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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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

CYVET: A Cyber-Physical Assurance Framework Based on a Semi-Supervised Vetting Approach

The project is designed to directly address the needs described in the Topic Area 4 (“Cybersecurity Verification and Validation”) of the CEDS’19 Research Call. The proposed CYVET system directly addresses the need to elevate the current industry capabilities to verify and validate OT cybersecurity and associated control system infrastructure. Currently, there is a significant gap in the energy sector’s capability in that regard during infrastructure improvement, equipment procurement, and compliance certification. CYVET provides that needed capability. CYVET is device and architecture agnostic and thus broadly applicable across the energy sector. The goal of this project is to develop and deliver a cybersecurity verification and validation framework testing capability to verify and validate OT equipment, software, and the underlying control system architecture. The primary project objectives are (i) Verification: the synthesis and reconciliation of standards and vendor supplied features, (ii) Validation: the generation, execution, and presentation of testing scripts of verified security features, and (3) Demonstration: apply the developed technology capabilities for verification and validation at a relevant end-user facility in the energy sector.

Lopez Jr, Juan↗
DOE OSTI2024

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability.

58 GEOSCIENCES↗
NASA NTRS1974

Motivation techniques for supervision

Guide has been published which deals with various aspects of employee motivation. Training methods are designed to improve communication between supervisors and subordinates, to create feeling of achievement and recognition for every employee, and to retain personnel confidence in spite of some negative motivators. End result of training is reduction or prevention of errors.

Gray, N. D.↗
NASA NTRS1981

Pixel labeling by supervised probabilistic relaxation

A simple modification to existing probabilistic relaxation procedures is suggested which allows the information contained in initial labels to exert an influence on the direction of relaxation throughout the process. In this manner, the initial labels assume more importance than with conventional algorithms and are used in combination with the outcome of relaxation at each iteration to produce a cooperative estimate of the correct label for a particular object. Pixel labeling examples are presented which show the performance that can be obtained with the modified algorithm. The procedure is readily generalized to allow other data to influence the process.

Richards, J. A.↗
NASA NTRS1981

Human supervision and microprocessor control of an optical tracking system

Gunners using small calibre anti-aircraft systems have not been able to track high-speed air targets effectively. Substantial improvement in the accuracy of surface fire against attacking aircraft has been realized through the design of a director-type weapon control system. This system concept frees the gunner to exercise a supervisory/monitoring role while the computer takes over continuous target tracking. This change capitalizes on a key consideration of human factors engineering while increasing system accuracy. The advanced system design, which uses distributed microprocessor control, is discussed at the block diagram level and is contrasted with the previous implementation.

Bigley, W. J.↗
NASA NTRS1982

Supervision of dynamic systems: Monitoring, decision-making and control

Effects of task variables on the performance of the human supervisor by means of modelling techniques are discussed. The task variables considered are: The dynamics of the system, the task to be performed, the environmental disturbances and the observation noise. A relationship between task variables and parameters of a supervisory model is assumed. The model consists of three parts: (1) The observer part is thought to be a full order optimal observer, (2) the decision-making part is stated as a set of decision rules, and (3) the controller part is given by a control law. The observer part generates, on the basis of the system output and the control actions, an estimate of the state of the system and its associated variance. The outputs of the observer part are then used by the decision-making part to determine the instants in time of the observation actions on the one hand and the controls actions on the other. The controller part makes use of the estimated state to derive the amplitude(s) of the control action(s).

White, T. N.↗
NASA NTRS1983

Shear zone ice deformation using supervised analysis of Seasat data

Spacecraft and aircraft Synthetic-Aperture Radar (SAR) images are commonly used to compute sea ice conditions including especially the drift velocities of ice features. Recent developments, described in this paper, in computer-aided analysis and in digital image processing have extended the analysis speed and accuracy of registration to the point that ice deformation can be examined on the geophysically important scales of about 100 m over tens of kilometers between two 'snapshots.' Examples are shown for the ice motion and rotation in the shear zone near the Alaska coast from Seasat data in 1978.

Carsey, F.↗
NASA NTRS1990

Vision science and technology for supervised intelligent space robots

The focus of recent work in robotic vision for application in intelligent space robots such as the Extravehicular Activity (EVA) Retriever is in visual function, that is, how information about the space world is derived and then conveyed to cognition. The goal of this work in visual function is first to understand how the relevant structure of the surrounding world is evidenced by regularities among the pixels of images, then to understand how these regularities are mapped on the premises that form the primitive elements of cognition, and then to apply these understandings with the elements of visual processing (algorithms) and visual mechanism (machine organization) to intelligent space robot simulations and test beds. Since visual perception is the process of recognizing regularities in images that are known on the basis of a model of the world to be reliable related to causal structure in the environment (because perception attaches meaning to the link between a conception of the environment and the objective environment), the work involves understanding generic, generally applicable models of world structure (not merely objects) and how that structure evidences itself in images.

Erickson, Jon D.↗
NASA NTRS1992

Automatic learning rate adjustment for self-supervising autonomous robot control

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

Arras, Michael K.↗