A Large Safeguards-Informed Hybrid Imagery Dataset for Computer Vision Research and Development.
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One aspect of the present disclosure is an imaging system including an optical sensor defining an optical axis. The system further includes a light source. The system may include an optical beam splitter, and may also include an optional diffusing lens that may be configured to diffuse and/or collimate light from the light source and direct light exiting the diffusing lens to the optical beam splitter. The optical beam splitter is configured to direct light from the light source along the optical axis of the optical sensor.
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The Oak Ridge Office of Environmental Management (EM) strives to be the leader in the Department of Energy's (DOE's) EM Complex regarding successful and safe project execution and stakeholder interactions that yield positive results. EM's goal has been to become 'Investment Worthy' and, in order to accomplish that important objective, has also had to improve communications both within and outside of the Department. One of our most important missions is to assist the Department in achieving the sustainability goals set forth in Executive Order 13514. In this regard, EM's primary role is to return land to beneficial use and reduce energy impacts and maintenance costs by demolishing unneeded and deteriorating structures and remediating environmental contamination. Recent accomplishments toward meeting these goals include significant progress in the decontamination and demolition of the country's largest facility, the former K-25 Gaseous Diffusion Building, constructed in 1942 to enrich uranium to help end World War II; the disposition of the first phase of Uranium-233 material from the Oak Ridge National Laboratory (ORNL) which involved the transfer of Zero Power Reactor Plates to the National Nuclear Security Administration (NNSA); and a host of other project successes associated with transuranic (TRU) waste processing, hot cell decontamination and demolition, remediation of highly contaminated soils and burial grounds, and removal of mercury from storm sewers and surface waters. With regard to successful stakeholder interactions, recent accomplishments include a new method for collaboration that has renewed EM's working relationship with the regulators, and success in completing an extensive consultation process with over a dozen parties on the historic preservation of the former Oak Ridge Gaseous Diffusion Plant, which is now called the East Tennessee Technology Park (ETTP). Regarding improved communications, EM has successfully revised Program priorities and has received buy-in from the leadership in Headquarters, the regulators, and the community. Issues EM was facing in 2009 are presented. Resulting lessons learned and subsequent changes that the Office has gone through in the past several years in order to improve performance in the safe execution of work, relationships with external stakeholders, and communications both internally and externally are discussed. Results of these efforts are provided as a summary of Program accomplishments, including a strong focus on the future. EM's motto, Moving to the Future by Cleaning up the Past, will be demonstrated through the Program's mission, which includes protecting the region's health and environment; ensuring the continuation of ongoing vital missions being conducted by DOE on the Oak Ridge Reservation; and making clean land available for future use at all three sites, with a near-term focus on Re-industrialization of ETTP. (authors)
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Rotating detonation engines (RDEs) theoretically achieve pressure gain by continuously propagating detonation waves in a cylindrical annulus. Research aims to implement them in gas turbines for improved propulsion and power generation efficiency. Current diagnostic methods, such as point measurements and optical diagnostics, face limitations due to high data acquisition rates required to analyze rapid detonation interactions. In contrast, image classification and time series classification achieve real-time capabilities with rates of 4 and 5 Hz, respectively. Object detection offers the highest time-step resolution at 20 μsec, while real-time methods require longer resolutions of 80 msec, highlighting the advancements in diagnostic capabilities for RDEs.
Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.
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