Factorization of multipliers in passivity and IQC analysis

Multipliers are often used to find conditions for the absolute stability of Lur’e systems. They can be used either in conjunction with passivity theory or within the more recent framework of integral quadratic constraints (IQCs). We compare the use of multipliers in both approaches. Passivity theory requires that the multipliers have a canonical factorization and it has been suggested in the literature that this represents an advantage of the IQC theory. We consider sufficient conditions on the nonlinearity class for the associated multipliers to have a canonical factorization.     

Crossing safety barriers: influence of children's morphological and functional variables

Thirty-three children between 3 and 6 years of age were asked to climb four different types of safety barriers. Morphological and functional variables of the children, which were expected to influence climbing or passing through skills, were collected. The influence of those variables on children’s success rate and time to cross was tested. No barrier offered a total restraining efficacy. The horizontal bars barrier was crossed by 97% of the children. In the group of children that succeeded in crossing the four barriers, mean time to cross the most difficult barrier was 15 s. Age was the best predictor for success in crossing most barriers but morphology and strength were important predictors of time to cross. The influence of anthropometric variables in time to cross was dependent upon the characteristics of the barrier. A good design of safety barriers should consider children’s age, morphology and strength.     

An open-source library for the numerical modeling of mass-transfer in solid oxide fuel cells

The generation of direct current electricity using solid oxide fuel cells (SOFCs) involves several interplaying transport phenomena. Their simulation is crucial for the design and optimization of reliable and competitive equipment, and for the eventual market deployment of this technology. An open-source library for the computational modeling of mass-transport phenomena in SOFCs is presented in this article. It includes several multicomponent mass-transport models (i.e. Fickian, Stefan–Maxwell and Dusty Gas Model), which can be applied both within porous media and in porosity-free domains, and several diffusivity models for gases. The library has been developed for its use with OpenFOAM^®, a widespread open-source code for fluid and continuum mechanics. The library can be used to model any fluid flow configuration involving multicomponent transport phenomena and it is validated in this paper against the analytical solution of one-dimensional test cases. In addition, it is applied for the simulation of a real SOFC and further validated using experimental data.Program summaryProgram title: multiSpeciesTransportModelsCatalogue identifier: AEKB_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEKB_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: GNU General Public LicenseNo. of lines in distributed program, including test data, etc.: 18 140No. of bytes in distributed program, including test data, etc.: 64 285Distribution format: tar.gzProgramming language:: C++Computer: Any x86 (the instructions reported in the paper consider only the 64 bit case for the sake of simplicity)Operating system: Generic Linux (the instructions reported in the paper consider only the open-source Ubuntu distribution for the sake of simplicity)Classification: 12External routines: OpenFOAM® (version 1.6-ext) (http://www.extend-project.de)Nature of problem: This software provides a library of models for the simulation of the steady state mass and momentum transport in a multi-species gas mixture, possibly in a porous medium. The software is particularly designed to be used as the mass-transport library for the modeling of solid oxide fuel cells (SOFC). When supplemented with other sub-models, such as thermal and charge-transport ones, it allows the prediction of the cell polarization curve and hence the cell performance.Solution method: Standard finite volume method (FVM) is used for solving all the conservation equations. The pressure-velocity coupling is solved using the SIMPLE algorithm (possibly adding a porous drag term if required). The mass transport can be calculated using different alternative models, namely Fick, Maxwell–Stefan or dusty gas model. The code adopts a segregated method to solve the resulting linear system of equations. The different regions of the SOFC, namely gas channels, electrodes and electrolyte, are solved independently, and coupled through boundary conditions.Restrictions: When extremely large species fluxes are considered, current implementation of the Neumann and Robin boundary conditions do not avoid negative values of molar and/or mass fractions, which finally end up with numerical instability. However this never happened in the documented runs. Eventually these boundary conditions could be reformulated to become more robust.Running time: From seconds to hours depending on the mesh size and number of species. For example, on a 64 bit machine with Intel Core Duo T8300 and 3 GBytes of RAM, the provided test run requires less than 1 second.     

Strongdeco: Expansion of analytical,strongly correlated quantum states into a many-body basis

We provide a Mathematica code for decomposing strongly correlated quantum states described by a first-quantized, analytical wave function into many-body Fock states. Within them, the single-particle occupations refer to the subset of Fock–Darwin functions with no nodes. Such states, commonly appearing in two-dimensional systems subjected to gauge fields, were first discussed in the context of quantum Hall physics and are nowadays very relevant in the field of ultracold quantum gases. As important examples, we explicitly apply our decomposition scheme to the prominent Laughlin and Pfaffian states. This allows for easily calculating the overlap between arbitrary states with these highly correlated test states, and thus provides a useful tool to classify correlated quantum systems. Furthermore, we can directly read off the angular momentum distribution of a state from its decomposition. Finally we make use of our code to calculate the normalization factors for Laughlin?s famous quasi-particle/quasi-hole excitations, from which we gain insight into the intriguing fractional behavior of these excitations.Program summaryProgram title: StrongdecoCatalogue identifier: AELA_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AELA_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 5475No. of bytes in distributed program, including test data, etc.: 31 071Distribution format: tar.gzProgramming language: MathematicaComputer: Any computer on which Mathematica can be installedOperating system: Linux, Windows, MacClassification: 2.9Nature of problem: Analysis of strongly correlated quantum states.Solution method: The program makes use of the tools developed in Mathematica to deal with multivariate polynomials to decompose analytical strongly correlated states of bosons and fermions into a standard many-body basis. Operations with polynomials, determinants and permanents are the basic tools.Running time: The distributed notebook takes a couple of minutes to run.     

Intelligent system for time series classification using support vector machines applied to supply-chain

To be able of anticipate demand is a key factor for commercial success in the supply-chain sector. The benefits can be grouped around two main concepts: firstly the optimization of operations through the development of optimal strategies for procurement and secondly the stock reduction that reduces storage costs, handling, etc. There is currently a variety of methods for making predictions, these methods vary from pure statistical methods such as exponential smoothing Holt-Winters or ARIMA models, to those based on artificial intelligence techniques like neural networks or fuzzy systems. However, despite being able to build accurate models, in managing the supply chain based on forecasts there is a problem known as “Forrester effect” irrespective of the model chosen. To monitor the impact of this effect, given the volume of information handled in large corporations, is a very expensive task (often manual) for such corporations because it requires investigating issues such as the adequacy of the model, allocation of known models to the sales time series, discovery of new patterns of behavior, etc. This article proposes an intelligent system based on support vector machines to solve problems concerning the allocation and discovery of new models. With this focus in mind, the system objective is to build groups of time series that share the same forecasting model. For the identification of new models, the system will assign “virtual models” for those groups that do not have a predefined pattern. Using the proposed method, it has been possible to group a sample of more than 14,000 time series (real data taken from a store) in around 70 categories, of which only 12 of them already grouped over 98% of the total.     

Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.     

Investigation of different load changes in wind farm by using FACTS devices

In this study, voltage, active power and reactive power variation of load bus were examined by Flexible AC Transmission Systems devices (FACTS) in grid-connected wind farm. In the system, FACTS devices, Static Synchronous Compensators (STATCOM), Static VAr Compensators (SVC), Static Synchronous Series Compensators (SSSC) and Thyristor Controlled Series Compensators (TCSC), were used. Also, dual feed induction generator (DFIG) was used as generator in the wind farm. This simulation study was done via Matlab/Simulink environment. According to different values of active and reactive loads at different times being attached to the system, shapes of load bus voltage, active power and reactive power variation were given, also the results were presented in tables. In the conclusion of this study, by means of FACTS devices that is used in the wind farm, instability was healed within a short time.     

Self-Organising Maps: A new way to screen the level of satisfaction of dialysis patients

Evaluation of patient satisfaction has become an important indicator for assessing health care quality. Fresenius Medical Care (FME) as a global provider of dialysis services through its NephroCare network has a strong interest in monitoring patient satisfaction.The aim of the paper is to test and validate a methodology for detecting a residual area of low satisfaction in dialysis patients.The FME Patient Satisfaction Programme questionnaire was distributed to haemodialysis (HD) patients treated in 335 centers of its network. It contained 79 questions covering various satisfaction aspects regarding Dialysis Unit, Dialysis Arrangement, Nurses, Doctors, etc.To analyse the data provided by the questionnaire, the Self-Organising Map (SOM) method was used. SOM is a neural network model for clustering and projecting high-dimensional data into a low-dimensional space, preserving topological relationships of original high-dimensional data spaces.10,632 HD patients completed the questionnaire. Mean age was 63.05 ± 14.93 years with 56.69% males. Response rate was 66%. Overall level of satisfaction was 1.99 (range from ?3 to +3). On average patients were very satisfied with all issues. Nevertheless, a group of patients, around 60 years old, balanced gender ratio, whose level of satisfaction was lower than 1, were highlighted.In the NephroCare clinics patient satisfaction with service is rather high. While traditional analysis usually stops here, the SOM method allows identification of areas of potential improvement for specific patient groups.     

A consensus model for group decision making problems with linguistic interval fuzzy preference relations

Sometimes, we find decision situations in which it is difficult to express some preferences by means of concrete preference degrees. In this paper, we present a consensus model for group decision making problems in which the experts use linguistic interval fuzzy preference relations to represent their preferences. This model is based on two consensus criteria, a consensus measure and a proximity measure, and on the concept of coincidence among preferences. We compute both consensus criteria in the three representation levels of a preference relation and design an automatic feedback mechanism to guide experts in the consensus reaching process.