Radio Location of Partial Discharge Sources: A Support Vector Regression Approach

Partial discharge (PD) can provide a useful forewarning of asset failure in electricity substations. A significant proportion of assets are susceptible to PD due to incipient weakness in their dielectrics. This paper examines a low cost approach for uninterrupted monitoring of PD using a network of inexpensive radio sensors to sample the spatial patterns of PD received signal strength. Machine learning techniques are proposed for localisation of PD sources. Specifically, two models based on Support Vector Machines (SVMs) are developed: Support Vector Regression (SVR) and Least-Squares Support Vector Regression (LSSVR). These models construct an explicit regression surface in a high dimensional feature space for function estimation. Their performance is compared to that of artificial neural network (ANN) models. The results show that both SVR and LSSVR methods are superior to ANNs in accuracy. LSSVR approach is particularly recommended as practical alternative for PD source localisation due to it low complexity

Left ventricular pseudoaneurysm

10.11909/j.issn.1671-5411.2017.01.010Journal of Geriatric Cardiology14178-8

A dynamic co-allocation service in multicluster systems

In multicluster systems, and more generally in grids, jobs may require co-allocation, i.e., the simultaneous allocation of resources such as processors in multiple clusters to improve their performance. In previous work, we have studied processor co-allocation through simulations. Here, we extend this work with the design and implementation of a dynamic processor co-allocation service in multicluster systems. While an implementation of basic co-allocation mechanisms has existed for some years in the form of the DUROC component of the Globus Toolkit, DUROC does not provide resource-brokering functionality or fault tolerance in the face of job submission or completion failures. Our design adds these two elements in the form of a software layer on top of DUROC. We have performed experiments that show that our co-allocation service works reliably