Plasma dynamics in the self-magnetic-pinch diode
The self-magnetic-pinch (SMP) diode1,2 is being developed as a source for flash x-ray radiography. The high electric field stresses applied to this diode and material heating from the intense electron beam generate electrode plasmas. While these plasmas are instrumental in beam formation and focusing, rapid plasma expansion from the electrodes can degrade radiographic performance.
2-D simulations of the self magnetic pinch diode
For many years AWE has used self magnetic (SM) pinch diodes on its lower voltage flash x-ray machines (MEVEX @0.8 MV and Mini-B @ 2 MV). The SM pinch diode has proved to be a reliable source for providing small diameter radiographic spot sizes (2.0 - 2.5 mm). With the recent emphasis on reduction of the x-ray spot size at higher voltages, one part of the diode research project has been to field SM pinch diodes at higher voltages. An electromagnetic PIC code, LSP, has been used to carry out 2-D simulations of the diode to support this project. Results of these code simulations will be presented. The simulations show good agreement with measured experimental machine voltage and current records. Good correlation is also achieved with old experimental data on diode performance. The simulations suggest further improvements in spot size reduction could be achieved with changes in the diode geometry.
Automatic classification of TV news articles based on telop character recognition
The purpose of this study is to develop a multimedia database system for TV news video data. TV news video data consist of speech, characters and images. In this study, telop characters are recognized and given to the news articles as indices for classification. At first, telop frames which include telop characters are detected and then the telop characters are extracted and recognized. Through morphological analysis of the recognized telop characters, keywords are extracted which consist of more than two characters. Their keywords are used as indices to classify the TV news articles. We carried out experiments for 30 days of NHK 5 minutes news and obtained 95.4% telop character extraction rate, 81.4% character recognition rate and 83.8% article classification rate. We improved the article classification rate by 43.4% through character recognition improvement from 44.1% to 81.4%.