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Notes: Neural network is considered as a parameter estimation tool in plasma controls for next generation tokamak such as ITER. The neural network has been reported to be so accurate and fast for plasma equilibrium identification that it may be applied to the control of complex tokamak plasmas. For this application, the reliability of the conventional neural network needs to be improved. In this study, a new idea of double neural network is developed to achieve this. The new idea has been applied to simple plasma position identification of KSTAR tokamak for feasibility test. Characteristics of the concept show higher reliability and fault tolerance even in severe faulty conditions, which may make neural network applicable to plasma control reliably and widely in future tokamaks. © 2001 American Institute of Physics.

Notes: To improve inherent shortcomings of statistical methods and apply them to the extraction of plasma equilibrium parameters in a fast timescale for real-time plasma control, new concepts of statistical methods such as principal component analysis-based neural network (NN), functional parametrization (FP)-based NN and double network are introduced by modifying NN and FP. These new methods are benchmarked and compared with the conventional techniques of NN and FP in a simple single-filament system. As a result of their applications to identification of plasma equilibrium parameters in the Korea Superconducting Tokamak Advanced Research tokamak, particularly, the double network concept among them has successfully achieved the improvement of drawbacks in the conventional methods. It is shown that more reliable results from the double network method can be obtained by combining several different statistical treatments as a primary network. Even in the case of nonoptimized methods united as a primary network, quite acceptable results can be achieved in the double network method. © 2001 American Institute of Physics.

Notes: A Thomson scattering system on the Hanbit magnetic mirror device has been installed to measure the electron temperature and density of the plasma in the central cell. The configuration is based on a standard 90° scattering scheme. The optical system consists of a Q-switched Nd:YAG laser, input optics, collection optics, spectrograph optics, detectors, and a data acquisition system. Although the laser beam path is about 50 m long and the background emissions are not low, the electron temperature measurements have been made at a single point on a shot-by-shot basis, in which the stray light was considerably suppressed by using a beam dump, a viewing dump, and baffles. The measured electron temperature is about 50–70 eV in experiments for plasma production and heating by ICRF of 200-kW-rf power using a slot antenna. A description of the installed system and the experimental results are presented. © 2001 American Institute of Physics.

Notes: SUMMARY: Pharmacokinetic studies were performed in 10 stable kidney transplantation patients who received microemulsion formulation (Neoral®) of cyclosporine A (CsA) twice daily. No agents having pharmacokinetic effects on CsA had been used in these patients. The values of various basic pharmacokinetic parameters were similar to those reported in Western literature. The complete area under the blood concentration–time curve (AUC) of CsA for the duration of 12 h (12-h AUC) was determined using the linear trapezoidal rule from seven concentrations at 0, 1, 2, 4, 6, 8, and 12 h after CsA administration. The mean values of 12-h AUC were 4603.63 ± 344.61 ng h/mL. CsA concentrations at 2 h after dosing (not the trough levels) showed the best correlation with the complete AUC (r2 = 0.9322). The abbreviated AUC of CsA was calculated either by stepwise multiple linear regression analysis or by the linear trapezoidal rule from a few sampling time points. Using stepwise multiple linear regression analysis, which was used in calculating abbreviated AUC in all previous studies, the model equation that had the highest correlation and the lowest prediction error with the complete AUC was derived by using CsA concentrations at 2 and 8 h after dosing (12-h AUC = 4.262C2 + 8.390C8− 669.417; r2 = 0.9808, absolute prediction error = 3.97 ± 0.96). Two model equations derived using the linear trapezoidal rule provided the best correlation with the complete AUC: (1) The two time points selected model equation 12-h AUC = 4C2 + 5C8; r2 = 0.9780, absolute prediction error = 6.41 ± 1.22). (2) The three time points selected model equation 12-h AUC = 4C0 + 3C2 + 5C6; r2 = 0.9475, absolute prediction error = 5.00 ± 1.41). When different pharmacokinetic data sets were applied to the model equations derived using regression analysis, the values of coefficients and the constant of the regression equation had changed from the initial equation. Thus, new model equations will emerge every time the new data are applied. In contrast, the values of coefficients in the model equation calculated using trapezoidal rule were unaltered when tested by the new pharmacokinetic data set. Thus, the abbreviated AUC derived using the linear trapezoidal rule would be simpler than and superior to that obtained using stepwise multiple linear regression analysis in prediction of the complete AUC.