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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

A knowledge-based system for controlling automobile traffic

Transportation network capacity variations arising from accidents, roadway maintenance activity, and special events as well as fluctuations in commuters' travel demands complicate traffic management. Artificial intelligence concepts and expert systems can be useful in framing policies for incident detection, congestion anticipation, and optimal traffic management. This paper examines the applicability of intelligent route guidance and control as decision aids for traffic management. Basic requirements for managing traffic are reviewed, concepts for studying traffic flow are introduced, and mathematical models for modeling traffic flow are examined. Measures for quantifying transportation network performance levels are chosen, and surveillance and control strategies are evaluated. It can be concluded that automated decision support holds great promise for aiding the efficient flow of automobile traffic over limited-access roadways, bridges, and tunnels.

Maravas, Alexander↗
NASA NTRS1998

Buffer Management Simulation in ATM Networks

This paper presents a simulation of a new dynamic buffer allocation management scheme in ATM networks. To achieve this objective, an algorithm that detects congestion and updates the dynamic buffer allocation scheme was developed for the OPNET simulation package via the creation of a new ATM module.

simulations Asynchronous Transfer Mode ATM Switch ↗
DOE OSTI2021

Two-color, intracavity pump–probe, cavity ringdown spectroscopy

Here, we report a proof-of-principle demonstration of intracavity pump–probe, cavity ringdown (CRD) detection in a three-mirror, traveling-wave cavity. With cavity-enhanced pump power and probe absorption path length, the technique is a generally applicable, high-sensitivity, high-selectivity detection method. In our experiments, the pump radiation is switched off during every other probe ringdown, which allows uncorrelated measurements of analyte and background cavity decay rates. The net, two-color signal from the difference between the pump-on and pump-off decay rates is immune to empty-CRD drifts and spectral overlaps from non-target molecular transitions. The immunity to the ringdown drifts allows longer signal-averaging and, thus, higher detection sensitivity. The ability to compensate for the background absorption enhances the detection selectivity in spectrally congested regions. Our technique is well-suited for trace-detection in the mid-IR region, where pump–probe schemes based on strong rovibrational transitions can be applied. In this work, two-color CRD detection is implemented on a ladder-type, three-level system based on the N 2 O, ν 3 = 1 ← 0, P(19) (pump) and ν 3 = 2 ← 1, R(18) (probe), rovibrational transitions. By frequency-locking two-quantum cascade lasers to the p-polarization (pump, Finesse = 5280) and s-polarization (probe, Finesse = 67 700) cavity modes, we achieve high intracavity pump power (36 W) and high probe ringdown rates (>2 kHz). The observed two-color spectra are simulated by a density-matrix, three-level system model that is solved under the constraints of the cavity resonance conditions. In addition to its background compensation capability, experimental flexibility in the selection of pump–probe schemes and signal insensitivity to intracavity laser power are further features that enhance the utility of our technique for mid-IR trace-detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Estimation of Arrivals on Green at Signalized Intersections Using Stop-Bar Video Detection

Across the world, traffic congestion is increasing with alarming rapidity. Traffic signal control effectiveness, in coordinated networks, is often investigated in relation to the type of vehicle arrivals at the signalized intersections. Recently, several transportation agencies have switched from traditional loop detectors to video detection. When video cameras are accompanied by computer vision, one can extract more information about traffic “dynamics” than by using traditional inductive loop detectors. Collecting arrival times of multiple vehicles after the first arrival at the stop-bar detector might be challenging when using inductive loop detectors (since after the first arrival, detector status is always occupied). However, emerging video detection systems allow tracking of each vehicle’s entrance time in the detection zone, departure time from the detection zone, and the type of vehicle. This information can be used to estimate vehicular arrival and departure times, which then can be fed into machine learning algorithms to estimate arrivals on green (AOG). However, such research ideas have not been documented so far. Thus, this paper presents an estimation model for AOG, which was developed using multigene genetic programming. A robust experimental dataset was collected from a highly calibrated and validated microsimulation model of an 11-intersection corridor in Chattanooga, TN. The results of the model’s performance analysis showed the high accuracy of the training-, testing-, and validation datasets. The practical benefit of this model is that it can be applied to estimate arrival types at intersections where only stop-bar video detection exists.

Engineering↗
DOE OSTI2021

Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. Here, the proposed method was tested with 34 traffic congestion cases in the Knoxville, Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2001

Automation of Data Traffic Control on DSM Architecture

The design of distributed shared memory (DSM) computers liberates users from the duty to distribute data across processors and allows for the incremental development of parallel programs using, for example, OpenMP or Java threads. DSM architecture greatly simplifies the development of parallel programs having good performance on a few processors. However, to achieve a good program scalability on DSM computers requires that the user understand data flow in the application and use various techniques to avoid data traffic congestions. In this paper we discuss a number of such techniques, including data blocking, data placement, data transposition and page size control and evaluate their efficiency on the NAS (NASA Advanced Supercomputing) Parallel Benchmarks. We also present a tool which automates the detection of constructs causing data congestions in Fortran array oriented codes and advises the user on code transformations for improving data traffic in the application.

Frumkin, Michael↗
NASA NTRS2000

Automatic Data Traffic Control on DSM Architecture

We study data traffic on distributed shared memory machines and conclude that data placement and grouping improve performance of scientific codes. We present several methods which user can employ to improve data traffic in his code. We report on implementation of a tool which detects the code fragments causing data congestions and advises user on improvements of data routing in these fragments. The capabilities of the tool include deduction of data alignment and affinity from the source code; detection of the code constructs having abnormally high cache or TLB misses; generation of data placement constructs. We demonstrate the capabilities of the tool on experiments with NAS parallel benchmarks and with a simple computational fluid dynamics application ARC3D.

Frumkin, Michael↗
NASA NTRS2006

Improving UDP/IP Transmission Without Increasing Congestion

Datagram Retransmission (DGR) is a computer program that, within certain limits, ensures the reception of each datagram transmitted under the User Datagram Protocol/Internet Protocol. [User Datagram Protocol (UDP) is considered unreliable because it does not involve a reliability-ensuring connection-initiation dialogue between sender and receiver. UDP is well suited to issuing of many small messages to many different receivers.] Unlike prior software for ensuring reception of UDP datagrams, DGR does not contribute to network congestion by retransmitting data more frequently as an ever-increasing number of messages and acknowledgements is lost. Instead, DGR does just the opposite: DGR includes an adaptive timeout-interval- computing component that provides maximum opportunity for reception of acknowledgements, minimizing retransmission. By monitoring changes in the rate at which message-transmission transactions are completed, DGR detects changes in the level of congestion and responds by imposing varying degrees of delay on the transmission of new messages. In addition, DGR maximizes throughput by not waiting for acknowledgement of a message before sending the next message. All DGR communication is asynchronous, to maximize efficient utilization of network connections. DGR manages multiple concurrent datagram transmission and acknowledgement conversations.

Burleigh, Scott↗
DOE OSTI2022

A data-centric weak supervised learning for highway traffic incident detection

Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models-random forest, k-nearest neighbors, and a support vector machine ensemble-and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Finally, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

Diagnostic accuracy of an ultrasonic multiple transducer cardiac imaging system

An ultrasonic multiple-transducer imaging system for intracardiac structure visualization is developed in order to simplify visualization of the human heart in vivo without radiation hazard or invasion of the body. Results of the evaluation of the diagnostic accuracy of the devised system in a clinical setting for adult patients are presented and discussed. Criteria are presented for recognition of mitral valva prolapse, mitral stenosis, pericardial effusion, atrial septal defect, and left ventricular dyssynergy. The probable cause for false-positive and false-negative diagnoses is discussed. However, hypertrophic myopathy and congestive myopathy were unable to be detected. Since only qualitative criteria were used, it was not possible to differentiate patients with left ventricular volume overload from patients without cardiac pathology.

Popp, R. L.↗
NASA NTRS2006

Implantable Wireless MEMS Sensors for Medical Uses

Sensors designed and fabricated according to the principles of microelectromechanical systems (MEMS) are being developed for several medical applications in outer space and on Earth. The designs of these sensors are based on a core design family of pressure sensors, small enough to fit into the eye of a needle, that are fabricated by a "dissolved wafer" process. The sensors are expected to be implantable, batteryless, and wireless. They would be both powered and interrogated by hand-held radio transceivers from distances up to about 6 in. (about 15 cm). One type of sensor would be used to measure blood pressure, particularly for congestive heart failure. Another type would be used to monitor fluids in patients who have hydrocephalus (high brain pressure). Still other types would be used to detect errors in delivery of drugs and to help patients having congestive heart failure.

Chimbayo, Alexander↗
NASA NTRS2016

Conflict Resolution for Wind-Optimal Aircraft Trajectories in North Atlantic Oceanic Airspace with Wind Uncertainties

Air traffic in the North Atlantic oceanic airspace (NAT) experiences very strong winds caused by jet streams. Flying wind-optimal trajectories increases individual flight efficiency, which is advantageous when operating in the NAT. However, as the NAT is highly congested during peak hours, a large number of potential conflicts between flights are detected for the sets of wind-optimal trajectories. Conflict resolution performed at the strategic level of flight planning can significantly reduce the airspace congestion. However, being completed far in advance, strategic planning can only use predicted environmental conditions that may significantly differ from the real conditions experienced further by aircraft. The forecast uncertainties result in uncertainties in conflict prediction, and thus, conflict resolution becomes less efficient. This work considers wind uncertainties in order to improve the robustness of conflict resolution in the NAT. First, the influence of wind uncertainties on conflict prediction is investigated. Then, conflict resolution methods accounting for wind uncertainties are proposed.

wind uncertainties↗
DOE OSTI2024

Mid-infrared trace detection with parts-per-quadrillion quantitation accuracy: Expanding frontiers of radiocarbon sensing

Detection sensitivity is a critical characteristic to consider during selection of spectroscopic techniques. However, high sensitivity alone is insufficient for spectroscopic measurements in spectrally congested regions. Two-color cavity ringdown spectroscopy (2C-CRDS), based on intra-cavity pump–probe detection, simultaneously achieves high detection sensitivity and selectivity. This combination enables mid-infrared detection of radiocarbon dioxide ( 14 CO 2 ) molecules in room-temperature CO 2 samples, with 1.4 parts-per-quadrillion (ppq, 10 − 15 ) sensitivity (average measurement precision) and 4.6-ppq quantitation accuracy (average calibrated measurement error for 21 samples from four separate trials) demonstrated on samples with 14 C/C up to ∼ 1.5 × natural abundance ( ∼ 1,800 ppq). These highly reproducible measurements, which are the most sensitive and quantitatively accurate in the mid-infrared, are accomplished despite the presence of orders-of-magnitude stronger, one-photon signals from other CO 2 isotopologues. This is a major achievement in laser spectroscopy. A room-temperature-operated, compact, and low-cost 2C-CRDS sensor for 14 CO 2 benefits a wide range of scientific fields that utilize 14 C for dating and isotope tracing, most notably atmospheric 14 CO 2 monitoring to track CO 2 emissions from fossil fuels. The 2C-CRDS technique significantly enhances the general utility of high-resolution mid-infrared detection for analytical measurements and fundamental chemical dynamics studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1995

Reliable multicast protocol specifications flow control and NACK policy

This appendix presents the flow and congestion control schemes recommended for RMP and a NACK policy based on the whiteboard tool. Because RMP uses a primarily NACK based error detection scheme, there is no direct feedback path through which receivers can signal losses through low buffer space or congestion. Reliable multicast protocols also suffer from the fact that throughput for a multicast group must be divided among the members of the group. This division is usually very dynamic in nature and therefore does not lend itself well to a priori determination. These facts have led the flow and congestion control schemes of RMP to be made completely orthogonal to the protocol specification. This allows several differing schemes to be used in different environments to produce the best results. As a default, a modified sliding window scheme based on previous algorithms are suggested and described below.

Callahan, John R.↗
NASA NTRS2005

Analysis of Aircraft Clusters to Measure Sector-Independent Airspace Congestion

The Distributed Air/Ground Traffic Management (DAG-TM) concept of operations* permits appropriately equipped aircraft to conduct Free Maneuvering operations. These independent aircraft have the freedom to optimize their trajectories in real time according to user preferences; however, they also take on the responsibility to separate themselves from other aircraft while conforming to any local Traffic Flow Management (TFM) constraints imposed by the air traffic service provider (ATSP). Examples of local-TFM constraints include temporal constraints such as a required time of arrival (RTA), as well as spatial constraints such as regions of convective weather, special use airspace, and congested airspace. Under current operations, congested airspace typically refers to a sector(s) that cannot accept additional aircraft due to controller workload limitations; hence Dynamic Density (a metric that is indicative of controller workload) can be used to quantify airspace congestion. However, for Free Maneuvering operations under DAG-TM, an additional metric is needed to quantify the airspace congestion problem from the perspective of independent aircraft. Such a metric would enable the ATSP to prevent independent aircraft from entering any local areas of congestion in which the flight deck based systems and procedures may not be able to ensure separation. This new metric, called Gaggle Density, offers the ATSP a mode of control to regulate normal operations and to ensure safety and stability during rare-normal or off-normal situations (e.g., system failures). It may be difficult to certify Free Maneuvering systems for unrestricted operations, but it may be easier to certify systems and procedures for specified levels of Gaggle Density that could be monitored by the ATSP, and maintained through relatively minor flow-rate (RTA type) restrictions. Since flight deck based separation assurance is airspace independent, the challenge is to measure congestion independent of sector boundaries. Figure 1 , reproduced from Ref. 1, depicts an example traffic situation. When the situation is analyzed by sector boundaries (left side of figure), a Dynamic Density metric would identify excessive congestion in the central sector. When the same traffic situation is analyzed independent of sector boundaries (right side of figure), a Gaggle Density metric would identify congestion in two dynamically defined areas covering portions of several sectors. The first step towards measuring airspace-independent congestion is to identify aircraft clusters, i.e., groups of closely spaced aircraft. The objective of this work is to develop techniques to detect and classify clusters of aircraft.

Bilimoria, Karl D.↗
NASA NTRS2018

Effects of Aircraft Health on Airspace Safety

This manuscript investigates the effects of aircraft health on the surrounding airspace, and proposes a methodology to understand how different aircraft-level faults (system faults, communication faults, etc.) can adversely affect the safety of the airspace, and qualitatively assess the impact of such faults on airspace safety metrics (such as congestion, controller/pilot workload, etc.). The topic of systems health management deals with continuously monitoring the performance of an engineering system, identifying and detecting the presence of faults, predicting the growth/progression of faults, computing the remaining useful life, and aiding online decision-making for the robust, continued operation of such engineering systems. The topic of real-time airspace modeling and safety analysis deals with defining and computing safety metrics for airspace operations in order to support risk-informed decision-making activities for various airspace entities including pilots, air traffic controllers, airlines, etc. This report presents recent research efforts that focus on combining multiple aspects of the aforementioned topics, and investigates the impact of aircraft-level faults on the airspace safety

Aircraft Health↗
NASA NTRS1972

Development of simulation techniques suitable for the analysis of air traffic control situations and instrumentation

A terminal area simulation is described which permits analysis and synthesis of current and advanced air traffic management system configurations including ground and airborne instrumentation and new and modified aircraft characteristics. Ground elements in the simulation include navigation aids, surveillance radars, communication links, air-route structuring, ATC procedures, airport geometries and runway handling constraints. Airborne elements include traffic samples with individual aircraft performance and operating characteristics and aircraft navigation equipment. The simulation also contains algorithms for conflict detection, conflict resolution, sequencing and pilot-controller data links. The simulation model is used to determine the sensitivities of terminal area traffic flow, safety and congestion to aircraft performance characteristics, avionics systems, and other ATC elements.

Source record↗