Method and system for monitoring and optimizing the operation of an alumina rotary kiln
US12038738B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 28, 2020 |
| Grant date | Jul 16, 2024 |
| Priority date | — |
| Expiry date | Apr 8, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/45132
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remains—in preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.