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At least 37 records · Page 2

Development and Evaluation of Fault-Tolerant Flight Control Systems

The research is concerned with developing a new approach to enhancing fault tolerance of flight control systems. The original motivation for fault-tolerant control comes from the need for safe operation of control elements (e.g. actuators) in the event of hardware failures in high reliability systems. One such example is modem space vehicle subjected to actuator/sensor impairments. A major task in flight control is to revise the control policy to balance impairment detectability and to achieve sufficient robustness. This involves careful selection of types and parameters of the controllers and the impairment detecting filters used. It also involves a decision, upon the identification of some failures, on whether and how a control reconfiguration should take place in order to maintain a certain system performance level. In this project new flight dynamic model under uncertain flight conditions is considered, in which the effects of both ramp and jump faults are reflected. Stabilization algorithms based on neural network and adaptive method are derived. The control algorithms are shown to be effective in dealing with uncertain dynamics due to external disturbances and unpredictable faults. The overall strategy is easy to set up and the computation involved is much less as compared with other strategies. Computer simulation software is developed. A serious of simulation studies have been conducted with varying flight conditions.

Song, Yong D.↗

Detection of Bi-Directionality in Strain-Gage Balance Calibration Data

An indicator variable was developed for both visualization and detection of bi-directionality in wind tunnel strain-gage balance calibration data. First, the calculation of the indicator variable is explained in detail. Then, a criterion is discussed that may be used to decide which gage outputs of a balance have bi- directional behavior. The result of this analysis could be used, for example, to justify the selection of certain absolute value or other even function terms in the regression model of gage outputs whenever the Iterative Method is chosen for the balance calibration data analysis. Calibration data of NASA s MK40 Task balance is analyzed to illustrate both the calculation of the indicator variable and the application of the proposed criterion. Finally, bi directionality characteristics of typical multi piece, hybrid, single piece, and semispan balances are determined and discussed.

Ulbrich, Norbert↗

Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings

Abstract We develop a machine learning approach to detect and discriminate elephants from other species, and to recognise important behaviours such as running and rumbling, based only on seismic data generated by the animals. We demonstrate our approach using data acquired in the Kenyan savanna, consisting of 8000 h seismic recordings and 250 k camera trap pictures. Our classifiers, different convolutional neural networks trained on seismograms and spectrograms, achieved 80%–90% balanced accuracy in detecting elephants up to 100 m away, and over 90% balanced accuracy in recognising running and rumbling behaviours from the seismic data. We release the dataset used in this study: SeisSavanna represents a unique collection of seismic signals with the associated wildlife species and behaviour. Our results suggest that seismic data offer substantial benefits for monitoring wildlife, and we propose to further develop our methods using dense arrays that could result in a seismic shift for wildlife monitoring.

Szenicer, Alexandre↗

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗

A 100Mbps coherent optical link demonstration using frequency stabilized solid state lasers

The design, implementation, and performance evaluation of a 100Mbps binary phase shift keying (BPSK) coherent optical link using frequency stabilized solid state lasers is described. In this demonstration, the transmitted data is binary phase modulated at 100Mbps using an external resonant cavity phase modulator. At the receiving end, the signal is detected using a balanced heterodyne detector. An auxiliary frequency tracking loop (FTL) is used to help the initial frequency acquisition before handing over to the carrier phase tracking loop. The FTL is implemented using a delay line discriminator, and the detected frequency error is fed back to the local oscillator laser to close the tracking loop. Phase coherent reception is achieved by synchronizing the carrier phase using a Costas tracking loop. The demonstration showed that a high data rate phase coherent optical link can be implemented using frequency stabilized solid state lasers.

Chen, CHIEN-C.↗

Detection and Use of Load and Gage Output Repeats of Wind Tunnel Strain-Gage Balance Data

Criteria are discussed that may be used for the detection of load and gage output repeats of wind tunnel strain-gage balance data. First, empirical thresholds are introduced that help determine if the loads or electrical outputs of a pair of balance calibration or check load data points match. A threshold of 0.01 percent of the load capacity is suggested for the identification of matching loads. Similarly, a threshold of 0.1 microV/V is recommended for the identification of matching electrical outputs. Two examples for the use of load and output repeats are discussed to illustrate benefits of the implementation of a repeat point detection algorithm in a balance data analysis software package. The first example uses the suggested load threshold to identify repeat data points that may be used to compute pure errors of the balance loads. This type of analysis may reveal hidden data quality issues that could potentially be avoided by making calibration process improvements. The second example uses the electrical output threshold for the identification of balance fouling. Data from the calibration of a six-component force balance is used to illustrate the calculation of the pure error of the balance loads.

strain-gage balance↗

FL‐ADS: Federated learning anomaly detection system for distributed energy resource networks

Abstract With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.

Purohit, Shaurya [Iowa State University Ames Iowa ↗

A directed search for extraterrestrial laser signals

The focus of NASA's Search for Extraterrestrial Intelligence (SETI) Program is on microwave frequencies, where receivers have the best sensitivities for the detection of narrowband signals. Such receivers, when coupled to existing radio telescopes, form an optimal system for broad area searches over the sky. For a directed search, however, such as toward specific stars, calculations show that infrared wavelengths can be equally as effective as radio wavelengths for establishing an interstellar communication link. This is true because infrared telescopes have higher directivities (gains) that effectively compensate for the lower sensitivities of infrared receivers. The result is that, for a given level of transmitted power, the signal to noise ratio for communications is equally as good at infrared and radio wavelengths. It should also be noted that the overall sensitivities of both receiver systems are quite close to their respective fundamental limits: background thermal noise for the radio frequency system and quantum noise for the infrared receiver. Consequently, the choice of an optimum communication frequency may well be determined more by the achievable power levels of transmitters rather than the ultimate sensitivities of receivers at any specific frequency. In the infrared, CO2 laser transmitters with power levels greater than 1 MW can already be built on Earth. For a slightly more advanced civilization, a similar but enormously more powerful laser may be possible using a planetary atmosphere rich in CO2. Because of these possibilities and our own ignorance of what is really the optimum search frequency, a search for narrowband signals at infrared frequencies should be a part of a balanced SETI Program. Detection of narrowband infrared signals is best done with a heterodyne receiver functionally identical to a microwave spectral line receiver. We have built such a receiver for the detection of CO2 laser radiation at wavelengths near 10 microns. The spectrometer uses a high-speed HgCdTe diode as the photomixer and a small CO2 laser as the local oscillator. Output signals in the intermediate frequency range 0.1-2.6 GHz are processed by a 1000-channel acousto-optic signal processor. The receiver is being used on a 1.5-m telescope on Mt. Wilson to survey a selected sample of 150 nearby stars. The current status of the work is discussed along with future project plans.

Betz, A.↗

Automating Detection and Diagnosis of Faults, Failures, and Underperformance in PV Plants

The project developed hybrid physics-based and machine-learning methods for near-real-time detection of balance-of-system faults (e.g., string, combiner, and tracker outages) in utility-scale Photovoltaic plants, achieving over 50% true positive rates with under 10% false positives and significantly reducing engineering setup time. In the extended phase, the scope expanded to plant-level underperformance analysis and industry benchmarking through the SUPER.epri.com platform. SUPER standardizes data processing and performance metrics across more than 9 GWac and 120+ plants, enabling robust comparisons and insights into loss rates, inverter downtime, and capacity degradation.

14 SOLAR ENERGY↗

System for interference signal nulling by polarization adjustment

A receiving system for automatically selecting a desired one of two approximately orthogonally polarized signals occupying the same bandwidth, is described. Received signals are provided by any orthomode antenna system at a pair of output ports, i.e., right hand and left hand circular polarizations or two linear polarizations. The received signals are then applied to the inputs of a hybrid junction to produce sum and difference signals. The resulting sum signal at one output port comprises components of the undesired one of two orthogonally polarized signals and is used to coherently detect and dynamically balance out the undesired signal components that are included at the difference signal port. The desired one of two orthogonally polarized signals is thereby provided at the difference port of the hybrid junction. Feedback loops are used to effect dynamic balancing.

Ohlson, J. E.↗

A robust CELP coder with source-dependent channel coding

A CELP coder using Source Dependent Channel Encoding (SDCE) for optimal channel error protection is introduced. With SDCE, each of the CELP parameters are encoded by minimizing a perceptually meaningful error criterion under prevalent channel conditions. Unlike conventional channel coding schemes, SDCE allows for optimal balance between error detection and correction. The experimental results show that the CELP system is robust under various channel bit error rates and displays a graceful degradation in SSNR as the channel error rate increases. This is a desirable property to have in a coder since the exact channel conditions cannot usually be specified a priori.

Sukkar, Rafid A.↗

Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs

Crewmembers returning from long-duration space flight face significant challenges due to the microgravity-induced inappropriate adaptations in balance/ sensorimotor function. The Neuroscience Laboratory at JSC is developing a method based on stochastic resonance to enhance the brain s ability to detect signals from the balance organs of the inner ear and use them for rapid improvement in balance skill, especially when combined with balance training exercises. This method involves a stimulus delivery system that is wearable/portable providing imperceptible electrical stimulation to the balance organs of the human body. Stochastic resonance (SR) is a phenomenon whereby the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular non-zero level of noise. This phenomenon of SR is based on the concept of maximizing the flow of information through a system by a non-zero level of noise. Application of imperceptible SR noise coupled with sensory input in humans has been shown to improve motor, cardiovascular, visual, hearing, and balance functions. SR increases contrast sensitivity and luminance detection; lowers the absolute threshold for tone detection in normal hearing individuals; improves homeostatic function in the human blood pressure regulatory system; improves noise-enhanced muscle spindle function; and improves detection of weak tactile stimuli using mechanical or electrical stimulation. SR noise has been shown to improve postural control when applied as mechanical noise to the soles of the feet, or when applied as electrical noise at the knee and to the back muscles.

Bloomberg, Jacob↗

Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs

Crewmembers returning from long-duration space flight face significant challenges due to the microgravity-induced inappropriate adaptations in balance/sensorimotor function. The Neuroscience Laboratory at JSC is developing a method based on stochastic resonance to enhance the brain's ability to detect signals from the balance organs of the inner ear and use them for rapid improvement in balance skill, especially when combined with balance training exercises. This method involves a stimulus delivery system that is wearable/portable and provides imperceptible electrical stimulation to the balance organs of the human body. Stochastic resonance (SR) is a phenomenon whereby the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular non-zero level of noise. This phenomenon of SR is based on the concept of maximizing the flow of information through a system by a non-zero level of noise. Application of imperceptible SR noise coupled with sensory input in humans has been shown to improve motor, cardiovascular, visual, hearing, and balance functions. SR increases contrast sensitivity and luminance detection; lowers the absolute threshold for tone detection in normal hearing individuals; improves homeostatic function in the human blood pressure regulatory system; improves noise-enhanced muscle spindle function; and improves detection of weak tactile stimuli using mechanical or electrical stimulation. SR noise has been shown to improve postural control when applied as mechanical noise to the soles of the feet, or when applied as electrical noise at the knee and to the back muscles. SR using imperceptible stochastic electrical stimulation of the vestibular system (stochastic vestibular stimulation, SVS) applied to normal subjects has shown to improve the degree of association between the weak input periodic signals introduced via venous blood pressure receptors and the heart-rate responses. Also, application of SVS over 24 hours improves the long-term heart-rate dynamics and motor responsiveness as indicated by daytime trunk activity measurements in patients with multi-system atrophy, Parkinson s disease, or both, including patients who were unresponsive to standard therapy for Parkinson s disease. Recent studies conducted at the NASA JSC Neurosciences Laboratories showed that imperceptible SVS, when applied to normal young healthy subjects, leads to significantly improved balance performance during postural disturbances on unstable compliant surfaces. These studies have shown the benefit of SR noise characteristic optimization with imperceptible SVS in the frequency range of 0-30 Hz, and amplitudes of stimulation have ranged from 100 to 400 microamperes.

Bloomberg, Jacob↗

Spectral attenuation coefficients from measurements of light transmission in bare ice on the Greenland Ice Sheet

Light transmission into bare glacial ice affects surface energy balance, biophotochemistry, and light detection and ranging (lidar) laser elevation measurements but has not previously been reported for the Greenland Ice Sheet. We present measurements of spectral transmittance at 350–900 nm in bare glacial ice collected at a field site in the western Greenland ablation zone (67.15∘ N, 50.02∘ W). Empirical irradiance attenuation coefficients at 350–750 nm are ∼ 0.9–8.0 m−1 for ice at 12–124 cm depth. The absorption minimum is at ∼ 390–397 nm, in agreement with snow transmission measurements in Antarctica and optical mapping of deep ice at the South Pole. From 350–530 nm, our empirical attenuation coefficients are nearly 1 order of magnitude larger than theoretical values for optically pure ice. The estimated absorption coefficient at 400 nm suggests the ice volume contained a light-absorbing particle concentration equivalent to ∼ 1–2 parts per billion (ppb) of black carbon, which is similar to pre-industrial values found in remote polar snow. The equivalent mineral dust concentration is ∼ 300–600 ppb, which is similar to values for Northern Hemisphere warm periods with low aeolian activity inferred from ice cores. For a layer of quasi-granular white ice (weathering crust) extending from the surface to ∼ 10 cm depth, attenuation coefficients are 1.5 to 4 times larger than for deeper bubbly ice. Owing to higher attenuation in this layer of near-surface granular ice, optical penetration depth at 532 nm is 14 cm (20 %) lower than asymptotic attenuation lengths for optically pure bubbly ice. In addition to the traditional concept of light scattering on air bubbles, our results imply that the granular near-surface ice microstructure of weathering crust is an important control on radiative transfer in bare ice on the Greenland Ice Sheet ablation zone, and we provide new values of flux attenuation, absorption, and scattering coefficients to support model development and validation.

IceSat-2↗

Evaluation, Analysis, and Application of Internal Strain-Gage Balance Data

Experimental processes, analytical methods, and numerical algorithms are described that may be used to predict the forces and moments of an internal strain–gage balance during a wind tunnel test. First, the control volume model of a strain–gage balance and the concepts of load state, load space, and output space are introduced. These important abstractions provide a better understanding of fundamental characteristics of different balance load prediction approaches. Then, the description of strain–gage balance data and the definition of the primary bridge sensitivity are discussed. Afterwards, basic elements of the calibration of a typical six–component balance are reviewed. Two fundamentally different balance load prediction methods, the processing of check loads, and related topics are also discussed. Three real–world balance data examples are reviewed in great detail to illustrate typical analysis results for a variety of strain–gage balance designs. Finally, important observations are summarized and recommendations are provided. – Additional information and detailed mathematical derivations can be found in the appendices of the document. They include the following topics: balance terminology, definitions of important statistical metrics, balance axis system conventions, balance load transformations, the combined load diagram, electrical output format options, bi–directional output characteristics, determination of the natural zeros, derivation of two balance load prediction methods, description of two tare load iteration algorithms, modeling of balance temperature effects, basics of three–component moment balances, definition of the percent contribution, detection of linear and near–linear dependencies in balance calibration data, a regression model search algorithm, balance interactions, and other related information.

strain-gage balance↗

The sunspot chromosphere-corona transition region

Data from simultaneous readings of the line intensities, Doppler shifts, and line widths of a sunspot ion emission lines between 1170-1700 A are reported. Subsonic and supersonic flows were observed in the same line of sight above the umbra. A reduction of coronal plasma over sunspots with an electron temperature exceeding 1,000,000 K was confirmed, concurrent with enhanced emission from the transition region plasma in the temperature range 200,000-1,000,000 K. The differential emission measure is noted to have been caused to shift because of the enhancement of the transition region plasma emission, where radiative losses dominated the energy balance. Calculations of the energy balance also indicated that a detected divergence in the enthalpy flux for the umbral downflows could balance the radiative losses in the electron temperature range 30,000-200,000 K.

Nicolas, K. R.↗