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Results for “DATA PROCESSING”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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NASA's High-End Computing Capability: Growing to Support Science Data Processing for The Earth System Observatory Missions
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Wind Lidar Data Processing and Results from the CPEX-AW Campaign
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Open Sourced Science for Earth System Observatory (ESO) Mission Science Data Processing Study: Workshop #2
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Open-Source NDE Simulation and Data Processing Tools from NASA - An Update
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Low Latency LEO Satellite Data Processing using Ground Station Observation Network (GSON) Framework: Towards a Near Real-time Global Cloud Composite (GCC)
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Enabling Open and Interoperable Science: Multi-omics Data Processing with Standardized Bioinformatics Workflows for Earth and Space Research
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LoVoCCS. I. Survey Introduction, Data Processing Pipeline, and Early Science Results
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GST Data-processing Workflow: Image Registration and Alignment
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Systems, methods and computer program products for self-tuning sensor data processing
Systems and methods are disclosed that include tools that utilize Dynamic Detector Tuning (DDT) software that identifies near-optimal parameter settings for each sensor using a neuro-dynamic programming (reinforcement learning) paradigm. DDT adapts parameter values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The DDT algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest. The disclosed systems and methods reduce the number of missed legitimate detections and the number of false detections, resulting in improved event detection.
System architecture and method of processing data therein
A system architecture encoded on a non-transitory computer readable medium, the system architecture includes a first protocol. The first protocol is configured to receive a plurality of outputs from an ICS used in controlling an industrial system. The first protocol is configured to receive a plurality of inputs from a physical module. The physical module includes at least one of a component, a sensor, or the ICS. Additionally, the system architecture includes a second protocol, wherein the second protocol is configured to validate the plurality of inputs from the first protocol. Moreover, the system architectures includes a third protocol, wherein the third protocol is configured to validate the plurality of outputs from the first protocol. Further, the system architecture includes a fourth protocol, wherein the fourth protocol is configured to manage the ICS based on the second protocol and the third protocol.
Towards Scalable Data Processing in Python with CLIPPy
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Intelligent Experiment Through Real-Time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors
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In-Line Membrane Thickness Mapping with Real-Time Data Processing
The goal of this project is commercialization of a novel, patented by NREL, non-contact, in-line quality control thickness mapping tool that will enable 100% area inspection of multilayer polymer film assemblies, such as a PEM on a casting substrate, during continuous high-volume R2R manufacturing.
Locating underground features with seismic data processing
Methods are presented for determining the location of underground features (e.g., CO 2 ). One method includes capturing, by sensors distributed throughout a region, seismic traces associated with seismic signals generated by a seismic source. For multiple sensors, active noise is identified or passive noise is measured within each seismic trace and values for attributes associated with the active or passive noise are determined. Further, an unsupervised machine-learning model, based on the values of the attributes, is utilized to determine noise characteristics for multiple sensors. The sensors are grouped in clusters based on the noise characteristics for each sensor. For multiple clusters, a noise filter is created based on the noise characteristics of the sensors in the cluster, and the noise filter of the cluster is applied, for multiple sensors, to the seismic traces of the sensor. Additionally, the filtered seismic traces are analyzed to determine a location of CO 2 underground.