Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

Implications of the Hemodynamic Optimization Approach Guided by Right Heart Catheterization in Patients with Severe Heart Failure

OBJECTIVE: To report the hemodynamic and functional responses obtained with clinical optimization guided by hemodynamic parameters in patients with severe and refractory heart failure. METHODS: Invasive hemodynamic monitoring using right heart catheterization aimed to reach low filling pressures and peripheral resistance. Frequent adjustments of intravenous diuretics and vasodilators were performed according to the hemodynamic measurements. RESULTS: We assessed 19 patients (age = 48±12 years and ejection fraction = 21±5%) with severe heart failure. The intravenous use of diuretics and vasodilators reduced by 12 mm Hg (relative reduction of 43%) pulmonary artery occlusion pressure (P<0.001), with a concomitant increment of 6 mL per beat in stroke volume (relative increment of 24%, P<0.001). We observed significant associations between pulmonary artery occlusion pressure and mean pulmonary artery pressure (r=0.76; P<0.001) and central venous pressure (r=0.63; P<0.001). After clinical optimization, improvement in functional class occurred (P< 0.001), with a tendency towards improvement in ejection fraction and no impairment to renal function. CONCLUSION: Optimization guided by hemodynamic parameters in patients with refractory heart failure provides a significant improvement in the hemodynamic profile with concomitant improvement in functional class. This study emphasizes that adjustments in blood volume result in imme-diate benefits for patients with severe heart failure.

Optimización en el cómputos de algoritmos de cuadratura para funciones especiales. Optimization of quadrature algorithms for special functions.

En nuestro proyecto anterior aproximamos el cálculo de una integral definida con integrandos de grandes variaciones funcionales. Nuestra aproximación paraleliza el algoritmo de cómputo de un método adaptivo de cuadratura, basado en reglas de Newton-Cote. Los primeros resultados obtenidos fueron comunicados en distintos congresos nacionales e internacionales; ellos nos permintieron comenzar con una tipificación de las reglas de cuadratura existentes y una clasificación de algunas funciones utilizadas como funciones de prueba. Estas tareas de clasificación y tipificación no las hemos finalizado, por lo que pretendemos darle continuidad a fin de poder informar sobre la conveniencia o no de utilizar nuestra técnica. Para llevar adelante esta tarea se buscará una base de funciones de prueba y se ampliará el espectro de reglas de cuadraturas a utilizar. Además, nos proponemos re-estructurar el cálculo de algunas rutinas que intervienen en el cómputo de la mínima energía de una molécula. Este programa ya existe en su versión secuencial y está modelizado utilizando la aproximación LCAO. El mismo obtiene resultados exitosos en cuanto a precisión, comparado con otras publicaciones internacionales similares, pero requiere de un tiempo de cálculo significativamente alto. Nuestra propuesta es paralelizar el algoritmo mencionado abordándolo al menos en dos niveles: 1- decidir si conviene distribuir el cálculo de una integral entre varios procesadores o si será mejor distribuir distintas integrales entre diferentes procesadores. Debemos recordar que en los entornos de arquitecturas paralelas basadas en redes (típicamente redes de área local, LAN) el tiempo que ocupa el envío de mensajes entre los procesadores es muy significativo medido en cantidad de operaciones de cálculo que un procesador puede completar. 2- de ser necesario, paralelizar el cálculo de integrales dobles y/o triples. Para el desarrollo de nuestra propuesta se desarrollarán heurísticas para verificar y construir modelos en los casos mencionados tendientes a mejorar las rutinas de cálculo ya conocidas. A la vez que se testearán los algoritmos con casos de prueba. La metodología a utilizar es la habitual en Cálculo Numérico. Con cada propuesta se requiere: a) Implementar un algoritmo de cálculo tratando de lograr versiones superadoras de las ya existentes. b) Realizar los ejercicios de comparación con las rutinas existentes para confirmar o desechar una mejor perfomance numérica. c) Realizar estudios teóricos de error vinculados al método y a la implementación. Se conformó un equipo interdisciplinario integrado por investigadores tanto de Ciencias de la Computación como de Matemática. Metas a alcanzar Se espera obtener una caracterización de las reglas de cuadratura según su efectividad, con funciones de comportamiento oscilatorio y con decaimiento exponencial, y desarrollar implementaciones computacionales adecuadas, optimizadas y basadas en arquitecturas paralelas.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.

Synthesis of plantwide control strategies using mixed integer optimization

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2012

Statistical analysis in multivariate sampling

Magdeburg, Univ., Fak. für Mathematik, Diss., 2011

Modeling, design, and optimization of radiofrequency micromechanical structures

Coupled Electromechanical Analysis, MEMS Modeling, MEMS, RF MEMS Switches, Defected Ground Structures, Reconfigurable Resonator

Multivariate Analysis for Animal Selection in Experimental Research

Background: Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective: To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods: The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results: The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion: The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

Optimization of radial jets mixing in cross-flow of combustion chambers using computational fluid dynamics

Cross-Flow, Radial Jets Mixing, Temperature Homogenization, Optimization, Combustion Chamber, CFD

Optimization of a procedure for emergency cooling and pressure relief for reactors with exothermal processes

Passive trip system, reactor trip, runaway reaction, batch reactor

Advancing the understanding of brain function with multivariate pattern analysis

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2009

Analysis, design, optimization and realization of compact high performance printed RF filters

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010

Design optimization of savonius and wells turbines

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2011

Polyhedral and combinatorial aspects in optimization

Magdeburg, Univ., Fak. für Mathematik, Habil.-Schr., 2012