Results for “difficult” — Page 14

DOE OSTI2020

A Review of Existing Test Methods for Occupancy Sensors

One of the key new features of connected lighting systems (CLS) is their ability to collect data from various types of integral sensors and share that data with other lighting or building systems. Occupancy and vacancy sensors have been widely adopted as an energy-saving strategy in buildings, yet published test methods for reproducibly characterizing their performance remain few and limited in their sophistication. As a result, it has been difficult to predict their performance in a specific application, and in practice, they frequently do not meet energy-savings expectations. Occupants at times remove or otherwise bypass occupancy sensors that hinder their work or otherwise do not perform as expected, thereby compromising the sensors’ potential to reduce energy consumption. Poor performance can result from multiple causes – ranging from fundamental limitations of the sensor technology, to misconfiguration, to poor placement in the room or space. Innovative occupancy sensors, some of them combining multiple sensing technologies, have come on the market over the years, with claims of improved performance compared to their predecessors; yet in practice, their performance has led to neither high deployment nor high user satisfaction in any significant application, nor has it differed enough from the performance of previous products to necessitate a test method that facilitated comparison between them. While the performance of both common and novel occupancy sensors has been the subject of many published research articles, the test methods that have been employed for them have typically been loosely described and incorporated custom equipment or techniques that render them difficult to reproduce, or have been limited in their ability to fairly characterize devices that utilize varying sensor technology. The lack of a well-described, technology-agnostic test method that yields reproducible results across different implementations has been a barrier to the commercial success of new occupancy sensor products, as users and specifiers who have been disappointed with previous products are often unwilling to take a chance with new ones. Motivated by a desire to fairly characterize new technologies that continue to enter the market and claim not only improved occupancy detection but, in some cases, also additional capabilities (e.g., the ability to measure traffic or discern between different object types), this report presents the results of a literature review of recent published, well-described test methods for characterizing occupancy sensor performance, as well as research articles containing ad-hoc test methods. The review also identifies and consolidates the best techniques for characterizing sensor performance in indoor spaces and identifies test method gaps that need to be filled in order to evaluate emerging technologies and products.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

A Review of Existing Test Methods for Occupancy Sensors

One of the key new features of connected lighting systems (CLS) is their ability to collect data from various types of integral sensors and share that data with other lighting or building systems. Occupancy and vacancy sensors have been widely adopted as an energy-saving strategy in buildings, yet published test methods for reproducibly characterizing their performance remain few and limited in their sophistication. As a result, it has been difficult to predict the performance of such sensors in a specific application, and in practice, they frequently do not meet energy-savings expectations. Occupants at times remove or otherwise bypass occupancy sensors that hinder their work or otherwise do not perform as expected, thereby compromising the sensors’ potential to reduce energy consumption. Poor performance can result from multiple causes – ranging from fundamental limitations of the sensor technology, to misconfiguration, to poor placement in the room or space. Innovative occupancy sensors, some of them combining multiple sensing technologies (i.e., multimodal), have come on the market over the years, with claims of improved performance compared to their predecessors. However, in practice, their performance has neither differed enough from the performance of previous products to necessitate a test method that facilitated comparison between them, nor has it led to high deployment or high user satisfaction in human-occupied spaces with persistent presence. While the performance of both common and novel occupancy sensors has been the subject of many published research articles, the test methods that have been employed for them typically have been loosely described and have incorporated custom equipment or techniques that render them difficult to reproduce, or have been limited in their ability to fairly characterize devices that utilize varying sensor technology. The lack of a fully described, technology-agnostic test method that yields reproducible results across different implementations has been a barrier to the commercial success of new occupancy-sensor products, as users and specifiers who have been disappointed with previous products are often unwilling to take a chance with new ones. Motivated by a desire to fairly characterize new technologies that continue to enter the market and claim not only improved occupancy detection but, in some cases, additional capabilities (e.g., the ability to measure traffic or discern between different object types), this report presents the results of a literature review of recently published fully described test methods for characterizing occupancy-sensor performance, as well as research articles containing ad-hoc test methods. The review also identifies and consolidates test conditions for characterizing sensor performance in indoor spaces and identifies apparent test method gaps that need to be filled in order to evaluate emerging technologies and products. The identified test-method conditions are intended to enable the development of a future technology-agnostic test method that facilitates occupancy-sensor performance characterization more-accurately representing performance in buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

How To Manual - 4.56

The "how to" document is designed to help walk the analyst through difficult aspects of software usage. It should supplement both the User's manual and the Theory document, by providing examples and detailed discussion that reduce learning time for complex set ups. These documents are intended to be used together. We will not formally list all parameters for an input here — see the User's manual for this. All the examples in the "How To" document are part of the Sierra/SD test suite, and each will run with no modification. The nature of this document casts together a number of rather unrelated procedures. Grouping them is difficult. Please try to use the table of contents and the index as a guide in finding the analyses of interest.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

High-Level Considerations for Access and Access Controls by Design

Nuclear power plants must be, by design and construction, robust structures and difficult to penetrate. Ideally, for security, the reactors would be sited underground, beneath a massive solid block, too thick to be penetrated by tools or explosives with all communications and power transfer lines also underground and fortified. Limiting access with difficult-to-penetrate physical barriers is going to be key for determining response and staffing requirements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

High-Level Considerations for Access and Access Controls by Design

The design and construction of a nuclear power plant must include robust structures and a security boundary that is difficult to penetrate. For security considerations, the reactors would ideally be sited underground, beneath a massive solid block, which would be too thick to be penetrated by tools or explosives. Additionally, all communications and power transfer lines would also be located underground and would be fortified against any possible design basis threats. Limiting access with difficult-to-penetrate physical barriers is a key aspect for determining response and staffing requirements. Considerations considered in a graded approach to physical protection are described.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Application of a machine learning algorithm (XGBoost) to offline RHIC luminosity optimization

The operation parameter optimization in 2020 RHIC low energy run is difficult. First, the RHIC luminosity is affected by many RHIC operation parameters, as well as affected by many Low Energy RHIC electron Cooling (LEReC) operation parameters. Second, the luminosity signal in this run is noisy and not sensitive to these parameter changes, especially when these parameters are very close to their optimized values. It is not easy to distinguish the effects of one parameter from all other operation parameters separately. Therefore, it is difficult to optimize the luminosity by varying these parameters one by one. To find a way for luminosity optimization, we analyze some operation parameters via a machine learning algorithm - XGBoost. After constructing a black-box surrogate model from XGBoost and plotting their partial dependency plots (PDF) and SHAP value plots for different operation parameters, we can find the effects of an individual parameter on the RHIC luminosity and optimize it accordingly.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Picking an Open Source License at LLNL: Guidance and Recommendations from the Computing Directorate

Science and innovation are at the core of LLNL’s mission, and open source software is a vehicle by which LLNL innovations reach the scientific community, the nation, and the world. It also serves as a vehicle for building collaborations, publicizing our capabilities, and attracting new employees. Over half of the software produced by LLNL is open source, and open source licenses are the legal tools that allow our software to be used widely. Choosing an appropriate license can be difficult, but it can be even more difficult to change licenses after your software has amassed a significant number of contributors, so it is important understand the potentially subtle implications of your license choice. This document provides a set of recommendations for the busy and uninitiated. They are not set in stone (there are always exceptions). Our goal is to provide default choices that align best with LLNL’s mission, along with a high-level rationale.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

High-Level Considerations for Access and Access Controls by Design

Nuclear power plants must be, by design and construction, robust structures and difficult to penetrate. Limiting access with difficult-to-penetrate physical barriers is going to be key for staffing reduction. Ideally, for security, the reactors would be sited underground, beneath a massive solid block, too thick to be penetrated by tools or explosives with all communications and power transfer lines also underground and fortified. Having the minimal possible number of access points and methods to completely block access from these points if a threat is detected will greatly help us justify staffing reduction.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Sierra/SD-- How To Manual - 4.58

The “how to” document is designed to help walk the analyst through difficult aspects of software usage. It should supplement both the User’s manual and the Theory document, by providing examples and detailed discussion that reduce learning time for complex set ups. These documents are intended to be used together. We will not formally list all parameters for an input here – see the User’s manual for this. All the examples in the “How To” document are part of the Sierra/SD test suite, and each will run with no modification. The nature of this document casts together a number of rather unrelated procedures. Grouping them is difficult. Please try to use the table of contents and the index as a guide in finding the analyses of interest.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Vehicle Tracking

The detection and tracking of vehicles moving at hypersonic speeds (> Mach 5) at altitudes of ~100 km has been of growing interest due to the ongoing development of hypersonic glide vehicles (HGVs) by the U.S., Russia and China. This challenge is notably different from traditional ballistic missile systems whose re-entry vehicles move at hypersonic velocities but at much higher altitudes on a ballistic course. Hypersonic glide vehicles are also designed to be able to make sudden turns along their flight path making prediction of their trajectories difficult. Their ability to maneuver, combined with their high velocity and altitude make them difficult to track. The combination of these characteristics presents a challenge for anyone attempting to defend against a hypersonic glide vehicle attack by generally shortening the time available to react. That reaction can involve moving potential targets or attempting to detect the hypersonic glide vehicle and intercept it before it reaches the intended target. Figure 1 compares the trajectory of hypersonic vehicles compared to that of a traditional ballistic missile.

42 ENGINEERING↗

DOE OSTI2021

Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms (Consortium on Nuclear Security Technologies (CONNECT) Q1 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. The challenge is that spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

High Throughput expression and characterization of laccases in Saccharomyces cerevisiae

Laccases are oxidative enzymes containing 4 conserved copper heteroatoms. Laccases catalyze cleavage of bonds in lignin using radical chemistry, yet their exact specificity for bonds (such as the β-O-4 or C-C) in lignin remains unknown and may vary with the diversity of laccases across fungi, plants and bacteria. Bond specificity may perhaps even vary for the same enzyme across different reaction conditions. Determining these differences has been difficult due to the fact that heterologous expression of soluble, active laccases has proven difficult. Here we describe the successful heterologous expression of functional laccases in two strains of Saccharomyces cerevisiae, including one we genetically modified with CRISPR. We phylogenically map the enzymes that we successfully expressed, compared to those that did not express. We also describe differences protein sequence differences and pH and temperature profiles and their ability to functionally express, leading to a potential future screening platform for directed evolution of laccases and other ligninolytic enzymes such as peroxidases.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Software Verification Toolkit (SVT): Survey on Available Software Verification Tools and Future Direction

Writing software is difficult. However, writing complex, well tested and designed, and functionally correct software is incredibly difficult. An entire field of study is devoted to the validation and verification of software to address this problem, and in this paper we analyze the landscape of currently available third party software. We have divided our analyses into three separate subsections with regards to software validation: formal methods, static analysis, and test generation. Formal verification is the most complex method in which to validate software correctness, but also the most thorough as it truly validates the mathematical validity of the source code. Static analysis generally is relegated to abstract syntax tree traversal techniques to find errors related to faulty software such as memory leaks or stack overflow issues. Automatic test generation is similar in implementation to static analysis, but pushes a bit further in verifying the boundedness of function inputs and outputs with regards to annotated or parsed criteria. The crux of this report is to analyze and describe the software tools that implement these techniques to validate and verify software. Pros and cons related to installation, utilization, and capabilities of the frameworks are described, and reproducible examples are provided with a focus on usability. The initial survey concluded that the most interesting tools of note are Z3, Isabelle/HOL, and TLA+ with regards to formal verification; and Infer, Frama-C, and SonarQube with regards to static analysis. With these tools in mind, a final conjecture is provided that describes future avenues of utilizing these tools for developing a verification framework to assist in validating existing software at Sandia National Laboratories.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Analysis of Defects in Metal Additive Manufacturing with Augmented Data Generation

Laser powder bed fusion (LPBF) is a method of additive manufacturing (AM) that selectively melts and fuses together microscopic metallic powder. LPBF offers the benefit of producing custom structures out of high strength metals that can be difficult to fabricate with conventional methods. The challenge of LPBF is that 3D printed structures often have internal pores due to process flaws. Pulsed thermal tomography (PTT) is a method for reconstructing the depth profile of materials, allowing the visualization internal voids in solids. In prior work, we developed a convolutional neural network (CNN) which, having been trained on simulated 2D PTT images of subsurface elliptical defects, was able to classify the semi-major radii, semi-minor radii, and angular orientation of the best-fit ellipses in previously unseen PTT images. The unseen PTT images contained subsurface irregular defect shapes imported from scanning electron microscopy (SEM) images of metallic LPBF-printed specimens. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN.

36 MATERIALS SCIENCE↗

DOE OSTI2022

The Critical Need for Innovations in Instruments and Techniques to Enable In-Situ Cold Ion and Cold Electron Measurements within the Magnetosphere

Relatively few studies have focused on the cold plasma populations of the magnetosphere due to difficulties associated with obtaining measurements. The cold particle populations are defined here as those with total energy approximately <100 eV which is an energy range for which measurements are difficult (regardless of species), but which often make up the bulk of the plasma density. These populations have known and suspected impacts on the structure and dynamics of the magnetosphere. Unfortunately, the lack of accurate measurements of the cold ion and cold electron populations through the magnetosphere makes closure of these science questions extremely difficult if not impossible. In a complex system of interconnected parts like the Earth’s magnetosphere, a lack of understanding of one part of the system (the cold electron and ion populations) is disabling. Hence, until cold electron and cold ion measurements are obtained robustly with every mission, the magnetospheric plasma environment cannot be completely understood. This will require innovations in plasma spectrometers and associated techniques required to obtain high-fidelity in-situ measurements of the cold ion and cold electron populations in the magnetosphere. This paper seeks to review the instruments and techniques that have been used to date and present possible options for future missions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗