Lithium ion conduction in Li5La3Ta2O12 and Li7La3Ta2O13 garnet-type materials

Electrical properties of the lithium garnets Li₅La₃Ta₂O₁₂ (L5LTO) and Li₇La₃Ta₂O₁₃ (L7LTO) are reported over a wide frequency range from 10 MHz to 0.1 Hz at different temperatures. The structural properties are characterized by powder X-ray diffraction, Scanning electron microscopy with energy dispersive X-ray spectroscopy and Fourier transformation Infrared spectroscopy. By means of the frame work of classical brick layer model (BLM) and of a finite element approach, the ion transport properties of grain and grain boundary for the lithium garnets were analyzed. The specific grain conductivity of 5.0?×?10^?6?S/cm at 40 °C is found for both lithium garnets. Specific grain conductivities and grain boundary conductivities are thermally activated, with activation energies found to be in the range of 0.55–0.61 eV. The total conductivity is found to be depending on the ion conduction of grain boundary. The information on the fraction of contact area α_contact between grains <0.25 is obtained by the finite element approach for Li₇La₃Ta₂O₁₃.     

Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

This article presents a hybrid model involving artificial neural networks and biogeography-based optimization for long-term forecasting of India's sector-wise electrical energy demand. It involves socio-economic indicators, such as population and per capita gross domestic product, and uses two artificial neural networks, which are trained through a biogeography-based optimization algorithm with a goal of perfect mapping of the input–output data in the non-linear space through obtaining the global best weight parameters. The biogeography-based optimization based training of the artificial neural network improves the forecasting accuracy and avoids trapping in local optima besides enhancing the convergence to the lowest mean squared error at the minimum number of iterations than existing approaches. The model requires an input and the year of the forecast and predicts the sector-wise energy demand. Forecasts up to the year 2025 are compared with those of the regression model, the artificial neural network model trained by back-propagation, and the artificial neural network model trained by harmony search algorithm to exhibit its effectiveness.     

An implementation of granular dynamics for simulating frictional elastic particles based on the DL_POLY code

We have modified Daresbury Laboratory's replicated data strategy (RDS) parallel molecular dynamics (MD) package DL_POLY (version 2.13) to study the granular dynamics of frictional elastic particles. DL_POLY [Smith and Forester, The DL_POLY_2 User Manual v2.13, 2001; Forester and Smith, The DL_POLY_2 Reference Manual v2.13, 2001] is a MD package originally developed to study liquid state and macromolecular systems by accounting for various molecular interaction forces. The particles of interest in this study are macroscopic grains in pharmaceutical powders, with sizes ranging from tens to hundreds of microns. We have therefore substituted the molecular interaction forces with contact forces (including linear-dashpot, HKK interaction forces and Coulombic friction) while taking advantage of the RDS scheme. In effect, we have created a parallel Discrete Element Simulation (DES) code. In this paper, we describe the modifications made to the original DL_POLY code and the results from the validation tests of the granular dynamics simulations for systems of monodisperse spherical particles settling under gravity. The code can also be utilized to study particle packings generated via uniaxial compaction and, in some cases, simultaneous application of shear, at constant strain.     

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.     

An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design

This paper addresses an evaluation of new heuristics solution procedures for the location of cross-docks and distribution centers in supply chain network design. The model is characterized by multiple product families, a central manufacturing plant site, multiple cross-docking and distribution center sites, and retail outlets which demand multiple units of several commodities. This paper describes two heuristics that generate globally feasible, near optimal distribution system design and utilization strategies utilizing the simulated annealing (SA) methodology. This study makes two important contributions. First, we continue the study of location planning for the cross-dock and distribution center supply chain network design problem. Second, we systematically evaluate the computational performance of this network design location model under more sophisticated heuristic control parameter settings to better understand interaction effects among the various factors comprising our experimental design, and present convergence results. The central idea of the paper is to evaluate the impact of geometric control mechanism vis-a-vis more sophisticated ones on solution time, quality, and convergence for two new heuristics. Our results suggest that integrating traditional simulated annealing with TABU search is recommended for this supply chain network design and location problem.     

Enhancing biometric recognition with spatio-temporal reasoning in smart environments

We discuss smart environments that identify and track their occupants using unobtrusive recognition modalities such as face, gait, and voice. In order to alleviate the inherent limitations of recognition, we propose spatio-temporal reasoning techniques based upon an analysis of the occupant tracks. The key idea underlying our approach is to determine the identity of a person based upon information from a track of events rather than a single event. We abstract a smart environment by a probabilistic state transition system in which each state records a set of individuals who are present in various zones of the smart environment. An event abstracts a recognition step, and the transition function defines the mapping between states upon the occurrence of an event. We express two forms of spatio-temporal reasoning in the form of transition functions: a track-based transition function and an error-correcting transition function. We also define the concepts of ‘precision’ and ‘recall’ to quantify the performance of the smart environment and provide experimental results to clarify the performance improvements from spatio-temporal reasoning. Our conclusion is that the state transition system is an effective abstraction of a smart environment and the application of spatial-temporal reasoning enhances the overall performance of a biometric recognition system.     

Optimality and stability of the K-hyperline clustering algorithm

K-hyperline clustering is an iterative algorithm based on singular value decomposition and it has been successfully used in sparse component analysis. In this paper, we prove that the algorithm converges to a locally optimal solution for a given set of training data, based on Lloyd’s optimality conditions. Furthermore, the local optimality is shown by developing an Expectation-Maximization procedure for learning dictionaries to be used in sparse representations and by deriving the clustering algorithm as its special case. The cluster centroids obtained from the algorithm are proved to tessellate the space into convex Voronoi regions. The stability of clustering is shown by posing the problem as an empirical risk minimization procedure over a function class. It is proved that, under certain conditions, the cluster centroids learned from two sets of i.i.d. training samples drawn from the same probability space become arbitrarily close to each other, as the number of training samples increase asymptotically.     

Texture and residual strain in two SiC/Ti-6-2-4-2 titanium composites

Residual strain and texture variations were measured in two titanium matrix composites reinforced with silicon carbide fibers (Ti/SiC) of similar composition but fabricated by different processing routes. Each composite comprised a Ti-6242 α/β matrix alloy containing vol 35 pct continuous SiC fibers. In one, the matrix was produced by a plasma sprayed (PS) route, and in the other by a wiredrawn (WD) process. The PS and WD composites were reinforced with SCS-6 (SiC) and Trimarc (SiC) fibers, respectively. The texture in the titanium matrices differed significantly. The titanium matrix for the PS material exhibited random texture pre and post fabrication of the composite. For the WD material, the starting texture of the monolithic titanium matrix was ≈17 times random, but after consolidation into composite form, it was ≈6 times random. No significant differences were noted in the fiber-induced matrix residual strains between the composites prepared by the two procedures. However, the Trimarc (WD) fibers recorded higher (≈1.3 times) compressive strains than the SCS-6 (PS) fibers. Stresses and stress balance results are reported. Plane-specific elastic moduli, measured in load tests on the unreinforced matrices, showed little difference. This article is based on a presentation made in the Symposium “Mechanisms and Mechanics of Composites Fracture” held October 11–15, 1998, at the TMS Fall Meeting in Rosemont, Illinois, under the auspices of the TMS-SMD/ASM-MSCTS Composite Materials Committee.     

Mixing matrix estimation using discriminative clustering for blind source separation

Mixing matrix estimation in instantaneous blind source separation (BSS) can be performed by exploiting the sparsity and disjoint orthogonality of source signals. As a result, approaches for estimating the unknown mixing process typically employ clustering algorithms on the mixtures in a parametric domain, where the signals can be sparsely represented. In this paper, we propose two algorithms to perform discriminative clustering of the mixture signals for estimating the mixing matrix. For the case of overdetermined BSS, we develop an algorithm to perform linear discriminant analysis based on similarity measures and combine it with K-hyperline clustering. Furthermore, we propose to perform discriminative clustering in a high-dimensional feature space obtained by an implicit mapping, using the kernel trick, for the case of underdetermined source separation. Using simulations on synthetic data, we demonstrate the improvements in mixing matrix estimation performance obtained using the proposed algorithms in comparison to other clustering methods. Finally we perform mixing matrix estimation from speech mixtures, by clustering single source points in the time-frequency domain, and show that the proposed algorithms achieve higher signal to interference ratio when compared to other baseline algorithms.