Computer-aided design and optimization of high-efficiency LLC series resonant converter

High conversion efficiency is desired in switch mode power supply converters. Computer-aided design optimization is emerging as a promising way to design power converters. In this work a systematic optimization procedure is proposed to optimize LLC series resonant converter full load efficiency. A mode solver technique is proposed to handle LLC converter steady-state solutions. The mode solver utilizes numerical nonlinear programming techniques to solve LLC-state equations and determine operation mode. Loss models are provided to calculate total component losses using the current and voltage information derived from the mode solver. The calculated efficiency serves as the objective function to optimize the converter efficiency. A prototype 300-W 400-V to 12-V LLC converter is built using the optimization results. Details of design variables, boundaries, equality/inequality constraints, and loss distributions are given. An experimental full-load efficiency of 97.07 is achieved compared to a calculated 97.4 efficiency. The proposed optimization procedure is an effective way to design high-efficiency LLC converters. © 1986-2012 IEEE.published_or_final_versio

Optimal and simultaneous designs of Hermitian transforms and masks for reducing intraclass separations of feature vectors for anomaly detection of diabetic retinopathy images

This paper proposes a novel methodology for the optimal and simultaneous designs of both Hermitian transforms and masks for reducing the intraclass separations of feature vectors for anomaly detection of diabetic retinopathy images. Each class of training images associates with a Hermitian transform, a mask and a known represented feature vector. The optimal and simultaneous designs of both the Hermitian transforms and the masks are formulated as least squares optimization problems subject to the Hermitian constraints. Since the optimal mask of each class of training images is dependent on the corresponding optimal Hermitian transform, only the Hermitian transforms are required to be designed. Nevertheless, the Hermitian transform design problems are optimization problems with highly nonlinear objective functions subject to the complex valued quadratic Hermitian constraints. This kind of optimization problems is very difficult to solve. To address the difficulty, this paper proposes a singular value decomposition approach for deriving a condition on the solutions of the optimization problems as well as an iterative approach for solving the optimization problems. Since the matrices characterizing the discrete Fourier transform, discrete cosine transform and discrete fractional Fourier transform are Hermitian, the Hermitian transforms designed by our proposed approach are more general than existing transforms. After both the Hermitian transforms and the masks for all classes of training images are designed, they are applied to test images. The test images will assign to the classes where the Euclidean 2-norms of the differences between the processed feature vectors of the test images and the corresponding represented feature vectors are minimum. Computer numerical simulation results show that the proposed methodology for the optimal and simultaneous designs of both the Hermitian transforms and the masks is very efficient and effective. The proposed technique is also very efficient and effective for reducing the intraclass separations of feature vectors for anomaly detection of diabetic retinopathy images. © 2012 IEEE

Parallel implementation of empirical mode decomposition for nearly bandlimited signals via polyphase representation

202208 bckwNot applicableSelf-funde

Joint generalized singular value decomposition and tensor decomposition for image super-resolution

202307 bckwAccepted ManuscriptSelf-fundedPublishe

Performing fractional delay via fractional singular spectrum analysis

202107 bcvcNot applicableOthersthe National Nature Science Foundation of China (no. U1701266, no. 61671163 and no. 62071128), the Team Project of the Education Ministry of the Guangdong Province (no. 2017KCXTD011), the Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent (no. 501130144), Hong Kong Innovation and Technology Commission, Enterprise Support Scheme (no. S/E/070/17)Early release12 month

A new bidimensional EMD algorithmand its applications

© 2014 IEEE.Empirical mode decomposition (EMD) is a powerful tool for analysis of non-stationaryand nonlinear signals, and has drawn significant attention in various engineeringapplication areas. This paper presents a new bidimensional EMD based on the adaptive anisotropic triangulations. Specifically, we define the local mean surface of the data, which is a key step in bidimensional EMD, by a locally weighted mean filter with variable window sizes that are determined by the adaptive anisotropic triangulations. Numerical experimentsshow that the proposed method achieves effective empirical mode decomposition for 2D signals

Image super resolution via combination of two dimensional quaternion valued singular spectrum analysis based denoising, empirical mode decomposition based denoising and discrete cosine transform based denoising methods

202307 bckwNot applicableSelf-fundedPublished12 month

A new bidimensional EMD algorithmand its applications

© 2014 IEEE.Empirical mode decomposition (EMD) is a powerful tool for analysis of non-stationaryand nonlinear signals, and has drawn significant attention in various engineeringapplication areas. This paper presents a new bidimensional EMD based on the adaptive anisotropic triangulations. Specifically, we define the local mean surface of the data, which is a key step in bidimensional EMD, by a locally weighted mean filter with variable window sizes that are determined by the adaptive anisotropic triangulations. Numerical experimentsshow that the proposed method achieves effective empirical mode decomposition for 2D signals