Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Automatic Data Processing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

WE-AB-BRB-12: Nanoscintillator Fiber-Optic Detector System for Microbeam Radiation Therapy Dosimetry

Purpose: Microbeam Radiation Therapy (MRT) is an experimental radiation therapy that has demonstrated a higher therapeutic ratio than conventional radiation therapy in animal studies. There are several roadblocks in translating the promising treatment technology to clinical application, one of which is the lack of a real-time, high-resolution dosimeter. Current clinical radiation detectors have poor spatial resolution and, as such, are unsuitable for measuring microbeams with submillimeter-scale widths. Although GafChromic film has high spatial resolution, it lacks the real-time dosimetry capability necessary for MRT preclinical research and potential clinical use. In this work we have demonstrated the feasibility of using a nanoscintillator fiber-optic detector (nanoFOD) system for real-time MRT dosimetry. Methods: A microplanar beam array is generated using a x-ray research irradiator and a custom-made, microbeam-forming collimator. The newest generation nanoFOD has an effective size of 70 µm in the measurement direction and was calibrated against a kV ion chamber (RadCal Accu-Pro) in open field geometry. We have written a computer script that performs automatic data collection with immediate background subtraction. A computer-controlled detector positioning stage is used to precisely measure the microbeam peak dose and beam profile by translating the stage during data collection. We test the new generation nanoFOD system, with increased active scintillation volume, against the previous generation system. Both raw and processed data are time-stamped and recorded to enable future post-processing. Results: The real-time microbeam dosimetry system worked as expected. The new generation dosimeter has approximately double the active volume compared to the previous generation resulting in over 900% increase in signal. The active volume of the dosimeter still provided the spatial resolution that meets the Nyquist criterion for our microbeam widths. Conclusion: We have demonstrated that real-time dosimetry of MRT microbeams is feasible using a nanoscintillator fiber-optic detector with integrated positioning system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

An LLVM Instrumentation Plug-in for Score-P

Reducing application runtime, scaling parallel applications to higher numbers of processes/threads, and porting applications to new hardware architectures are tasks necessary in the software development process. Therefore, developers have to investigate and understand application runtime behavior. Tools such as monitoring infrastructures that capture performance relevant data during application execution assist in this task. The measured data forms the basis for identifying bottlenecks and optimizing the code.Monitoring infrastructures need mechanisms to record application activities in order to conduct measurements. Automatic instrumentation of the source code is the preferred method in most application scenarios. We introduce a plug-in for the LLVM infrastructure that enables automatic source code instrumentation at compile-time. In contrast to available instrumentation mechanisms in LLVM/Clang, our plug-in can selectively include/exclude individual application functions. This enables developers to fine-tune the measurement to the required level of detail while avoiding large runtime overheads due to excessive instrumentation.

Tschüter, Ronny↗
DOE OSTI2021

Parameterization of arctic hydrometeor physics using new precipitation measurement technologies: Final Report

Predictions of precipitation are highly sensitive to the accuracy of parameterized growth and sedimentation processes, especially in remote regions such as the Arctic where observations are scarce. The goal of this study was to combine measurements of meteorological conditions and retrievals from passive and active remote sensors to provide refined parameterizations of precipitation properties and processes with a particular focus on the Arctic. To accomplish the project goals, new instrumentation was used from the Oliktok Point Mobile Facility and the Utqiagvik (Barrow) North Slope of Alaska ARM site focusing in particular on Multi-Angle Snowflake Camera (MASC) data. The MASC is the first device able to automatically photograph precipitation particles in free-fall from multiple angles while simultaneously measuring their fall speed. The MASC installed at the ARM Oliktok Point and Utqiagvik facilities was used to examine the nature of Arctic precipitation, taking particular advantage of the unique suite of precipitation, meteorological, and remote sensing instrumentation that is available at the high latitude sites. Combined with radiometer data we were able to explore the relationship between precipitation particle characteristics and the clouds where the snow is created. Ground-based wind measurements provided detailed data on turbulence. Further theoretical work explored the physical mechanisms controlling precipitation size distributions, the physics determining hydrometeor fallspeed, and the existence of general solutions to the Navier-Stokes equations for falling particles. Outreach work introduced hydrometeor classification to classrooms across the United States.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Generalizable Web User Interface for Scalable and Streamlined Deployment of Building Energy Management Systems in Small and Medium-Sized Commercial Buildings

Small and medium-sized commercial buildings (SMCBs) comprise 94% of US commercial buildings yet face significant barriers to implementing building energy management systems despite advances in smart device technology. Existing solutions present critical limitations: cloud-based API solutions simplify deployment but create vendor lock-in constraints; commercial integrated software solutions ensure compatibility via standardized protocols but require substantial cost and technical expertise; open-source IoT platforms offer cost-effective vendor independence but provide insufficient standardized protocol support for commercial building automation. This research presents a generalizable web user interface framework that bridges the gap between evolving smart device capabilities and lagging software infrastructure for SMCBs. The proposed system integrates VOLTTRON open-source middleware with an automated configuration converter that transforms unified specifications written in YAML, a human-readable data-serialization format, into system-specific files, streamlining manual setup processes. The vendor-agnostic architecture supports industry-standard protocols (BACnet and Modbus) and semantic building models while providing adaptive web interfaces that dynamically adjust to various building configurations. Demonstrations through simulation-based testing and a field deployment show automatic interface adaptation across heterogeneous HVAC systems and multizone monitoring. The automated configuration converter also substantially reduces labor-intensive setup.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗
DOE OSTI2022

Ring artifact and Poisson noise attenuation via volumetric multiscale nonlocal collaborative filtering of spatially correlated noise

X-ray micro-tomography systems often suffer from high levels of noise. In particular, severe ring artifacts are common in reconstructed images, caused by defects in the detector, calibration errors, and fluctuations producing streak noise in the raw sinogram data. Furthermore, the projections commonly contain high levels of Poissonian noise arising from the photon-counting detector. This work presents a 3-D multiscale framework for streak attenuation through a purposely designed collaborative filtering of correlated noise in volumetric data. A distinct multiscale denoising step for attenuation of the Poissonian noise is further proposed. By utilizing the volumetric structure of the projection data, the proposed fully automatic procedure offers improved feature preservation compared with 2-D denoising and avoids artifacts which arise from individual filtering of sinograms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Grid connection management of distributed generators on the basis of renewable energy sources

The concept of distributed energy of renewable energy sources is becoming increasingly popular. It is not simply supplementary but a real alternative to the conventional power supply from centralized power grids. Distributed electricity generators integrated into the Unified Energy System are a complex system in which the relationship between the observed parameters and the system state is ambivalent and ambiguous. Therefore, the approach to management in automatic control systems based on control of the output parameters is inappropriate in nature. In this case, the elaboration of a control action should be carried out on the basis of data on the current state of the object of control. Issues of the grid connection of distributed generators and control of their process parameters are considered.

14 SOLAR ENERGY↗
DOE OSTI2023

Bi-Level Linear Programming Model for Automatic Load Shedding: A Distributed Wide-Area Measurement System-based Solution

Load shedding is currently implemented as a two-step based approach. In the first step, manual load shedding is taken place, were system operators, using estimates, inform distribution utilities of predicted stressful conditions. Information provided include the potential use of energy reserves, as well as load shedding amount. In a second step, automatic load shedding is done. The latter is realized using protection relays. While considering frequency variation, pre-defined values of load to be shed and correspondent number of stages for such to be realized are transformed into relay settings. Under-frequency protection relays use only local measurements towards decision making, thus operate in a decentralized architecture. Decision making is done in milliseconds plus breaker time. While this approach has provided much system reliability, considering the new smart grid paradigm, where system dynamics are much faster due to increasing renewable resources penetration, in some operating conditions it will generate sub-optimal solutions, such as islanding. Phasor measurement units provide a source of information which can be useful for this problem. Centralized architecture-based solutions for automatic load shedding, as present in the state-of-the-art, require though total processing times which are not acceptable for real-life implementation. In this work, considering the above, a bi-level linear programming model is presented. The model is implemented considering a distributed architecture while leveraging phasor measurement units data. The upper-level model estimates the current system state. Results of this model are embedded in a lower-level model, which decision variables are the location and load value to be shed. Easy-to-implement model, built-on the classic weighted least squares solution, highlight potential aspects towards real-life applications.

Bretas, Arturo Suman↗
DOE OSTI2019

The QCECOR Value-Added Product: Quality-Controlled Eddy Correlation Flux Measurements

Eddy correlation flux (ECOR) measurement system stations are used by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility to provide surface turbulence flux measurements. In recent years, ARM has significantly enhanced its ECOR systems and made them available at all its primary research observatories and mobile facilities. However, the original ECOR data were not corrected through common eddy correlation corrections and still contain suspicious data due to instrument and measurement uncertainties. Such uncertainties in the ECOR surface turbulence fluxes hampered their uses by the user community. To improve the ECOR data, we develop a value-added product (VAP) —the Quality-Controlled Eddy Correlation Flux (QCECOR) — by applying the following necessary corrections and quality control (QC) procedures: Eddy correlation corrections. Determination of the quality-controlled energy fluxes from the corrected ECOR fluxes. Stringent QC checks. Flagging possible incorrect fluxes due to hydrometeor impacts using the collocated surface energy balance system (SEBS) wetness measurements. The QCECOR VAP currently contains two variables: surface latent heat flux (LH) and sensible heat flux (SH), together with their QC flags. When SEBS are collocated with ECOR, the wetness measurements from SEBS are used to flag the LH that may be incorrect due to hydrometeors such as precipitation, dew, or frost. An indeterminate flag is given to those that fail the wetness test. The QCECOR data are currently available at the Southern Great Plains (SGP) atmospheric observatory for the ARM extended facilities of E1, E3, E5, E6, E10, E14, E16, E21, E24, E31, E33, E37, E38, E39, and E41 updated to 2018. The data are also available at the following AMF sites: PYE (Point Reyes, 2005), NIM (Niamey, Niger, 2006), FKB (Black Forest, Germany, 2007), HFE (Shouxian, China, 2008), GRW (Azores, 2009 to 2010), and MAO (Manacapuru, Brazil, 2014-2015). The algorithm is now in the process of moving to production mode. After that, QCECOR will run automatically for all available ARM sites.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Sequential Decision Making (SDM) for Mesh Refinement and Model Selection in Multiscale, Multi-Physics Applications

Intelligent automation and decision support are needed to enhance computational efficiency and robustness in multiscale and multi-physics problems, including materials science, manufacturing, and climate and weather modeling. Current scientific computing approaches for enabling decisions by scientists fail to explore the role of learning, reasoning, and probabilistic planning. Often these decisions are not performed in real-time during the computation but are made prior to the start of the computation, which must be interrupted in order to make changes to the prior choices. Such interruptions at different stages of the computation increase the total computing time and the need for a human expert to frequently monitor the results. State of art scientific computing methods consist of rule-based algorithms that cannot automatically adapt to a dynamically changing computing environment. The development of a Sequential Decision Making (SDM) framework will automate scientific computing by optimizing the policies for mesh refinement, time-stepping, model and algorithm selection, resource allocation, and pre and post-processing. Our agent SDM framework for scientific computing will consist of data-driven learning (Classifier), automated reasoning (contextual knowledge), and probabilistic planning (Reinforcement Learning). In this project, we focused on three problems to demonstrate our SDM framework on a set of ordinary and partial differential equations. Classification of Lorenz system regions using Feed-Forward Neural Networks examined learning in the SDM framework. On the other hand, reasoning and planning in the SDM framework were used in two problems: adaptive time-stepping for nonlinear ODEs using on-policy RL algorithms, and adaptive mesh refinement for 2-D PDEs using off-policy RL algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Patient-specific quantification of image quality: An automated method for measuring spatial resolution in clinical CT images

Purpose: To develop and validate an automated technique for evaluating the spatial resolution characteristics of clinical computed tomography (CT) images. Methods: Twenty one chest and abdominopelvic clinical CT datasets were examined in this study. An algorithm was developed to extract a CT resolution index (RI) analogous to the modulation transfer function from clinical CT images by measuring the edge-spread function (ESF) across the patient’s skin. A polygon mesh of the air-skin boundary was created. The faces of the mesh were then used to measure the ESF across the air-skin interface. The ESF was differentiated to obtain the line-spread function (LSF), and the LSF was Fourier transformed to obtain the RI. The algorithm’s ability to detect the radial dependence of the RI was investigated. RIs measured with the proposed method were compared with a conventional phantom-based method across two reconstruction algorithms (FBP and iterative) using the spatial frequency at 50% RI, f{sub 50}, as the metric for comparison. Three reconstruction kernels were investigated for each reconstruction algorithm. Finally, an observer study was conducted to determine if observers could visually perceive the differences in the measured blurriness of images reconstructed with a given reconstruction method. Results: RI measurements performed with the proposed technique exhibited the expected dependencies on the image reconstruction. The measured f{sub 50} values increased with harder kernels for both FBP and iterative reconstruction. Furthermore, the proposed algorithm was able to detect the radial dependence of the RI. Patient-specific measurements of the RI were comparable to the phantom-based technique, but the patient data exhibited a large spread in the measured f{sub 50}, indicating that some datasets were blurrier than others even when the projection data were reconstructed with the same reconstruction algorithm and kernel. Results from the observer study substantiated this finding. Conclusions: Clinically informed, patient-specific spatial resolution can be measured from clinical datasets. The method is sufficiently sensitive to reflect changes in spatial resolution due to different reconstruction parameters. The method can be applied to automatically assess the spatial resolution of patient images and quantify dependencies that may not be captured in phantom data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Multisensor Agile Adaptive Sampling (MAAS): A Methodology to Collect Radar Observations of Convective Cell Life Cycle

Abstract Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Sample registration software for process automation in the Neutron Activation Analysis (NAA) Facility in Malaysia nuclear agency

Neutron Activation Analysis (NAA) had been established in Nuclear Malaysia since 1980s. Most of the procedures established were done manually including sample registration. The samples were recorded manually in a logbook and given ID number. Then all samples, standards, SRM and blank were recorded on the irradiation vial and several forms prior to irradiation. These manual procedures carried out by the NAA laboratory personnel were time consuming and not efficient. Sample registration software is developed as part of IAEA/CRP project on ‘Development of Process Automation in the Neutron Activation Analysis (NAA) Facility in Malaysia Nuclear Agency (RC17399)’. The objective of the project is to create a pc-based data entry software during sample preparation stage. This is an effective method to replace redundant manual data entries that needs to be completed by laboratory personnel. The software developed will automatically generate sample code for each sample in one batch, create printable registration forms for administration purpose, and store selected parameters that will be passed to sample analysis program. The software is developed by using National Instruments Labview 8.6.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Automated annotation of scientific texts for ML-based keyphrase extraction and validation

Advanced omics technologies and facilities generate a wealth of valuable data daily; however, the data often lack the essential metadata required for researchers to find, curate, and search them effectively. The lack of metadata poses a significant challenge in the utilization of these data sets. Machine learning (ML)–based metadata extraction techniques have emerged as a potentially viable approach to automatically annotating scientific data sets with the metadata necessary for enabling effective search. Text labeling, usually performed manually, plays a crucial role in validating machine-extracted metadata. However, manual labeling is time-consuming and not always feasible; thus, there is a need to develop automated text labeling techniques in order to accelerate the process of scientific innovation. This need is particularly urgent in fields such as environmental genomics and microbiome science, which have historically received less attention in terms of metadata curation and creation of gold-standard text mining data sets. In this paper, we present two novel automated text labeling approaches for the validation of ML-generated metadata for unlabeled texts, with specific applications in environmental genomics. Our techniques show the potential of two new ways to leverage existing information that is only available for select documents within a corpus to validate ML models, which can then be used to describe the remaining documents in the corpus. The first technique exploits relationships between different types of data sources related to the same research study, such as publications and proposals. The second technique takes advantage of domain-specific controlled vocabularies or ontologies. In this paper, we detail applying these approaches in the context of environmental genomics research for ML-generated metadata validation. Our results show that the proposed label assignment approaches can generate both generic and highly specific text labels for the unlabeled texts, with up to 44% of the labels matching with those suggested by a ML keyword extraction algorithm.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Power Plant Model Validation Tool

The PPMV is used to validate generator model using disturbance recordings. The PPMV tool contains a collection of power plant models and model validation studies, as well as disturbance recordings from a number of historic grid events. The user can import data from a new disturbance into the database, which converts PMU and SCADA data into GE PSLF format, and then run the tool to validate (or invalidate) the model for a specific power plant against its actual performance. The PNNL PPMV tool enables the automation of the process of power plant model validation using disturbance recordings. The tool uses PMU and SCADA measurements as input information. The tool automatically adjusts all required EPCL scripts and interacts with GE PSLF in the batch mode. The main tool features includes: The tool interacts with GE PSLF; The tool uses GE PSLF Play-In Function for generator model validation; Database of projects (model validation studies); Database of the historic events; Database of the power plant; The tool has advanced visualization capabilities; and the tool automatically generates reports.

Source record↗
DOE OSTI2014

SWAP-Assembler: scalable and efficient genome assembly towards thousands of cores

Background: There is a widening gap between the throughput of massive parallel sequencing machines and the ability to analyze these sequencing data. Traditional assembly methods requiring long execution time and large amount of memory on a single workstation limit their use on these massive data. Results: This paper presents a highly scalable assembler named as SWAP-Assembler for processing massive sequencing data using thousands of cores, where SWAP is an acronym for Small World Asynchronous Parallel model. In the paper, a mathematical description of multi-step bi-directed graph (MSG) is provided to resolve the computational interdependence on merging edges, and a highly scalable computational framework for SWAP is developed to automatically preform the parallel computation of all operations. Graph cleaning and contig extension are also included for generating contigs with high quality. Experimental results show that SWAP-Assembler scales up to 2048 cores on Yanhuang dataset using only 26 minutes, which is better than several other parallel assemblers, such as ABySS, Ray, and PASHA. Results also show that SWAP-Assembler can generate high quality contigs with good N50 size and low error rate, especially it generated the longest N50 contig sizes for Fish and Yanhuang datasets. Conclusions: In this paper, we presented a highly scalable and efficient genome assembly software, SWAPAssembler. Compared with several other assemblers, it showed very good performance in terms of scalability and contig quality. This software is available at: https://sourceforge.net/projects/swapassembler

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Computer systems for annotation of single molecule fragments

There are provided computer systems for visualizing and annotating single molecule images. Annotation systems in accordance with this disclosure allow a user to mark and annotate single molecules of interest and their restriction enzyme cut sites thereby determining the restriction fragments of single nucleic acid molecules. The markings and annotations may be automatically generated by the system in certain embodiments and they may be overlaid translucently onto the single molecule images. An image caching system may be implemented in the computer annotation systems to reduce image processing time. The annotation systems include one or more connectors connecting to one or more databases capable of storing single molecule data as well as other biomedical data. Such diverse array of data can be retrieved and used to validate the markings and annotations. The annotation systems may be implemented and deployed over a computer network. They may be ergonomically optimized to facilitate user interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Soil Segregation Methods for Reducing Transportation and Disposal Costs - 13544

At Formerly Utilized Sites Remedial Action Program (FUSRAP) sites where the selected alternative for contaminated soil is excavation and off-site disposal, the most significant budget items of the remedial action are the costs for transportation and disposal of soil at an off-site facility. At these sites, the objective is to excavate and dispose of only those soils that exceed derived concentration guideline levels. In situ soil segregation using gross gamma detectors to guide the excavation is often challenging at sites where the soil contamination is overlain by clean soil or where the contaminated soil is located in isolated, subsurface pockets. In addition, data gaps are often identified during the alternative evaluation and selection process, resulting in increased uncertainty in the extent of subsurface contamination. In response, the U.S. Army Corps of Engineers, Buffalo District is implementing ex situ soil segregation methods. At the remediated Painesville Site, soils were excavated and fed through a conveyor-belt system, which automatically segregated them into above- and below-cleanup criteria discharge piles utilizing gamma spectroscopy. At the Linde Site and the Shallow Land Disposal Area (SLDA) Site, which are both in the remediation phase, soils are initially segregated during the excavation process using gross gamma detectors and then transported to a pad for confirmatory manual surveying and sampling. At the Linde Site, the ex situ soils are analyzed on the basis of a site-specific method, to establish compliance with beneficial reuse criteria that were developed for the Linde remediation. At the SLDA Site, the ex situ soils are surveyed and sampled based on Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) final status survey guidance to demonstrate compliance with the derived concentration guideline levels. At all three sites, the ex situ soils that meet the site- specific DCGLs are retained on-site and used as backfill material. This paper describes the ex situ soil segregation methods, the considerations of each method, and the estimated cost savings from minimizing the volume of soil requiring transportation and off-site disposal. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Optimizing Tensor Contraction Expressions for Hybrid CPU-GPU Execution

Tensor contractions are generalized multidimensional matrix multiplication operations that widely occur in quantum chemistry. Efficient execution of tensor contractions on Graphics Processing Units (GPUs) requires several challenges to be addressed, including index permutation and small dimension-sizes reducing thread block utilization. Moreover, to apply the same optimizations to various expressions, we need a code generation tool. In this paper, we present our approach to automatically generate CUDA code to execute tensor contractions on GPUs, including management of data movement between CPU and GPU. To evaluate our tool, GPU-enabled code is generated for the most expensive contractions in CCSD(T), a key coupled cluster method, and incorporated into NWChem, a popular computational chemistry suite. For this method, we demonstrate speedup over a factor of 8.4 using one GPU (instead of one core per node) and over 2.6 when utilizing the entire system using hybrid CPU+GPU solution with 2 GPUs and 5 cores (instead of 7 cores per node). Finally, we analyze the implementation behavior on future GPU systems.

CUDA↗