SINGLE STAGE LED DRIVER TECHNOLOGY WITH LOW FREQUENCY RIPPLE CANCELLATION

Because of high efficacy, long lifespan, and environment-friendly operation, LED lighting devices become more and more popular in every part of our life, such as ornament/interior lighting, outdoor lightings and flood lighting. The LED driver is the most critical part of the LED lighting fixture. It heavily affects the purchasing cost, operation cost as well as the light quality. Design a high efficiency, low component cost and flicker-free LED driver is the goal. The conventional single-stage LED driver can achieve low cost and high efficiency. However, it inevitably produces significant twice-line-frequency lighting flicker, which adversely affects our health. The conventional two-stage LED driver can achieve flicker-free LED driving at the expenses of significantly adding component cost, design complexity and low the efficiency. The basic ripple cancellation LED driving method has been proposed in chapter three. It achieves a high efficiency and a low component cost as the single-stage LED driver while also obtaining flicker-free LED driving performance. The basic ripple cancellation LED driver is the foundation of the entire thesis. As the research evolving, another two ripple cancellation LED drivers has been developed to improve different aspects of the basic ripple cancellation LED driver design. The primary side controlled ripple cancellation LED driver has been proposed in chapter four to further reduce cost on the control circuit. It eliminates secondary side compensation circuit and an opto-coupler in design while at the same time maintaining flicker-free LED driving. A potential integrated primary side controller can be designed based on the proposed LED driving method. The energy channeling ripple cancellation LED driver has been proposed in chapter five to further reduce cost on the power stage circuit. In previous two ripple cancellation LED drivers, an additional DC-DC converter is needed to achieve ripple cancellation. A power transistor has been used in the energy channeling ripple cancellation LED driving design to successfully replace a separate DC-DC converter and therefore achieved lower cost. The detailed analysis supports the theory of the proposed ripple cancellation LED drivers. Simulation and experiment have also been included to verify the proposed ripple cancellation LED drivers.

Estimation of Sample Size and Power For Quantile Regression

Quantile regression (QR) was first introduced by Roger Koenker and Gilbert Bassett in 1978. It is robust to outliers which affect least squares estimator on a large scale in linear regression. Instead of modeling mean of the response, QR provides an alternative way to model the relationship between quantiles of the response and covariates. Therefore, QR can be widely used to solve problems in econometrics, environmental sciences and health sciences. Sample size is an important factor in the planning stage of experimental design and observational studies. In ordinary linear regression, sample size may be determined based on either precision analysis or power analysis with closed form formulas. There are also methods that calculate sample size based on precision analysis for QR like C.Jennen-Steinmetz and S.Wellek (2005). A method to estimate sample size for QR based on power analysis was proposed by Shao and Wang (2009). In this paper, a new method is proposed to calculate sample size based on power analysis under hypothesis test of covariate effects. Even though error distribution assumption is not necessary for QR analysis itself, researchers have to make assumptions of error distribution and covariate structure in the planning stage of a study to obtain a reasonable estimate of sample size. In this project, both parametric and nonparametric methods are provided to estimate error distribution. Since the method proposed can be implemented in R, user is able to choose either parametric distribution or nonparametric kernel density estimation for error distribution. User also needs to specify the covariate structure and effect size to carry out sample size and power calculation. The performance of the method proposed is further evaluated using numerical simulation. The results suggest that the sample sizes obtained from our method provide empirical powers that are closed to the nominal power level, for example, 80%.