Information Processing Device Neural Network Signal Accuracy
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Solution Overview
Problem
Current information processing devices face challenges in achieving high processing accuracy when dealing with signals of varying characteristics, such as recording density, as they often rely on a single neural network to process diverse attributes, leading to suboptimal results.
Innovation Solution
The implementation of an information processing device that utilizes multiple neural networks as processing models, allowing for the selection of an appropriate model based on the acquired signal characteristics, and processing the signal using both a first and a second neural network to derive a third output for enhanced '0/1' determination accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single neural network is used to process signals with varying characteristics, then device complexity is reduced, but processing accuracy deteriorates
Solution Approach 1:
The system divides the signal processing task into multiple specialized neural networks, each trained to handle specific signal characteristics or attribute ranges. This segmentation allows each network to optimize its processing for particular conditions, thereby improving overall processing accuracy while managing complexity through functional division.
Solution Approach 2:
The system changes the parameter of network selection based on signal attributes. By detecting signal characteristics and selecting or switching between appropriate neural networks, the system adapts its processing capability to match the input signal properties, improving accuracy without requiring all networks to operate simultaneously.
2Measurement precision
If multiple neural networks are used to process signals, then processing accuracy is improved, but device complexity increases
Solution Approach 1:
The system implements dynamic network selection or switching based on real-time signal characteristics. Instead of statically deploying all neural networks, the system dynamically chooses the most appropriate network for each input signal, maintaining high processing accuracy while reducing the effective complexity by activating only necessary components.
Solution Approach 2:
Different parts of the system (different neural networks) are assigned different specialized functions or optimization targets based on local signal characteristics. Each neural network is tailored to handle specific types or ranges of signals, creating local optimization that improves overall system accuracy without requiring universal complexity.
Data Source
AI summary
According to one embodiment, an information processing device includes an acquisition part, and a processor. The acquisition part is configured to acquire a reproduction signal obtained from a recording part. The recording part includes a recording medium. The reproduction signal includes a first signal corresponding to information recorded in the recording medium. The processor is configured to derive a first output and a second output. The first output is obtained by first information being processed by a first processing model. The first information includes the first signal. The second output is obtained by the first information being processed by a second processing model. The processor is configured to output a result of processing the first information based on a third output. The third output is obtained based on the first output, the second output, and the first information.