Lukas Foss's The Prairie (1942): Building Community through Music

Lukas Foss's The Prairie, a seven-movement cantata for SATB soloists, mixed chorus, and orchestra, is a well-crafted and audience-friendly setting of the poem of the same name by Carl Sandburg. Acclaimed at its 1944 premiere, The Prairie has not enjoyed a robust performance history due in large part to numerous inconsistencies between the score and parts which render the work challenging to perform. Heretofore, each conductor has individually located these inconsistencies and made the necessary adjustments for performances. In this document, resources have been assembled which identify and correct many of the inconsistencies, thus facilitating more performances in the future. The Prairie is an ideal vehicle for a project-based chorus, which is itself an opportunity to create a new community. A correlation may exist between "community" and "vulnerability;" that is, when a choral singer feels comfortably connected to fellow singers, he or she may be more likely to open up to new ways of making sounds for the ultimate service of the music. Results are inconclusive, but the project points to a successful strategy for creating an affirming new community and to positive musical and nonmusical results of intentional community-building.

Vulnerability and robustness of SME supply chains: An empirical study of risk and disturbance management of fresh food processors in a developing market

The aim of this paper is to analyse vulnerability and robustness of small and medium size enterprises (SMEs) supply chains and to consider contextual factors that might influence the success of their disturbance management: Risky product and business environment. By using an exploratory case study it is shown how these contextual factors attribute vulnerability sources, contribute to the robustness of a company’s performance and supply chain vulnerability, as well as how a company seeks to manage internal and external vulnerability sources. The exploratory case is based on a fresh food supply chain of a manufacturing SME operating in a developing market.
Case findings suggest that fresh food supply chains of a manufacturing SME in developing markets are prone to disruptions of their logistics and production processes due to ‘riskiness’ of fresh food products, the ‘riskiness’ of developing markets, as well as ‘riskiness’ of SMEs themselves. However, this does not necessarily indicate the vulnerability of an SME and its entire supply chain. Findings indicate that SMEs can be very successful in disturbance management by selective use of redesign strategies that aim to prevent or reduce the impact of disturbances. More precise, it is likely that an SME can achieve robust performance by employing preventive redesign strategies in managing disturbances that result from internal, company related vulnerability sources, while impact reduction strategies are likely to contribute to robust performance of an SME if used to manage disturbances that result from internal, supply chain related vulnerability sources, as well as external vulnerability sources.

Neuro-genetic PID autotuning: time invariant case

The Proportional, Integral and Derivative (PID) controllers are widely used in induxtrial applications. Their popularity comes from their robust performance and also from their functional simplicity.

New methods for PID autotuning

Despite the developments in the control theory and technology achieved in the last decade, PID controllers still remain the type of controller most used in industry. This fact is due to its simplicity (only three terms to tune) and to their robust performance.

Rotation Invariant Object Recognition from One Training Example

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. Such a descriptor--based on a set of oriented Gaussian derivative filters-- is used in our recognition system. We report here an evaluation of several techniques for orientation estimation to achieve rotation invariance of the descriptor. We also describe feature selection based on a single training image. Virtual images are generated by rotating and rescaling the image and robust features are selected. The results confirm robust performance in cluttered scenes, in the presence of partial occlusions, and when the object is embedded in different backgrounds.

Effectiveness of asynchronous channel assignment scheme in heterogeneous WLANs

Due to their popularity, dense deployments of wireless local area networks (WLANs) are becoming a common feature of many cities around the world. However, with only a limited number of channels available, the problem of increased interference can severely degrade the performance of WLANs if an effective channel assignment scheme is not employed. Previous studies on channel assignment in WLANs almost always assume that all access points (AP) employ the same channel assignment scheme which is clearly unrealistic. On the other hand, to the best of our knowledge, the interaction between different channel assignment schemes has also not been studied before. Therefore, in this paper, we investigate the effectiveness of our earlier proposed asynchronous channel assignment scheme in these heterogeneous WLANs scenarios. Simulation results show that our proposed scheme is still able to provide robust performance gains even in these scenarios.

Supervised subspace learning with multi-class Lagrangian SVM on the Grassmann manifold

Learning robust subspaces to maximize class discrimination is challenging, and most current works consider a weak connection between dimensionality reduction and classifier design. We propose an alternate framework wherein these two steps are combined in a joint formulation to exploit the direct connection between dimensionality reduction and classification. Specifically, we learn an optimal subspace on the Grassmann manifold jointly minimizing the classification error of an SVM classifier. We minimize the regularized empirical risk over both the hypothesis space of functions that underlies this new generalized multi-class Lagrangian SVM and the Grassmann manifold such that a linear projection is to be found. We propose an iterative algorithm to meet the dual goal of optimizing both the classifier and projection. Extensive numerical studies on challenging datasets show robust performance of the proposed scheme over other alternatives in contexts wherein limited training data is used, verifying the advantage of the joint formulation.

Estudo comparativo do desempenho de controladores robustos aplicados a um sistema de tanques acoplados.

Currently the uncertain system has attracted much academic community from the standpoint of scientific research and also practical applications. A series of mathematical approaches emerge in order to troubleshoot the uncertainties of real physical systems. In this context, the work presented here focuses on the application of control theory in a nonlinear dynamical system with parametric variations in order and robustness. We used as the practical application of this work, a system of tanks Quanser associates, in a configuration, whose mathematical model is represented by a second order system with input and output (SISO). The control system is performed by PID controllers, designed by various techniques, aiming to achieve robust performance and stability when subjected to parameter variations. Other controllers are designed with the intention of comparing the performance and robust stability of such systems. The results are obtained and compared from simulations in Matlab-simulink.

Observer-based state-feedback versus H-infinity output feedback control solved by LMI approach for applications in smart structures

This paper aims with the use of linear matrix inequalities approach (LMIs) for application in active vibration control problems in smart strutures. A robust controller for active damping in a panel was designed with piezoelectrical actuators in optimal locations for illustration of the main proposal. It was considered, in the simulations of the closed-loop, a model identified by eigensystem realization algorithm (ERA) and reduced by modal decomposition. We tested two differents techniques to solve the problem. The first one uses LMI approach by state-feedback based in an observer design, considering several simultaneous constraints as: a decay rate, limited input on the actuators, bounded output peak (output energy) and robustness to parametic uncertainties. The results demonstrated the vibration attenuation in the structure by controlling only the first modes and the increased damping in the bandwidth of interest. However, it is possible to occur spillover effects, because the design has not been done considering the dynamic uncertainties related with high frequencies modes. In this sense, the second technique uses the classical H. output feedback control, also solved by LMI approach, considering robustness to residual dynamic to overcome the problem found in the first test. The results are compared and discussed. The responses shown the robust performance of the system and the good reduction of the vibration level, without increase mass.

Preliminary Overview of the Economies of Latin America and the Caribbean 2011

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ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

Variable structure control with chattering elimination and guaranteed stability for a generalized T-S model

In this paper, a fuzzy based Variable Structure Control (VSC) with guaranteed stability is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. The main contribution of this work is that, firstly, new functions for chattering reduction and error convergence without sacrificing invariant properties are proposed, which is considered the main drawback of the VSC control. Secondly, the global stability of the controlled system is guaranteed.The well known weighting parameters approach, is used in this paper to optimize local and global approximation and modeling capability of T-S fuzzy model.A one link robot is chosen as a nonlinear unstable system to evaluate the robustness, effectiveness and remarkable performance of optimization approach and the high accuracy obtained in approximating nonlinear systems in comparison with the original T-S model. Simulation results indicate the potential and generality of the algorithm. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved with the proposed FLC-VSC controller. The effectiveness of the proposed controller is proven in front of disturbances and noise effects.

Variable structure control with chattering reduction of a generalized T-S model

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. New functions for chattering reduction and error convergence without sacrificing invariant properties are proposed. The main feature of the proposed method is that the switching function is added as an additional fuzzy variable and will be introduced in the premise part of the fuzzy rules; together with the state variables. In this work, a tuning of the well known weighting parameters approach is proposed to optimize local and global approximation and modelling capability of the Takagi-Sugeno (T-S) fuzzy model to improve the choice of the performance index and minimize it. The main problem encountered is that the T-S identification method can not be applied when the membership functions are overlapped by pairs. This in turn restricts the application of the T-S method because this type of membership function has been widely used in control applications. The approach developed here can be considered as a generalized version of the T-S method. An inverted pendulum mounted on a cart is chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of the proposed estimation approach in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the chattering reduction algorithm. In this paper, we prove that the proposed estimation algorithm converge the very fast, thereby making it very practical to use. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved.

On stability in nonsequential MIMO QFT designs

This paper reexamines the stability of uncertain closed-loop systems resulting from the nonsequential (NS) MIMO QFT design methodology. By combining the effect of satisfying both the robust stability and robust performance specifications in a NS MIMO QFT design, a proof for the stability of the uncertain closed-loop system is derived. The stability theorem proves that, subject to the satisfaction of a critical necessary and sufficient condition, the original NS MIMO QFT design methodology will provide a robustly stable closed-loop system. This necessary and sufficient condition provides a useful existence test for a successful NS MIMO QFT design. The results expose the salient features of the NS MIMO QFT design methodology. Two 2 x 2 MIMO design examples are presented to illustrate the key features of the stability, theorem.

An aggregate measure of financial ratios using a multiplicative DEA model

This paper examines the problems in the definition of the General Non-Parametric Corporate Performance (GNCP) and introduces a multiplicative linear programming as an alternative model for corporate performance. We verified and tested a statistically significant difference between the two models based on the application of 27 UK industries using six performance ratios. Our new model is found to be a more robust performance model than the previous standard Data Envelopment Analysis (DEA) model.