Model Development of Inhaled Toxicants and Neural Network Model Development and Optimization
Closed Special Notice Posted
- Solicitation number
- W912HZ23T2355
- Agency
-
Engineer Research and Development Center
US Army Corps of Engineers, Department of Defense
- Responses due
- Set-aside
- Total Small Business
Opportunity facts
- NAICS code
- 541714 Research and Development in Biotechnology (except Nanobiotechnology)
- Place of performance
- Vicksburg, Mississippi 39180, United States
Notice details come from SAM.gov. Updated .
About this opportunity
The US Army Corps of Engineers intends to award a sole source contract to Bennett Aerospace, Inc. for the development of a computational model to assess the risks of toxic industrial chemicals and materials released into the air, and the development and optimization of several biologically based neural network models from field data. The computational model will merge a machine learning model predicting wind velocity fields in urban environments with a framework assessing the risks of toxic substances, and integrate this with algorithms and models showing how toxins move through urban environments. The neural network models will correlate input data such as aerial spectral data and drone collected images with desired outputs, such as field measurements of size/location/type of ground cover, aquatic plants, or understory layer of vegetation.
Services and products to be provided:
- Development of a computational model to assess the risks of toxic industrial chemicals and materials released into the air.
- Integration of the computational model with algorithms and models showing how toxins move through urban environments.
- Testing of the computational model in defined test locations.
- Development and optimization of several biologically based neural network models from field data.
- Correlation of input data with desired outputs in the neural network models.
Notice text
THIS IS A NOTICE OF INTENT TO AWARD A SOLE SOURCE CONTRACT AND IS NOT A REQUST FOR COMPETITIVE QUOTES. The US Army Corps of Engineers, Engineer Research and Development Center (ERDC), Environmental Laboratory (EL) intends to issue an award on a sole source basis (IAW FAR 13.106-1(b)(1)) to Bennett Aerospace, Inc. CAGE code: 61DA9 LOCATED 1 Glenwood Ave, Raleigh, NC 27603 for Model Development of Inhaled Toxicants and Neural Network Model Development and Optimization Task 1- Model Development of Inhaled Toxicants
The contractor shall merge a machine learning model developed to predict wind velocity fields in urban environments with a computational framework developed to assess the risks of toxic industrial chemicals (TICs) and toxic industrial materials (TIMs) released into the air at a proposed location. The contactor shall also integrate this modified risk model (mRM) with algorithms and models that deliver how the toxin moves through urban environments. The main goal from this research task is to develop a single computing platform that is able to incorporate exposure risk from high fidelity wind flows that guide toxic plumes around buildings in an operational area. Both the computational model and framework produced should integrate computer code that can be utilized across different programming languages and packages. Code should be suitable for both high performance computing platforms (e.g., HPC) and standard desktop computers. Lastly, the model shall be tested in defined test locations which allow for a vast array of expected outcomes.
Task 2 – Neural Network Model Development and Optimization
The contractor shall develop several biologically based neural network models from field data. The developed model should correlate input data such as aerial spectral data and drone collected images with desired outputs, such as field measurements of size/location/type of ground cover, aquatic plants, or understory layer of vegetation. Additionally, the neural network model should be able to train input data to some kind of regression output that approximates a statistical distribution. For example, input data should correlate with higher order moments of the data distributions collected for observations like foliage size, height, or spatial extent. Neural network models should be formulated with multiple layers and layers of different size.
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