A new approach for sequential experimental design for model discrimination

Model discrimination procedures are useful tools for selection of the best mathematical models to be used to represent a specific chemical process. The present paper presents and discusses a new sequential discrimination procedure, which makes use of model probabilities and concentrates the efforts on models with higher probabilities. Model probabilities are determined based on simple statistical arguments. Four numerical examples illustrate the application of the proposed discrimination procedure. The obtained results indicate that the new procedure is able to discriminate kinetic models with fewer experiments when compared to other procedures and also indicates when model discrimination is not possible and, thus, when the sequential design must be halted. Furthermore, the speed of the proposed discrimination procedure can be controlled by tuning a design parameter which reflects the analyst's mood (confidence) towards the discrimination problem and allows for increase or decrease of the number of experiments required for model discrimination during the sequential procedure.     

A novel electromyographic signal simulator for muscle contraction studies

Mathematical simulation has been widely used in biomedical and biological sciences. In the case of the surface electromyographic (SEMG) activity, some models have been proposed aiming to study muscle contraction strategies that are used during different tasks and conditions. Most of SEMG simulators are based on energy modulation of a Gaussian noise. This work proposes a novel simulator in which the user-defined parameters are associated with the motor units (MUs) recruitment and their firing rate. Comparison between the mean spectrum of real SEMG signals collected in isometric contraction of the muscle biceps brachii and the mean spectrum obtained from simulated SEMG signals showed a good agreement, pointing the proposed simulator seems to be capable to generate consistent electromyographic signals in time and frequency domains and that can be used in many studies, in particular in the evaluation of automatic methods aimed to detect muscular contraction.     

A visual digital library approach for time-oriented scientific primary data

Digital Library support for textual and certain types of non-textual documents has significantly advanced over the last years. While Digital Library support implies many aspects along the whole library workflow model, interactive and visual retrieval allowing effective query formulation and result presentation are important functions. Recently, new kinds of non-textual documents which merit Digital Library support, but yet cannot be fully accommodated by existing Digital Library technology, have come into focus. Scientific data, as produced for example, by scientific experimentation, simulation or observation, is such a document type. In this article we report on a concept and first implementation of Digital Library functionality for supporting visual retrieval and exploration in a specific important class of scientific primary data, namely, time-oriented research data. The approach is developed in an interdisciplinary effort by experts from the library, natural sciences, and visual analytics communities. In addition to presenting the concept and to discussing relevant challenges, we present results from a first implementation of our approach as applied on a real-world scientific primary data set. We also report from initial user feedback obtained during discussions with domain experts from the earth observation sciences, indicating the usefulness of our approach.     

A Framework for Integrating Non-Functional Requirements into Conceptual Models

The development of complex information systems calls for conceptual models that describe aspects beyond entities and activities. In particular, recent research has pointed out that conceptual models need to model goals, in order to capture the intentions which underlie complex situations within an organisational context. This paper focuses on one class of goals, namely non-functional requirements (NFR), which need to be captured and analysed from the very early phases of the software development process. The paper presents a framework for integrating NFRs into the ER and OO models. This framework has been validated by two case studies, one of which is very large. The results of the case studies suggest that goal modelling during early phases can lead to a more productive and complete modelling activity.       

Rheological properties of wheat flour and quality characteristics of pan bread as modified by partial additions of wheat bran or whole grain wheat flour

The use of bran and whole grain flour changes dough rheology and causes difficulties in manufacturing bakery products. The aim of this study was to analyse the influence of substituting refined wheat flour (WF) by wheat bran (WB; 5%, 10%, 20%, 30% and 40%) or whole grain wheat flour (WGWF; 10%, 20%, 30%, 40% and 50%) on dough rheological properties and pan bread quality characteristics. The addition of WB and WGWF increased water absorption and resistance to extension and decreased stability, extensibility and peak viscosity. Effects with WB were more pronounced. The presence of WB or WGWF increased crumb moisture content, firmness and hardness and decreased specific volume of pan bread. It is important to set new farinographic and extensographic standards when using WB and WGWF, allowing for a correct correlation between rheology and quality characteristics of bakery products, as the same standards used for WF are not valid.     

MOSFET threshold voltage: Definition,extraction, and some applications

This paper exploits a universal current-based definition of the threshold voltage (V_T) and discusses some direct methods to measure it. The consistency, accuracy, and sensitivity of the extraction procedures to second-order effects are examined through numerical simulations and experimental measurements. In addition to three procedures based on dc current measurements we propose an automatic V_T-extractor circuit which allows the direct determination of the threshold voltage with minimum influence of second-order effects.     

A New Active Tilting-Pad Bearing: Nonlinear Modeling and Feedback Control

This article introduces a new type of active fluid film bearing and its feedback control. In particular, the active adjustment of the angular velocity of the pads of a tilting-pad bearing in response to changes in the operating conditions of the rotating machine is proposed. This is motivated by the observation that there is more control authority in the pad tilting motion than in its radial translation. To this end, a dynamic model for the bearing system is first developed, inclusive of the nonlinear hydrodynamic force for the infinitely short bearing case. A model-based controller is then constructed, based on measurements of the journal position and velocity and pad tilting angles, to ensure that the journal is asymptotically regulated to the bearing center. Numerical simulations illustrate the performance of the active bearing under the proposed control in comparison with the bearing's standard passive mode of operation.     

Further results on strict Lyapunov functions for rapidly time-varying nonlinear systems

We explicitly construct global strict Lyapunov functions for rapidly time-varying nonlinear control systems. The Lyapunov functions we construct are expressed in terms of oftentimes more readily available Lyapunov functions for the limiting dynamics which we assume are uniformly globally asymptotically stable. This leads to new sufficient conditions for uniform global exponential, uniform global asymptotic, and input-to-state stability of fast time-varying dynamics. We also construct strict Lyapunov functions for our systems using a strictification approach. We illustrate our results using several examples.     

Nonlinear parameter estimation through particle swarm optimization

Parameter estimation procedures are very important in the chemical engineering field for development of mathematical models, since design, optimization and advanced control of chemical processes depend on model parameter values obtained from experimental data. Model nonlinearity makes the estimation of parameter and the statistical analysis of parameter estimates more difficult and more challenging. In this work, it is shown that many of these difficulties can be overcome with the use of heuristic optimization methods, such as the particle swarm optimization (PSO) method. Parameter estimation problems are solved here with PSO and it is shown that the PSO method is efficient for both minimization and construction of the confidence region of parameter estimates. Moreover, it is shown that the elliptical approximation of confidence regions of nonlinear model parameters can be very poor sometimes and that more accurate likelihood confidence regions can be constructed with PSO, allowing for more reliable statistical analysis of the significance of parameter estimates.