RESUMO
A new neutral particle analyzer (NPA) diagnostic based on single crystal chemical vapor deposition (sCVD) diamond detector that provides measurements of fast ions has been designed and installed on HL-2A tokamak. Diamond detectors have been applied in some magnetic confinement fusion devices due to their outstanding properties of compact size and radiation hardness. This DNPA can measure energies above 13.4 keV. The line of sight (LOS) of the DNPA intersects with the NBI No. 2 with a tangency radius of 154.8 cm. Due to the pitch angle defined by the LOS and geometry of the diagnostic, the DNPA is mainly sensitive to trapped ions. To interpret the energy spectrum and verify the feasibility of the design of the DNPA, a Monte Carlo code called FIDASIM, which is a synthetic diagnostic code that simulates fast ion D-alpha and NPA signals, is applied to model the neutral flux reaching the detector. The results show that the flux is mainly contributed by the low energy fast ions (E < 10 keV) and it is mainly coming from the active components, the passive signal is dominant in the high energy region (E > 15 keV). The modeling features the ability to distinguish between active and passive signals, and the simulated strong passive signals are suggested to come from charge exchange between cold neutrals and fast ions around the plasma edge. In addition, despite the large ratio of halo neutrals, essentially it has a limited contribution to the energy spectrum.
RESUMO
Autofocusing (AF) criterion functions are critical to the performance of a passive autofocusing system in automatic video microscopy. Most of the autofocusing criterion functions proposed are dependent on the imaging system and image captured by the objective being focused or ranged. This dependence destabilizes the performance of the system when the criterion functions are applied to objectives with different characteristics. In this paper, a new design method for autofocusing criterion functions is introduced. This method enables the system to have the ability to tell the texture directional information of the objective. Based on this information, the optimal focus criterion function specific to one texture direction is designed, voiding blindly using autofocusing functions which cannot perform well when applied to the certain surface and can even lead to failure of the whole process. In this way, we improved the self-adaptability, robustness, reliability and focusing accuracy of the algorithm. First, the grey-level co-occurrence matrices of real-time images are calculated in four directions. Next, the contrast values of the four matrices are computed and then compared. The result reflects the directional information of the measured objective surfaces. Finally, with the directional information, an adaptive criterion function is constructed. To demonstrate the effectiveness of the new focus algorithm, we conducted experiments on different texture surfaces and compared the results with those obtained by existing algorithms. The proposed algorithm excellently performs with different measured objectives.
