New Calibration Method for Implementing Machine Learning in Low-Cost Sensor Applications
| Autor: | Julie Whitney, Ryan Bradley, George T.-C. Chiu, Nikhil Bajaj, Niko Jay Murrell |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
education.field_of_study Integrated design Computer science business.industry Manufacturing process 010401 analytical chemistry Population Cyber-physical system 02 engineering and technology Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences Reference space 020901 industrial engineering & automation Component (UML) Scalability Calibration Artificial intelligence Electrical and Electronic Engineering business education Instrumentation computer |
| Zdroj: | IEEE Sensors Letters. 4:1-4 |
| ISSN: | 2475-1472 |
| Popis: | Hardware cost and manufacturing process variation constrain the use of machine learning in low-cost sensor applications. This letter describes a novel method to manage some of those critical limitations. Reference calibration mapping is a method that creates a reference space from a single sensor and then transforms the output from the remaining sensor population into that reference space. The method results in the ability to utilize low-cost hardware and reduce the required training set. This letter applies the method to a media sensing system in a laser printer application. The resulting system lowered component and development costs while meeting stringent manufacturing and performance requirements |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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