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First Iteration (first + iteration)

Distribution by Scientific Domains
Engineering75%

Selected Abstracts

Bayesian Networks and Adaptive Management of Wildlife Habitat

CONSERVATION BIOLOGY, Issue 4 2010
ALISON L. HOWES
herramientas para la toma de decisiones; incertidumbre ecológica; pastoreo feral; regímenes de quema; validación de modelos Abstract:,Adaptive management is an iterative process of gathering new knowledge regarding a system's behavior and monitoring the ecological consequences of management actions to improve management decisions. Although the concept originated in the 1970s, it is rarely actively incorporated into ecological restoration. Bayesian networks (BNs) are emerging as efficient ecological decision-support tools well suited to adaptive management, but examples of their application in this capacity are few. We developed a BN within an adaptive-management framework that focuses on managing the effects of feral grazing and prescribed burning regimes on avian diversity within woodlands of subtropical eastern Australia. We constructed the BN with baseline data to predict bird abundance as a function of habitat structure, grazing pressure, and prescribed burning. Results of sensitivity analyses suggested that grazing pressure increased the abundance of aggressive honeyeaters, which in turn had a strong negative effect on small passerines. Management interventions to reduce pressure of feral grazing and prescribed burning were then conducted, after which we collected a second set of field data to test the response of small passerines to these measures. We used these data, which incorporated ecological changes that may have resulted from the management interventions, to validate and update the BN. The network predictions of small passerine abundance under the new habitat and management conditions were very accurate. The updated BN concluded the first iteration of adaptive management and will be used in planning the next round of management interventions. The unique belief-updating feature of BNs provides land managers with the flexibility to predict outcomes and evaluate the effectiveness of management interventions. Resumen:,El manejo adaptativo es un proceso interactivo de recopilación de conocimiento nuevo relacionado con el comportamiento de un sistema y el monitoreo de las consecuencias ecológicas de las acciones de manejo para refinar las opciones de manejo. Aunque el concepto se originó en la década de los 1970s, rara vez es incorporado activamente en la restauración ecológica. Las redes Bayesianas (RBs) están emergiendo como herramientas eficientes para la toma de decisiones ecológicas en el contexto del manejo adaptativo, pero los ejemplos de su aplicación en este sentido son escasos. Desarrollamos una RB en el marco del manejo adaptativo que se centra en el manejo de los efectos del pastoreo feral y los regímenes de quemas prescritas sobre la diversidad de aves en bosques subtropicales del este de Australia. Construimos la RB con datos para predecir la abundancia de aves como una función de la estructura del hábitat, la presión de pastoreo y las quemas prescritas. Los resultados del análisis de sensibilidad sugieren que la presión de pastoreo incrementó la abundancia de melífagos agresivos, que a su vez tuvieron un fuerte efecto negativo sobre paserinos pequeños. Posteriormente se llevaron a cabo intervenciones de manejo para reducir la presión del pastoreo feral y quemas prescritas, después de las cuales recolectamos un segundo conjunto de datos de campo para probar la respuesta de paserinos pequeños a estas medidas. Utilizamos estos datos, que incorporaron cambios ecológicos que pueden haber resultado de la intervención de manejo, para validar y actualizar la RB. Las predicciones de la abundancia de paserinos pequeños bajo las nuevas condiciones de hábitat y manejo fueron muy precisas. La RB actualizada concluyó la primera iteración de manejo adaptativo y será utilizada para la planificación de la siguiente ronda de intervenciones de manejo. La característica única de actualización de la RBs permite que los manejadores tengan flexibilidad para predecir los resultados y evaluar la efectividad de las intervenciones de manejo. [source]

On extrinsic information of good binary codes operating over Gaussian channels

EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS, Issue 2 2007
M. Peleg
We show that the extrinsic information about the coded bits of any good (capacity achieving) binary code operating over a Gaussian channel is zero when the channel capacity is lower than the code rate and unity when capacity exceeds the code rate, that is, the extrinsic information transfer (EXIT) chart is a step function of the signal to noise ratio and independent of the code. It follows that, for a common class of iterative receivers where the error correcting decoder must operate at first iteration at rate above capacity (such as in turbo equalization, iterative channel estimation, parallel and serial concatenated coding and the like), classical good codes which achieve capacity over the Additive White Gaussian Noise Channel are not effective and should be replaced by different new ones. Copyright © 2006 AEIT. [source]

An adaptive multigrid iterative approach for frictional contact problems

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 7 2006
S. A. Mohamed
Abstract The objective of this paper is the construction of a robust strategy towards adaptively solving Signorini's frictional contact problems. The frictional contact problem between a linearly elastic body and rigid foundation is formulated as a classical boundary value problem of the elastic body but associated with special inequality conditions on the contact surface. A new iterative approach is presented to solve the problem on a given mesh. In the first iteration the candidate nodes are assumed to be in micro-slip contact and then proceeding to update the contact status according to the actual displacements and stresses obtained at the end of each increment. An efficient multigrid method is developed to solve the discrete problems of different iterations. The proposed iterative procedure is integrated with an error indicator and automatic grid generator to construct an adaptive multigrid method. Numerical results of the convergence rates, automatically generated grid sequence, contact stresses and strains as well as two parametric studies are presented to prove the efficiency of the proposal. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Model reference adaptive iterative learning control for linear systems

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 9 2006
A. Tayebi
Abstract In this paper, we propose a model reference adaptive control (MRAC) strategy for continuous-time single-input single-output (SISO) linear time-invariant (LTI) systems with unknown parameters, performing repetitive tasks. This is achieved through the introduction of a discrete-type parametric adaptation law in the ,iteration domain', which is directly obtained from the continuous-time parametric adaptation law used in standard MRAC schemes. In fact, at the first iteration, we apply a standard MRAC to the system under consideration, while for the subsequent iterations, the parameters are appropriately updated along the iteration-axis, in order to enhance the tracking performance from iteration to iteration. This approach is referred to as the model reference adaptive iterative learning control (MRAILC). In the case of systems with relative degree one, we obtain a pointwise convergence of the tracking error to zero, over the whole finite time interval, when the number of iterations tends to infinity. In the general case, i.e. systems with arbitrary relative degree, we show that the tracking error converges to a prescribed small domain around zero, over the whole finite time interval, when the number of iterations tends to infinity. It is worth noting that this approach allows: (1) to extend existing MRAC schemes, in a straightforward manner, to repetitive systems; (2) to avoid the use of the output time derivatives, which are generally required in traditional iterative learning control (ILC) strategies dealing with systems with high relative degree; (3) to handle systems with multiple tracking objectives (i.e. the desired trajectory can be iteration-varying). Finally, simulation results are carried out to support the theoretical development. Copyright © 2006 John Wiley & Sons, Ltd. [source]