An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

Parametric FIR Design of Propagation Loss Filters in Digital Waveguide String Models

One of the attractive features of sound synthesis by physical modeling is the potential to build acoustic-sounding digital instruments that offer more flexibility and different options in its design and control than their real-life counterparts. In order to develop such virtual-acoustic instruments, the models they are based on need to be fully parametric, i.e., all coefficients employed in the model are functions of physical parameters that are controlled either online or at the (offline) design stage. In this letter we show how propagation losses can be parametrically incorporated in digital waveguide string models with the use of zero-phase FIR filters. Starting from the simplest possible design in the form of a three-tap FIR filter, a higher-order FIR strategy is presented and discussed within the perspective of string sound synthesis with digital waveguide models.

COLREGs-based collision avoidance strategies for unmanned surface vehicles

The aim of this paper is to report the preliminary development of an automatic collision avoidance technique for unmanned marine craft based on standardised rules, COLREGs, defined by the International Maritime Organisation. It is noted that all marine surface vessels are required to adhere to COLREGs at all times in order to minimise or eliminate the risk of collisions. The approach presented is essentially a reactive path planning algorithm which provides feedback to the autopilot of an unmanned vessel or the human captain of a manned ship for steering the craft safely. The proposed strategy consists of waypoint guidance by line-of-sight coupled with a manual biasing scheme. This is applied to the dynamic model of an unmanned surface vehicle. A simple PID autopilot is incorporated to ensure that the vessel adheres to the generated seaway. It is shown through simulations that the resulting scheme is able to generate viable trajectories in the presence of both stationary and dynamic obstacles. Rules 8 and 14 of the COLREGs, which apply to the amount of manoeuvre and to a head-on scenario respectively are simulated. A comparison is also made with an offline or deliberative grid-based path planning algorithm which has been modified to generate COLREGs-compliant routes.

Sliding window two-thermocouple sensor characterization for variable flow environments

A two-thermocouple sensor characterization method for use in variable flow applications is proposed. Previous offline methods for constant velocity flow are extended using sliding data windows and polynomials to accommodate variable velocity. Analysis of Monte-Carlo simulation studies confirms that the unbiased and consistent parameter estimator outperforms alternatives in the literature and has the added advantage of not requiring a priori knowledge of the time constant ratio of thermocouples. Experimental results from a test rig are also presented. © 2008 The Institute of Measurement and Control.

Rethinking Local Government Support for Youth Arts:The Case of NOISE South Dublin

Within Ireland, interest in strategically supporting young people’s participation in the arts has increased. Additionally, awareness of the Internet’s potential for promot- ing engagement with the arts has grown. Addressing national directives and local needs assessments, South Dublin County Council’s Arts Office initiated NOISE South Dublin (http://www.noisesouthdublin.com), an interactive Web site based on Australia Council’s NOISE project (http://www.noise.net), to promote the creative development of young people in the county. This article presents the practical chal- lenges and potential of youth arts Web-based programs for harnessing the creative engagement of youth. It concludes that the Internet is only useful if it expands online engagement offline.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

In situ diagnostics and prognostics of solder fatigue in IGBT modules for electric vehicle drives

This paper proposes an in situ diagnostic and prognostic (D&P) technology to monitor the health condition of insulated gate bipolar transistors (IGBTs) used in EVs with a focus on the IGBTs' solder layer fatigue. IGBTs' thermal impedance and the junction temperature can be used as health indicators for through-life condition monitoring (CM) where the terminal characteristics are measured and the devices' internal temperature-sensitive parameters are employed as temperature sensors to estimate the junction temperature. An auxiliary power supply unit, which can be converted from the battery's 12-V dc supply, provides power to the in situ test circuits and CM data can be stored in the on-board data-logger for further offline analysis. The proposed method is experimentally validated on the developed test circuitry and also compared with finite-element thermoelectrical simulation. The test results from thermal cycling are also compared with acoustic microscope and thermal images. The developed circuitry is proved to be effective to detect solder fatigue while each IGBT in the converter can be examined sequentially during red-light stopping or services. The D&P circuitry can utilize existing on-board hardware and be embedded in the IGBT's gate drive unit.

Vertex Clustering of Augmented Graph Streams

In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity.