Study on patient-induced radioactivity during proton treatment in hengjian proton medical facility.

At present, increasingly more proton medical facilities have been established globally for better curative effect and less side effect in tumor treatment. Compared with electron and photon, proton delivers more energy and dose at its end of range (Bragg peak), and has less lateral scattering for its much larger mass. However, proton is much easier to produce neutron and induced radioactivity, which makes radiation protection for proton accelerators more difficult than for electron accelerators. This study focuses on the problem of patient-induced radioactivity during proton treatment, which has been ignored for years. However, we confirmed it is a vital factor for radiation protection to both patient escort and positioning technician, by FLUKA's simulation and activation formula calculation of Hengjian Proton Medical Facility (HJPMF), whose energy ranges from 130 to 230MeV. Furthermore, new formulas for calculating the activity buildup process of periodic irradiation were derived and used to study the relationship between saturation degree and half-life of nuclides. Finally, suggestions are put forward to lessen the radiation hazard from patient-induced radioactivity.

Analysis and experimental verification of new power flow control for grid-connected inverter with LCL filter in microgrid.

Microgrid is an effective way to integrate the distributed energy resources into the utility networks. One of the most important issues is the power flow control of grid-connected voltage-source inverter in microgrid. In this paper, the small-signal model of the power flow control for the grid-connected inverter is established, from which it can be observed that the conventional power flow control may suffer from the poor damping and slow transient response. While the new power flow control can mitigate these problems without affecting the steady-state power flow regulation. Results of continuous-domain simulations in MATLAB and digital control experiments based on a 32-bit fixed-point TMS320F2812 DSP are in good agreement, which verify the small signal model analysis and effectiveness of the proposed method.

Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler.

This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed.