure further provides an electronic device using the signal detection method.