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.

Assessing the case for mandatory folate fortification : policy-making in the face of scientific uncertainties

This paper presents the view that policymakers face scientific uncertainties in assessing the case for mandatory folate fortification as a policy response to epidemiological evidence of the relationship between folate and neural tube defects. Moreover, the resolution of these uncertainties is confounded by the under-resourced state of nutrition information systems in Australia and New Zealand. The uncertainties relate to potential risks and benefits associated with the intervention for the target group and the population in general. These risks and benefits reflect the mismatch between evidence and policy that arises when addressing a presumed genetic abnormality in at-risk individuals with an intervention that is population-wide in its scope. There is an urgent need to conduct ongoing national nutrition surveys and monitor and evaluate policy interventions to strengthen the capacity of nutrition information systems to inform decision-making for this current, and future, public health nutrition policy.

Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification

Clustering of multivariate data is a commonly used technique in ecology, and many approaches to clustering are available. The results from a clustering algorithm are uncertain, but few clustering approaches explicitly acknowledge this uncertainty. One exception is Bayesian mixture modelling, which treats all results probabilistically, and allows comparison of multiple plausible classifications of the same data set. We used this method, implemented in the AutoClass program, to classify catchments (watersheds) in the Murray Darling Basin (MDB), Australia, based on their physiographic characteristics (e.g. slope, rainfall, lithology). The most likely classification found nine classes of catchments. Members of each class were aggregated geographically within the MDB. Rainfall and slope were the two most important variables that defined classes. The second-most likely classification was very similar to the first, but had one fewer class. Increasing the nominal uncertainty of continuous data resulted in a most likely classification with five classes, which were again aggregated geographically. Membership probabilities suggested that a small number of cases could be members of either of two classes. Such cases were located on the edges of groups of catchments that belonged to one class, with a group belonging to the second-most likely class adjacent. A comparison of the Bayesian approach to a distance-based deterministic method showed that the Bayesian mixture model produced solutions that were more spatially cohesive and intuitively appealing. The probabilistic presentation of results from the Bayesian classification allows richer interpretation, including decisions on how to treat cases that are intermediate between two or more classes, and whether to consider more than one classification. The explicit consideration and presentation of uncertainty makes this approach useful for ecological investigations, where both data and expectations are often highly uncertain.

An effective non-parametric method for globally clustering genes from expression profiles

Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorithms have been proposed, these are usually confronted with difficulties in meeting the requirements of both automation and high quality. In this paper, we propose a novel algorithm for clustering genes from their expression profiles. The unique features of the proposed algorithm are twofold: it takes into consideration global, rather than local, gene correlation information in clustering processes; and it incorporates clustering quality measurement into the clustering processes to implement non-parametric, automatic and global optimal gene clustering. The evaluation on simulated and real gene data sets demonstrates the effectiveness of the algorithm.

State and input simultaneous estimation for a class of time-delay systems with uncertainties

This brief addresses the problem of estimation of both the states and the unknown inputs of a class of systems that are subject to a time-varying delay in their state variables, to an unknown input, and also to an additive uncertain, nonlinear disturbance. Conditions are derived for the solvability of the design matrices of a reduced-order observer for state and input estimation, and for the stability of its dynamics. To improve computational efficiency, a delay-dependent asymptotic stability condition is then developed using the linear matrix inequality formulation. A design procedure is proposed and illustrated by a numerical example.

Parametric conditional frailty models for recurrent cardiovascular events in the lipid study

Background Analysis of recurrent event data is frequently needed in clinical and epidemiological studies. An important issue in such analysis is how to account for the dependence of the events in an individual and any unobserved heterogeneity of the event propensity across individuals.Methods We applied a number of conditional frailty and nonfrailty models in an analysis involving recurrent myocardial infarction events in the Long-Term Intervention with Pravastatin in Ischaemic Disease study. A multiple variable risk prediction model was developed for both males and females. Results A Weibull model with a gamma frailty term fitted the data better than other frailty models for each gender. Among nonfrailty models the stratified survival model fitted the data best for each gender. The relative risk estimated by the elapsed time model was close to that estimated by the gap time model. We found that a cholesterol-lowering drug, pravastatin (the intervention being tested in the trial) had significant protective effect against the occurrence of myocardial infarction in men (HR¼0.71, 95% CI0.60–0.83). However, the treatment effect was not significant in women due to smaller sample size (HR¼0.75, 95% CI 0.51–1.10). There were no significant interactions between the treatment effect and each recurrent MI event (p¼0.24 for men and p¼0.55 for women). The risk of developing an MI event for a male who had an MI event during follow-up was about 3.4 (95% CI 2.6–4.4) times the risk compared with those who did not have an MI event. The corresponding relative risk for a female was about 7.8 (95% CI 4.4–13.6). Limitations The number of female patients was relatively small compared with their male counterparts, which may result in low statistical power to find real differences in the effect of treatment and other potential risk factors.Conclusions The conditional frailty model suggested that after accounting for all the risk factors in the model, there was still unmeasured heterogeneity of the risk for myocardial infarction, indicating the effect of subject-specific risk factors. These risk prediction models can be used to classify cardiovascular disease patients into different risk categories and may be useful for the most effective targeting of preventive therapies for cardiovascular disease.

Parametric modelling and the tessellation of architectural surfaces

Parametric modelling is gaining in popularity as both a fabrication and design tool, but its application in the architectural design industry has not been widely explored. This form of modelling has the ability to generate complex forms with intuitively reactive components, allowing designers to express and
fabricate structures previously too laborious and geometrically complex to realise. The key aim of the paper is to address the increasing need for seamless and bi-directional connectivity between the design, modelling and
fabrication ambit.

Comments on "Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties"

This note points out that the time complexity of the main multiple-surface sliding control (MSSC) algorithm in Huang and Chen [Huang, A. C. & Chen, Y. C. (2004). Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties. Automatica, 40(11), 1939-1945] is O(2^n). Here, we propose a simplified recursive design MSSC algorithm with time complexity O(n), and, using mathematical induction, we show that this algorithm agrees with this MSSC law.

A simple parametric method for reducing sample sizes in gut passage time trials

Seed dispersal is now regularly analyzed using spatially explicit models, relying in part on frugivore gut passage times to produce model outputs. In determining species-specific gut passage times, there is a trade-off in sample size between minimizing collection effort and maintaining statistical reliability. Here we demonstrate that a two-parameter lognormal parametric distribution reliably fits empirical gut passage time distributions and is easily parameterized using relatively small data sets of approximately 30 defecations. We suggest this approach as a statistically reliable substitute for larger empirical gut passage data sets in seed dispersal modeling, and also as a way of using published gut passage data sets to parameterize new models.

Non-Deterministic Exploration through Parametric Design

This paper explores non-deterministic parametric modelling as a design tool. Specifically, it addresses the application of parametric variables to the generation of a conceptual bridge design and the use of repeatable discrete components to the conceptual form. In order to control the generation of the bridge form, a set of design variables based on the concept of a law curve have been developed.These design variables are applied and tested through interactive modelling and variation, driven by manipulating the law curve. Combining this process with the application and control of a repeatable element, known as a Representative Volumetric Element (RVE), allows for the development and exploration of a design solution that could not be achieved through the use of conventional computer modelling.The competition brief for the Australian Institute of Architects (AIA) ‘Dialectical Bridge’ has been used as a case study to demonstrate the use of non-deterministic parametric modelling as a design tool.The results of the experimentation with parametric variables, the law curve and representative volumetric elements (RVE) are presented in the paper.