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.     

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.     

Effect of the Distribution of Manganese Ions on the Properties of Mn-Doped (Ba,Y)TiO3 PTCR Ceramics

The electrical properties and microstructure of (Ba,Y)TiO₃ PTCR ceramics were studied. The results indicate that the Mn ions increase the intergranular barrier height and produce a high-resistance layer on the grain surface. The temperature-dependent resistances of the grain bulk, surface layer, and grain boundaries, the temperature coefficient of resistance, and the magnitude of the varistor effect were assessed as a function of Mn content.     

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.     

Science in High Magnetic Fields: What Could Be Learned?

High magnetic fields are one of the most powerful tools available to scientists for the study, modification and control of matter. This includes the knowledge on correlations effects, interaction mechanisms, structural information and understanding of mesoscopic effects. In this context, a review of recent scientific achievements at the Grenoble High Magnetic Laboratory is given to illustrate, on specific examples, the power of the Magnetic Field probe.     

Structural Changes Induced in Nonstoichiometric Titanium Carbide by High-Temperature Deformation

The microstructure evolution in nonstoichiometric titanium carbide is studied during high-temperature deformation at high strain rates and low strains (shock compression) and at slow strain rates and high strains (superplastic regime). The results demonstrate that high-temperature deformation in a broad range of strain rates offers a means of controlling the microstructure of titanium carbide. By varying deformation conditions, one can obtain materials differing in microstructure and chemical composition, in particular, with equilibrium and nonequilibrium microstructures. Accordingly, the physicochemical properties of such materials also differ.     

On the physical and logical topology design of large-scale optical networks

We consider the problem of designing a network of optical cross-connects (OXCs) to provide end-to-end lightpath services to large numbers of label switched routers (LSRs). We present a set of heuristic algorithms to address the combined problem of physical topology design (i.e., determine the number of OXCs required and the fiber links among them) and logical topology design (i.e., determine the routing and wavelength assignment for the lightpaths among the LSRs). Unlike previous studies which were limited to small topologies with a handful of nodes and a few tens of lightpaths, we have applied our algorithms to networks with hundreds or thousands of LSRs and with a number of lightpaths that is an order of magnitude larger than the number of LSRs. In order to characterize the performance of our algorithms, we have developed lower bounds which can be computed efficiently. We present numerical results for up to 1000 LSRs and for a wide range of system parameters such as the number of wavelengths per fiber, the number of transceivers per LSR, and the number of ports per OXC. The results indicate that it is possible to build large-scale optical networks with rich connectivity in a cost-effective manner, using relatively few but properly dimensioned OXCs.     

Failure mode transition and energy dissipation in naturally occurring composites

The microstructural aspects of compressive inelastic deformation in balsa wood are investigated with emphasis on the failure mode transition and its effects on energy dissipation characteristics. The architectural features as well as the composite character of cell wall ultrastructure are discussed in a framework to understand the complex interrelationship between microstructure and macroscopic behavior in this extremely lightweight cellular biocomposite. Based on this discussion and experimental results, it is concluded that the biomimetic approach may prove to be a viable strategy in designing composite structures with high specific energy absorption capacity.