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Results for “identification”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Linear system identification - The application of Lion's identification scheme to a third order system with noisy input-output measurements

A linear system identification technique developed by Lion is adapted for use on a third-order system with six unknown parameters and noisy input-output measurements. A digital computer is employed so that rapid identification takes place with only two state variable filters. Bias in the parameter estimates is partially eliminated by a signal-to-noise ratio testing procedure.

Brown, C. M., Jr.↗
NASA NTRS2005

Multiple Layer Identification Label Using Stacked Identification Symbols

An automatic identification system and method are provided which employ a machine readable multiple layer label. The label has a plurality of machine readable marking layers stacked one upon another. Each of the marking layers encodes an identification symbol detectable using one or more sensing technologies. The various marking layers may comprise the same marking material or each marking layer may comprise a different medium having characteristics detectable by a different sensing technology. These sensing technologies include x-ray, radar, capacitance, thermal, magnetic and ultrasonic. A complete symbol may be encoded within each marking layer or a symbol may be segmented into fragments which are then divided within a single marking layer or encoded across multiple marking layers.

Harry F Schramm↗
NASA NTRS2005

Multiple layer identification label using stacked identification symbols

An automatic identification system and method are provided which employ a machine readable multiple layer label. The label has a plurality of machine readable marking layers stacked one upon another. Each of the marking layers encodes an identification symbol detectable using one or more sensing technologies. The various marking layers may comprise the same marking material or each marking layer may comprise a different medium having characteristics detectable by a different sensing technology. These sensing technologies include x-ray, radar, capacitance, thermal, magnetic and ultrasonic. A complete symbol may be encoded within each marking layer or a symbol may be segmented into fragments which are then divided within a single marking layer or encoded across multiple marking layers.

Schramm, Harry F.↗
NASA NTRS2006

Multiple concurrent recursive least squares identification with application to on-line spacecraft mass-property identification

The present invention is a method for identifying unknown parameters in a system having a set of governing equations describing its behavior that cannot be put into regression form with the unknown parameters linearly represented. In this method, the vector of unknown parameters is segmented into a plurality of groups where each individual group of unknown parameters may be isolated linearly by manipulation of said equations. Multiple concurrent and independent recursive least squares identification of each said group run, treating other unknown parameters appearing in their regression equation as if they were known perfectly, with said values provided by recursive least squares estimation from the other groups, thereby enabling the use of fast, compact, efficient linear algorithms to solve problems that would otherwise require nonlinear solution approaches. This invention is presented with application to identification of mass and thruster properties for a thruster-controlled spacecraft.

Wilson, Edward↗
NASA NTRS2010

A Parameter Identification Method for Helicopter Noise Source Identification and Physics-Based Semi-Empirical Modeling

A new physics-based parameter identification method for rotor harmonic noise sources is developed using an acoustic inverse simulation technique. This new method allows for the identification of individual rotor harmonic noise sources and allows them to be characterized in terms of their individual non-dimensional governing parameters. This new method is applied to both wind tunnel measurements and ground noise measurements of two-bladed rotors. The method is shown to match the parametric trends of main rotor Blade-Vortex Interaction (BVI) noise, allowing accurate estimates of BVI noise to be made for operating conditions based on a small number of measurements taken at different operating conditions.

Greenwood, Eric, II↗
DOE OSTI2014

Concealed identification symbols and nondestructive determination of the identification symbols

The concealing of one or more identification symbols into a target object and the subsequent determination or reading of such symbols through non-destructive testing is described. The symbols can be concealed in a manner so that they are not visible to the human eye and/or cannot be readily revealed to the human eye without damage or destruction of the target object. The identification symbols can be determined after concealment by e.g., the compilation of multiple X-ray images. As such, the present invention can also provide e.g., a deterrent to theft and the recovery of lost or stolen objects.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Milling stability identification using Bayesian machine learning

This paper describes automated identification of the milling stability boundary using Bayesian machine learning and experiments. The Bayesian machine learning process begins with the user’s initial beliefs about milling stability. This “prior” is a distribution that uses all available information, which may be based only on experience or may be informed by physics-based model predictions. Experiments are then completed to update this prior by calculating the “posterior,” a modified probabilistic description of the milling stability limit based on the new information. The approach is demonstrated and results are presented for both numerical and experimental cases.

Karandikar, Jaydeep↗
DOE OSTI2024

Identification of Serine-Containing Microcystins by UHPLC-MS/MS Using Thiol and Sulfoxide Derivatizations and Detection of Novel Neutral Losses

Microcystins (MCs) are hepatotoxic cyclic heptapeptides produced by cyanobacteria, and their structural diversity has led to the discovery of more than 300 congeners to date. However, with known amino acid combinations, many more MC congeners are theoretically possible, suggesting many remain unidentified. Herein, two novel serine (Ser)-containing MCs were putatively identified in a Lake Erie cyanobacterial harmful algal bloom (cyanoHAB), using high-resolution UHPLC-MS as well as thiol and sulfoxide derivatization procedures. These MCs contain an α,β-unsaturated carbonyl on methyl dehydroalanine (Mdha) residue that undergoes Michael addition to produce a thiol-derivatized MC. Derivatization reactions using various thiolation reagents were followed by MS/MS, and two Python codes were used for data analysis and structural elucidation of MCs. Two novel MCs containing Ser at position 1 (i.e., next to Mdha) were putatively identified as [Ser 1 ]MC-RR and [Ser 1 ]MC-YR. Using thiol- and sulfoxide-modified [Ser 1 ]MCs, identifications were confirmed by the observation of specific neutral losses of the oxidized thiols or sulfoxides in CID-MS/MS spectra in both positive and negative electrospray ionization (ESI) modes. These novel neutral losses are unique for MCs with Mdha and an adjacent Ser residue. In conclusion, data suggest that a gas-phase reaction occurs between oxygen from adjacent Ser residue and sulfur of the Mdha-bonded thiol or sulfoxide, which leads to the formation and detection of stable cyclic MC ions in MS/MS spectra at m/z values corresponding to the loss of oxidized thiols or oxidized sulfoxides from Ser 1 -containing MCs.

Premathilaka, Sanduni H.↗
DOE OSTI2023

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.

17 WIND ENERGY↗
DOE OSTI2016

Performance of b-jet identification in the ATLAS experiment

The identification of jets containing b hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing b hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b-tagging algorithm used in the online trigger are also presented. The b-jet tagging efficiency, the c-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of b jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.

43 PARTICLE ACCELERATORS↗