Interactive Power Analysis Graphs for Experiment Design
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Solution Overview
Problem
Designing experiments to achieve specific power and quality metrics is challenging due to the complexity of determining optimal sample sizes and parameters, as existing tools lack interactive and adjustable analysis graphs that allow users to visualize and adjust settings effectively.
Innovation Solution
A computer profiler tool generates interactive adjustable power analysis graphs, allowing users to visualize the relationship between adjustable settings and power profiles, enabling the determination of optimal input parameters by iteratively adjusting settings to meet desired design quality metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing tools are used for power analysis, then basic calculations can be performed, but interactive and adjustable analysis graphs for visualizing relationships between settings and power profiles are not provided
Solution Approach 1:
The patent creates visual copies of power analysis data through graphical interfaces. Multiple graphs are generated that replicate the relationships between settings and power profiles in visual form, allowing users to see rather than just calculate results. This copying approach transforms abstract numerical data into tangible visual representations without adding physical complexity to the system.
Solution Approach 2:
The patent replaces traditional mechanical calculation methods with computational graphing systems. Instead of manual power analysis calculations, the system uses computer-generated interactive graphs that automatically visualize relationships. This substitution eliminates the need for complex manual computations while providing enhanced visualization capabilities through software-based graphical interfaces.
2Productivity
If manual calculation methods are used to determine optimal sample sizes and parameters, then basic analysis can be performed, but iterative adjustment and visualization of settings are time-consuming and inefficient
Solution Approach 1:
The patent performs preliminary power analysis calculations and generates complete sets of power profiles before the user needs them. Multiple graphs showing relationships between different settings and power outcomes are pre-computed and made available for immediate visualization. This preliminary action eliminates the need for users to perform time-consuming iterative calculations during the experiment design process.
Solution Approach 2:
The patent implements feedback mechanisms where the system automatically recalculates and updates power analysis graphs based on user-selected parameters. When users adjust settings, the system provides immediate visual feedback through updated graphs showing how changes affect power profiles. This automated feedback loop eliminates manual recalculation time and allows rapid exploration of different experimental designs.
3Measurement precision
If comprehensive power analysis with multiple settings is performed, then detailed insights into experiment design quality can be obtained, but the complexity of analyzing and adjusting multiple parameters increases
Solution Approach 1:
The patent segments the complex power analysis into multiple separate graphical representations. Each graph focuses on specific relationships between settings and power profiles, breaking down the overall analysis into manageable visual components. This segmentation allows users to examine individual aspects of design quality without being overwhelmed by the complexity of all parameters simultaneously.
Solution Approach 2:
The patent transforms multi-parameter power analysis data into visual dimensions through graphical interfaces. Instead of presenting complex tabular data requiring users to mentally process multiple parameters, the system creates visual dimensions through graphs where relationships between settings and power outcomes are spatially represented. This dimensional transformation makes complex parameter relationships easier to understand and adjust.
Data Source
AI summary
A computing device receives a target value for a design quality metric. The target value indicates a desired quality of a design of an experiment and is related to an input parameter by a response curve. The computing device also validates the target value as being in a feasibility range for the design quality metric, and if so, determines a candidate value for the input parameter that yields a calculated value for the design quality metric. To determine the calculated value, the computing device iteratively adjusts the candidate value until the calculated value is within a predetermined tolerance of the target value. The computing device then updates an interactive graph visually representing the calculated value for the design quality metric as a function of the candidate value for the input parameter and outputs a graph visually representing the relationship that exists between the candidate value and the calculated value.