Simulation Device for Onboard System Dimensioning
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Solution Overview
Problem
Existing methods for determining the appropriate dimensioning of onboard systems, particularly in critical applications like avionics and aerospace, face challenges in accurately approximating the distribution laws using Extreme Value Theory (EVT), specifically with Gumbel and generalized Pareto distributions, leading to uncertainties in processing power requirements and execution times.
Innovation Solution
A simulation device comprising a memory, selector, evaluator, sorter, and estimator that processes datasets to determine the most probable Gumbel and generalized Pareto distributions, allowing for precise estimation of extreme values and processing power needs by segmenting data subsets and calculating form, scale, and localization factors using statistical methods like maximum likelihood and moment methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If processor power is increased to meet growing computational requirements of onboard systems, then processing capability is improved, but electrical power consumption and heat generation worsen
Solution Approach 1:
The patent changes the parameter of processor power selection by using statistical analysis to determine the minimum necessary processing capability. Instead of continuously oversizing processors, the system analyzes measurement data to identify the actual computational requirements, allowing selection of processors that meet needs without excess power consumption or heat generation.
2Power
If processor power is increased to meet growing computational requirements of onboard systems, then processing capability is improved, but heat generation worsens
Solution Approach 1:
The patent addresses heat generation by changing the parameter of processor power selection through statistical analysis. By determining the actual minimum processing requirements from measurement data, the system avoids selecting overly powerful processors that would generate excessive heat, thus resolving the contradiction between processing capability and thermal management.
3Measurement precision
If statistical analysis with multiple data subsets and form factor calculation is performed to accurately determine distribution laws, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
The patent applies segmentation by dividing the dataset into multiple data subsets based on different form factors. This segmentation allows the system to analyze different portions of the data independently, improving the accuracy of distribution law approximation while organizing the complexity into manageable, structured components rather than a monolithic complex system.
Solution Approach 2:
The patent replaces complex mechanical or manual analysis methods with automated computational evaluation. The evaluator automatically calculates form factors and determines the best-fit distribution law using statistical algorithms, substituting what would otherwise require complex manual procedures with efficient computational methods that reduce overall system complexity.
Data Source
AI summary
The invention relates to a simulation device including: —a memory (2) receiving a dataset representing parameter measurements; —a selector (4) capable of producing a set of sub-datasets from the dataset, each sub-dataset from said set including consecutive data from the dataset, the number of which is a whole integer received as a variable by the selector (4); —an evaluator (6) capable of calculating, from a value set defining a curve, a form factor indicating a probability of correspondence between the curve defined by the value set and a Gumbel curve; and—a driver (8) arranged so as to call the selector (4) with a plurality of numbers for generating a plurality of sub-dataset sets, select the data, the measurement value of which is highest, within each sub-dataset from each sub-dataset set, create a plurality of curve datasets by combining the data selected from each set, call the evaluator (6) with the plurality of curve datasets, and select the curve dataset for which the form factor indicates the highest probability.