Hyperspectral Image Target Detection Using Deep Ensembles for Robust Uncertainty Quantification.
Abstract not provided.
Sahay, Rajeev↗
SEARCH · Engineering Papers
Results for “Detection”
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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Detection and classification capabilities of two multibeam sonars
Multibeam sonars can be used to detect and classify marine fauna under conditions when optical sensors are ineffective. In this work, we compare the detection and classification capabilities of two multibeam sonars with different operating frequencies, the Tritech Gemini 720is (720 kHz) and the Teledyne BlueView M900-2250 (2250 kHz). The two sonars were deployed with overlapping swaths in a narrow tidal channel with peak currents of approximately 2 m/s where seals, schools of fish, and diving birds were intermittently present. In comparing data concurrently acquired by both sonars, we observe differences in the appearance of detected targets and detection capabilities. The detected target tracks are classified using a random forest model as either individual biological targets (flora and fauna, including fish, diving birds, seals, and plant matter), fish schools, or nonbiological targets (including entrained air and sonar artifacts). Despite the observed differences in detection capabilities for the two sonars, automatic classification distinguishes between target classes with precision and recall above 0.7 and 0.9, respectively. Furthermore, these results suggest that while similar methodologies can be used for data analysis, some outcomes from environmental studies using multibeam sonars may be instrument-specific.
16 TIDAL AND WAVE POWER↗
Towards increasing the performance of FTICR-MS with signal detection at frequency multiples: Signal theory and numerical study
Not Available
Wu, Qinghao (ORCID:000000031665567X)↗
Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data
Abstract We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored (grizy) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as “outliers” from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected $\sim\!\! 60\%$–$70\%$ of the quasars and $60\%$ of the XELGs without labeled training data. In reference to the spectral information of local galaxies at z = 0.05–0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, deep anomaly detection is an effective tool that can search rare objects, and, ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.
Astronomy & Astrophysics↗
Network Anomaly Detection Using Federated Learning
The internet is turning out to be an integral part of every-one's lives as more and more devices are being connected to serve societal needs. Our work is motivated by two ma-jor observations. Firstly, one drawback of connecting to the network is the threat of network attacks that can compromise users' private information, leading to data loss and adversely affecting productivity. There are several traditional security mechanisms to defend against these attacks, such as firewalls, virtual private networks (VPNs), demilitarized zones (DMZs), and vulnerability scanners. One way to prevent these attacks is early detection and prevention. However, these kinds of architecture do not scale very well because of their centralized nature. Secondly, we observe from heuristics and data set distributions that the majority of the requests made to a server are innocuous. Therefore, almost all server request data sets are highly imbalanced, weighted highly towards the harmless requests.
Marfo, William↗
Nanopore Activity Assays for Detection of Biomarker Protease Activity: Design and Testing of Substrates for Both Nanopore Sequencing and PCR-Based Detection Methods
The work performed in this project has demonstrated the ability to construct proteolytic enzyme substrates that are PCR and sequencing-readable reporter molecules. Specifically, the goal was to detect those reporter molecules via PCR and Oxford Nanopore Technologies MinION sequencing methods following exposure to the biomarker protease thrombin. The assay development focused on binding the constructed peptide-oligonucleotide chimera to immobilized streptavidin. The action of thrombin on the peptide portion of the molecule released the oligonucleotide for detection. Detection of protease activity was demonstrated in a concentration-dependent manner using MALDI-MS, RT-PCR and DNA sequencing. Additional steps to remove background release of reporter molecules during the assay was used to improve the difference in detected oligonucleotide reporter following protease activity. Additional steps in assay development will be to (1) test the assay in an appropriate matrix, (2) investigate detection using additional DNA sequencing platforms and (3) demonstrate multiplexed detection of multiple protease markers in a single reaction.
59 BASIC BIOLOGICAL SCIENCES↗
Nondestructive Damage Detection of Concrete With Alkali-Silica Reactions Using Coda Wave and Anomaly Detection
An anomaly detection model for early damage detection for concrete structures undergoing alkali-silica reaction (ASR) is presented. It is difficult to detect ASR initiation and early damage without a reference expansion measurement. Coda waves, or the multiply scattered portion of ultrasonic waves, have been found to be indicative of small changes in complex material such as concrete. The relationship between concrete damage and relative velocity change and decorrelation of coda waves has been studied, but a generalized model which detects when damage occurs in a concrete structure is still lacking. The presented method uses features extracted from coda waves to detect early damage in concrete structures. The model uses unsupervised learning and only requires data from undamaged structures for training. During the training process, the reconstruction error of the training data is minimized. When the data collected from damaged concrete structures is used as an input of the model, it returns high reconstruction errors that indicate the occurrence of damage in the structures. The performance of the model is validated using experimental studies and has been shown to generalize across two different ASR specimens.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Evaluation, Analysis and Results of the DANTi Flight Test Data, the DAIDALUS Detect and Avoid Algorithm, and the DANTi Concept for Detect and Avoid in the Cockpit
The DAIDALUS Detect and Avoid Algorithm [1] was developed to address the operational needs of Unmanned Aerial Systems (UAS) and meet the Minimum Operational Performance Standards for Detect and Avoid [2]. The DANTi (Detect and Avoid iN the cockpit) concept [3], developed at the National Aeronautics and Space Administration (NASA) Langley Research Center, leverages advancements achieved in surveillance and Detect and Avoid technologies for unmanned aircraft systems as a safety enhancing capability for pilots of manned aircraft. Pilots operating under Visual Flight Rules and not receiving Air Traffic Control radar services rely on see and avoid to remain well clear of other aircraft and avoid collisions. The DANTi concept has been conceived as a safety enhancement capability to remain well clear and avoid potential collisions. The DANTi concept uses a traffic display to provide situational awareness, conflict detection, alerting, and guidance to remain well clear.
Detect and Avoid↗
Sensitivity and Detection Limit of Spectroscopic-Grade Perovskite CsPbBr 3 Crystal for Hard X-Ray Detection
We report spectroscopic-grade single crystal detectors can register the energies of individual X-ray interactions enabling photon-counting systems with superior resolution over traditional photoconductive X-ray detection systems. Current technical challenges have limited the preparation of perovskite semiconductors for energy-discrimination X-ray photon-counting detection. Here, this work reports the deployment of a spectroscopic-grade CsPbBr 3 Schottky detector under reverse bias for continuum hard X-ray detection in both the photocurrent and spectroscopic schemes. High surface barriers of approximate to ≈ 1 eV are formed by depositing solid bismuth and gold contacts. The spectroscopic response under a hard X-ray source is assessed in resolving the characteristic X-ray peak. The methodology in enhancing X-ray sensitivity by controlling the X-ray energies and flux, and voltage, is described. The X-ray sensitivity varies between a few tens to over 8000 μC Gy air -1 cm -2 . The detectable dose rate of the CsPbBr 3 detectors is as low as 0.02 nGy air s -1 in the energy discrimination configuration. Finally, the unbiased CsPbBr 3 device forms a spontaneous contact potential difference of about 0.7 V enabling high quality of the CsPbBr 3 single crystals to operate in "passive" self-powered X-ray detection mode and the X-ray sensitivity is estimated as 14 μC Gy air -1 cm -2 . The great potential of spectroscopic-grade CsPbBr 3 devices for X-ray photon-counting systems is anticipated in this work.
36 MATERIALS SCIENCE↗
A cosine-based correlation information entropy approach for building automatic fault detection baseline construction
Building automatic fault detection and diagnosis (AFDD) technologies have shown great potential for energy savings. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Existing research using physics-based knowledge and models for AFDD has mainly taken a trial-and-error approach to determine if a given baseline is sufficient via empirical experiments. A mechanism to support decisions such as how many samples and what samples should be included in the baseline is currently lacking. In this study, a data-driven method for AFDD baseline construction based on information entropy is developed. The entropy is derived based on cosine similarity among typical building automation system measurements in conjunction with outdoor weather information. The performance of the proposed method is evaluated using real building data. Evaluation results indicate that the fault detection strategy adopting the proposed method has similar or better accuracy in detecting faults compared to the same fault detection strategy using the baseline construction method from the literature. Additionally, the use of entropy enables the proposed method to automatically construct and assess the baseline consisting of information-rich samples.
42 ENGINEERING↗
Improving Non-Destructive Detection Technology Through SAVY Feature Detection
Surveillance of special nuclear material (SNM) storage containers is required by the DOE to assess their integrity across the Complex. This work aims to improve the task of container inspections by leveraging automation through machine learning (ML) tools to reduce the human-intensive effort and expert-level knowledge needed to assess container status. A field-deployable, non-destructive technology was designed using off-the-shelf components to collect multiple images from different perspectives of containers in storage to detect both spatial features of interest and anomalies of concern. Nine ML models were generated using unique training datasets and parameters. Learned features include SAVY surface regions including the body side wall, collar, lid, filter, and printed/etched information. Average Precision (AP) is used to calculate detection performance when both viewing previously seen environments and previously unseen environments. The application of image transformations and resolution scaling while training greatly improved the detection performance in unseen environments, and significantly increasing the number of computation iterations improved detection performance on previously seen environments. Additional capabilities were developed including the novel detection of procedural non-compliance and the ability to localize anomalies relative to SAVY surface features.
97 MATHEMATICS AND COMPUTING↗
In situ detection of tropospheric OH, HO2, NO2, and NO by laser-induced fluorescence in detection chambers at reduced pressures
For detection of OH and HO2, we have met or exceeded most of the goals of the proposal for this grant. We have succeeded in building a prototype instrument for the detection of OH and HO2 that has a detection sensitivity of about 2 x 10 exp 4 (OH molecules/cc)/(ct/s), about five times greater than proposed. The current minimum detectable OH of 1.4 x 10 exp 5 OH molecules/cc (S/N=2; 30 second integration) is less than half that proposed, and will be much lower once scattered light levels are reduced. The instrument displays other important properties. First, interfering signals from other gases and OH losses on the inlet appear to be small under laboratory and field conditions. Second, three different calibration methods, two external to the inlet, give similar results, to within 30 percent. Third, the chemical conversion efficiency of HO2 to OH by addition of reagent NO is better than 90 percent. All of these factors give us great confidence that this technique works. The instrument has gone through several variations that have not affected its potential performance but have affected its use. We were able to undergo the first field trials away from State College in June 1992, when we were able to participate informally in the ROSE experiment in rural Alabama, a year ahead of the proposed schedule for field studies. Because this field experiment was our first and the data analysis was complicated by instrument instabilities, we have not yet released the observations. None-the-less, we have gained insight into instrument design. We have not yet completed all of the proposed work. The calibration systems need to be improved to reduce the 50 percent to 100 percent uncertainties to less than 30 percent. Interference signals from ambient gases and possible losses on the inlet under field conditions need to be quantified. Finally, the detection of NO2 and NO with laser induced fluorescence is only now being seriously pursued.
Brune, William H.↗
Demonstration of Selective Single-Barium Ion Detection with Dry Diazacrown Ether Naphthalimide Turn-on Chemosensors
Single-molecule fluorescence imaging (SMFI) of gas-phase ions has been proposed for “barium tagging,” a burgeoning area of research in particle physics to detect individual barium daughter ions. This has potential to significantly enhance the sensitivity of searches for neutrinoless double-beta decay (0νββ) that is obscured by background radiation events. The chemistry required to make such sensitive detection of Ba 2+ by SMFI in dry Xe gas at solid interfaces has implications for solid-phase detection methods but has not been demonstrated. Here, we synthesized simple, robust, and effective Ba 2+ -selective chemosensors capable of function within ultrapure high-pressure 136 Xe gas. Turn-on fluorescent naphthalimide-(di)azacrown ether chemosensors were Ba 2+ -selective and achieved SMFI in a polyacrylamide matrix. Here, fluorescence and NMR experiments supported a photoinduced electron transfer mechanism for turn-on sensing. Ba 2+ selectivity was achieved with computational calculations correctly predicting the fluorescence responses of sensors to barium, mercury, and potassium ions. With these molecules, dry-phase single-Ba 2+ ion imaging with turn-on fluorescence was realized using an oil-free microscopy technique for the first time—a significant advance toward single-Ba 2+ ion detection within large volumes of 136 Xe, plausibly enabling a background-independent technique to search for the hypothetical process of 0νββ.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Using Temporal Information from Human Mobility Data to Detect Anchor Points
Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.
McBride, Liz [ORNL] (ORCID:0000000286925869)↗
Wave Detection and Tracking Within a Rotating Detonation Engine Through Object Detection
As the operational time window of experimental rotating detonation engines (RDEs) is expanded and the technology matures toward integration within gas turbines, monitoring techniques must evolve to offer computationally efficient and highly time-resolved diagnostics. In this study, computer vision object detection methodology that seeks to reduce data processing time and calculate wave velocity within drastically reduced time intervals as compared to traditional high-frame-rate RDE images analysis techniques is proposed. The adapted you-only-look-once object detection network is trained to detect individual detonation waves within single down-axis RDE images. The wave location and rotational direction detected within a frame are tracked through a series of high-speed images to calculate the frame-to-frame wave velocity with the time-step resolution of $\mathrm{20 μs}$ across a series of frames. The analysis of the annotation box size and image linearization effects is presented, demonstrating the lowest frame-to-frame velocity total uncertainty of $\mathrm{±3.8\%}$ and the highest classification speed of 9.5 frames per second using linearized images. Linearized images “unwrap” the RDE annulus pixel region to a reduced image size. Here, this new method offers great reductions in data processing times and unsteady detonation behavior insight at intervals more comparable to the timescales of detonation wave interactions via the application of machine learning to experimental RDE data.
33 ADVANCED PROPULSION SYSTEMS↗
Dynamical Classification of Trans-Neptunian Objects Detected by the Dark Energy Survey
The outer solar system contains a large number of small bodies (known as trans-Neptunian objects or TNOs) that exhibit diverse types of dynamical behavior. The classification of bodies in this distant region into dynamical classes—subpopulations that experience similar orbital evolution—aids in our understanding of the structure and formation of the solar system. In this work, we propose an updated dynamical classification scheme for the outer solar system. This approach includes the construction of a new (automated) method for identifying mean motion resonances. We apply this algorithm to the current data set of TNOs observed by the Dark Energy Survey (DES) and present a working classification for all of the DES TNOs detected to date. Our classification scheme yields 1 inner centaur, 19 outer centaurs, 21 scattering disk objects, 47 detached TNOs, 48 securely resonant objects, 7 resonant candidates, and 97 classical belt objects. Among the scattering and detached objects, we detect 8 TNOs with semimajor axes greater than 150 au.
79 ASTRONOMY AND ASTROPHYSICS↗
The physics of heterodyne detection in the far-infrared: Transition from electric-field to photon-absorption detection in a simple system
The history of heterodyne detection is reviewed from the radiowave to the optical regions of the electromagnetic spectrum with emphasion the submillimeter/far infrared. The transition from electric field to photon absorption detection in a simple system is investigated. The response of an isolated two level detector to a coherent source of incident radiation is calculated for both heterodyne and video detection. When the processes of photon absorption and photon emission cannot be distinguished, the relative detected power at double- and sum-frequencies is found to be multiplied by a coefficient, which is less than or equal to unity, and which depends on the incident photon energy and on the effective temperature of the system.
Teich, M. C.↗