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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.”
T. Craciunescu, A. Murari, I. Tiseanu, J. Vega, JET-EFDA Contributors
Fusion Science and Technology | Volume 62 | Number 2 | October 2012 | Pages 339-346
Selected Paper from the Seventh Fusion Data Validation Workshop 2012 (Part 1) | doi.org/10.13182/FST12-A14625
Articles are hosted by Taylor and Francis Online.
Multifaceted asymmetric radiation from the edge (MARFE) instabilities may reduce confinement leading to harmful disruptions. They cause a significant increase in impurity radiation, and therefore, they leave a clear signature in the video data. This information can be exploited for automatic identification and tracking. A MARFE classifier, based on the phase congruency theory, has been developed and adjusted to extract the structural information in the images of Joint European Torus (JET) cameras. This approach has the advantage of using a dimensionless quantity and providing information that is invariant to image illumination, contrast, and magnification. The method was tested on JET experimental data and has proved to provide a good prediction rate.
