Results for “intelligence analysis” — Page 65

NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗

DOE OSTI2021

Outlook for artificial intelligence and machine learning at the NSLS-II

Abstract We describe the current and future plans for using artificial intelligence and machine learning (AI/ML) methods at the National Synchrotron Light Source II (NSLS-II), a scientific user facility at the Brookhaven National Laboratory. We discuss the opportunity for using the AI/ML tools and techniques developed in the data and computational science areas to greatly improve the scientific output of large scale experimental user facilities. We describe our current and future plans in areas including from detecting and recovering from faults, optimizing the source and instrument configurations, streamlining the pipeline from measurement to insight, through data acquisition, processing, analysis. The overall strategy and direction of the NSLS-II facility in relation to AI/ML is presented.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

SETI low-frequency feed design study for DSS 24

The Search for Extraterrestrial Intelligence Sky Survey project requires operation from 1 to 10 GHz on the beam waveguide (BWG) antenna DSS 24. The BWG reflectors are undersized in the 1- to 3.02-GHz range, resulting in poor performance. Horn designs and a method for implementing 1- to 3.02-GHz operation on DSS 24 are presented. A combination of a horn and a shaped feed reflector placed above the main reflector is suggested. The horn and feed reflector could be hidden in the RF shadow of the subreflector and struts. Results from computer analysis of this design indicate that adequate performance could be achieved.

Stanton, P. H.↗

NASA NTRS1994

The design and implementation of EPL: An event pattern language for active databases

The growing demand for intelligent information systems requires closer coupling of rule-based reasoning engines, such as CLIPS, with advanced data base management systems (DBMS). For instance, several commercial DBMS now support the notion of triggers that monitor events and transactions occurring in the database and fire induced actions, which perform a variety of critical functions, including safeguarding the integrity of data, monitoring access, and recording volatile information needed by administrators, analysts, and expert systems to perform assorted tasks; examples of these tasks include security enforcement, market studies, knowledge discovery, and link analysis. At UCLA, we designed and implemented the event pattern language (EPL) which is capable of detecting and acting upon complex patterns of events which are temporally related to each other. For instance, a plant manager should be notified when a certain pattern of overheating repeats itself over time in a chemical process; likewise, proper notification is required when a suspicious sequence of bank transactions is executed within a certain time limit. The EPL prototype is built in CLIPS to operate on top of Sybase, a commercial relational DBMS, where actions can be triggered by events such as simple database updates, insertions, and deletions. The rule-based syntax of EPL allows the sequences of goals in rules to be interpreted as sequences of temporal events; each goal can correspond to either (1) a simple event, or (2) a (possibly negated) event/condition predicate, or (3) a complex event defined as the disjunction and repetition of other events. Various extensions have been added to CLIPS in order to tailor the interface with Sybase and its open client/server architecture.

Giuffrida, G.↗

NASA NTRS1999

High Data Rate Instrument Study

The High Data Rate Instrument Study was a joint effort between the Jet Propulsion Laboratory (JPL) and the Goddard Space Flight Center (GSFC). The objectives were to assess the characteristics of future high data rate Earth observing science instruments and then to assess the feasibility of developing data processing systems and communications systems required to meet those data rates. Instruments and technology were assessed for technology readiness dates of 2000, 2003, and 2006. The highest data rate instruments are hyperspectral and synthetic aperture radar instruments which are capable of generating 3.2 Gigabits per second (Gbps) and 1.3 Gbps, respectively, with a technology readiness date of 2003. These instruments would require storage of 16.2 Terebits (Tb) of information (RF communications case of two orbits of data) or 40.5 Tb of information (optical communications case of five orbits of data) with a technology readiness date of 2003. Onboard storage capability in 2003 is estimated at 4 Tb; therefore, all the data created cannot be stored without processing or compression. Of the 4 Tb of stored data, RF communications can only send about one third of the data to the ground, while optical communications is estimated at 6.4 Tb across all three technology readiness dates of 2000, 2003, and 2006 which were used in the study. The study includes analysis of the onboard processing and communications technologies at these three dates and potential systems to meet the high data rate requirements. In the 2003 case, 7.8% of the data can be stored and downlinked by RF communications while 10% of the data can be stored and downlinked with optical communications. The study conclusion is that only 1 to 10% of the data generated by high data rate instruments will be sent to the ground from now through 2006 unless revolutionary changes in spacecraft design and operations such as intelligent data extraction are developed.

Schober, Wayne↗

DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

actuated signal control↗

NASA NTRS2016

Semantic Representation and Scale-Up of Integrated Air Traffic Management Data

Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.

data management↗

DOE OSTI2017

Smart Manufacturing Technologies and Data Analytics for Improving Energy Efficiency in Industrial Energy Systems

Smart manufacturing and advanced data analytics can help the manufacturing sector unlock energy efficiency from the equipment level to the entire manufacturing facility and the whole supply chain. These technologies can make manufacturing industries more competitive, with intelligent communication systems, real-time energy savings, and increased energy productivity. Smart manufacturing can give all employees in an organization the actionable information they need, when they need it, so that each person can contribute to the optimal operation of the corporation through informed, data-driven decision making. This paper examines smart technologies and data analytics approaches for improving energy efficiency and reducing energy costs in process-supporting energy systems. It dives into energy-saving improvement opportunities through smart manufacturing technologies and sophisticated data collection and analysis. The energy systems covered in this paper include those with motors and drives, fans, pumps, air compressors, steam, and process heating.

Nimbalkar, Sachin U.↗

DOE OSTI2017

Smart Manufacturing Technologies and Data Analytics for Improving Energy Efficiency in Industrial Energy Systems

Smart manufacturing and advanced data analytics can help the manufacturing sector unlock energy efficiency from the equipment level to the entire manufacturing facility and the whole supply chain. These technologies can make manufacturing industries more competitive, with intelligent communication systems, real-time energy savings, and increased energy productivity. Smart manufacturing can give all employees in an organization the actionable information they need, when they need it, so that each person can contribute to the optimal operation of the corporation through informed, data-driven decision making. This paper examines smart technologies and data analytics approaches for improving energy efficiency and reducing energy costs in process-supporting energy systems. It dives into energy-saving improvement opportunities through smart manufacturing technologies and sophisticated data collection and analysis. The energy systems covered in this paper include those with motors and drives, fans, pumps, air compressors, steam, and process heating.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1988

Grasping with mechanical intelligence

Many robotic hands have been designed and a number have been built. Because of the difficulty of controlling and using complex hands, which usually have nine or more degrees of freedom, the simple one- or two-degree-of-freedom gripper is still the most common robotic end effector. A new category of device is presented: a medium-complexity end effector. With three to five degrees of freedom, such a tool is much easier to control and use, as well as more economical, compact and lightweight than complex hands. In order to increase the versatility, it was necessary to identify grasping primitives and to implement them in the mechanism. In addition, power and enveloping grasps are stressed over fingertip and precision grasps. The design is based upon analysis of object apprehension types, requisite characteristics for active sensing, and a determination of necessary environmental interactions. Contained are the general concepts necessary to the design of a medium-complexity end effector, an analysis of typical performance, and a computer simulation of a grasp planning algorithm specific to this type of mechanism. Finally, some details concerning the UPenn Hand-a tool designed for the research laboratory-are presented.

Ulrich, Nathan Thatcher↗

DOE OSTI2017

A Bernoulli Gaussian Watermark for Detecting Integrity Attacks in Control Systems

We examine the merit of Bernoulli packet drops in actively detecting integrity attacks on control systems. The aim is to detect an adversary who delivers fake sensor measurements to a system operator in order to conceal their effect on the plant. Physical watermarks, or noisy additive Gaussian inputs, have been previously used to detect several classes of integrity attacks in control systems. In this paper, we consider the analysis and design of Gaussian physical watermarks in the presence of packet drops at the control input. On one hand, this enables analysis in a more general network setting. On the other hand, we observe that in certain cases, Bernoulli packet drops can improve detection performance relative to a purely Gaussian watermark. This motivates the joint design of a Bernoulli-Gaussian watermark which incorporates both an additive Gaussian input and a Bernoulli drop process. We characterize the effect of such a watermark on system performance as well as attack detectability in two separate design scenarios. Here, we consider a correlation detector for attack recognition. We then propose efficiently solvable optimization problems to intelligently select parameters of the Gaussian input and the Bernoulli drop process while addressing security and performance trade-offs. Finally, we provide numerical results which illustrate that a watermark with packet drops can indeed outperform a Gaussian watermark.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Assessment of Condition Monitoring Methods and Technologies for Inservice Inspection and Testing of Nuclear Power Plant Components

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to explore the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components. The advanced technologies considered in this work are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), physics-based models, and digital twins (DT). The interest in the application of advanced technologies for condition monitoring in nuclear power plants continues to grow, and current and future licensees are expected to implement advanced technologies as part of their inservice inspection (ISI) and inservice testing (IST) programs. This report delineates the outcomes of an exploratory investigation into the implementation of advanced condition monitoring technologies to address ISI and IST requirements. A thorough review was conducted of the existing regulatory requirements for ISI and IST, along with an analysis of associated industry practices. Additionally, a state-of-the-art assessment was performed on advanced condition monitoring technologies frequently employed in non-nuclear sectors. This research incorporated two nuclear-specific case studies to illustrate the application of these technologies within the current nuclear fleet. The report provides an exhaustive discussion on the technical challenges, considerations, and opportunities associated with the deployment of advanced condition monitoring technologies. The following are key considerations in the application of advanced technologies for the ISI and IST of nuclear power plant components: • Developing adequate verification and validation procedures to confirm the functional and non-functional requirements, • Developing technical capabilities to conduct real-time asset condition monitoring, • Establishing guidance and protocol for modeling and simulation tools to continuously meet regulatory requirements, • Addressing trustworthiness, explainability, and interpretability of ML/AI methods, • Evaluating maintenance activities to maintain an adequate safety margin and avoid undesirable conditions, • Establishing cybersecure condition monitoring programs associated with a computer-based software system, and • Establishing standardized evaluation metrics for advanced condition monitoring programs. Interest in the use of advanced technologies for condition monitoring in ISI and IST programs continues to grow, and the technology is expected to experience rapid and wide industry adoption in the near future. Adoption of advanced technologies for condition monitoring could have novel and unique impacts on regulatory activities associated with ISI and IST programs. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of ISI and IST programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1987

Development of Moire machine vision

Three dimensional perception is essential to the development of versatile robotics systems in order to handle complex manufacturing tasks in future factories and in providing high accuracy measurements needed in flexible manufacturing and quality control. A program is described which will develop the potential of Moire techniques to provide this capability in vision systems and automated measurements, and demonstrate artificial intelligence (AI) techniques to take advantage of the strengths of Moire sensing. Moire techniques provide a means of optically manipulating the complex visual data in a three dimensional scene into a form which can be easily and quickly analyzed by computers. This type of optical data manipulation provides high productivity through integrated automation, producing a high quality product while reducing computer and mechanical manipulation requirements and thereby the cost and time of production. This nondestructive evaluation is developed to be able to make full field range measurement and three dimensional scene analysis.

Harding, Kevin G.↗

NASA NTRS1988

Telerobotics for the efficient utilization of space

Telerobotics is a new technology that is being developed to perform remote manipulation in space. It seeks to accommodate the needs arising from a rapidly increasing investment in space assets and from the large costs deriving from human operations in space, especially EVA. Telerobotics applies advanced automation and artificial intelligence technology and combines the immediacy of execution of teleoperation (the replication at a distance of the physical motions of the operator) with the efficiency and precision of supervised robotic autonomy (the accomplishment of assignments through machine task decomposition and interpretation of sensor information). One of the key goals of this approach is the achievement of a seamless transition between teleoperation and supervised autonomy. The basis of this technology and of the NASA telerobotics research and development activities are described. They consist of five elements: core research, laboratory integration testbed, mission analysis, application demonstrations and flight experiments. Current advances, both in research and system integration are reported including the first integration of the laboratory testbed and quantitative comparisons obtained in a buoyant facility. Planned future goals are outlined.

Varsi, Giulio↗

DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

42 ENGINEERING↗

NASA NTRS2016

Semantic Representation and Scale-Up of Integrated Air Traffic Management Data

Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.

air traffic management↗

NASA NTRS2005

Pods: a Powder Delivery System for Mars In-situ Organic, Mineralogic and Isotopic Analysis Instruments

Many Mars in situ instruments require fine-grained high-fidelity samples of rocks or soil. Included are instruments for the determination of mineralogy as well as organic and isotopic chemistry. Powder can be obtained as a primary objective of a sample collection system (e.g., by collecting powder as a surface is abraded by a rotary abrasion tool (RAT)), or as a secondary objective (e.g, by collecting drill powder as a core is drilled). In the latter case, a properly designed system could be used to monitor drilling in real time as well as to deliver powder to analytical instruments which would perform complementary analyses to those later performed on the intact core. In addition, once a core or other sample is collected, a system that could transfer intelligently collected subsamples of power from the intact core to a suite of analytical instruments would be highly desirable. We have conceptualized, developed and tested a breadboard Powder Delivery System (PoDS) intended to satisfy the collection, processing and distribution requirements of powder samples for Mars in-situ mineralogic, organic and isotopic measurement instruments.

Saha, C. P.↗

DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗