Electromagnetic loss modeling and demagnetization analysis for high speed permanent magnet machine

This paper presents a research work on the electromagnetic loss modeling and demagnetization analysis for a high-speed permanent magnet machine (HSPMM). The iron loss is estimated by improved modeling considering harmonics and rotational magnetic field effects to achieve high precision; rotor eddy current loss is researched and comprehensively investigated using finite-element method (FEM). The auxiliary slot and PM beveling are also proposed to reduce the rotor eddy current loss for machine at high-speed operation. Temperature-dependent PM demagnetization modeling is utilized in HSPMM FEM analysis to investigate machine performance due to temperature variation, while optimized rotor structures are proposed and comparatively researched by FEM to improve the machine anti-demagnetization capability in harsh conditions. The HSPMM temperature is estimated based on the calculated loss results and machine computational fluid dynamic modeling. Experimental measurements on the prototype machine verify the effectiveness of the machine electromagnetic and thermal modeling in the paper

A Tale of Two Blogs: Lessons Learned Establishing The Top Shelf and La Cocina Histórica at the University of Texas at San Antonio

When establishing a blog, Special Collections departments face a variety of decisions that will affect the future shape of the blog and its readership. The University of Texas at San Antonio (UTSA) Libraries Special Collections is in the unusual position of publishing two blogs with distinct audiences and content: a general department blog, The Top Shelf, and a collection-specific blog, La Cocina Histórica. This article examines various strategies employed by both of these blogs in the areas of content, targeted audience, management and authorship responsibility, media exposure, and platform-choice and how those strategies affect blog readership

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues

Harnessing brain power at NUI Maynooth

The Department of Electronic Engineering at NUI Maynooth is involved in exciting interdisciplinary work in the biomedical, digital signal processing, control and electronic systems areas. Here Tomas Ward, Seán McLoone and Shirley Coyle highlight three specific projects

Utilising Mobile Phone RSSI Metric for Human Activity Detection

Recent research into urban analysis through the use of mobile device usage statistics has presented a need for the collection of this data independently from mobile network operators. In this paper we propose that cumulative received signal strength indications (RSSI) for overall mobile device transmissions in an area may provide such independent information. A process for the detection of high density areas within the RSSI temporal data set will be demonstrated. Finally, future applications for this collection method are discussed and we highlight its potential to complement traditional metric analysis techniques, for the representation of intensity of urban and local activities and their evolution through time and space

Exploiting A Priori Time Constant Ratio Information in Difference Equation Two-Thermocouple Sensor Characterization

The characterization of thermocouple sensors for temperature measurement in varying-flow enviroments is a challenging problem. Recently, the authors introduced novel different-equation-based algorithms that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. In particular, a linear least squares (LS) formulation of the characterization problem, which yields unbiased estimates when identified using generalized total LS, was introduced. These algorithms assume that time constants do not change during operation and are, therefore, appropriate for temperature measurement in homogenous constant-velocity liquid of gas flows. This paper develops an alternative B formulation of the characterization problem that has the major advantage of allowing exploitation of a priori knowledge of the ratio of the sensor time constants, thereby facilitating the implementation of computationally efficient algorithms that are less sensitive to measurement noise. A number of variants of the B formulation are developed, and appropriate unbiased estimators are identified. Monte Carlo simulation results are used to support the analysis

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.Comment: 20 pages, 3 figure