SPSS step by step - essentials for social and political science

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LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

European Union Biomedical Research Law and Policy and Citizen Science

In this chapter I focus on the EU's emerging biomedical research law and policy and examine the development of citizen science in this setting. The chapter argues that while what the analysis reveals might not be specific to the EU, attention to this organisation underlines important but often overlooked aspects of citizen science. That is, citizen science is (being) made less about promoting substantive involvement by citizens in the fashioning of biomedical trajectories and their empowerment as participants that pursue aims defined by themselves rather than others. Instead citizen science is underpinned by a more longstanding EU level approach to participation in science-based issues that sees it being harnessed, shaped and directed towards supporting the production and legitimation of organisational identity and sociotechnical order (in this case the EU’s). Within biomedical research law and policy citizen science might therefore be expected to support market-optimised biomedical futures and a dynamic internal market and economy. Citizen science is thereby implicated in the delineation of the boundaries of responsibility and accountability (and blame) for the (non-)realisation of public health priorities and objectives. In this way law and policy on participation and citizen science might support current research trajectories that do not serve all health needs.

A Tower-Shadow Model for Wind Turbines Using a Wavelet-Prony Method

The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency pulsations contained within active power flow. A primary concern is excitation of low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of interconnection between the Northern and Southern power system networks. In order to determine whether the prevalence of wind generation has a negative effect (excites modes) or positive impact (damping of modes) on the power system, oscillations must be measured and characterised. Using time – frequency methods, this paper presents work that has been conducted to extract features from low-frequency active power pulsations to determine the composition of oscillatory modes which may impact on dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.

A Sensitivity Approach for Eliminating Clashes from Computer Aided Design Model Assemblies

Clashes occur when components in an assembly unintentionally violate others. If clashes are not identified and designed out before manufacture, product function will be reduced or substantial cost will be incurred in rework. This paper introduces a novel approach for eliminating clashes by identifying which parameters defining the part features in a computer aided design (CAD) assembly need to change and by how much. Sensitivities are calculated for each parameter defining the part and the assembly as the change in clash volume due to a change in each parameter value. These sensitivities give an indication of important parameters and are used to predict the optimum combination of changes in each parameter to eliminate the clash. Consideration is given to the fact that it is sometimes preferable to modify some components in an assembly rather than others and that some components in an assembly cannot be modified as the designer does not have control over their shape. Successful elimination of clashes has been demonstrated in a number of example assemblies.

Investigation of AIMD based charging strategies for EVs connected to a low-voltage distribution network

In this paper we consider charging strategies that mitigate the impact of domestic charging of EVs on low-voltage distribution networks and which seek to reduce peak power by responding to time-ofday pricing. The strategies are based on the distributed Additive Increase and Multiplicative Decrease (AIMD) charging algorithms proposed in [5]. The strategies are evaluated using simulations conducted on a custom OpenDSS-Matlab platform for a typical low voltage residential feeder network. Results show that by using AIMD based smart charging 50% EV penetration can be accommodated on our test network, compared to only 10% with uncontrolled charging, without needing to reinforce existing network infrastructure. © Springer-Verlag Berlin Heidelberg 2013.

An Extended Event Reasoning Framework for Decision Support under Uncertainty

To provide in-time reactions to a large volume of surveil- lance data, uncertainty-enabled event reasoning frameworks for CCTV and sensor based intelligent surveillance system have been integrated to model and infer events of interest. However, most of the existing works do not consider decision making under uncertainty which is important for surveillance operators. In this paper, we extend an event reasoning framework for decision support, which enables our framework to predict, rank and alarm threats from multiple heterogeneous sources.