Inclusion of PMU current phasor measurements in a power system state estimator

Three different methods of inclusion of current measurements by phasor measurement units (PMUs) in a power sysetm state estimator is investigated. A comprehensive formulation of the hybrid state estimator incorporating conventional, as well as PMU measurements, is presented for each of the three methods. The behaviour of the elements because of the current measurements in the measurement Jacobian matrix is examined for any possible ill-conditioning of the state estimator gain matrix. The performance of the state estimators are compared in terms of the convergence properties and the varian in the estimated states. The IEEE 14-bus and IEEE 300-bus systems are used as test beds for the study.

Tangible programming elements for young children

Tangible programming elements offer the dynamic and programmable properties of a computer without the complexity introduced by the keyboard, mouse and screen. This paper explores the extent to which programming skills are used by children during interactions with a set of tangible programming elements: the Electronic Blocks. An evaluation of the Electronic Blocks indicates that children become heavily engaged with the blocks, and learn simple programming with a minimum of adult support.

Cell density alters matrix accumulation in two distinct fractions and the mechanical integrity of alginate-chondrocyte constructs

Chondrocyte density in articular cartilage is known to change with the development and growth of the tissue and may play an important role in the formation of a functional extracellular matrix (ECM). The objective of this study was to determine how initial chondrocyte density in an alginate hydrogel affects the matrix composition, its distribution between the cell-associated (CM) and further removed matrix (FRM) fractions, and the tensile mechanical properties of the developing engineered cartilage. Alginate constructs containing primary bovine chondrocytes at densities of 0, 4, 16, and 64 million cells/ml were fabricated and cultured for 1 or 2 weeks, at which time structural, biochemical, and mechanical properties were analyzed. Both matrix content and distribution varied with the initial cell density. Increasing cell density resulted in an increasing content of collagen and sulfated-glycosaminoglycan (GAG) and an increasing proportion of these molecules localized in the CM. While the equilibrium tensile modulus of cell-free alginate did not change with time in culture, the constructs with highest cell density were 116% stiffer than cell-free controls after 2 weeks of culture. The equilibrium tensile modulus was positively correlated with total collagen (r2 = 0.47, p < 0.001) and GAG content (r2 = 0.68, p < 0.001), and these relationships were enhanced when analyzing only those matrix molecules in the CM fraction (r2 = 0.60 and 0.72 for collagen and GAG, respectively, each p < 0.001). Overall, the results of this study indicate that initial cell density has a considerable effect on the developing composition, structure, and function of alginate–chondrocyte constructs.

Modulation of depth-dependent properties in tissue-engineered cartilage with a semi-permeable membrane and perfusion : a continuum model of matrix metabolism and transport

The functional properties of cartilaginous tissues are determined predominantly by the content, distribution, and organization of proteoglycan and collagen in the extracellular matrix. Extracellular matrix accumulates in tissue-engineered cartilage constructs by metabolism and transport of matrix molecules, processes that are modulated by physical and chemical factors. Constructs incubated under free-swelling conditions with freely permeable or highly permeable membranes exhibit symmetric surface regions of soft tissue. The variation in tissue properties with depth from the surfaces suggests the hypothesis that the transport processes mediated by the boundary conditions govern the distribution of proteoglycan in such constructs. A continuum model (DiMicco and Sah in Transport Porus Med 50:57-73, 2003) was extended to test the effects of membrane permeability and perfusion on proteoglycan accumulation in tissue-engineered cartilage. The concentrations of soluble, bound, and degraded proteoglycan were analyzed as functions of time, space, and non-dimensional parameters for several experimental configurations. The results of the model suggest that the boundary condition at the membrane surface and the rate of perfusion, described by non-dimensional parameters, are important determinants of the pattern of proteoglycan accumulation. With perfusion, the proteoglycan profile is skewed, and decreases or increases in magnitude depending on the level of flow-based stimulation. Utilization of a semi-permeable membrane with or without unidirectional flow may lead to tissues with depth-increasing proteoglycan content, resembling native articular cartilage.

A material model for flexural crack simulation in reinforced concrete elements using ABAQUS

This paper presents a material model to simulate load induced cracking in Reinforced Concrete (RC) elements in ABAQUS finite element package. Two numerical material models are used and combined to simulate complete stress-strain behaviour of concrete under compression and tension including damage properties. Both numerical techniques used in the present material model are capable of developing the stress-strain curves including strain softening regimes only using ultimate compressive strength of concrete, which is easily and practically obtainable for many of the existing RC structures or those to be built. Therefore, the method proposed in this paper is valuable in assessing existing RC structures in the absence of more detailed test results. The numerical models are slightly modified from the original versions to be comparable with the damaged plasticity model used in ABAQUS. The model is validated using different experiment results for RC beam elements presented in the literature. The results indicate a good agreement with load vs. displacement curve and observed crack patterns.

An evaluation of alternative media for pebble matrix filtration using clay balls and recycled crushed glass

Protecting slow sand filters (SSFs) from high-turbidity waters by pretreatment using pebble matrix filtration (PMF) has previously been studied in the laboratory at University College London, followed by pilot field trials in Papua New Guinea and Serbia. The first full-scale PMF plant was completed at a water-treatment plant in Sri Lanka in 2008, and during its construction, problems were encountered in sourcing the required size of pebbles and sand as filter media. Because sourcing of uniform-sized pebbles may be problematic in many countries, the performance of alternative media has been investigated for the sustainability of the PMF system. Hand-formed clay balls made at a 100-yearold brick factory in the United Kingdom appear to have satisfied the role of pebbles, and a laboratory filter column was operated by using these clay balls together with recycled crushed glass as an alternative to sand media in the PMF. Results showed that in countries where uniform-sized pebbles are difficult to obtain, clay balls are an effective and feasible alternative to natural pebbles. Also, recycled crushed glass performed as well as or better than silica sand as an alternative fine media in the clarification process, although cleaning by drainage was more effective with sand media. In the tested filtration velocity range of ð0:72–1:33Þ m=h and inlet turbidity range of (78–589) NTU, both sand and glass produced above 95% removal efficiencies. The head loss development during clogging was about 30% higher in sand than in glass media.

Quantifying axial deformation of columns using vibration characteristics

Column elements at a certain level in building are subjected to loads from different tributary areas. Consequently, differential axial deformation among these elements occurs. Adverse effects of differential axial deformation increase with building height and geometric complexity. Vibrating wire, electronic strain and external mechanical strain gauges are used to measure the axial deformations to take adequate provisions to mitigate the adverse effects. These gauges require deploying in or on the elements during their construction in order to acquire necessary measurements continuously. The use of these gauges is therefore inconvenient and uneconomical. This highlights the need for a method to quantify the axial deformation using ambient measurements. This paper proposes a comprehensive vibration based method. The unique capabilities of the proposed method present through an illustrative example.

Quantifying axial deformations of core shear walls using modal parameters

Differential axial deformation between column elements and shear wall elements of cores increase with building height and geometric complexity. Adverse effects due to the differential axial deformation reduce building performance and life time serviceability. Quantifying axial deformations using ambient measurements from vibrating wire, external mechanical and electronic strain gauges in order to acquire adequate provisions to mitigate the adverse effects is well established method. However, these gauges require installing in or on elements to acquire continuous measurements and hence use of these gauges is uneconomical and inconvenient. This motivates to develop a method to quantify the axial deformations. This paper proposes an innovative method based on modal parameters to quantify axial deformations of shear wall elements in cores of buildings. Capabilities of the method are presented though an illustrative example.

Minimising capacitive couplings and distributing copper losses in planar magnetic elements

Planar magnetic elements are becoming a replacement for their conventional rivals. Among the reasons supporting their application, is their smaller size. Taking less bulk in the electronic package is a critical advantage from the manufacturing point of view. The planar structure consists of the PCB copper tracks to generate the desired windings .The windings on each PCB layer could be connected in various ways to other winding layers to produce a series or parallel connection. These windings could be applied coreless or with a core depending on the application in Switched Mode Power Supplies (SMPS). Planar shapes of the tracks increase the effective conduction area in the windings, brings about more inductance compared to the conventional windings with the similar copper loss case. The problem arising from the planar structure of magnetic inductors is the leakage current between the layers generated by a pulse width modulated voltage across the inductor. This current value relies on the capacitive coupling between the layers, which in its turn depends on the physical parameters of the planar scheme. In order to reduce this electrical power dissipation due to the leakage current and Electromagnetic Interference (EMI), reconsideration in the planar structure might be effective. The aim of this research is to address problem of these capacitive coupling in planar layers and to find out a better structure for the planar inductance which offers less total capacitive coupling and thus less thermal dissipation from the leakage currents. Through Finite Element methods (FEM) several simulations have been carried out for various planar structures. The labs prototypes of these structures are built with the similar specification of the simulation cases. The capacitive couplings of the samples are determined with Spectrum Analyser whereby the test analysis verified the simulation results.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.