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Case study highlighting the design, implementation strategies, and continuous performance monitoring of NREL's Research Support Facility data center.
This paper introduces SOL, which is both an efficient data representation for sensor measurements and network statistics, and a complete low-power wireless sensor management system that builds around it. A SOL system consists of multiple low-power wireless mesh networks in which motes connected to sensors and actuators send data to a single server. It offers multi-tier data replication and the associated data recovery. SOL is used in 3 pilot deployments for micro-climate monitoring, building automation and agriculture applications. In conjunction with Metronome Systems' NeoMote and Manager devices, SOL is a turn-key ready-to-deploy end-to-end system.
Conventional tomographic reconstruction typically depends on centralized servers for both data storage and computation, leading to concerns about memory limitations and data privacy. Distributed reconstruction algorithms mitigate these issues by partitioning data across multiple nodes, reducing server load and enhancing privacy. However, these algorithms often encounter challenges related to memory constraints and communication overhead between nodes. In this paper, we introduce a decentralized Alternating Directions Method of Multipliers (ADMM) with configurable quantization. By distributing local objectives across nodes, our approach is highly scalable and can efficiently reconstruct images while adapting to available resources. To overcome communication bottlenecks, we propose two quantization techniques based on K-means clustering and JPEG compression. Numerical experiments with benchmark images illustrate the tradeoffs between communication efficiency, memory use, and reconstruction accuracy.
The Belle II experiment at KEK observed its first collisions in the summer of 2018. Processing the large amounts of data that will be produced requires conditions data to be readily available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. This was accomplished by relying on industry-standard tools and methods: the conditions database is built as an HTTP REST service using tools such as Swagger for the API interface development, Payara for the Java EE application server, and Squid for the caching proxy. This article presents the design of the Belle II conditions database production environment as well as details about the capabilities and performance during both Monte Carlo campaigns and data reprocessing.
The report objective is to explore the Wide Bandgap (WBG) Power Electronics (PE) market, applications, and potential energy savings in order to identify key areas where further resources and investments of the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (DOE EERE) would have the most impact on U.S. competiveness. After considering the current market, several potential near-term application areas were identified as having significant market and energy savings potential with respect to clean energy applications: (1) data centers (uninterruptible power supplies and server power supplies); (2) renewable energy generation (photovoltaic-solar and wind); (3) motor drives (industrial, commercial and residential); (4) rail traction; and, (5) hybrid and electric vehicles (traction and charging). After the initial explorative analyses, it became clear that, SiC, not GaN, would be the principal WBG power device material for the chosen markets in the near future. Therefore, while GaN is discussed when appropriate, this report focuses on SiC devices, other WBG applications (e.g., solid-state transformers, combined heat and power, medical, and wireless power), the GaN market, and GaN specific applications (e.g., LiDAR, 5G) will be explored at a later date. In addition to the market, supply and value chain analyses addressed in Section 1 of this report, a SWOT (Strength, Weakness, Opportunity, Threat) analysis and potential energy savings analysis was conducted for each application area to identify the major potential WBG application area(s) with a U.S. competitiveness opportunity in the future.
NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.
Comprehensive aircraft system health-state awareness is critical for maintaining safe, efficient growth in global operations, enabling higher levels of autonomy, and facilitating new forms of aviation. Maintainers, vehicle operators, air traffic controllers, dispatchers, pilots, autonomous systems, and other decision-makers must have reliable real-time knowledge of the vehicle health, the health of its critical composite systems, predictions of how health changes with time, and forecasts of how its capabilities change with health degradation to preserve safety and efficiency. Providing this information in a reliable manner in computationally constrained environments and across a wide range of vehicles and systems continues to be a challenge. This challenge can be partially resolved through cloud computing, where the execution of prognostic and diagnostic algorithms is performed on a network of remote servers hosted on the internet. NASA is developing a cloud computing service, Prognostics As-A-Service (PaaS), that explores the feasibility and challenges of cloud-enhanced prognostics. Though such a system has broad applicability, this research effort is focused on aviation applications.
The Compact Muon Solenoid (CMS) is a CERN multi-purpose experiment that exploits the physics of the Large Hadron Collider (LHC). The Detector Control System (DCS) is responsible for ensuring the safe, correct and efficient operation of the experiment, and has contributed to the recording of high quality physics data. The DCS is programmed to automatically react to the LHC operational mode. CMS sub-detectors bias voltages are set depending on the machine mode and particle beam conditions. An operator provided with a small set of screens supervises the system status summarized from the approximately 6M monitored parameters. Using the experience of nearly two years of operation with beam the DCS automation software has been enhanced to increase the system efficiency by minimizing the time required by sub-detectors to prepare for physics data taking. From the infrastructure point of view the DCS will be subject to extensive modifications in 2012. The current rack mounted control PCs will be replaced by a redundant pair of DELL Blade systems. These blade servers are a high-density modular solution that incorporates servers and networking into a single chassis that provides shared power, cooling and management. This infrastructure modification associated with the migration to blade servers will challenge the DCS software and hardware factorization capabilities. The on-going studies for this migration together with the latest modifications are discussed in the paper.
Federated Learning (FL) has garnered significant attention for its potential to protect user privacy while enhancing model training efficiency. For that reason, FL has found its use in various domains, from health care to industrial engineering, especially where data cannot be easily exchanged due to sensitive information or privacy laws. However, recent research has demonstrated that FL protocols can be easily compromised by active reconstruction attacks executed by dishonest servers. These attacks involve the malicious modification of global model parameters, allowing the server to obtain a verbatim copy of users' private data by inverting their gradient updates. Tackling this class of attack remains a crucial challenge due to the strong threat model. In this paper, we propose a defense mechanism, namely OASIS, based on image augmentation that effectively counteracts active reconstruction attacks while preserving model performance. We first uncover the core principle of gradient inversion that enables these attacks and theoretically identify the main conditions by which the defense can be robust regardless of the attack strategies. We then construct our defense with image augmentation showing that it can undermine the attack principle. Comprehensive evaluations demonstrate the efficacy of the defense mechanism highlighting its feasibility as a solution.
Finite difference method, as a traditional numerical solution to neutron diffusion equation, although considered simpler and more precise than the coarse mesh nodal methods, has a bottle neck to be widely applied caused by the huge memory and unendurable computation time it requires. In recent years, the concept of General-Purpose computation on GPUs has provided us with a powerful computational engine for scientific research. In this study, a GPU-Accelerated multi-group 3D neutron diffusion code based on finite difference method was developed. First, a clean-sheet neutron diffusion code (3DFD-CPU) was written in C++ on the CPU architecture, and later ported to GPUs under NVIDIA's CUDA platform (3DFD-GPU). The IAEA 3D PWR benchmark problem was calculated in the numerical test, where three different codes, including the original CPU-based sequential code, the HYPRE (High Performance Pre-conditioners)-based diffusion code and CITATION, were used as counterpoints to test the efficiency and accuracy of the GPU-based program. The results demonstrate both high efficiency and adequate accuracy of the GPU implementation for neutron diffusion equation. A speedup factor of about 46 times was obtained, using NVIDIA's Geforce GTX470 GPU card against a 2.50 GHz Intel Quad Q9300 CPU processor. Compared with the HYPRE-based code performing in parallel on an 8-core tower server, the speedup of about 2 still could be observed. More encouragingly, without any mathematical acceleration technology, the GPU implementation ran about 5 times faster than CITATION which was speeded up by using the SOR method and Chebyshev extrapolation technique. (authors)
The objective of this project is to design, develop, and demonstrate a next-generation, federal safety- and emission-complaint driving feedback system that can be deployed across the existing vehicle fleet and improve fleet average fuel efficiency by at least 2%. The project objective was achieved with the driving feedback system that encourages fuel-efficient vehicle travel and operation through: 1) Eco-Routing Navigation module that suggests the most fuel-efficient route from one stop to the next, 2) Eco-Driving Feedback module that provides sensible information, recommendation, and warning regarding fuel-efficient vehicle operation, and 3) Eco-Score and Eco-Rank module that provides a means for driving performance tracking, self-evaluation, and peer comparison. The system also collects and stores vehicle travel and operation data, which are used by Algorithm Updating module to customize the other modules for specific vehicles and adapts them to specific drivers over time. The driving feedback system was designed and developed as an aftermarket technology that can be retrofitted to vehicles in the existing fleet. It consists of a mobile application for smart devices running Android operating system, a vehicle on-board diagnostics connector, and a data server. While the system receives and utilizes real-time vehicle and engine data from the vehicle’s controller area network bus through the vehicle’s on-board diagnostic connector, it does not modify or interfere with the vehicle’s controller area network bus, and thus, is in compliance with federal safety and emission regulations. The driving feedback system was demonstrated and then installed on 45 vehicles from three different fleets for field operational test. These include 15 private vehicles of the general public, 15 pickup trucks of the California Department of Transportation that are assigned to individual employees for business use, and 15 shuttle buses of the Riverside Transit Agency that are used for paratransit service. Detailed vehicle travel and operation data including route taken, driving speed, acceleration, braking, and the corresponding fuel consumption, were collected both before and during the test period. The data analysis results show that the fleet average fuel efficiency improvements for the three fleets with the use of the driving feedback system are in the range of 2% to 9%. The economic viability of the driving feedback system is high. A fully deployed system would require capital investment in smart device ($150-$350) and on-board diagnostics connector ($50-$100) as well as paying operating costs for wireless data plan and subscription fees ($20-$30 per month) for connecting to the data server and receiving various system services. For individual consumers who already own a smart device (such as smartphone) and commercial fleets that already use some kind of telematics services, the costs for deploying this driving feedback system would be much lower.
The sPHENIX experiment is currently under commissioning at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Lab (BNL). The Time Projection Chamber (TPC) serves as a tracking detector for the experiment. The sPHENIX TPC uses a stack of four Gas Electron Multipliers (GEMs) as a gain stage in a reduced ion back-flow configuration. To mitigate the damaging effects of sparks in the GEMs, an online spark monitoring system was created. Once the system detects a spark in a GEM stack, the voltages across the GEMs in that stack can be lowered to prevent further sparking without affecting the gain and efficiency of the other modules. Spark signals are coupled out of the GEM stack by a pick-off capacitor attached to the bottom of the bottom GEM. Custom PCBs convert the oscillatory spark signal into a mono-polar pulse that is then digitized. The software then saves the waveform in a server and uses experimentally derived thresholds to determine how to react. As a result, the system has so far proven to be effective at improving the stability of the TPC and preventing damaging events while collecting cosmic ray data.
Here, an integrated workflow for fast-ion analysis was developed by adapting the One Modeling Framework for Integrated Task (OMFIT) workflow manager to support a standard and unified analysis platform for KSTAR users. The newly established analysis suite offers a graphical user interface–based workflow to enable users to readily access and handle experimental data archived in various data formats and servers. Further, users can analyze the data by importing modules designed for conducting certain tasks, such as profile fitting, equilibrium reconstruction, and postprocessing of tokamak data. The procedures for preparing the inputs for fast-ion simulations are streamlined by a common workflow manager, which enables the parallel processing of various tasks to efficiently analyze large fast-ion datasets. The OMFIT platform comprises a flexible Python-based application that enables users to freely manipulate the Python scripts for applications that are unavailable in the standard workflow. The framework also offers mapping tools to translate the output data into the Integrated Modeling and Analysis Suite format to maintain application compatibility for future ITER burning plasma experiments.
The Belle II experiment at KEK is preparing for taking first collision data in early 2018. For the success of the experiment it is essential to have information about varying conditions available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. The Belle II Conditions Database was designed to make maintenance as easy as possible. To this end, a HTTP REST service was developed with industry-standard tools such as Swagger for the API interface development, Payara for the Java EE application server, and the Hazelcast in-memory data grid for support of scalable caching as well as transparent distribution of the service across multiple sites. On the client side, the online and offline software has to be able to obtain conditions data from the Belle II Conditions Database in a robust and reliable way under very different situations. As such the client side interface to the Belle II Conditions Database has been designed with a variety of access mechanisms which allow the software to be used with and without an internet connection. Different methods to access the payload information are implemented to allow for a high level of customization per site and to simplify testing of new payloads locally. Changes to the conditions data are usually handled transparently but users can actively check whether an object has changed or register callback functions to be called whenever a conditions data object is updated. In addition a command line user interface has been developed to simplify inspection and modification of the database contents.
Data parallel languages, such as High Performance fortran, can be successfully applied to a wide range of numerical applications. However, many advanced scientific and engineering applications are multidisciplinary and heterogeneous in nature, and thus do not fit well into the data parallel paradigm. In this paper we present Opus, a language designed to fill this gap. The central concept of Opus is a mechanism called ShareD Abstractions (SDA). An SDA can be used as a computation server, i.e., a locus of computational activity, or as a data repository for sharing data between asynchronous tasks. SDAs can be internally data parallel, providing support for the integration of data and task parallelism as well as nested task parallelism. They can thus be used to express multidisciplinary applications in a natural and efficient way. In this paper we describe the features of the language through a series of examples and give an overview of the runtime support required to implement these concepts in parallel and distributed environments.
Federated Learning (FL) has amassed notable attention for its ability to preserve user privacy while emphasizing the retainment of model training efficiency. Due to this potential, FL has been integrated in many domains, such as healthcare, finance, law, and industrial engineering, where data cannot be easily exchanged due to sensitive information and strict privacy laws. However, current research has indicated that FL protocols are easily compromised by active data reconstruction attacks employed by actively dishonest servers. The malicious modification of global model parameters allows an actively dishonest server to obtain a direct copy of users’ private data via gradient inversion. Here, this class of attacks is highly underexplored and continues to be a major challenge due to the intense threat model. In this paper, we propose OASIS as a scalable and modality-agnostic defense based on data augmentation that counteracts active data reconstruction attacks while preserving model performance. To generalize our defense, we uncover the intuition behind gradient inversion that enables these attacks and theoretically establish the conditions by which the defense can be considered robust regardless of attack design. From this, we formulate our defense with data augmentation that illustrates its ability to undermine the attack principle. We evaluate OASIS on five real-world datasets–two image-based (ImageNet and CIFAR100) and three text-based (Wikitext, Stack Overflow, and Shakespeare)–which span diverse uses cases such as vision tasks and language modeling. Comprehensive evaluations on these datasets exhibit the efficacy of OASIS and highlight its feasibility as a solution.
To perform dosimetry and risk assessment, NASA collects astronaut ionizing radiation exposure data from space flight, medical imaging and therapy, aviation training activities and prior occupational exposure histories. Career risk of exposure induced death (REID) from radiation is limited to 3 percent at a 95 percent confidence level. The Radiation Health Office at Johnson Space Center (JSC) is implementing a program to integrate the gathering, storage, analysis and reporting of astronaut ionizing radiation dose and risk data and records. This work has several motivations, including more efficient analyses and greater flexibility in testing and adopting new methods for evaluating risks. The foundation for these improvements is a set of software tools called the Astronaut Radiation Exposure Analysis System (AREAS). AREAS is a series of MATLAB(Registered TradeMark)-based dose and risk analysis modules that interface with an enterprise level SQL Server database by means of a secure web service. It communicates with other JSC medical and space weather databases to maintain data integrity and consistency across systems. AREAS is part of a larger NASA Space Medicine effort, the Mission Medical Integration Strategy, with the goal of collecting accurate, high-quality and detailed astronaut health data, and then securely, timely and reliably presenting it to medical support personnel. The modular approach to the AREAS design accommodates past, current, and future sources of data from active and passive detectors, space radiation transport algorithms, computational phantoms and cancer risk models. Revisions of the cancer risk model, new radiation detection equipment and improved anthropomorphic computational phantoms can be incorporated. Notable hardware updates include the Radiation Environment Monitor (which uses Medipix technology to report real-time, on-board dosimetry measurements), an updated Tissue-Equivalent Proportional Counter, and the Southwest Research Institute Radiation Assessment Detector. Also, the University of Florida hybrid phantoms, which are flexible in morphometry and positioning, are being explored as alternatives to the current NASA computational phantoms.
SQL-RAMS (where "SQL" signifies Structured Query Language and "RAMS" signifies Rocketdyne Automated Management System) is a successor to the legacy version of RAMS -- a computer program used to manage all work, nonconformance, corrective action, and configuration management on rocket engines and ground support equipment at Stennis Space Center. The legacy version resided in the File-Maker Pro software system and was constructed in modules that could act as standalone programs. There was little or no integration among modules. Because of limitations on file-management capabilities in FileMaker Pro, and because of difficulty of integration of FileMaker Pro with other software systems for exchange of data using such industry standards as SQL, the legacy version of RAMS proved to be limited, and working to circumvent its limitations too time-consuming. In contrast, SQL-RAMS is an integrated SQL-server-based program that supports all data-exchange software industry standards. Whereas in the legacy version, it was necessary to access individual modules to gain insight into a particular workstatus document, SQL-RAMS provides access through a single-screen presentation of core modules. In addition, SQL-RAMS enables rapid and efficient filtering of displayed statuses by predefined categories and test numbers. SQL-RAMS is rich in functionality and encompasses significant improvements over the legacy system. It provides users the ability to perform many tasks, which in the past required administrator intervention. Additionally, many of the design limitations have been corrected, allowing for a robust application that is user centric.