Weak monotonicity of Lehmer and Gini means

We analyze directional monotonicity of several mixture functions in the direction (1,1...,1), called weak monotonicity. Our particular focus is on power weighting functions and the special cases of Lehmer and Gini means. We establish limits on the number of arguments of these means for which they are weakly monotone. These bounds significantly improve the earlier results and hence increase the range of applicability of Gini and Lehmer means. We also discuss the case of affine weighting functions and find the smallest constant which ensures weak monotonicity of such mixture functions.

Characteristics evaluation of a μ-Synthesis H∞ controller for a grid-connected three-phase photovoltaic system

This paper presents a μ-Synthesis H∞ Controller for regulating the switching signal of the inverter connected with a three-phase photovoltaic (PV) system. To facilitate the control design, the system is represented in terms of state space realization with uncertainties. The control design involves selecting proper weighting functions and performing synthesis. The controller order is reduced by Henkel-norm method. Simulations are carried out to evaluate the characteristics of the controller under parametric uncertainties. It is found out that the proposed controller is inherently stable, possesses significantly small tracking error, and preserves nominal performance, robust stability and robust performance for the grid-connected three-phase PV system.

Characteristics evaluation of an LMI-synthesis H∞ controller for a superconducting magnetic energy systems applied in power systems

© 2015 IEEE.This paper presents an H« controller synthesised based on linear matrix inequalities (LMI) for a current source converter based superconducting magnetic energy systems (SMESs) connected to a node of power systems where the regulation of grid current has considered as a control objective. To facilitate the control design, the system is represented in terms of state space realization with uncertainties. The control design involves selecting proper weighting functions and performing LMI-synthesis. The controller order is reduced by Henkel-norm method. Simulations are carried out to evaluate the characteristics of the controller under parametric uncertainties. It is found out that the proposed controller is inherently stable, possesses significantly small tracking error, and preserves robust performance for the SMES.

Minimum-order filter design for partial state estimation of linear systems with multiple time-varying state delays and unknown input delays

Designing delay-dependent functional observers for LTI systems with multiple known time-varying state delays and unknown time-varying input delays is studied. The input delays are arbitrary, but the state delays should be upper-bounded. In addition, two scenarios of slow-varying and fast-varying state delays are investigated. The results of the paper can also be considered as one of the first contributions considering unknown-input functional observer design for linear systems with multiple time-varying state delays. Based on the Lyapunov Krasovskii approach, delay-dependent sufficient conditions of the exponential stability of the observer in each scenario are established in terms of linear matrix inequalities. Because of using effective techniques, such as the descriptor transformation and an advanced weighted integral inequality, the proposed stability criteria can result in larger stability regions compared with the other papers that study functional observers for time-varying delay systems. Furthermore, to help with the design procedure, a genetic algorithm-based scheme is proposed to adjust a weighting matrix in the established linear matrix inequalities. Two numerical examples illustrate the design procedure and demonstrate the efficacy of the proposed observer in each scenario.

Fitting aggregation functions to data: part I-linearization and regularization

The use of supervised learning techniques for fitting weights and/or generator functions of weighted quasi-arithmetic means – a special class of idempotent and nondecreasing aggregation functions – to empirical data has already been considered in a number of papers. Nevertheless, there are still some important issues that have not been discussed in the literature yet. In the first part of this two-part contribution we deal with the concept of regularization, a quite standard technique from machine learning applied so as to increase the fit quality on test and validation data samples. Due to the constraints on the weighting vector, it turns out that quite different methods can be used in the current framework, as compared to regression models. Moreover, it is worth noting that so far fitting weighted quasi-arithmetic means to empirical data has only been performed approximately, via the so-called linearization technique. In this paper we consider exact solutions to such special optimization tasks and indicate cases where linearization leads to much worse solutions.

Multi-criteria evaluation of wave energy projects on the south-east Australian coast

In the last decade, multiple studies focusing on national-scale assessments of the ocean wave energy resource in Australia identified the Southern Margin to be one of the most energetic areas worldwide suitable for the extraction of wave energy for electricity production. While several companies have deployed single unit devices, the next phase of development will most likely be the deployment of parks with dozens of units, introducing the risk of conflicts within the marine space. This paper presents a geo-spatial multi-criteria evaluation approach to identify optimal locations to deploy a wave energy farm while minimizing potential conflicts with other coastal and offshore users. The methodology presented is based around five major criteria: ocean wave climatology, nature of the seabed, distance to key infrastructure, environmental factors and potential conflict with other users such as shipping and fisheries. A case study is presented for an area off the south-east Australian coast using a total of 18 physical, environmental and socio-economic parameters. The spatial restrictions associated with environmental factors, wave climate, as well as conflict of use, resulted in an overall exclusion of 20% of the study area. Highly suitable areas identified ranged between 11 and 34% of the study area based on scenarios with varying criteria weighting. By spatially comparing different scenarios we identified persistence of a highly suitable area of 700 km2 off the coast of Portland across all model domains investigated. We demonstrate the value of incorporation spatial information at the scale relevant to resource exploitation when examining multiple criteria for optimal site selection of Wave Energy Converters over broad geographic regions.

Adverse drug reaction classification with deep neural networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.

Hilbert Space Valued Signal Plus Noise Models: Analysis of Structural Breaks under High Dimensionality and Temporal Dependence

This thesis is concerned with change point analysis for time series, i.e. with detection of structural breaks in time-ordered, random data. This long-standing research field regained popularity over the last few years and is still undergoing, as statistical analysis in general, a transformation to high-dimensional problems. We focus on the fundamental »change in the mean« problem and provide extensions of the classical non-parametric Darling-Erdős-type cumulative sum (CUSUM) testing and estimation theory within highdimensional Hilbert space settings. In the first part we contribute to (long run) principal component based testing methods for Hilbert space valued time series under a rather broad (abrupt, epidemic, gradual, multiple) change setting and under dependence. For the dependence structure we consider either traditional m-dependence assumptions or more recently developed m-approximability conditions which cover, e.g., MA, AR and ARCH models. We derive Gumbel and Brownian bridge type approximations of the distribution of the test statistic under the null hypothesis of no change and consistency conditions under the alternative. A new formulation of the test statistic using projections on subspaces allows us to simplify the standard proof techniques and to weaken common assumptions on the covariance structure. Furthermore, we propose to adjust the principal components by an implicit estimation of a (possible) change direction. This approach adds flexibility to projection based methods, weakens typical technical conditions and provides better consistency properties under the alternative. In the second part we contribute to estimation methods for common changes in the means of panels of Hilbert space valued time series. We analyze weighted CUSUM estimates within a recently proposed »high-dimensional low sample size (HDLSS)« framework, where the sample size is fixed but the number of panels increases. We derive sharp conditions on »pointwise asymptotic accuracy« or »uniform asymptotic accuracy« of those estimates in terms of the weighting function. Particularly, we prove that a covariance-based correction of Darling-Erdős-type CUSUM estimates is required to guarantee uniform asymptotic accuracy under moderate dependence conditions within panels and that these conditions are fulfilled, e.g., by any MA(1) time series. As a counterexample we show that for AR(1) time series, close to the non-stationary case, the dependence is too strong and uniform asymptotic accuracy cannot be ensured. Finally, we conduct simulations to demonstrate that our results are practically applicable and that our methodological suggestions are advantageous.

Contact lens prescribing trends 2015 : The Efron, Morgan and Woods 16th annual survey of Australian contact lens prescribing habits

The 16th annual survey ofAustralian contact lens prescribingwas conducted from January toApril 2015. The same format as inprevious years was employed. Ane-mail was sent to all members ofOptometry Australia with a link to adownloadable questionnaire, and arequest that this be accessed, printedand completed to provide detailsof the first 10 patients fitted withcontact lenses after receipt of thequestionnaire.The survey was specifically designedto be straightforward to completewhile capturing key informationabout their patients. Practitionerswere asked general questions aboutthemselves. For each contact lensfitting, they were requested tocomplete the following details: dateof fitting, new fitting or refitting, ageand sex of patient, lens material, lensdesign, frequency of replacement,times per week of wear, modality(daily or extended wear) and caresystem. Practitioners were asked toreturn the questionnaire by fax, postor e-mail.Completed questionnaires relatingto 353 contact lens fittings werereturned, which provides a soundbasis for a meaningful analysis. Eachfitting was given a weighting based onthe number of lenses fitted per yearby the practitioner, based on the dateinformation on the form. This meansthat data generated by practitionerswho have a higher frequency of fittingcontact lenses were afforded a higherweighting than those taking longer tofit the 10 patients with lenses.This discussion concentratesprimarily on data relating to newlens fittings as opposed to refittings.We believe that new fittings are amore sensitive barometer of currentpatterns and future trends, whereasrefittings are more indicative ofprevious fitting behaviours.In keeping with other markets aroundthe world,1 the majority of lenses (62per cent) were fitted to females. Theaverage age of contact lens wearersat the time of fitting was 32.2 ± 16.5years. The age at fitting ranged fromeight to 75 years.

Stability enhancement of DFIG wind turbine using LQR pitch control over rated wind speed

A novel pitch control design method is proposed for the doubly fed induction generator (DFIG) wind turbine (WT) using linear quadratic regulator (LQR). A seven-order model represents the DFIG WT which is linearized by truncated Taylor series expansion. A systematic approach is adopted to determine the weighting matrices in LQR design for the optimal solution. Simulations have been carried out to compare the performance of the proposed LQR pitch control method against a PI pitch control for small and large disturbances. It is shown that the proposed control method enhances low-voltage ride-through capability and improves system damping under large disturbances.

Evaluation of breeding strategies for polledness in dairy cattle using a newly developed simulation framework for quantitative and Mendelian traits

Background: Intensified selection of polled individuals has recently gained importance in predominantly horned dairy cattle breeds as an alternative to routine dehorning. The status quo of the current polled breeding pool of genetically-closely related artificial insemination sires with lower breeding values for performance traits raises questions regarding the effects of intensified selection based on this founder pool. Methods: We developed a stochastic simulation framework that combines the stochastic simulation software QMSim and a self-designed R program named QUALsim that acts as an external extension. Two traits were simulated in a dairy cattle population for 25 generations: one quantitative (QMSim) and one qualitative trait with Mendelian inheritance (i.e. polledness, QUALsim). The assignment scheme for qualitative trait genotypes initiated realistic initial breeding situations regarding allele frequencies, true breeding values for the quantitative trait and genetic relatedness. Intensified selection for polled cattle was achieved using an approach that weights estimated breeding values in the animal best linear unbiased prediction model for the quantitative trait depending on genotypes or phenotypes for the polled trait with a user-defined weighting factor. Results: Selection response for the polled trait was highest in the selection scheme based on genotypes. Selection based on phenotypes led to significantly lower allele frequencies for polled. The male selection path played a significantly greater role for a fast dissemination of polled alleles compared to female selection strategies. Fixation of the polled allele implies selection based on polled genotypes among males. In comparison to a base breeding scenario that does not take polledness into account, intensive selection for polled substantially reduced genetic gain for this quantitative trait after 25 generations. Reducing selection intensity for polled males while maintaining strong selection intensity among females, simultaneously decreased losses in genetic gain and achieved a final allele frequency of 0.93 for polled. Conclusions: A fast transition to a completely polled population through intensified selection for polled was in contradiction to the preservation of high genetic gain for the quantitative trait. Selection on male polled genotypes with moderate weighting, and selection on female polled phenotypes with high weighting, could be a suitable compromise regarding all important breeding aspects.

Iowa Seat Belt Use Survey 2013 Data Collection Methodology Report, September 26, 2013

In an effort to achieve greater consistency and comparability in state-wide seat belt use reporting, the National Highway Traffic Safety Administration (NHTSA) issued new requirements in 2011 for observing and reporting future seat belt use. The requirements included the involvement of a qualified statistician in the sampling and weighting portions of the process as well as a variety of operational details. The Iowa Governor’s Traffic Safety Bureau contracted with Iowa State University’s Survey & Behavioral Research Services (SBRS) in 2011 to develop the study design and data collection plan for the State of Iowa annual survey that would meet the new requirements of the NHTSA. A seat belt survey plan for Iowa was developed by SBRS with statistical expertise provided by Zhengyuan Zhu, Ph.D., Associate Professor of Statistics at Iowa State University and was approved by NHTSA on March 19, 2012.

Iowa Seat Belt Use Survey 2014 Data Collection Methodology Report, September 25, 2014

In an effort to achieve greater consistency and comparability in state-wide seat belt use reporting, the National Highway Traffic Safety Administration (NHTSA) issued new requirements in 2011 for observing and reporting future seat belt use. The requirements included the involvement of a qualified statistician in the sampling and weighting portions of the process as well as a variety of operational details. The Iowa Governor’s Traffic Safety Bureau contracted with Iowa State University’s Survey & Behavioral Research Services (SBRS) in 2011 to develop the study design and data collection plan for the State of Iowa annual survey that would meet the new requirements of the NHTSA. A seat belt survey plan for Iowa was developed by SBRS with statistical expertise provided by Zhengyuan Zhu, Ph.D., Associate Professor of Statistics at Iowa State University and was approved by NHTSA on March 19, 2012.

Iowa Seat Belt Use Survey 2015 Data Collection Methodology Report, September 9, 2015

In an effort to achieve greater consistency and comparability in state-wide seat belt use reporting, the National Highway Traffic Safety Administration (NHTSA) issued new requirements in 2011 for observing and reporting future seat belt use. The requirements included the involvement of a qualified statistician in the sampling and weighting portions of the process as well as a variety of operational details. The Iowa Governor’s Traffic Safety Bureau contracted with Iowa State University’s Survey & Behavioral Research Services (SBRS) in 2011 to develop the study design and data collection plan for the State of Iowa annual survey that would meet the new requirements of the NHTSA. A seat belt survey plan for Iowa was developed by SBRS with statistical expertise provided by Zhengyuan Zhu, Ph.D., Associate Professor of Statistics at Iowa State University and Director of the Center for Survey Statistics and Methodology. The plan was approved by NHTSA on March 19, 2012.

L'Intégration d'un Module d'Étude Internet Dans la Classe de Comptabilité: Perceptions des Élèves et les Résultats Académiques

Implemented in the context of Business Administration students enrolled in a college level three year technology program, this research investigated students’ perceptions and academic results concurrent with the implementation of an online web module designed to facilitate student self-study. The students involved in this research were enrolled in a program that, while offering a broad education in business disciplines, specialized in the field of accounting. As a result, students were enrolled in academically rigorous accounting courses in each of the six semesters of the program. The weighting of these accounting courses imposes a significant self-study component – typically matching or exceeding the time spent in class. In this context many of the students enrolled in the Business Administration Program have faced difficulties completing the self-study component of the course effectively as demonstrated in low homework completion rates, low homework grade averages and ultimately low success rates in the courses. In an attempt to address this situation this research studied the implementation of a web-based self-study module. Through this module students could access a number of learning tools that were designed to facilitate the self-study process under the premise that more effective self-study learning tools will help remove obstacles and provide more timely confirmation of learning during student self-study efforts. This research collected data from a single cohort of students drawn from the first three sequential accounting courses of the Business Administration Program. The web-based self-study module was implemented in the third of the three sequential accounting courses. The first two of these courses implemented a traditional manual self-study environment. Data collected from the three accounting courses included homework completion rates, homework, exam and final grades for the respective courses. In addition the web-study module allowed the automatic reporting of student usage of a number of specific online learning tools. To complement the academic data, students were surveyed to gain insight into their perceptions of the effectiveness of the web-based system. The research provided a number of interesting insights. First among these was a confirmation of the importance of the self-study process in the academic achievement of the learners. Regardless of the self-study environment, manual or web-enhanced, a significant positive correlation existed between the students’ self-study results, demonstrated in both homework completion rates and homework averages and the corresponding final grades. These results confirm the importance of self-study found generally in the prevailing academic literature regarding students enrolled in higher education. In addition, the web-enhanced learning environment implemented during the third accounting course coincided with significantly higher homework completion rates and corresponding homework averages: homework completion rates in particular increased from a combined average of 63% in the first two accounting courses to 93% in the web-enhanced context of the third accounting course. Moreover, the homework completion rates of the web-enhanced course were evenly distributed across the cohort of students. A quartile-based analysis was subsequently completed. Quartiles were constructed by ranking the students according to their combined average homework completion rates from the first two manual self-study courses, Accounting I and II. The quartile-based homework completion rates for the manual self-study courses Accounting I and II were subsequently compared to the results these same quartiles of students achieved in the web-based self-study within Accounting III. While the first two courses demonstrated significantly uneven homework completion rates across the quartiles ranging from 31% to 91% homework completion rates, the differences among the four quartiles within the web-enhanced module, with an average homework completion rate of 93%, were statistically insignificant. Congruent with the positive academic results observed in the third, web-enhanced course, through the corresponding survey, students expressed a strong attitude in favor of the online self-study environment. This research was designed to add to the existing research that studies the implementation of learning in an online setting. Specifically, the research was designed to explore a middle ground of online learning – a web-enhanced course – a context that supplements the classroom experience rather than replacing it. The web-enhanced accounting course demonstrated impressive favorable results, both academically and in terms of students' perception of the system; these results suggest that a web-enhanced environment can provide learning tools that facilitate the self-study process while providing a structured learning environment that can help developing learners reach their potential.