Machine learning cuts through the fog in aerial dispersal of chemical threats.
Understanding and predicting the dispersion and concentration of chemical weapons is a central element in protecting the joint force. Military commanders and staffs now plan large-scale combat operations using methods to predict where the enemy will plant chemical weapons on the battlefield. A reconnaissance team in protective gear would confirm these estimates.
To increase the probability of detecting chemical weapons, the Defense Threat Reduction Agency's (DTRA) Chemical and Biological Technology Division, as the Joint Science and Technology Office for Chemical and Biological Defense (JSTO), is an essential component of chemical and biological defense. plays the role of The program partners with scientists at Pacific Northwest National Laboratory (PNNL) to apply deep learning, a subset of machine learning, in the area of quantifying hazardous materials using 2D images, such as photographs of chemical plumes. Reveal the functionality.
Deep learning is often, but not always, formed through the implementation of neural networks. A neural network is a system of mathematical layers that identifies and extracts important features from almost any type of data, including numbers, categories, images, audio, and video. Convolutional neural networks (CNNs) interpret individual pixels in an image and assign weights to specific features or groups of pixels within each image to help classify the entire image. A CNN model is first trained on a set of labeled images. This means that the model is provided with the correct category for each of the initial images. Once the model is sufficiently trained, it can identify similarities and key features within a dataset and calculate the probability that an image belongs to any number of existing categories. The category with the highest resulting probability becomes the category assigned to the image.
In collaboration with JSTO, PNNL researchers created a CNN model designed to classify chemical plumes so that they can be characterized. They used images obtained from publicly available sources that simulate the plumes exhibited by many chemical weapons. The photos collected are a combination of ground, air, and satellite images of multiple volcanic plume events. The results of this study preliminarily demonstrated the high effectiveness of deep learning models in classifying volcanic plume data and showed that they can reduce human bias and errors.
JSTO plans to apply the final CNN model to the problem of quantifying hazardous materials on the battlefield from two-dimensional images. This capability allows commanders in post-war environments to better plan subsequent maneuvers to counter the adversary's use of chemical area denial systems in sustained (long-term) or non-persistent (short-term) forms. You will be able to evaluate it. This technology allows commanders to maintain combat power and maximize capability against the continued use of chemical weapons against U.S. joint and allied forces. The results of this study could lead to further integration of deep learning into military decision-making processes, creating opportunities for joint force leaders to plan military operations based on more accurate chemical threat predictions.
Machine learning is an emerging field that ensures more efficient and accurate analysis of complex topics. JSTO's exploration of machine learning capabilities in chemical warfare countermeasures will enable more reliable modeling and prediction of threats to combatants on the battlefield, enabling better-informed decision-making for joint force commanders It will be.
POC: Richard Fry, richard.n.fry.civ@mail.mil
| Obtained data: | April 24, 2024 |
| Post date: | April 24, 2024 16:51 |
| Story ID: | 469441 |
| position: | FT.Belvoir, Virginia, USA |
| Web view: | 19 |
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