17994993. Generating Training Samples via 3D Modeling for Greenhouse Gas Emission Detection simplified abstract (Saudi Arabian Oil Company)
Contents
- 1 Generating Training Samples via 3D Modeling for Greenhouse Gas Emission Detection
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Generating Training Samples via 3D Modeling for Greenhouse Gas Emission Detection - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Generating Training Samples via 3D Modeling for Greenhouse Gas Emission Detection
Organization Name
Inventor(s)
Ali Almadan of Houston TX (US)
Damian Pablo San Roman Alerigi of Al Khobar (SA)
Generating Training Samples via 3D Modeling for Greenhouse Gas Emission Detection - A simplified explanation of the abstract
This abstract first appeared for US patent application 17994993 titled 'Generating Training Samples via 3D Modeling for Greenhouse Gas Emission Detection
Simplified Explanation
The computer-implemented method described in the abstract involves generating training samples for machine learning-based greenhouse gas emission detection using 3D modeling. Here are some bullet points to explain the patent/innovation:
- Generating 3D models of real-world facilities and gas plume emissions.
- Building a virtual environment based on the 3D models.
- Capturing images or video of the virtual environment.
- Assigning labels to objects in the images or videos.
Potential Applications
This technology could be applied in industries such as environmental monitoring, emissions control, and climate change research.
Problems Solved
This technology helps in accurately detecting and monitoring greenhouse gas emissions, which is crucial for environmental protection and regulatory compliance.
Benefits
The benefits of this technology include improved accuracy in detecting emissions, cost-effective monitoring, and better understanding of emission sources.
Potential Commercial Applications
One potential commercial application of this technology could be in providing services for greenhouse gas emission monitoring and reporting to industries and regulatory bodies.
Possible Prior Art
One possible prior art could be the use of remote sensing technologies for monitoring greenhouse gas emissions from industrial facilities.
What are the limitations of this technology in real-world implementation?
The limitations of this technology in real-world implementation could include the need for high-quality 3D modeling data, computational resources for simulation, and potential challenges in scaling the system for large-scale monitoring.
How does this technology compare to traditional methods of greenhouse gas emission detection?
This technology offers a more advanced and automated approach to greenhouse gas emission detection compared to traditional methods, which may rely on manual data collection and analysis. The use of 3D modeling and machine learning can improve accuracy and efficiency in detecting emissions.
Original Abstract Submitted
A computer-implemented method for generating training samples via 3D modeling for machine learning-based greenhouse gas emission detection is described. The method includes generating at least one 3D model corresponding to a real-world facility and at least one 3D model corresponding to gas plume emissions and building a virtual environment comprising a 3D field based on the at least one 3D model corresponding to the real-world facility and the at least one 3D model corresponding to gas plume emissions. The method also includes capturing images or video of the 3D field via simulation of the virtual environment and assigning labels to objects in the images or videos.
