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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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.”
Jiancheng Hou, Jiahui Hu, Xiaofeng Han, Jianhua Yang, Jichao Wang, Teng Wang, Jingang Chen
Fusion Science and Technology | Volume 81 | Number 5 | July 2025 | Pages 425-436
Research Article | doi.org/10.1080/15361055.2024.2431783
Articles are hosted by Taylor and Francis Online.
Accurately and rapidly obtaining the position of the plasma boundary is a necessary condition for the stable operation of tokamaks. In order to address the various shortcomings of the magnetic measurement methods commonly used in modern tokamak devices and to meet the development trend of diagnostic methods for fusion devices, it is necessary to meet the more stringent operational requirements in the future. Therefore, it is necessary to conduct research on optical diagnostic methods.Traditional optical methods often rely on complex physical models and manual feature extraction. This paper proposes a method based on deep learning algorithms that reconstructs the plasma boundary position solely through image reconstruction. For the plasma image recognition task on the Experimental Advanced Superconducting Tokamak (EAST), we utilized a multiband and high-speed visible endoscope diagnostic system to construct the dataset required for building neural network models. We proposed an improved lightweight U-Net network model to identify optical boundaries. Subsequently, we introduced the convolutional block attention module (CBAM) to further extract image information, and we addressed the overfitting issue of the neural network by employing the Dropkey regularizer. Finally, we utilized the skeleton refinement algorithm to extract boundary coordinates and mapped them to the Equilibrium Fitting code (EFIT) reconstruction results using the CatBoost algorithm. The method proposed in this paper can convert the plasma optical boundary coordinates obtained from visible light cameras into tokamak-level plane coordinates, thereby achieving the reconstruction of plasma shapes on EAST. This approach circumvents the issues encountered by magnetic measurement and traditional optical methods. From the experimental results, it can be observed that compared to the original U-Net model, the optical boundary recognition model proposed in this paper has improved the mean accuracy, recall, F1 score, and mean intersection over union metrics by 3.99%, 8.06%, 2.74%, and 3.73%, respectively, on the test set. Additionally, the average error of the boundary reconstruction algorithm compared to the EFIT data is only 6.45 mm.
