Page 29 - Book of Abstracts 2023

Book of Abstracts | 2023
System for the refining of radioactive material Me-4-6
By: Sharon Sharon Dotan Sharondot2@gmail com com Aviraz Aviraz Lahyany Aviraz13@gmail com com 1 1 1 1 1 1 2 Advisors: Dr Etan Fisher Mr Mr Eyal Gimshi Mr Mr Erez Secly
1Shamoon College of Engineering Be'er-Sheva 2Isotopia Molecular Imaging Petah Tikva
Creating a a a a a a a a a a a a chemotherapeutic material for prostate cancer treatment requires mixing acids with radioactive substances The most efficient method involves irradiating the the material in in in in in in an an ampoule ampoule breaking the the the ampoule ampoule in in in in in in a a a a a a a a a a a controlled environment and flowing the the acids through it This precise process requires manual operation potentially exposing operators to to to radiation radiation Our project is developing an an an automated system for preventing radiation radiation exposure and delivering accurate results This process involved research design finite element analysis (FEA) experimentation and and and component and and and material characterization that resulted in in a a a a a a a a a a a a a a a a a controlled and and and highly accuracy system for breaking ampoules Keywords: prostate cancer quartz ampules refining radioactive materials Feedback control for vibration suppression based on on on on on particle swarm optimization
Me-4-7
By: Maor Lotem maorlotem@gmail com Advisor: Dr Ziv Brand
Shamoon College of Engineering Beer-Sheva In the the field of control engineering there are are dynamic models that are are difficult to to simulate or whose linearization would lead to to inaccuracy In the machining process this phenomenon is is true of of vibrations Our project deals with finding optimal gain constants for a a a a a a a a a a a a a a a restraint system of of of an an engraving machine machine blade with the the help of of the the machine-learning algorithm algorithm “Particle swarm optimization” (PSO) This algorithm algorithm produces optimal gains by using the the the input and output of of the the the system without needing to recognize its dynamic model Results of of the the the the algorithm are are compared with results obtained by the the conventional “Linear quadratic regulator” method Keywords: engraving machine-learning optimization
PSO vibrations 29