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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Detection of dark states in two-dimensional electronic photon-echo signals via ground-state coherence

Several recent experiments report on possibility of dark-state detection by means of so called beating maps of two-dimensional photon-echo spectroscopy [Ostroumov et al., Science 340, 52 (2013); Bakulin et al., Ultrafast Phenomena XIX (Springer International Publishing, 2015)]. The main idea of this detection scheme is to use coherence induced upon the laser excitation as a very sensitive probe. In this study, we investigate the performance of ground-state coherence in the detection of dark electronic states. For this purpose, we simulate beating maps of several models where the excited-state coherence can be hardly detected and is assumed not to contribute to the beating maps. The models represent strongly coupled electron-nuclear dynamics involving avoided crossings and conical intersections. In all the models, the initially populated optically accessible excited state decays to a lower-lying dark state within few hundreds femtoseconds. We address the role of Raman modes and of interstate-coupling nature. Our findings suggest that the presence of low-frequency Raman active modes significantly increases the chances for detection of dark states populated via avoided crossings, whereas conical intersections represent a more challenging task.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Muon detection studied by pulse-height energy analysis: Novel converter arrangements

Muons are conventionally measured by a plastic scintillator–photomultiplier detector. Muons from processes in ultra-dense hydrogen H(0) are detected here by a novel type of converter in front of a photomultiplier. The muon detection yield can be increased relative to that observed with a plastic scintillator by at least a factor of 100, using a converter of metal, semiconductor (Ge), or glass for interaction with the muons penetrating through the metal housing of the detector. This detection process is due to transient formation of excited nuclei by the well-known process of muon capture, giving beta decay. The main experimental results shown here are in the form of beta electron energy spectra detected directly by the photomultiplier. Events which give a high-energy tail in the energy spectra are probably due to gamma photons from the muons. Sharp and intense x-ray peaks from a muonic aluminium converter or housing material are observed. The detection conversion in glass and Ge converters has a time constant of the order of many minutes to reach the final conversion level, while the process in metal converters is stabilized faster. The time constants are not due to lifetimes of the excited nuclei or neutrons but are due to internal charging in the insulating converter material. Interaction of this charging with the high voltage in the photomultiplier is observed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Efficient and robust quantum random number generation by photon number detection

We present an efficient and robust quantum random number generator based upon high-rate room temperature photon number detection. We employ an electric field-modulated silicon avalanche photodiode, a type of device particularly suited to high-rate photon number detection with excellent photon number resolution to detect, without an applied dead-time, up to 4 photons from the optical pulses emitted by a laser. By both measuring and modeling the response of the detector to the incident photons, we are able to determine the illumination conditions that achieve an optimal bit rate that we show is robust against variation in the photon flux. We extract random bits from the detected photon numbers with an efficiency of 99% corresponding to 1.97 bits per detected photon number yielding a bit rate of 143 Mbit/s, and verify that the extracted bits pass stringent statistical tests for randomness. Our scheme is highly scalable and has the potential of multi-Gbit/s bit rates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Time-frequency energy density precipitation method for time-of-flight extraction of narrowband Lamb wave detection signals

The time-of-flight of the Lamb wave provides an important basis for defect evaluation in metal plates and is the input signal for Lamb wave tomographic imaging. However, the time-of-flight can be difficult to acquire because of the Lamb wave dispersion characteristics. This work proposes a time-frequency energy density precipitation method to accurately extract the time-of-flight of narrowband Lamb wave detection signals in metal plates. In the proposed method, a discrete short-time Fourier transform is performed on the narrowband Lamb wave detection signals to obtain the corresponding discrete time-frequency energy density distribution. The energy density values at the center frequency for all discrete time points are then calculated by linear interpolation. Next, the time-domain energy density curve focused on that center frequency is precipitated by least squares fitting of the calculated energy density values. Finally, the peak times of the energy density curve obtained relative to the initial pulse signal are extracted as the time-of-flight for the narrowband Lamb wave detection signals. An experimental platform is established for time-of-flight extraction of narrowband Lamb wave detection signals, and sensitivity analysis of the proposed time-frequency energy density precipitation method is performed in terms of propagation distance, dispersion characteristics, center frequency, and plate thickness. For comparison, the widely used Hilbert–Huang transform method is also implemented for time-of-flight extraction. The results show that the time-frequency energy density precipitation method can accurately extract the time-of-flight with relative error of <1% and thus can act as a universal time-of-flight extraction method for narrowband Lamb wave detection signals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Feasibility studies for the detection of O{sub 2} in an Earth-like exoplanet

We present the results of simulations on the detectability of O{sub 2} in the atmosphere of Earth twins around nearby low mass stars using high resolution transmission spectroscopy. We explore such detectability with each of the three upcoming Extremely Large Telescopes (ELTs), i.e., GMT, TMT, and E-ELT, and high resolution spectrographs, assuming such instruments will be available in all ELTs. With these simulations we extend previous studies by taking into account atmospheric refraction in the transmission spectrum of the exo-Earth and observational white and red noise contributions. Our studies reveal that the number of transits necessary to detect O{sub 2} in the atmosphere of an Earth twin around an M dwarf is by far higher than the number of transits estimated by Snellen et al. In addition, our simulations show that, when accounting for typical noise levels associated with observations in the optical and near-infrared, the O{sub 2} A band at 760 nm is more favorable for detecting the exoplanetary signal than the O{sub 2} band at 1268 nm for all the spectral types, except M9V. We conclude that, unless unpredicted instrumental limitations arise, the implementation of pre-slit optics such as image slicers appears to be key to significantly improving the yield of this particular science case. However, even in the most optimistic cases, we conclude that the detection of O{sub 2} in the atmosphere of an Earth twin will only be feasible with the ELTs if the planet is orbiting a bright close by (d ≤ 8 pc) M dwarf with a spectral type later than M3.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A Spitzer search for transits of radial velocity detected super-Earths

Unlike hot Jupiters or other gas giants, super-Earths are expected to have a wide variety of compositions, ranging from terrestrial bodies like our own to more gaseous planets like Neptune. Observations of transiting systems, which allow us to directly measure planet masses and radii and constrain atmospheric properties, are key to understanding the compositional diversity of the planets in this mass range. Although Kepler has discovered hundreds of transiting super-Earth candidates over the past 4 yr, the majority of these planets orbit stars that are too far away and too faint to allow for detailed atmospheric characterization and reliable mass estimates. Ground-based transit surveys focus on much brighter stars, but most lack the sensitivity to detect planets in this size range. One way to get around the difficulty of finding these smaller planets in transit is to start by choosing targets that are already known to host super-Earth sized bodies detected using the radial velocity (RV) technique. Here we present results from a Spitzer program to observe six of the most favorable RV-detected super-Earth systems, including HD 1461, HD 7924, HD 156668, HIP 57274, and GJ 876. We find no evidence for transits in any of their 4.5 μm flux light curves, and place limits on the allowed transit depths and corresponding planet radii that rule out even the most dense and iron-rich compositions for these objects. We also observed HD 97658, but the observation window was based on a possible ground-based transit detection that was later ruled out; thus the window did not include the predicted time for the transit detection recently made by the Microvariability and Oscillations of Stars space telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

GALEX detection of shock breakout in type IIP supernova PS1-13arp: Implications for the progenitor STAR wind

We present the GALEX detection of a UV burst at the time of explosion of an optically normal supernova (SN) IIP (PS1-13arp) from the Pan-STARRS1 survey at z = 0.1665. The temperature and luminosity of the UV burst match the theoretical predictions for shock breakout in a red supergiant (RSG), but with a duration a factor of ∼50 longer than expected. We compare the NUV light curve of PS1-13arp to previous GALEX detections of SNe IIP and find clear distinctions that indicate that the UV emission is powered by shock breakout, and not by the subsequent cooling envelope emission previously detected in these systems. We interpret the ∼1 day duration of the UV signal with a shock breakout in the wind of an RSG with a pre-explosion mass-loss rate of ∼10{sup −3} M{sub ⊙} yr{sup −1}. This mass-loss rate is enough to prolong the duration of the shock breakout signal, but not enough to produce an excess in the optical plateau light curve or narrow emission lines powered by circumstellar interaction. This detection of non-standard, potentially episodic high mass loss in an RSG SN progenitor has favorable consequences for the prospects of future wide-field UV surveys to detect shock breakout directly in these systems, and provide a sensitive probe of the pre-explosion conditions of SN progenitors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

EXTRASOLAR BINARY PLANETS. II. DETECTABILITY BY TRANSIT OBSERVATIONS

We discuss the detectability of gravitationally bound pairs of gas-giant planets (which we call “binary planets”) in extrasolar planetary systems that are formed through orbital instability followed by planet–planet dynamical tides during their close encounters, based on the results of N-body simulations by Ochiai et al. (Paper I). Paper I showed that the formation probability of a binary is as much as ∼10% for three giant planet systems that undergo orbital instability, and after post-capture long-term tidal evolution, the typical binary separation is three to five times the sum of the physical radii of the planets. The binary planets are stable during the main-sequence lifetime of solar-type stars, if the stellarcentric semimajor axis of the binary is larger than 0.3 AU. We show that detecting modulations of transit light curves is the most promising observational method to detect binary planets. Since the likely binary separations are comparable to the stellar diameter, the shape of the transit light curve is different from transit to transit, depending on the phase of the binary’s orbit. The transit durations and depth for binary planet transits are generally longer and deeper than those for the single planet case. We point out that binary planets could exist among the known inflated gas-giant planets or objects classified as false positive detections at orbital radii ≳0.3 AU, propose a binary planet explanation for the CoRoT candidate SRc01 E2 1066, and show that binary planets are likely to be present in, and could be detected using, Kepler-quality data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

ON DETECTING MILLISECOND PULSARS AT THE GALACTIC CENTER

The lack of detected pulsars at the Galactic Center (GC) region is a long-standing mystery. We argue that the high stellar density in the central parsec around the GC is likely to result in a pulsar population dominated by millisecond pulsars (MSPs), similar to the situation in globular cluster environments. Earlier GC pulsar searches have been largely insensitive to such an MSP population, accounting for the lack of pulsar detections. We estimate the best search frequency for such an MSP population with present and upcoming broad-band radio telescopes for two possible scattering scenarios, the “weak-scattering” case suggested by the recent detection of a magnetar close to the GC, and the “strong-scattering” case, with the scattering screen located close to the GC. The optimal search frequencies are ≈8 GHz (weak-scattering) and ≈25 GHz (strong-scattering), for pulsars with periods 1–20 ms, assuming that GC pulsars have a luminosity distribution similar to that those in the rest of the Milky Way. We find that 10–30 hr integrations with the Very Large Array and the Green Bank Telescope would be sufficient to detect MSPs at the GC distance in the weak-scattering case. However, if the strong-scattering case is indeed applicable to the GC, observations with the full Square Kilometre Array would be needed to detect the putative MSP population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

DOUBLE COMPACT OBJECTS. III. GRAVITATIONAL-WAVE DETECTION RATES

The unprecedented range of second-generation gravitational-wave (GW) observatories calls for refining the predictions of potential sources and detection rates. The coalescence of double compact objects (DCOs)—i.e., neutron star–neutron star (NS–NS), black hole–neutron star (BH–NS), and black hole–black hole (BH–BH) binary systems—is the most promising source of GWs for these detectors. We compute detection rates of coalescing DCOs in second-generation GW detectors using the latest models for their cosmological evolution, and implementing inspiral-merger-ringdown gravitational waveform models in our signal-to-noise ratio calculations. We find that (1) the inclusion of the merger/ringdown portion of the signal does not significantly affect rates for NS–NS and BH–NS systems, but it boosts rates by a factor of ∼1.5 for BH–BH systems; (2) in almost all of our models BH–BH systems yield by far the largest rates, followed by NS–NS and BH–NS systems, respectively; and (3) a majority of the detectable BH–BH systems were formed in the early universe in low-metallicity environments. We make predictions for the distributions of detected binaries and discuss what the first GW detections will teach us about the astrophysics underlying binary formation and evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

High-field quench behavior and dependence of hot spot temperature on quench detection voltage threshold in a Bi 2 Sr 2 CaCu 2 O x coil

Here, small insert coils have been built using a multifilamentary Bi 2 Sr 2 CaCu 2 O x round wire, and characterized in background fields to explore the quench behaviors and limits of Bi 2 Sr 2 CaCu 2 O x superconducting magnets, with an emphasis on assessing the impact of slow normal zone propagation on quench detection. Using heaters of various lengths to initiate a small normal zone, a coil was quenched safely more than 70 times without degradation, with the maximum coil temperature reaching 280 K. Coils withstood a resistive voltage of tens of mV for seconds without quenching, showing the high stability of these coils and suggesting that the quench detection voltage shall be greater than 50 mV to not to falsely trigger protection. The hot spot temperature for the resistive voltage of the normal zone to reach 100 mV increases from ~40 K to ~80 K with increasing the operating wire current density J o from 89 A/mm -2 to 354 A/mm -2 whereas for the voltage to reach 1 V, it increases from ~60 K to ~140 K, showing the increasing negative impact of slow normal zone propagation on quench detection with increasing J o and the need to limit the quench detection voltage to < 1 V. These measurements, coupled with an analytical quench model, were used to access the impact of the maximum allowable voltage and temperature upon quench detection on the quench protection, assuming to limit the hot spot temperature to <300 K.

43 PARTICLE ACCELERATORS↗
DOE OSTI2014

DETECTABILITY OF GLYCINE IN SOLAR-TYPE SYSTEM PRECURSORS

Glycine (NH{sub 2}CH{sub 2}COOH) is the simplest amino acid relevant to life. Its detection in the interstellar medium is key to understanding the formation mechanisms of pre-biotic molecules and their subsequent delivery onto planetary systems. Glycine has been extensively searched for toward hot molecular cores, although these studies did not yield any firm detection. In contrast to hot cores, low-mass star forming regions, in particular their earliest stages represented by cold pre-stellar cores, may be better suited for the detection of glycine as well as more relevant to the study of pre-biotic chemistry in young solar system analogs. We present one-dimensional spherically symmetric radiative transfer calculations of the glycine emission expected to arise from the low-mass pre-stellar core L1544. Water vapor has recently been reported toward this core, indicating that a small fraction of the grain mantles in L1544 (∼0.5%) has been injected into the gas phase. Assuming that glycine is photo-desorbed together with water in L1544, and considering a solid abundance of glycine on ices of ∼10{sup –4} with respect to water, our calculations reveal that several glycine lines between 67 GHz and 80 GHz have peak intensities larger than 10 mK. These results show for the first time that glycine could reach detectable levels in cold objects such as L1544. This opens up the possibility of detecting glycine, and other pre-biotic species, at the coldest and earliest stages in the formation of solar-type systems with near-future instrumentation such as the Band 2 receivers of ALMA.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Source-agnostic gravitational-wave detection with recurrent autoencoders

Abstract We present an application of anomaly detection techniques based on deep recurrent autoencoders (AEs) to the problem of detecting gravitational wave (GW) signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e. without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other AE architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent AE outperforms other AEs based on different architectures. The class of recurrent AEs presented in this paper could complement the search strategy employed for GW detection and extend the discovery reach of the ongoing detection campaigns.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Deep Learning-Based Data Forgery Detection in Automatic Generation Control

Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Network and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Concept Study of Robotic Camera-Based Foreign Object Detection for EV Wireless Charging

Wireless charging of an electric vehicle (EV) is an emerging charging technology promising convenient, autonomous, and highly efficient EV charging without requiring heavy gauge cables. However, due to the strong electromagnetic field created by this process that surrounds the wireless charger, the presence of foreign objects can detrimentally interact with it, thus affecting wireless power transfer (WPT) performance or leading to harmful and unwanted safety risks. This paper presents the results for a concept study on a robotic camera-based foreign object detection (FOD) system, as a supplement to the industry-existing overlapped FOD coil array method, for EV wireless charging. A Raspberry PI 4 control board and compatible Raspberry PI Camera Module 2 are used to implement camera-based object detection. The FOD program was developed using a state-of-the-art deep learning object detection model with the OpenCV and Pytorch library and is compatible with camera module hardware. A dry-run test with Raspberry PI and a camera module was conducted and the preliminary FOD function was verified. The feasibility assessment is also validated by comparing the performance of five existing state-of-the-art deep learning object detection models for vehicles, animals, persons, and metals subsets, respectively. Satisfactory performance on the benchmark datasets is observed by the tests, but further improvements are needed in future work when detecting small-sized metallic objects. A programable robotic car is also under development as ongoing work for carrying the Raspberry PI and camera module while moving for the maintenance process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Unobtrusive Sensor-Based Occupancy Facing Direction Detection and Tracking Using Advanced Machine Learning Algorithms

Facing direction detection plays a critical role in human-computer interaction and has a wide application in surveillance systems, driving awareness recognition, smart home appliances, computer games, and so on. Current detection methods are mainly focused on extracting specific patterns from user's optical images, which raises concerns on privacy invasion and these detection techniques do not usually work in a dark environment. To address these concerns, here this paper proposes an activity recognition system guided by an unobtrusive sensor (ARGUS). By using a low pixel infrared thermopile array sensor, ARGUS is capable of identifying five facing directions (left 45°/90°, right 45°/90°, and front) through the support vector machine classifier. Also two feature extraction methods are compared. One is manually-defined and the other is based on a pre-trained convolutional neural network (CNN) model. The facing direction detection accuracy resulting from manually-defined features reaches 85.3%, 90.6%, and 85.2% at detection distance of 0.6, 1.2, and 1.8 m, respectively. The level of accuracy resulted from using pre-trained CNN features demonstrates a much more reliable performance (89.1%, 95.3%, and 95.1% at distance of 0.6, 1.2, and 1.8 m, respectively). In addition, ARGUS has been successfully applied for occupancy tracking with a root mean square error of 0.19 m.

42 ENGINEERING↗