Denoising Model Training for Accurate Target Signal Detection
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Solution Overview
Problem
Existing detection systems face challenges in accurately detecting detection targets due to noise interference, necessitating effective noise cancellation methods.
Innovation Solution
A detection method involving a denoising model trained using noise signals to produce denoised signals, utilizing unsupervised machine learning models like autoencoders, particularly variational autoencoders, to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise cancellation is performed using conventional methods, then detection accuracy is improved, but the complexity of the detection system increases
Solution Approach 1:
The patent replaces conventional mechanical or algorithmic noise filtering methods with a deep learning-based denoising model. The model uses neural networks to automatically learn and remove noise patterns from detection signals, substituting complex signal processing algorithms with a trained artificial intelligence system that achieves better noise cancellation performance while simplifying the overall detection pipeline.
2Ease of manufacture
If a denoising model is trained using unsupervised machine learning, then the training process becomes simpler, but the model requires more data and computational resources
Solution Approach 1:
The patent implements self-service by using unsupervised learning where the denoising model automatically learns noise patterns and cleaning strategies from raw data without requiring manual labeling or supervision. The system serves itself by autonomously identifying noise characteristics and optimizing its denoising capabilities through iterative training on unlabeled datasets, eliminating the need for expensive annotated training data.
Data Source
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AI summary
A detection system includes: a detection signal obtainer 306 configured to obtain a signal from a detection target as a detection signal; and a denoised signal obtainer 307 configured to input the detection signal into a denoising model 304 trained using a noise signal in the absence of the detection target to obtain at least one denoised signal that has undergone denoising.