Open Source Telecommunications Framework for Time-Series Data from Smart Grid Sensors.

This paper presents a framework for a telecommunications interface which allows data from sensors embedded in Smart Grid applications to reliably archive data in an appropriate time-series database. The challenge in doing so is two-fold, firstly the various formats in which sensor data is represented, secondly the problems of telecoms reliability. A prototype of the authors' framework is detailed which showcases the main features of the framework in a case study featuring Phasor Measurement Units (PMU) as the application. Useful analysis of PMU data is achieved whenever data from multiple locations can be compared on a common time axis. The prototype developed highlights its reliability, extensibility and adoptability; features which are largely deferred from industry standards for data representation to proprietary database solutions. The open source framework presented provides link reliability for any type of Smart Grid sensor and is interoperable with existing proprietary database systems, and open database systems. The features of the authors' framework allow for researchers and developers to focus on the core of their real-time or historical analysis applications, rather than having to spend time interfacing with complex protocols.

Series Introduction: Centenary Classics

The Centenary Classics contains six titles in this special edition series. The year 2016 marks the beginning of the centenary period of the Irish Free State's establishment. This beautifully produced limited edition series examines the fascinating time of change and evolution in the Ireland of 100 years ago. Each volume is a first-hand account of individuals or events during the 1913-23 revolutionary period. They are each introduced by leading experts and academics in the field - giving a contemporary analysis of the original text - while a general series introduction by Fearghal McGarry sets the scene of the period. The complete series collectively tells the story of the birth of the Irish nation and consist of the following six titles: A Chronicle of Jails - Darrell Figgis; Civil War in Ulster - Joseph Johnston; Free State or Republic? - Padraig de Burca and John F. Boyle; Rising Out - Ernie O'Malley; Victory and Woe - Mossie Harnett and The Victory of Sinn Fein - P. S. O'Hegarty.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Determinants of pediatric cataract program outcomes and follow-up in a large series in Mexico.

PURPOSE:

To report determinants of outcomes and follow-up in a large Mexican pediatric cataract project.

SETTING:

Hospital Luis Sanchez Bulnes, Mexico City, Mexico.

METHODS:

Data were collected prospectively from a pediatric cataract surgery program at the Hospital Luis Sanchez Bulnes, implemented by Helen Keller International. Preoperative data included age, sex, baseline visual acuity, type of cataract, laterality, and presence of conditions such as amblyopia. Surgical data included vitrectomy, capsulotomy, complications, and use of intraocular lenses (IOLs). Postoperative data included final visual acuity, refraction, number of follow-up visits, and program support for follow-up.

RESULTS:

Of 574 eyes of 415 children (mean age 7.1 years +/- 4.7 [SD]), IOLs were placed in 416 (87%). At least 1 follow-up was attended by 408 patients (98.3%) (mean total follow-up 3.5 +/- 1.8 months); 40% of eyes achieved a final visual acuity of 6/18 or better. Children living farther from the hospital had fewer postoperative visits (P = .04), while children receiving program support had more visits (P = .001). Factors predictive of better acuity included receiving an IOL during surgery (P = .04) and provision of postoperative spectacles (P = .001). Predictive of worse acuity were amblyopia (P = .003), postoperative complications (P = .0001), unilateral surgery (P = .0075), and female sex (P = .045).

CONCLUSIONS:

The results underscore the importance of surgical training in reducing complications, early intervention before amblyopia (observed in 40% of patients) can develop, and vigorous treatment if amblyopia is present. The positive impact of program support on follow-up is encouraging, although direct financial support may pose a problem for sustainability. More work is needed to understand reasons for worse outcomes in girls.

Parallel Time Series Analysis and Income Inequality: A New Approach to Comparative Case Studies

This case study deals with the role of time series analysis in sociology, and its relationship with the wider literature and methodology of comparative case study research. Time series analysis is now well-represented in top-ranked sociology journals, often in the form of ‘pooled time series’ research designs. These studies typically pool multiple countries together into a pooled time series cross-section panel, in order to provide a larger sample for more robust and comprehensive analysis. This approach is well suited to exploring trans-national phenomena, and for elaborating useful macro-level theories specific to social structures, national policies, and long-term historical processes. It is less suited however, to understanding how these global social processes work in different countries. As such, the complexities of individual countries - which often display very different or contradictory dynamics than those suggested in pooled studies – are subsumed. Meanwhile, a robust literature on comparative case-based methods exists in the social sciences, where researchers focus on differences between cases, and the complex ways in which they co-evolve or diverge over time. A good example of this is the inequality literature, where although panel studies suggest a general trend of rising inequality driven by the weakening power of labour, marketisation of welfare, and the rising power of capital, some countries have still managed to remain resilient. This case study takes a closer look at what can be learned by applying the insights of case-based comparative research to the method of time series analysis. Taking international income inequality as its point of departure, it argues that we have much to learn about the viability of different combinations of policy options by examining how they work in different countries over time. By taking representative cases from different welfare systems (liberal, social democratic, corporatist, or antipodean), we can better sharpen our theories of how policies can be more specifically engineered to offset rising inequality. This involves a fundamental realignment of the strategy of time series analysis, grounding it instead in a qualitative appreciation of the historical context of cases, as a basis for comparing effects between different countries.

Series-L/parallel-tuned Class-E power amplifier analysis

An analysis of the operation of a new series-L/parallel-tuned Class-E amplifier and its equivalence to the classic shunt-C/series-tuned Class-E amplifier are presented. The first reported closed form design equations for the series-L/parallel-tuned topology operating under ideal switching conditions are given, including the switch current and voltage in steady state, the circuit component values, the peak values of switch current and voltage and the power-output capability. Theoretical analysis is confirmed by numerical simulation for a 500 mW (27 dBm), 10% bandwidth, 5 V series-L/parallel-tuned, then, shunt-C/series-tuned Class-E power amplifier, operating at 2.5 GHz. Excellent agreement between theory and simulation results is achieved.

Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.