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Remembering ANS member Gil Brown
Brown
The nuclear community is mourning the loss of Gilbert Brown, who passed away on July 11 at the age of 77 following a battle with cancer.
Brown, an American Nuclear Society Fellow and an ANS member for nearly 50 years, joined the faculty at Lowell Technological Institute—now the University of Massachusetts–Lowell—in 1973 and remained there for the rest of his career. He eventually became director of the UMass Lowell nuclear engineering program. After his retirement, he remained an emeritus professor at the university.
Sukesh Aghara, chair of the Nuclear Engineering Department Heads Organization, noted in an email to NEDHO members and others that “Gil was a relentless advocate for nuclear energy and a deeply respected member of our professional community. He was also a kind and generous friend—and one of the reasons I ended up at UMass Lowell. He served the university with great dedication. . . . Within NEDHO, Gil was a steady presence and served for many years as our treasurer. His contributions to nuclear engineering education and to this community will be dearly missed.”
Gonzalo Farias, Sebastián Dormido-Canto, Jesús Vega, Ignacio Pastor, Matilde Santos
Fusion Science and Technology | Volume 63 | Number 1 | January 2013 | Pages 20-25
Selected Paper from Seventh Fusion Data Validation Workshop 2012 (Part 3) | doi.org/10.13182/FST12-477
Articles are hosted by Taylor and Francis Online.
Stray light is the main source of noise on the Thomson scattering diagnostic images of the TJ-II stellarator. The diagnostic provides temperature and density profiles of the plasma. A charge-coupled-device camera acquires images that are disturbed by noise, which, in some cases, can produce unreliable profiles. In this paper we describe three different approaches to reduce or mitigate the stray light on these images: exhaustive detection, extraction of regions with connected components, and extraction of regions with the approach of region growing. The performance of the two most interesting techniques is evaluated by a validation process. This process quantifies the noise eliminated by each method.
