Contamination Source Localization Using Neural Network Sensors
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Solution Overview
Problem
Existing technologies are slow and indirect in identifying the location of contamination sources within designated spaces, such as clean rooms, leading to delayed responses in managing air contamination, which can significantly impact manufacturing quality.
Innovation Solution
An apparatus and method using a neural network trained to estimate the location of contamination sources by measuring contaminant concentration with sensors, allowing for quick and direct identification of contamination sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional methods are used to identify contamination sources, then the identification process is indirect and slow, but the system complexity remains low
Solution Approach 1:
The patent replaces traditional mechanical/physical contamination tracking methods with an optical sensing system combined with neural network algorithms. Sensors detect contaminant concentrations and the neural network processes this data to rapidly identify contamination sources, substituting indirect mechanical detection with direct optical measurement and intelligent computation.
Solution Approach 2:
The patent introduces a neural network as an intermediary between sensor measurements and contamination source identification. The neural network acts as a computational mediator that processes sensor data and maps it to contamination source locations, enabling rapid and accurate identification without direct physical tracking of contaminants.
2Measurement precision
If sensors are used to measure contaminant concentration, then measurement capability is improved, but the ability to directly locate contamination source remains insufficient
Solution Approach 1:
The patent implements a feedback mechanism where sensor measurements of contaminant concentrations are continuously fed into the neural network, which then outputs contamination source location information. This feedback loop transforms raw concentration measurements into actionable location data, enabling both precise measurement and effective source identification.
Solution Approach 2:
The patent transforms the parameter set from only contaminant concentration measurements to include both concentration data and inferred location information. By changing the output parameters of the system from purely measurement-based to measurement-plus-location-based, the patent recovers the lost location information while maintaining measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid and accurate estimation of contamination source locations, enabling timely measures to minimize damage by controlling the environment and removing contaminants effectively.
Implementation Method 1
estimate the location of the contamination source by using a neural network trained to output the location of the contamination source when the measured concentration is input
Data Source
AI summary
An apparatus for estimating a location of a contamination source in a designated space includes a processor, and a memory operatively connected to the processor and storing instructions that, when executed by the processor, cause the apparatus to measure a concentration of a contaminant diffusing from the contamination source within the designated space by using at least one sensor and estimate the location of the contamination source based on the measured concentration, where the instructions cause the processor to estimate the location of the contamination source by using a neural network trained to output the location of the contamination source when the measured concentration is input.