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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Modeling the Virtual Machine Launching Overhead under Fermicloud

FermiCloud is a private cloud developed by the Fermi National Accelerator Laboratory for scientific workflows. The Cloud Bursting module of the FermiCloud enables the FermiCloud, when more computational resources are needed, to automatically launch virtual machines to available resources such as public clouds. One of the main challenges in developing the cloud bursting module is to decide when and where to launch a VM so that all resources are most effectively and efficiently utilized and the system performance is optimized. However, based on FermiCloud’s system operational data, the VM launching overhead is not a constant. It varies with physical resource (CPU, memory, I/O device) utilization at the time when a VM is launched. Hence, to make judicious decisions as to when and where a VM should be launched, a VM launch overhead reference model is needed. The paper is to develop a VM launch overhead reference model based on operational data we have obtained on FermiCloud and uses the reference model to guide the cloud bursting process.

Garzoglio, Gabriele↗
DOE OSTI2017

An automatic input/output test experience of FPGA-based logic controller with I/O stimulator - 282

This paper describes an automatic testing approach instead of a traditional manual test for an FPGA-based Logic Controller (FLC). It mainly describes an automatic testing of FLC using an I/O stimulator with the initiated manual action. The FLC was developed by Doosan Heavy Industry and KAERI in S. Korea. We also developed a test-bed like I/O stimulator for a burning test based on the operational scenario. Signal sources from the I/O stimulator contain an analog input, an analog output, a digital input and a digital output, a programmable power supply, an RS232C serial interface, and a dual port Ethernet interface. LabVIEW Programming was used for the automatic testing. The FLC consists of a general processor module (FPM-01), a complex processor module (FPM-02), analog input modules (FAI-01), analog output modules (FAO-01), digital input modules (FDI-01), digital output modules (FDO-01), a data link module (FDL-01), and a network module (FNW-01). Among these, in this paper, automatic digital I/O testing and automatic analog I/O testing are described. In addition, for the accuracy of the automatic testing, two graphic recorders are used. One is the output results of general processor based testing, and the other is the output results of complex processor based testing. These are different company products. Redundancy recording was found in the test results. The test results were synchronized internally with the existing I/O stimulator and externally with two graphic recorders. Calibrations of all of the equipment were made before the system testing. Before the testing started, a test plan and the test procedure were set up in advance, and we performed test scenario-based automatic functional testing for the FLC as the qualification process. It can be several executed depending on the scenario and conditional parameters of the test. For validation of the accuracy and reliability in our automated test, manual testing was also performed. Manual tests were conducted in two ways. The first one was a manual test using Fluke 754 and Fluke 715. The other one was using embedded manual features of the I/O stimulator. By performing the automatic input and output testing using the I/O stimulator, the time and expense of FLC testing can be reduced significantly compared with a conventional manual test. We rechecked the automatic test results using Fluke754 manually. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2011

Improving the Automatic Inversion of Digital ISIS-2 Ionogram Reflection Traces into Topside Vertical Electron-Density Profiles

The topside-sounders on the four satellites of the International Satellites for Ionospheric Studies (ISIS) program were designed as analog systems. The resulting ionograms were displayed on 35-mm film for analysis by visual inspection. Each of these satellites, launched between 1962 and 1971, produced data for 10 to 20 years. A number of the original telemetry tapes from this large data set have been converted directly into digital records. Software, known as the TOPside Ionogram Scalar with True-height (TOPIST) algorithm has been produced that enables the automatic inversion of ISIS-2 ionogram reflection traces into topside vertical electron-density profiles Ne(h). More than million digital Alouette/ISIS topside ionograms have been produced and over 300,000 are from ISIS 2. Many of these ISIS-2 ionograms correspond to a passive mode of operation for the detection of natural radio emissions and thus do not contain ionospheric reflection traces. TOPIST, however, is not able to produce Ne(h) profiles from all of the ISIS-2 ionograms with reflection traces because some of them did not contain frequency information. This information was missing due to difficulties encountered during the analog-to-digital conversion process in the detection of the ionogram frame-sync pulse and/or the frequency markers. Of the many digital topside ionograms that TOPIST was able to process, over 200 were found where direct comparisons could be made with Ne(h) profiles that were produced by manual scaling in the early days of the ISIS program. While many of these comparisons indicated excellent agreement (<10% average difference over the entire profile) there were also many cases with large differences (more than a factor of two). Here we will report on two approaches to improve the automatic inversion process: (1) improve the quality of the digital ionogram database by remedying the missing frequency-information problem when possible, and (2) using the above-mentioned comparisons as teaching examples of how to improve the original TOPIST software.

Benson, R. F.↗
DOE OSTI2021

A Commercial Building Plug Load Management System that Uses Internet of Things Technology to Automatically Identify Plugged-In Devices and Their Locations

Plug and process loads (PPLs) account for a large portion of U.S. commercial building energy use. There is a huge potential to reduce whole building consumption by targeting PPLs for energy savings measures or implementing some form of plug load management (PLM). Despite this potential, there has yet to be a widely adopted commercial PLM technology. This paper describes the Automatic Type and Location Identification System (ATLIS), a PLM system framework with automatic and dynamic load detection (ADLD). ADLD gives PLM systems the ability to automatically identify devices as they are plugged into the outlets of a building. The ATLIS framework takes advantage of smart, connected devices to identify device locations in a building, meter and control their power, and communicate this information to a central database. ATLIS includes five primary capabilities: location identification, communication, control, energy metering, and data storage. A laboratory proof of concept (PoC) demonstrated all but the energy metering capability, and these capabilities were validated using a series of system tests. The PoC was able to identify when a device was plugged into an outlet and the location of the device in the building. When a device was moved, the PoC's dashboard and database were automatically updated with the new location. The PoC implemented controls to devices from the system dashboard so that devices maintained correct schedules regardless of where they were plugged in within the building. ATLIS's primary technology application is improved PLM, but other applications include asset management, energy audits, and interoperability for grid-interactive efficient buildings. An ATLIS-based system could also be used to direct power to critical devices, such as ventilators, during a brownout or blackout. Such a framework is an opportunity to make PLM more widespread and reduce the amount of energy consumed by PPLs in current and future commercial buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2021

Alert Classification for the ALeRCE Broker System: The Light Curve Classifier

We present the first version of the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream and colors obtained from AllWISE and ZTF photometry. We apply a balanced random forest algorithm with a two-level scheme where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes among 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and Gaia DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with ≥6 g-band or ≥6 r-band detections in ZTF (868,371 sources as of 2020 June 9), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the ALeRCE Explorer website (http://alerce.online).

47 OTHER INSTRUMENTATION↗
NASA NTRS1990

A flight expert system for on-board fault monitoring and diagnosis

An architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing, and recovering from inflight faults is described. A prototype was implemented and an attempt was made to automate the knowledge acquisition process by employing a learning by being told methodology. The scope of acquired knowledge ranges from domain knowledge, including the information about objects and their relationships, to the procedural knowledge associated with the functionality of the mechanisms. AKAS (automatic knowledge acquisition system) is the constructed prototype for demonstration proof of concept, in which the expert directly interfaces with the knowledge acquisition system to ultimately construct the knowledge base for the particular application. The expert talks directly to the system using a natural language restricted only by the extent of the definitions in an analyzer dictionary, i.e., the interface understands a subset of concepts related to a given domain. In this case, the domain is the electrical system of the Boeing 737. Efforts were made to define and employ heuristics as well as algorithmic rules to conceptualize data produced by normal and faulty jet engine behavior examples. These rules were employed in developing the machine learning system (MLS). The input to MLS is examples which contain data of normal and faulty engine behavior and which are obtained from an engine simulation program. MLS first transforms the data into discrete selectors. Partial descriptions formed by those selectors are then generalized or specialized to generate concept descriptions about faults. The concepts are represented in the form of characteristic and discriminant descriptions, which are stored in the knowledge base and are employed to diagnose faults. MLS was successfully tested on jet engine examples.

Ali, Moonis↗
DOE OSTI2020

EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on Geothermal Reservoir Engineering. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained), S-wave velocity (km/s), and S-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained). The Updated_MEQ_catalog text file contains event origin time, x(m), y(m), z(m), error in x (m), error in y (m), error in z (m), and RMS misfit (millisecond). The 3D_seismic_P-wave_velocity_model animation file shows slices of the 3D P-wave velocity model. The 3D_seismic_S-wave_velocity_model animation file shows slices of the 3D S-wave velocity model. The Interactive_MEQ_locations API file is an interactive visualization of the updated microseismic event locations. The visualization allows users to view the event locations by dragging, rotating, and zooming in. References: Chai, C., Maceira, M., Santos-Villalobos, H. J., Venkatakrishnan, S. V., Schoenball, M., and EGS Collab Team, 2020, Automatic Seismic Phase Picking Using Deep Learning for the EGS Collab Project, in PROCEEDINGS, 45th Workshop on Geothermal Reservoir Engineering, edited, Stanford University, Stanford, California, 45, 1266-1276.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

Recently Graph Neural Networks (GNNs) have drawn tremendous attentions due to their unique capability to extend the Machine Learning (ML) approaches to broadly defined applications with unstructured data, especially graphs. Comparing with other ML modalities, the acceleration of GNNs is as critical but even more challenging due to the irregularity and heterogeneity from graph typologies that together limit the performance. Existing efforts mainly focus on handling graphs’ irregularity, however, have not studied the heterogeneity. To this end, in this work, we propose H-GCN, a PL-AIE-based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal ACAPs to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity and processes them using PL and the newly emerged AIE respectively. To further improve the performance, we explore the sparsity support of AIE and develop an efficient density-aware method to map tiles of SpMM onto the systolic tensor array automatically. Compared with the current state-of-the-art GCN accelerator, HGCN achieves on average 1.5× speedups.

Zhang, Chengming↗
DOE OSTI2021

Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high–quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear–Test–Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. Here, we compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

58 GEOSCIENCES↗
NASA NTRS1990

Validation environment for AIPS/ALS: Implementation and results

The work is presented which was performed in porting the Fault Injection-based Automated Testing (FIAT) and Programming and Instrumentation Environments (PIE) validation tools, to the Advanced Information Processing System (AIPS) in the context of the Ada Language System (ALS) application, as well as an initial fault free validation of the available AIPS system. The PIE components implemented on AIPS provide the monitoring mechanisms required for validation. These mechanisms represent a substantial portion of the FIAT system. Moreover, these are required for the implementation of the FIAT environment on AIPS. Using these components, an initial fault free validation of the AIPS system was performed. The implementation is described of the FIAT/PIE system, configured for fault free validation of the AIPS fault tolerant computer system. The PIE components were modified to support the Ada language. A special purpose AIPS/Ada runtime monitoring and data collection was implemented. A number of initial Ada programs running on the PIE/AIPS system were implemented. The instrumentation of the Ada programs was accomplished automatically inside the PIE programming environment. PIE's on-line graphical views show vividly and accurately the performance characteristics of Ada programs, AIPS kernel and the application's interaction with the AIPS kernel. The data collection mechanisms were written in a high level language, Ada, and provide a high degree of flexibility for implementation under various system conditions.

Segall, Zary↗
DOE OSTI2018

Efficient Removal of Fluoride Using Polypyrrole-Modified Biochar Derived from Slow Pyrolysis of Pomelo Peel: Sorption Capacity and Mechanism

In this study, a novel adsorbent was developed by loading polypyrrole (PPy) onto biochar (BC) prepared by slow pyrolysis of pomelo peel. This PPy-grafted BC mainly exhibited anion-exchange behavior, which dramatically increased fluoride adsorption capacity. The effects of various factors (adsorbent dosage, pyrrole concentration, initial solution pH, co-existing anions and temperature) on the fluoride adsorption were investigated. The adsorbent was characterized by scanning electronic microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and automatic titration methods. Results showed that the optimized adsorbent (BP-0.1) performed excellently at pH 2.8–10.0, and possessed positive charge at pH < 8.6. The adsorption isotherm data were fitted well by the Langmuir model and the maximum adsorption capacity was 18.52 mg/g at 25 ± 2 °C. The adsorption kinetics followed the pseudo-second-order model and adsorption equilibrium was achieved at 24 h. Although both HCO{sub 3}{sup −} and CO{sub 3}{sup 2−} had great influence on F{sup −} adsorption, there was no significant impact in the presence of Cl{sup −}, NO{sub 3}{sup −} and SO{sub 4}{sup 2−}. Real groundwater study showed that 2.5 g/L of BP-0.1 could effectively reduce F{sup −} from 10.0 to 1.4 mg/L at pH 5.0. Thermodynamic parameters confirmed the spontaneous and endothermic nature of the adsorption process. Effect of pH, XPS analysis and the change of amount between Cl{sup −} and F{sup −} in the adsorption process revealed that ion exchange and electrostatic attraction were involved in the adsorption process and ion exchange was the main adsorption mechanism.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Towards intelligent diagnostic system employing integration of mathematical and engineering model

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1990

Numerical derivative techniques for trajectory optimization

The adoption of robust numerical optimization techniques in trajectory simulation programs has resulted in powerful design and analysis tools. These trajectory simulation/optimization programs are widely used, and a representative list includes the GTS system, the POST program, and newer collocation methods such as OTIS and FONPAC. All of these programs rely on optimization algorithms which require objective function and constraint gradient data during the iteration process. However, most trajectory optimization problems lack simple analytical expressions for these derivatives. In the general case a function evaluation involves integrating aerodynamic, propulsive, and gravity forces over multiple trajectory phases with complex control models. With the newer collocation methods, the integration is replaced by defect constraints and cubic approximations for the state. While analytic gradient expressions can sometimes be derived for trajectory optimization problems, the derivation is cumbersome, time consuming, and prone to mistakes. Fortunately, an alternate method exists for the gradient evaluation, namely finite difference approximations. In this paper some finite difference gradient techniques developed for use with the GTS system are presented. These techniques include methods for computing first and second partial derivatives of single and multiple sets of functions. A key feature of these methods is an error control mechanism which automatically adjusts the perturbation size to obtain accurate derivative values.

Hallman, Wayne P.↗
NASA NTRS2004

Knowledge Driven Image Mining with Mixture Density Mercer Kernels

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels; which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Srivastava, Ashok N.↗
NASA NTRS2004

Knowledge Driven Image Mining with Mixture Density Mercer Kernals

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper we present the theory of Mercer Kernels; describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Srivastava, Ashok N.↗
DOE OSTI2012

Design and performance of a combined secondary ion mass spectrometry-scanning probe microscopy instrument for high sensitivity and high-resolution elemental three-dimensional analysis

State-of-the-art secondary ion mass spectrometry (SIMS) instruments allow producing 3D chemical mappings with excellent sensitivity and spatial resolution. Several important artifacts however arise from the fact that SIMS 3D mapping does not take into account the surface topography of the sample. In order to correct these artifacts, we have integrated a specially developed scanning probe microscopy (SPM) system into a commercial Cameca NanoSIMS 50 instrument. This new SPM module, which was designed as a DN200CF flange-mounted bolt-on accessory, includes a new high-precision sample stage, a scanner with a range of 100 {mu}m in x and y direction, and a dedicated SPM head which can be operated in the atomic force microscopy (AFM) and Kelvin probe force microscopy modes. Topographical information gained from AFM measurements taken before, during, and after SIMS analysis as well as the SIMS data are automatically compiled into an accurate 3D reconstruction using the software program 'SARINA,' which was developed for this first combined SIMS-SPM instrument. The achievable lateral resolutions are 6 nm in the SPM mode and 45 nm in the SIMS mode. Elemental 3D images obtained with our integrated SIMS-SPM instrument on Al/Cu and polystyrene/poly(methyl methacrylate) samples demonstrate the advantages of the combined SIMS-SPM approach.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗