Training and optimization of an artificial neural network controlling a hybrid power filter

A hybrid power compensator (HPC) consisting of a static VAr compensator and a dynamic compensator needs to be optimally controlled during the compensation of nonlinear loads. The HPC must be controlled to meet minimum requirements in terms of power factor and harmonic distortion, while at the same time minimizing its total cost. An artificial neural network (ANN) is used to control the HPC amidst a very dynamic power system environment. The performance of a reference ANN is evaluated while controlling an HPC connected to a typical nonlinear industrial load. The training and performance of the ANN is then optimized in terms of training set size, training set packing and ANN topology and the performance compared to the reference ANN. This paper highlights the importance of optimising the mentioned ANN parameters to achieve optimum ANN training and modeling accuracy. The results obtained reveals that the application of an ANN in controlling an HPC is feasible given that the ANN parameters are chosen appropriately.     

Analytic models for the latency and steady-state throughput of TCP Tahoe, Reno, and SACK

Continuing the process of improvements made to TCP through the addition of new algorithms in Tahoe and Reno, TCP SACK aims to provide robustness to TCP in the presence of multiple losses from the same window. In this paper we present analytic models to estimate the latency and steady-state throughput of TCP Tahoe, Reno, and SACK and validate our models using both simulations and TCP traces collected from the Internet. In addition to being the first models for the latency of finite Tahoe and SACK flows, our model for the latency of TCP Reno gives a more accurate estimation of the transfer times than existing models. The improved accuracy is partly due to a more accurate modeling of the timeouts, evolution of cwnd during slow start and the delayed ACK timer. Our models also show that, under the losses introduced by the droptail queues which dominate most routers in the Internet, current implementations of SACK can fail to provide adequate protection against timeouts and a loss of roughly more than half the packets in a round will lead to timeouts. We also show that with independent losses SACK performs better than Tahoe and Reno and, as losses become correlated, Tahoe can outperform both Reno and SACK.     

A VQ-based blind image restoration algorithm

Learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.     

Synthesis of ZSM‐5 Films and Monoliths with Bimodal Micro/Mesoscopic Structures

A route to synthesize ZSM‐5 crystals with a bimodal micro/mesoscopic pore system has been developed in this study; the successful incorporation of the mesopores within the ZSM‐5 structure was performed using tetrapropylammonium hydroxide (TPAOH)‐impregnated mesoporous materials containing carbon nanotubes in the pores, which were encapsulated in the ZSM‐5 crystals during a solid rearrangement process within the framework. Such mesoporous ZSM‐5 zeolites can be readily obtained as powders, thin films, or monoliths.     

On trading wavelengths with fibers: a cost-performance based study

We consider the effect of multiple fibers on wavelength division multiplexing networks without wavelength conversion. We study networks with dynamic wavelength routing and develop accurate analytical models to compare various possible options using single- and multiple-fiber networks. We use results of an analytical model and simulation-based studies to evaluate the blocking performance and cost of multifiber networks. The number of fibers required providing high performance in multifiber networks and their costs are compared. A case is made for using multiple fibers in each link with fewer wavelengths instead of using a single fiber with many wavelengths. In particular, we show that a network with four fibers per link and with four wavelengths on each fiber without any wavelength conversion on any node yields similar same performance as the networks with one fiber per link and 16 wavelengths per fiber on each link and with full wavelength conversion capability on all nodes. In addition, the multifiber network may also offer the cost advantage depending on the relative cost of components. We develop a parametric cost model to show that multiple fibers in each link are an attractive option. Finally, such multifiber networks also has fault tolerance, with respect to a single fiber failure, already built into the system.     

QR factoring to compute the GCD of univariate approximate polynomials

We present a stable and practical algorithm that uses QR factors of the Sylvester matrix to compute the greatest common divisor (GCD) of univariate approximate polynomials over /spl Ropf/[x] or /spl Copf/[x]. An approximate polynomial is a polynomial with coefficients that are not known with certainty. The algorithm of this paper improves over previously published algorithms by handling the case when common roots are near to or outside the unit circle, by splitting and reversal if necessary. The algorithm has been tested on thousands of examples, including pairs of polynomials of up to degree 1000, and is now distributed as the program QRGCD in the SNAP package of Maple 9.     

Moving Least-Squares Differential Quadrature Method for Free Vibration of Antisymmetric Laminates

In this paper, the moving least-squares differential quadrature (MLSDQ) method is employed for free vibration of thick antisymmetric laminates based on the first-order shear deformation theory. The generalized displacements of the laminates are independently approximated with the centered moving least-squares (MLS) technique within each domain of influence. The MLS nodal shape functions and their partial derivatives are computed quickly through back-substitutions after only one LU decomposition. Subsequently, the weighting coefficients in the MLSDQ discretization are determined with the nodal partial derivatives of the MLS shape functions. The MLSDQ method combines the merits of both the differential quadrature and meshless methods which can be conveniently applied to complex domains and irregular discretizations without loss of implementation efficiency and numerical accuracy. The natural frequencies of the laminates with various edge conditions, ply angles, and shapes are calculated and compared with the existing solutions to study the numerical accuracy and stability of the MLSDQ method. Effects of support size, order of completeness of basis functions, and node irregularity on the numerical accuracy are investigated in detail.     

Derivation of a second-order switching surface in the boundary control of buck converters

A second-order switching surface in the boundary control of buck converters is derived in this letter. The formulated switching surface can make the overall converter exhibit better steady-state and transient behaviors than the one with a first-order switching surface. The switching surface is derived by estimating the state trajectory movement after a switching action, resulting in a high state trajectory velocity along the switching surface. This phenomenon accelerates the trajectory moving toward the target operating point. The proposed control scheme has been successfully applied to a 120-W buck converter. The large-signal performance and a comparison with the first-order switching surface have been studied.     

Characteristic Features of the Optimal Processing of the Results of Measurements in the Case of a Random Observation Function

An analysis is made of the characteristic features and problems of the optimal processing of the results of measurements in the case of a random observation function utilizing a nonlinear Kalman filter. A method is proposed for increasing the convergence domain of the filter with additional processing of measurements in the frequency domain.     

Influence of device engineering on the analog and RF performances of SOI MOSFETs

This work presents a systematic comparative study of the influence of various process options on the analog and RF properties of fully depleted (FD) silicon-on-insulator (SOI), partially depleted (PD) SOI, and bulk MOSFET's with gate lengths down to 0.08 /spl mu/m. We introduce the transconductance-over-drain current ratio and Early voltage as key figures of merits for the analog MOS performance and the gain and the transition and maximum frequencies for RF performances and link them to device engineering. Specifically, we investigate the effects of HALO implantation in FD, PD, and bulk devices, of film thickness in FD, of substrate doping in SOI, and of nonstandard channel engineering (i.e., asymmetric Graded-channel MOSFETs and gate-body contacted DTMOS).