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Neuromorphic Graph Algorithms: Cycle Detection, Odd Cycle Detection, and Max Flow
Neuromorphic computing is poised to become a promising computing paradigm in the post Moore’s law era due to its extremely low power usage and inherent parallelism. Spiking neural networks are the traditional use case for neuromorphic systems, and have proven to be highly effective at machine learning tasks such as control problems. More recently, neuromorphic systems have been applied outside of the arena of machine learning, primarily in the field of graph algorithms. Neuromorphic systems have been shown to perform graph algorithms faster and with lower power consumption than their traditional (GPU/CPU) counterparts, and are hence an attractive option for a co-processing unit in future high performance computing systems, where graph algorithms play a critical role. In this paper, we present a neuromorphic implementation of cycle detection, odd cycle detection, and the Ford-Fulkerson max-flow algorithm. We further evaluate the performance of these implementations using the NEST neuromorphic simulator by using spike counts and simulation time as proxies for energy consumption and run time. In addition to gains inherent in neuromorphic systems, we show that within the neuromorphic implementations early stopping criteria can be implemented to further improve performance.
Pipeline Defects Detection and Classification Based on Distributed Fiber Sensors and Neural Networks
An integrated approach is presented to detect pipeline defects using distributed acoustic sensors and machine learning. Over 80% accuracy for defected pipe identification was achieved with defect depth classifications with error less than 1-mm.
Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting
Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.
SRF cavity instability detection with machine learning at CEBAF
During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detectfast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. An unsupervised learning framework has been developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and our framework, along with recent successes in detecting anomalous cavity behavior. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.
Advances in High-Resolution Radiation Detection Using 4H-SiC Epitaxial Layer Devices
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Detection, Estimation, and Modulation Theory. Part 1 - Detection, Estimation, and Linear Modulation Theory.
Detection, estimation and linear modulation theory, Part 1, covering random processes, signal detection in noise and continuous waveforms
Carbon-14 radiation detection system for life detection experiments
Carbon 14 radiation detection system for life detection experiments
Measured electric field intensities near electric cloud discharges detected by the Kennedy Space Center's Lightning Detection and Ranging System, LDAR
For a summer thunderstorm, for which simultaneous, airborne electric field measurements and Lightning Detection and Ranging (LDAR) System data was available, measurements were coordinated to present a picture of the electric field intensity near cloud electrical discharges detected by the LDAR System. Radar precipitation echos from NOAA's 10 cm weather radar and measured airborne electric field intensities were superimposed on LDAR PPI plots to present a coordinated data picture of thunderstorm activity.
Application of infrared radiometers for airborne detection of clear air turbulence and low level wind shear, airborne infrared low level wind shear detection test
The feasibility of infrared optical techniques for the advance detection and avoidance of low level wind shear (LLWS) or low altitude wind shear hazardous to aircraft operations was investigated. A primary feasibility research effort was conducted with infrared detectors and instrumentation aboard the NASA Ames Research Center Learjet. The main field effort was flown on the NASA-Ames Dryden B57B aircraft. The original approach visualized a forward-looking, infrared transmitting (KRS-5) window through which signals would reach the detector. The present concept of a one inch diameter light pipe with a 45 deg angled mirror enables a much simpler installation virtually anywhere on the aircraft coupled with the possibility of horizontal scanning via rotation of the forward directed mirror. Present infrared detectors and filters would certainly permit ranging and horizontal scanning in a variety of methods. CRT display technology could provide a contoured picture with possible shear intensity levels from the infrared detection system on the weather radar or a small adjunct display. This procedure shoud be further developed and pilot evaluated in a light aircraft such as a Cessna 207 or equivalent.
In situ detection of tropospheric OH, HO2, NO2, and NO by laser-induced fluorescence in detection chambers at reduced pressures
This report is a brief summary of the status of work on the grant entitled 'In situ detection of tropospheric OH, HO2, NO2, and NO by laser induced fluorescence in detection chambers at low pressures'. The first version of the instrument is essentially complete and operational for about six months, and we continue to make improvements on the instrument sensitivity and reliability. We are focusing our efforts on improving our understanding of the operating characteristics of the instrument - particularly the inlet transmission for OH and HO2, the exact character of the air flow around and within the instrument, and the efficiency of the chemical conversion of HO2 to OH. We are also in the process of converting this laboratory instrument into a field worthy instrument that we can take to remote sites for measurements.
NASA airborne radar wind shear detection algorithm and the detection of wet microbursts in the vicinity of Orlando, Florida
The algorithms used in the NASA experimental wind shear radar system for detection, characterization, and determination of windshear hazard are discussed. The performance of the algorithms in the detection of wet microbursts near Orlando is presented. Various suggested algorithms that are currently being evaluated using the flight test results from Denver and Orlando are reviewed.
In situ detection of tropospheric OH, HO2, NO2, and NO by laser-induced fluorescence in detection chambers at reduced pressures
This report is a brief summary of the status of work on the grant entitled 'In situ detection of tropospheric OH, HO2, NO2, and NO by laser induced fluorescence in detection chambers at low pressures.' The basic instrument characteristics have been established, and have been reported in a manuscript, included as an appendix to this report, that has been accepted by the Journal of Geophysical Research. Currently, two efforts are under way. First, instrument tests and calibrations are continuing. These efforts include field measurements and an informal inter comparison in Colorado last August and September. Second, new technologies in lasers and detectors are being implemented to make the instrument smaller, lighter, and more energy efficient. Such instrument modifications are essential for measurements from aircraft, high scaffolding in forests, and ships.
Detection of Deforestation and Land Conversion in Rondonia, Brazil Using Change Detection Techniques
Fires associated with tropical deforestation, land conversion, and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rond6nia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 was examined in a 94 370-ha area of active deforestation to map land cover change. The Tasseled Cap (TC) transformation was used to enhance the contrast between forest, cleared areas, and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 78.1% (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opened to land exhibiting regrowth with infrequent clearing.
Searches for new interstellar molecules, including a tentative detection of aziridine and a possible detection of propenal
Rotational spectroscopy at millimeter wavelengths is a powerful means of investigating the chemistry of dense interstellar clouds. These regions can exhibit an interesting complement of gas phase molecules, including relatively complex organics. Here we report the tentative first astronomical detection of aziridine (ethylenimine), the possible detection of propenal (acrolein), and upper limits on the abundances of cyclopropenone, furan, hydroxyethanal (glycolaldehyde), thiohydroxylamine (NH2SH), and ethenol (vinyl alcohol) in various interstellar clouds.
Detection and Modeling of High-Dimensional Thresholds for Fault Detection and Diagnosis
Many Fault Detection and Diagnosis (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like oil pressure too high, analog sensor values need to be discretized using a suitablethreshold. Time series of analog and discrete sensor readings are processed and discretized as they come in. This task isusually performed by the wrapper code'' of the FDD system, together with signal preprocessing and filtering. In practice,selecting the right threshold is very difficult, because it heavily influences the quality of diagnosis. If a threshold causesthe alarm trigger even in nominal situations, false alarms will be the consequence. On the other hand, if threshold settingdoes not trigger in case of an off-nominal condition, important alarms might be missed, potentially causing hazardoussituations. In this paper, we will in detail describe the underlying statistical modeling techniques and algorithm as well as the Bayesian method for selecting the most likely shape and its parameters. Our approach will be illustrated by several examples from the Aerospace domain.
Kepler Planet Detection Metrics: Pixel-Level Transit Injection Tests of Pipeline Detection Efficiency for Data Release 25
This document describes the results of the fourth pixel-level transit injection experiment, which was designed to measure the detection efficiency of both the Kepler pipeline (Jenkins 2002, 2010; Jenkins et al. 2017) and the Robovetter (Coughlin 2017). Previous transit injection experiments are described in Christiansen et al. (2013, 2015a,b, 2016).In order to calculate planet occurrence rates using a given Kepler planet catalogue, produced with a given version of the Kepler pipeline, we need to know the detection efficiency of that pipeline. This can be empirically determined by injecting a suite of simulated transit signals into the Kepler data, processing the data through the pipeline, and examining the distribution of successfully recovered transits. This document describes the results for the pixel-level transit injection experiment performed to accompany the final Q1-Q17 Data Release 25 (DR25) catalogue (Thompson et al. 2017)of the Kepler Objects of Interest. The catalogue was generated using the SOC pipeline version 9.3 and the DR25 Robovetter acting on the uniformly processed Q1-Q17 DR25 light curves (Thompson et al. 2016a) and assuming the Q1-Q17 DR25 Kepler stellar properties (Mathur et al. 2017).