Heliumspeech Unscrambler Using Multi-Objective Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing saturation diving heliumspeech unscramblers fail to effectively adapt to changing depths and have poor performance, especially when diving depths exceed 200 meters, due to their inability to utilize the limited working vocabularies and personal speech characteristics of divers, leading to communication challenges that can threaten diver safety.
Innovation Solution
A method for saturation diving heliumspeech unscrambling based on multi-objective optimization, which involves selecting an appropriate filter, constructing phonetic and working word libraries, generating standard and heliumspeech libraries, determining filter impulse response coefficients using multi-objective optimization algorithms, and continuously updating these coefficients to correct and unscramble heliumspeech signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual adjustment of frequency domain or time domain characteristics is used in existing heliumspeech unscramblers, then the device complexity is reduced, but the adaptability to changing diving depths deteriorates and unscrambling performance becomes poor
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating filter coefficients based on real-time diving depth information and diver speech characteristics. The system transitions from static manual adjustment to dynamic automated adaptation, allowing the unscrambler to respond to changing depth conditions and maintain optimal performance throughout the diving operation.
Solution Approach 2:
The system performs self-adjustment by automatically determining filter coefficients through multi-objective optimization without requiring manual intervention. The unscrambler uses the diver's own speech samples and depth information to autonomously configure optimal parameters, eliminating the need for operator adjustment while improving adaptability.
2Ease of operation
If machine learning with small samples is used for heliumspeech unscrambling, then the ease of operation is improved, but the unscrambling effectiveness deteriorates
Solution Approach 1:
The system collects speech samples from each diver during normal operations and pre-processes them to build personalized speech profiles before actual diving operations. This preliminary preparation of diver-specific characteristics enables the multi-objective optimization to start with informed initial parameters, improving effectiveness while maintaining ease of operation.
Solution Approach 2:
The patent transforms the unscrambling approach by changing from generic machine learning models to diver-specific parameter optimization. By adjusting filter coefficients based on individual diver characteristics and real-time depth conditions, the system achieves both operational simplicity and high effectiveness through personalized parameter adaptation.
3Manufacturing precision
If filter coefficients are fixed for specific diving depths, then the manufacturing precision is improved, but the adaptability to depth changes deteriorates
Solution Approach 1:
The system replaces fixed depth-specific filter coefficients with dynamic coefficients that automatically adapt to changing diving depths. The multi-objective optimization continuously adjusts parameters based on real-time depth information, maintaining high precision at each depth level while enabling seamless transitions between depths without requiring pre-configured coefficient sets.
Solution Approach 2:
The unscrambler implements feedback mechanisms by monitoring actual diving depth and comparing unscrambling performance, then using this information to adjust filter coefficients in real-time. This closed-loop approach maintains manufacturing precision at each depth while providing adaptability to depth changes through continuous optimization based on feedback signals.
Data Source
AI summary
The present application discloses a method and a system for saturation diving heliumspeech unscrambling based on multi-objective optimization. In a system including a diver and a filter at least, a working language phonetic symbol library and a common working word library for divers are constructed. The divers read them one by one, and a phonetic symbol standard speech library, a phonetic symbol heliumspeech library and a common working word speech library are generated. The filter uses the multi-objective optimization algorithm to design its impulse response coefficients, corrects and unscrambles the tagged and sampled heliumspeech signal word by word, and continuously updates the impulse response coefficients to complete the perfect heliumspeech unscrambling.