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Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Proving DRACO will deliver
The United States is now closer than it has been in over five decades to launching the first nuclear thermal rocket into space, thanks to DRACO—the Demonstration Rocket for Agile Cislunar Orbit.
Gonzalo Farias, Ernesto Fabregas, Sebastián Dormido-Canto, Jesús Vega, Sebastián Vergara
Fusion Science and Technology | Volume 76 | Number 8 | November 2020 | Pages 925-932
Technical Paper | doi.org/10.1080/15361055.2020.1820804
Articles are hosted by Taylor and Francis Online.
Anomaly detection addresses the problem of finding unexpected values in data sets. Often, these anomalies, also known as outliers, discordant values, or exceptions, describe patterns in the behavior of the data. Anomaly detection is important because it frequently involves significant and critical information in many application domains. In the case of nuclear fusion, there is a wide variety of anomalies that could be related to plasma behaviors, such as disruptions or low-high (L-H) transitions. In this context, there are known and unknown anomalies, where unknown anomalies represent the largest proportion of the total that can be found in nuclear fusion. This paper presents a study of the application of deep learning and architecture called Autoencoder to detect anomalies predicting (encode-decode) in a discharge.