Data Filtration and Distributed Computer Architecture for Expert Systems

1991 ◽  
Author(s):  
Stephen A. Trovato ◽  
Robert A. Touchton
Keyword(s):  
Expert System ◽  
Power Plant ◽  
Computer Architecture ◽  
Nuclear Plant ◽  
Verification And Validation ◽  
Sensor Data ◽  
Emergency Situations ◽  
Plant Monitoring ◽  
Indian Point

Abstract The timely recognition and accurate classification of emergency situations is an important task for nuclear plant operators. The Reactor Emergency Action Level Monitor (REALM) expert system was developed to provide advice to operators during emergency situations. A technique for filtering plant sensor data was developed for REALM and is broadly applicable to power plant monitoring tasks. Innovative computer architecture and verification and validation methods were developed during a research program to implement REALM at Con Edison’s Indian Point 2 nuclear plant.

scholarly journals Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

2021 ◽  
Author(s):  
Alexander Bigalke ◽  
Lasse Hansen ◽  
Jasper Diesel ◽  
Mattias P. Heinrich
Keyword(s):  
Point Cloud ◽  
Drug Dosage ◽  
Sensor Data ◽  
Patient Specific ◽  
Point Cloud Data ◽  
Weight Estimation ◽  
Visual Sensor ◽  
Cloud Data ◽  
Emergency Situations ◽  
3D Cnn

Abstract Purpose Body weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in emergency situations. Machine learning-based methods have recently been shown to perform accurate weight estimation from point cloud data. The proposed methods, however, are designed for controlled conditions in terms of visibility and position of the patient, which limits their practical applicability. In this work, we aim to decouple accurate weight estimation from such specific conditions by predicting the weight of covered patients from voxelized point cloud data. Methods We propose a novel deep learning framework, which comprises two 3D CNN modules solving the given task in two separate steps. First, we train a 3D U-Net to virtually uncover the patient, i.e. to predict the patient’s volumetric surface without a cover. Second, the patient’s weight is predicted from this 3D volume by means of a 3D CNN architecture, which we optimized for weight regression. Results We evaluate our approach on a lying pose dataset (SLP) under two different cover conditions. The proposed framework considerably improves on the baseline model by up to $${16}{\%}$$ 16 % and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to $${52}{\%}$$ 52 % . Conclusion We present a novel pipeline to estimate the weight of patients, which are covered by a blanket. Our approach relaxes the specific conditions that were required for accurate weight estimates by previous contactless methods and thus constitutes an important step towards fully automatic weight estimation in clinical practice.

scholarly journals Using Machine Learning and Wearable Inertial Sensor Data for the Classification of Fractal Gait Patterns in Women and Men During Load Carriage

2021 ◽  
Vol 185 ◽  
pp. 282-291
Author(s):  
Nizam U. Ahamed ◽  
Kellen T. Krajewski ◽  
Camille C. Johnson ◽  
Adam J. Sterczala ◽  
Julie P. Greeves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Sensor ◽  
Load Carriage ◽  
Sensor Data ◽  
Gait Patterns

Design, calibration, and application of an airborne gamma spectrometer system in Switzerland

Geophysics ◽  
1997 ◽  
Vol 62 (5) ◽  
pp. 1369-1378 ◽  
Author(s):  
Georg F. Schwarz ◽  
Ladislaus Rybach ◽  
Emile E. Klingelé
Keyword(s):  
Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Gamma Ray ◽  
Point Sources ◽  
Swiss Alps ◽  
Artificial Radioactivity ◽  
Radiation Level ◽  
Satellite Navigation System ◽  
Emergency Situations

Airborne radiometric surveys are finding increasingly wider applications in environmental mapping and monitoring. They are the most efficient tool to delimit surface contamination and to locate lost radioactive sources. To secure radiometric capability in survey and emergency situations, a new sensitive airborne system has been built that includes an airborne spectrometer with 256 channels and a sodium iodide detector with a total volume of 16.8 liters. A rack mounted PC with memory cards is used for data acquisition, with a GPS satellite navigation system for positioning. The system was calibrated with point sources using a mathematical correction to take into account the effects of gamma‐ray scattering in the ground and in the atmosphere. The calibration was complemented by high precision ground gamma spectrometry and laboratory measurements on rock samples. In Switzerland, two major research programs make use of the capabilities of airborne radiometric measurements. The first one concerns nuclear power plant monitoring. The five Swiss nuclear installations (four power plants and one research facility) and the surrounding regions of each site are surveyed annually. The project goal is to monitor the dose‐rate distribution and to provide a documented baseline database. The measurements show that all sites (with the exception of the Gösgen power plant) can be identified clearly on the maps. No artificial radioactivity that could not be explained by the Chernobyl release or earlier nuclear weapons tests was detected outside of the fenced sites of the nuclear installations. The second program aims at a better evaluation of the natural radiation level in Switzerland. The survey focused on the crystalline rocks of the Central Massifs of the Swiss Alps because of their relatively high natural radioactivity and lithological variability.

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