Error Occurrence Level Identification via Non-Parametric Frequency Comparison
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Solution Overview
Problem
Existing methods for analyzing error information in electronic apparatuses are limited by their reliance on parametric methods, which fail to accurately identify error distributions since error data does not follow a normal distribution, leading to ineffective error detection and analysis.
Innovation Solution
An electronic apparatus and control method that utilize non-parametric verification by comparing frequency information of error occurrences within a target time interval to a standard time interval, employing algorithms like the Mann-Whitney test and Kruskal-Wallis test to identify error occurrence levels and provide feedback for error detection and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If parametric methods are used to analyze error information, then the analysis process is simple, but the measurement precision of error distribution is insufficient because error data does not follow normal distribution
Solution Approach 1:
The patent changes the statistical method parameter from parametric (assuming normal distribution) to non-parametric (Mann-Whitney U test, Kruskal-Wallis test) to accommodate error data that does not follow normal distribution. This parameter change enables accurate error distribution analysis while maintaining systematic analysis procedures.
2Measurement precision
If non-parametric verification is applied to compare frequency information, then the measurement precision of error occurrence level is improved, but the device complexity increases due to additional processing steps
Solution Approach 1:
The patent introduces frequency information as an intermediary metric that translates raw error data into comparable distributions. By comparing frequency information between target and standard time intervals using non-parametric tests, the system achieves precise error occurrence level identification without requiring complex direct analysis of raw error data.
Solution Approach 2:
The patent replaces simple error counting with statistical hypothesis testing (Mann-Whitney U test, Kruskal-Wallis test) to determine error occurrence levels. This substitution of mechanical counting with statistical analysis enables precise identification of error distributions while following systematic testing procedures.
3Device complexity
If error information is simply summed without distribution analysis, then the processing complexity is low, but the loss of information about error patterns occurs
Solution Approach 1:
The patent segments error information into frequency distributions across different time intervals rather than treating it as a single aggregated value. By dividing error data into target time interval and standard time interval frequency distributions, the system preserves error pattern information while maintaining manageable processing complexity through structured segmentation.
Data Source
AI summary
An electronic apparatus is provided. The electronic apparatus includes a storage storing error-related information of an external electronic apparatus, and a processor configured to obtain first error-related information with respect to a target time interval and second error-related information with respect to a standard time interval including the target time interval and time intervals other than the target time interval, from the storage, obtain frequency information for each number of error occurrences with respect to the target time interval based on the first error-related information and frequency information for each number of error occurrences with respect to the standard time interval based on the second error-related information, and compare the frequency information for each number of error occurrences with respect to the target time interval with the frequency information for each number of error occurrences with respect to the standard time interval to identify an error occurrence level with respect to the target time interval.