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Here, the vector potential was introduced to a linear magnetohydrodynamics (MHD) simulation code, and the modified and original simulation results were compared. The effects of the broken solenoidal condition on the perturbed magnetic field in linear MHD simulations based on real coordinates were investigated. The results showed that the modified code can successfully remove the error in this condition, and the modified code was confirmed to work appropriately. Incorrect results can be yielded by an error in the condition, especially in analyses of the mode structure.

This paper explains about fundamental working principles, governing equations, implementation problems, and experimental results of a new power factor correction (PFC) topology. In this proposed topology, bridgeless technique is applied to a critical-conduction-mode (CRM) interleaved boost PFC in order to gain fundamental understanding towards high efficiency, high performance, low-cost, simple control scheme, and low conducted electromagnetic interference (EMI) circuit. This application is targeted for low to middle power applications that normally employs continuous or interleaved boost converter., INTELEC 2009 - 2009 International Telecommunications Energy Conference : Incheon, South Korea, 2009.10.18-2009.10.22, INTELEC 2009, pp.1-5; 2009

A modified zero-current-switched quasi-resonant (ZCS-QR) boost converter is employed in a power factor correction (PFC) application. The main goal is to achieve a small-size low-noise PFC circuit based on single-switch boost topology. A clamp diode has been added to avoid the voltage ringing problem that is originally generated across the ZCS-QR switch during its turn-off period. The converter operation states after clamp diode application are presented. The PFC circuit control scheme implementation is also described. An experimental circuit with 100 V rms ac of 50 Hz input and 330 V dc output has been built. A high efficiency of 90% and the proof of compliance to the IEC61000-3-2 class D have been confirmed by experiment., 24th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2009; Washington, DC; 15 February 2009 through 19 February 2009, Applied Power Electronics Conference and Exposition, pp.1165-1169; 2009

In conventional least square (LS) regressions for nonlinear problems, it is not easy to obtain analytical derivatives with respect to target parameters that comprise a set of normal equations. Even if the derivatives can be obtained analytically or numerically, one must take care to choose the correct initial values for the iterative procedure of solving an equation, because some undesired, locally optimized solutions may also satisfy the normal equation. In the application of genetic algorithms (GAs) for nonlinear LS, it is not necessary to use normal equations, and a GA is also capable of avoiding localized optima. However, convergence of population and reliability of solutions depends on the initial domain of parameters, similarly to the choice of initial values in the above mentioned method using the normal equation. To overcome this disadvantage of applying GAs for nonlinear LS, we propose to use an adaptive domain method (ADM) in which the parameter domain can change dynamically by using several real-coded GAs with short lifetimes. Through an example problem, we demonstrate improvements in terms of both the convergence and the reliability by ADM. A further merit in the proposed method is that it does not require any specialized knowledge about GAs or their tuning. Therefore, the nonlinear LS by ADM with GAs are accessible to general scientists for various applications in many fields

In conventional least square (LS) regressions for nonlinear problems, it is not easy to obtain analytical derivatives with respect to target parameters that comprise a set of normal equations. Even if the derivatives can be obtained analytically or numerically, one must take care to choose the correct initial values for the iterative procedure of solving an equation, because some undesired, locally optimized solutions may also satisfy the normal equation. In the application of genetic algorithms (GAs) for nonlinear LS, it is not necessary to use normal equations, and a GA is also capable of avoiding localized optima. However, convergence of population and reliability of solutions depends on the initial domain of parameters, similarly to the choice of initial values in the above mentioned method using the normal equation. To overcome this disadvantage of applying GAs for nonlinear LS, we propose to use an adaptive domain method (ADM) in which the parameter domain can change dynamically by using several real-coded GAs with short lifetimes. Through an example problem, we demonstrate improvements in terms of both the convergence and the reliability by ADM. A further merit in the proposed method is that it does not require any specialized knowledge about GAs or their tuning. Therefore, the nonlinear LS by ADM with GAs are accessible to general scientists for various applications in many fields