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At least 433 records · Page 24

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NASA NTRS2015

CloudSat Anomaly and Return to the A-Train: Lessons Learned for Satellite Constellations

In April 2011, CloudSat suffered a severe battery anomaly, leaving the space-craft in emergency mode without the ability to command or maneuver the spacecraft. Before the team was able to recover spacecraft operability, CloudSat passed close to the Aqua satellite in the A-Train and then exited the A-Train. A new mode of operations, termed Daylight Only Operations (DO-Op) mode was developed to enable CloudSat to resume science operations in an orbit under the A-Train by November 2011, and in July 2012 CloudSat re-entered the A-Train. This paper describes challenges and lessons-learned during the anomaly, the exit from the A-Train and the return to the A-Train. These lessons-learned may ap-ply to other current and future satellite constellations in Earth orbit.

formation-flying↗

NASA NTRS2018

Addressing Human Error in International Space Station Flight Control Teams: Advances in Ground Training for Science Operators

In flight control, as with any human in the loop system, operator error is an inevitable reality. On the International Space Station (ISS) where crew time and physical resources are precious and often irreplaceable, operator errors can result in significant, irreversible consequences. Flight controllers at the Payload Operations Integration Center (POIC) located at NASA's Marshall Space Flight Center (MSFC) in Huntsville, Alabama know this reality well. At the POIC, operator errors can be caused by a variety of factors, from poor hardware or software design to environmental factors such as time pressure or fatigue. The most difficult errors to address, however, are those which result from ineffective teamwork. To address these teamwork errors, trainers at the POIC have drawn best practices from high reliability industries as well as from our sister ISS control center at the Johnson Space Center (JSC) in Houston, Texas, to develop and implement a new training program focused specifically on teamwork skills. This training program, called Team Skills Training, is inspired by modern and historical training programs developed by NASA and is specifically tailored to the needs of the payload operations flight control team at the POIC. The program consists of training for both certified and trainee flight controllers, and covers team skills topics such as situational awareness, leadership, and communication skills. To maximize effectiveness, the program uses novel instructional techniques which extend beyond the classroom to encourage students to apply what they have learned to their day to day work. Minimizing operator errors in flight control is an endeavor which requires constant vigilance and continuous improvement. At the POIC, Team Skills Training is an important step in this journey.

Harris, Samantha S.↗

NASA NTRS2018

Addressing Human Error in International Space Station Flight Control Teams: Advances in Ground Training for Science Operators

In flight control, as with any human in the loop system, operator error is an inevitable reality. On the International Space Station (ISS) where crew time and physical resources are precious and often irreplaceable, operator errors can result in significant, irreversible consequences. Flight controllers at the Payload Operations Integration Center (POIC) located at NASA’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama know this reality well. At the POIC, operator errors can be caused by a variety of factors, from poor hardware or software design to environmental factors such as time pressure or fatigue. The most difficult errors to address, however, are those which result from ineffective teamwork.Academic research in teamwork has resulted in the identification of many factors which make cross-functional teaming difficult, including leadership, trust building, and communication challenges. These factors, especially when combined with the challenging environmental factors flight control teams must contend with daily, make the goal of minimizing operator errors in payload operations challenging to achieve. To address such teamwork errors, trainers at the POIC have drawn best practices from high reliability industries such as commercial aviation, healthcare, and nuclear power plants, as well as from our sister ISS control center in Houston, Texas, to develop and institute a new training program focused specifically on teamwork skills.This training program, called the Team Skills Curriculum, is based on the concept of Crew Resource Management (CRM) which was developed by NASA in the 1970s for the commercial aviation industry in response to a series of aviation disasters resulting from ineffective teamwork. CRM was later tailored by the Johnson Space Center (JSC) for use in astronaut and flight control training. The result, called Space Flight Resource Management (SFRM) was formally introduced into manned spaceflight training in the late 90s. SFRM has evolved over the years, but the focus has remained on helping operators develop the skills needed to work as part of an effective team. Using these concepts as well as the latest research in cross-functional teaming and data on specific errors occurring at the POIC, trainers in the integrated flight control training branch created a custom training program for both new and certified payload operations specialists.

Harris, Samantha S.↗

NASA NTRS2018

GEONEX: Progressive Conditional Generative Adversarial Training Using Transfer learning

Obtaining accurate segmentation on large scale images is an open problem in deep learning. The main problem is the amount of labeled data that exists for large scale images. Traditionally, the common solution to this problem is to crop the large images into smaller images to increase the amount of available data and train a Conditional Generative Adversarial Network (CGAN). CGANs are currently the state of the art in image to image translation and provide better accuracy than the traditional method of training a encoder based conv-net architecture to minimize the loss at each pixel. This method can produce noisy and discontinuous images with inaccurate results. We seek to solve this problem by utilizing the concepts of transfer learning and progressive training to create a CGAN that can segment large scale images with a limited amount of labeled data. In transfer learning we recognize that many learned features are applicable to many classes from multiple domains. This introduces the concept of feature reusability, which is the basis for finetuning. Progressive training got its start in training models on the same images at different resolutions. In this work we instead train a GAN on increasing image scales by transferring the weights from the smaller scales to the larger scales. The learned features at the smaller scales are continually reused and applied to larger scales to create a CGAN that can perform accurate segmentation on large scale images. We apply this method to detect building footprints on very high-resolution overhead imagery (e.g Digital Globe and high resolution airborne platforms).

GEONEX↗

NASA NTRSDate not supplied

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka↗

NASA NTRS2023

A Global Land Cover Training Dataset From 1984 to 2020

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

Radost Stanimirova↗

DOE OSTI2018

Exploring flexible communications for streamlining DNN ensemble training pipelines

Parallel training of a Deep Neural Network (DNN) ensemble on a cluster of nodes is a common practice to train multiple models in order to construct a model with a higher prediction accuracy, or to quickly tune the parameters of a training model. Existing ensemble training pipelines perform a great deal of redundant operations, resulting in unnecessary CPU usage, or even poor pipeline performance. In order to remove these redundancies, we need pipelines with more communication flexibility than existing DNN frameworks can provide. This project investigates a series of designs to improve pipeline flexibility and adaptivity, while also increasing performance. We implement our designs using Tensorflow with Horovod, and test it using several large DNNs in a large scale GPU cluster, the Titan supercomputer at Oak Ridge National Lab. Our results show that with the new flexible communication schemes, the CPU time spent during training is reduced by 2-11X. Furthermore, our implementation can achieve up to 10X speedups when CPU core limits are imposed. Our best pipeline also reduces the average power draw of the ensemble training process by 5--16% when compared to the baseline.

Pittman, Randall↗

DOE OSTI2023

Selection of Training Sets for 235U Source Detection Classifiers Using Gamma Signatures

The machine learning methods for classifiers to detect low level radiation sources are of interest when suitable training data sets are available. Their application and performance assessment, however, involves the aspects of over-fitting and training data selection that are somewhat uncommon in other existing methods for this task.We study U-235 gamma signatures using data sets collected by 21 NaI detectors under controlled conditions. The gamma spectra are collected by the detectors located at different distances from the source, and we study their choice as training sets for classifiers to detect a source. The detectors form the near, middle and outer groups based on the distance to source. The classifiers based on the outer group are susceptible to over-fitting, that is, they achieve low training error but incur much higher testing error in independent tests. The other two groups achieve lower training error and comparable testing error, and the near group achieves the overall lowest error. In detecting a source at an unknown distance, the farther detectors in the middle group achieve the overall lowest testing error with limited over-fitting, thereby indicating the complex dependencies between the training and classifier performance.

Rao, Nageswara↗

DOE OSTI2025

GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism

Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their superior performance in various graph analytical tasks. Mini-batch training is commonly used to train GNNs on large graphs, and data parallelism is the standard approach to scale mini-batch training across multiple GPUs. Data parallel approaches contain redundant work as subgraphs sampled by different GPUs contain significant overlap. To address this issue, we introduce a hybrid parallel mini-batch training paradigm called Split parallelism. Split parallelism avoids redundant work by splitting the sampling, loading, and training of each mini-batch across multiple GPUs. Split parallelism, however, introduces communication overheads that can be more than the savings from removing redundant work. We further present a lightweight partitioning algorithm that probabilistically minimizes these overheads. We implement spllit parllelism in GSplit and show that it outperforms state-of-the-art mini-batch training systems like DGL, Quiver, and P3.

Lim, Seung-Hwan [ORNL] (ORCID:0000000194616866)↗

DOE OSTI2022

A Subcritical Assembly for Training and Education Use at the Oak Ridge National Laboratory [Abstract]

A near-final design has been completed for a new subcritical assembly at Oak Ridge National Laboratory (ORNL). This subcritical assembly will support the Nuclear Criticality Safety Program (NCSP) training and education program to augment current subcritical assembly capabilities at Sandia National Laboratories (SNL) and at the National Criticality Experiments Research Center (NCERC). Other collaborations have been proposed with the Sunrise Consortium and others. This proposed subcritical assembly uses legacy AGN-201M research reactor fuel plates that are available from the Y-12 National Security Complex. This subcritical assembly contains approximately 620 grams of 235 U as UO 2 particulates distributed homogeneously in radiation-stabilized polyethylene. The fuel plates will have a graphite neutron reflector to obtain a core multiplication, M, from 10 to 50, corresponding to a k eff of 0.9 to 0.95, respectively. The subcritical assembly will be able to support at least four experiments for the training courses: (1) the addition of fissile material to increase neutron multiplication (mass), (2) a core separation experiment (interaction), (3) the effect of adding moderators to the core (moderation), and (4) the effect of adding neutron absorbers to the core (poison/absorption). The proposed ORNL subcritical assembly will provide additional student bandwidth to support NCSP training courses and to provide for some backup capacity if nuclear facility operations are disrupted at SNL or NCERC due to some unforeseen issue. The subcritical assembly will be designed to be an inherently safe subcritical assembly—subcritical under all normal and abnormal conditions—and it will provide the capability to conduct hands-on training to support NCSP and general nuclear criticality safety (NCS) staff training and qualification goals.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

In Silico Versus In Situ: The Challenging Landscape of Nuclear Criticality Safety Training

Nuclear criticality safety grew out of the ranks of experimentalists studying the physics of chain-reacting systems at critical experiment facilities. Consequently, critical experiment facilities provide the best forum for conducting training in nuclear criticality safety. As the nuclear renaissance gains traction, there is an increased demand to train personnel in nuclear criticality safety (NCS). Strides have been made in both on-line and virtual reality based NCS training. While these are perhaps a necessary component of NCS training, hands-on training at critical experiments facilities remains the most effective means of developing competency for fissionable material handlers, managers of fissionable material operations, criticality safety analysts, and experimentalists. This paper explores the challenges of developing and maintaining workforce competency in nuclear criticality safety to support safe and efficient fissionable material operations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Investigation of candidates for reactor produced radioactive materials in support of radiological training exercises

Bromine-82, Potassium-42 and Copper-64 have been successfully adopted as radioactive surrogates for outdoor large area contamination training. The goal of this project was to discover new materials that could supplement potassium bromide (KBr) and copper pellets in radiological dispersal device (RDD) training events to reduce the down time of the training fields and to broaden the toolbox of the RDD surrogate training event program at Idaho National Laboratory. Of the ten different materials investigated, sodium nitrite, gallium metal, and gallium oxide presented the greatest promise as potential materials to replace potassium bromide in RDD training events.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2017

Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials

Purpose: To demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiation therapy clinical trial plan quality-control system. Methods and Materials: We analyzed 86 patients with stage IB through IVA cervical cancer treated with intensity modulated radiation therapy at 2 institutions according to the standards of the INTERTECC (International Evaluation of Radiotherapy Technology Effectiveness in Cervical Cancer, National Clinical Trials Network identifier: 01554397) protocol. The protocol used a planning target volume and 2 primary organs at risk: pelvic bone marrow (PBM) and bowel. Secondary organs at risk were rectum and bladder. Initial unfiltered dose-volume histogram (DVH) estimation models were trained using all 86 plans. Refined training sets were created by removing sub-optimal plans from the unfiltered sample, and DVH estimation models… and DVH estimation models were constructed by identifying 30 of 86 plans emphasizing PBM sparing (comparing protocol-specified dosimetric cutpoints V{sub 10} (percentage volume of PBM receiving at least 10 Gy dose) and V{sub 20} (percentage volume of PBM receiving at least 20 Gy dose) with unfiltered predictions) and another 30 of 86 plans emphasizing bowel sparing (comparing V{sub 40} (absolute volume of bowel receiving at least 40 Gy dose) and V{sub 45} (absolute volume of bowel receiving at least 45 Gy dose), 9 in common with the PBM set). To obtain deliverable KBP plans, refined models must inform patient-specific optimization objectives and/or priorities (an auto-planning “routine”). Four candidate routines emphasizing different tradeoffs were composed, and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine that best met protocol objectives in the 51-patient training sample (KBP{sub FINAL}), protocol-specific DVH metrics and normal tissue complication probability were compared for original versus KBP{sub FINAL} plans across the 35-patient validation set. Paired t tests were used to test differences between planning sets. Results: KBP{sub FINAL} plans outperformed manual planning across the validation set in all protocol-specific DVH cutpoints. The mean normal tissue complication probability for gastrointestinal toxicity was lower for KBP{sub FINAL} versus validation-set plans (48.7% vs 53.8%, P<.001). Similarly, the estimated mean white blood cell count nadir was higher (2.77 vs 2.49 k/mL, P<.001) with KBP{sub FINAL} plans, indicating lowered probability of hematologic toxicity. Conclusions: This work demonstrates that a KBP system can be efficiently trained and refined for use in radiation therapy clinical trials with minimal effort. This patient-specific plan quality control resulted in improvements on protocol-specific dosimetric endpoints.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

PracticeCALM: Coaching Anxiety Lessening Methods for Radiation Therapists: A Pilot Study of a Skills-Based Training Program in Radiation Oncology

PracticeCALM is an 8-week on-the-job training program to teach radiation therapists (RTs) techniques for assisting patients who are experiencing treatment-related anxiety. Twelve clinical RTs in a regional oncology program were recruited on a volunteer basis to participate in the training. A mixed-method approach was undertaken to evaluate perceived benefits to clinical practice. The quantitative findings from the Perceived Stress Scale (PSS) showed there were no changes before and after training. Qualitative findings showed significant benefits to RTs, including (1) ability to empathically attune more effectively and earlier to signs of anxiety in patients; (2) improved confidence and self-efficacy for effectively intervening in difficult treatment situations; and (3) enhanced creative problem solving in partnership with patients to assist the acutely anxious patient. The PSS results suggest that intrinsic and extrinsic stressors are a variable but consistent part of the everyday reality of RT practice. A training program focusing on the therapeutic presence dimension of RT practice has a direct impact on effectiveness in assisting anxious patients undergoing RT. When embedded in the clinical life of a radiation therapy department, such a training program has the potential to capture the lived practice wisdom and creative skills of RTs, and effectively mediate these skills across a team through group discussions and documentation on patient charts.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2017

Transforming Magnetic Resonance Imaging Education through Simulation-Based Training

Clinical instructors are facing the challenge of limited clinical training sites as the opportunities to train on actual patients become less available. In addition, work regulations, productivity requirements, and patients’ awareness of trainees “practicing” on them has led to a decline in training opportunities. These factors reduced training experience into fewer direct patient encounters, and fewer opportunities to perform procedures. This study assesses the perception of students on the MRI simulation training for undergraduate medical imaging students.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Trial by Fire? Rulewriters, Training, the BLM, and EPA

Individual bureaucrats are ultimately tasked with solving complex problems and are expected to do so cost-effectively. The purpose of this paper is to examine an infrequent perspective of administrative rulemaking - training. Simply put, our goal is to examine original interview data with federal rulewriters in two agencies - the Environmental Protection Agency and the Bureau of Land Management to understand how bureaucrats are trained to carry out rules. This exploratory study suggests the importance of training for the next generation of rulewriters. In particular, we demonstrate existing informal training can be effective, but more mechanisms (e.g. formal training and mentoring) is necessary.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

SU-F-E-16: A Specific Training Package for Medical Physicists in Support to Nuclear and Radiological Emergency Situations

Purpose: To provide the professional medical physicists with adequate competencies and skills in order to help them get prepared to support Nuclear or Radiological Emergency (NRE) situations. Methods: Although clinical medical physicists working have in-depth knowledge in radiation dosimetry, including dose reconstruction and dose measurements, they are usually not involved in NRE situations. However, in a few instances where medical physicists were involved in NREs, it appeared that many lacked specific knowledge and skills that are required in such situations. This lack of specific knowledge and skills is probably due to the fact that most current medical physics curricula do not include a specific module on this topic. As a response to this finding, the IAEA decided to initiate a project to develop a specific training package to help prepare medical physicists to support NRE situations. The training package was developed with the kind support of the Government of Japan and in collaboration with Fukushima Medical University (FMU) and the National Institute of Radiological Sciences (NIRS). Results: The first International Workshop to test the training package was held in Fukushima, Japan in June 2015. It consisted of lectures, demonstrations, simulation, role play, and practical sessions followed by discussions. The training was delivered through 14 modules which were prepared with the support of 12 lecturers. A knowledge assessment test was done before the workshop, followed by the same test done at the end of the Workshop, to assess the knowledge acquired during the training. Conclusion: The Workshop was successfully implemented. The overall rating of the workshop by the participants was excellent and all participants reported that they acquired a good understanding of the main issues that are relevant to medical physics support in case of NRE situations. They are expected to disseminate the knowledge to other medical physicists in their countries.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Grid-Ready Energy Analytics Training with Data (“GREAT with Data”)

GridEd is a collaborative educational initiative consisting of the Electric Power Research Institute (EPRI), 5 Partner Universities (Stony Brook University, The University of Texas at Austin, University of California – Riverside, Virginia Tech, Washington State University), and participating industry sponsors. This educational initiative focuses on developing and training the next generation of power engineers so they can help shape the electric grid of the future by anticipating and fulfilling the needs of changing electric industry requirements. GridEd is leveraging electric industry research to educate a future electric grid workforce by empowering new and continuing education students, not only to become competent and well-informed engineers, but also to participate and influence major technological, social, and policy decisions that address critical global challenges. GridEd’s activities are centered around four core pillars: Enhancement of university power systems engineering curricula; Professional development and training for a diverse electric industry workforce; Stimulating students to join the movement for the next generation of power engineers, and; Improve workforce development efforts in the electric utility industry. Major accomplishments over the course of the project were: Over 50 unique professional short courses were delivered by more than 30 instructors across the GridEd network; Over 3,600 unique learners, many who took multiple courses, received more than 27,000 professional development hours (PDH) and over 1,000 certificates of completion; Approximately 2,900 unique learners undertook a course that was offered LIVE online or in-person; Approximately 700 unique learners undertook a course that was offered as computer-based training (CBT); Over 35 unique university courses were delivered by more than 30 instructors to 1,500 university students across the GridEd network; One-hundred-and-fifty-seven (157) students were funded to completed 43 student projects in topics of power systems and data science, and; Six (6) Historically Black Colleges and Universities (HBCUs) recruited as Affiliate Universities via Utility Partners. The professional training initiative and workforce development activities launched by this project will be sustained through EPRI’s collaborative business model with industry. Stimulating students to join the power engineering workforce of the future and the enhancement of tertiary training may continue to need government support.

14 SOLAR ENERGY↗