Security constrained optimal active power flow via network model and interior point method

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Simulations under Ideal and Nonideal Conditions for Characterization of a Passive Doppler Geographical Location System Using Extension of Data Reception Network

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Discoloration process modeling by neural network

The photo-oxidation of acid orange 52 dye was performed in the presence of H2O2, utilizing UV light, aiming the discoloration process modeling and the process variable influence characterization. The discoloration process was modeled by the use of feedforward neural network. Each sample was characterized by five independent variables (dye concentration, pH, hydrogen peroxide volume, temperature and time of operation) and a dependent variable (absorbance). The neural model has also provided, through Garson Partition coefficients and the Pertubation method, the independent variable influence order determination. The results indicated that the time of operation was the predominant variable and reaction mean temperature was the lesser influent variable. The neural model obtained presented coefficients of correlation on the order 0.98, for sets of trainability, validation and testing, indicating the power of prediction of the model and its character of generalization. (c) 2007 Elsevier B.V. All rights reserved.

Interior point algorithm for linear programming used in transmission network synthesis

This article presents a well-known interior point method (IPM) used to solve problems of linear programming that appear as sub-problems in the solution of the long-term transmission network expansion planning problem. The linear programming problem appears when the transportation model is used, and when there is the intention to solve the planning problem using a constructive heuristic algorithm (CHA), ora branch-and-bound algorithm. This paper shows the application of the IPM in a CHA. A good performance of the IPM was obtained, and then it can be used as tool inside algorithm, used to solve the planning problem. Illustrative tests are shown, using electrical systems known in the specialized literature. (C) 2005 Elsevier B.V. All rights reserved.

Identification of lubricant contamination by biodiesel using vibration analysis and neural network

Purpose - The purpose of this paper is to provide information on lubricant contamination by biodiesel using vibration and neural network.Design/methodology/approach - The possible contamination of lubricants is verified by analyzing the vibration and neural network of a bench test under determinated conditions.Findings - Results have shown that classical signal analysis methods could not reveal any correlation between the signal and the presence of contamination, or contamination grade. on other hand, the use of probabilistic neural network (PNN) was very successful in the identification and classification of contamination and its grade.Research limitations/implications - This study was done for some specific kinds of biodiesel. Other types of biodiesel could be analyzed.Practical implications Contamination information is presented in the vibration signal, even if it is not evident by classical vibration analysis. In addition, the use of PNN gives a relatively simple and easy-to-use detection tool with good confidence. The training process is fast, and allows implementation of an adaptive training algorithm.Originality/value - This research could be extended to an internal combustion engine in order to verify a possible contamination by biodiesel.

Perinatal factors associated with early deaths of preterm infants born in Brazilian Network on Neonatal Research centers

Objective: To evaluate perinatal factors associated with early neonatal death in preterm infants with birth weights (BW) of 400-1,500 g.Methods: A multicenter prospective cohort study of all infants with BW of 400-1,500 g and 23-33 weeks of gestational age (GA), without malformations, who were born alive at eight public university tertiary hospitals in Brazil between June of 2004 and May of 2005. Infants who died within their first 6 days of life were compared with those who did not regarding maternal and neonatal characteristics and morbidity during the first 72 hours of life. Variables associated with the early deaths were identified by stepwise logistic regression.Results: A total of 579 live births met the inclusion criteria. Early deaths occurred in 92 (16%) cases, varying between centers from 5 to 31%, and these differences persisted after controlling for newborn illness severity and mortality risk score (SNAPPE-II). According to the multivariate analysis, the following factors were associated with early intrahospital neonatal deaths: gestational age of 23-27 weeks (odds ratio - OR = 5.0; 95%CI 2.7-9.4), absence of maternal hypertension (OR = 1.9; 95%CI 1.0-3.7), 5th minute Apgar 0-6 (OR = 2.8; 95%CI 1.4-5.4), presence of respiratory distress syndrome (OR = 3.1; 95%CI 1.4-6.6), and network center of birth.Conclusion: Important perinatal factors that are associated with early neonatal deaths in very low birth weight preterm infants can be modified by interventions such as improving fetal vitality at birth and reducing the incidence and severity of respiratory distress syndrome. The heterogeneity of early neonatal rates across the different centers studied indicates that best clinical practices should be identified and disseminated throughout the country.

A comparative tunnelling network approach to assess interspecific competition effects in termites

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Analysis of a hyper-diverse seed dispersal network: modularity and underlying mechanisms

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bacterial cellulose/poly(3-hydroxybutyrate) composite membranes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function

Economic Dispatch (ED) problems have recently been solved by artificial neural networks approaches. In most of these dispatch models, the cost function must be linear or quadratic. Therefore, functions that have several minimum points represent a problem to the simulation since these approaches have not accepted nonlinear cost function. Another drawback pointed out in the literature is that some of these neural approaches fail to converge efficiently towards feasible equilibrium points. This paper discusses the application of a modified Hopfield architecture for solving ED problems defined by nonlinear cost function. The internal parameters of the neural network adopted here are computed using the valid-subspace technique, which guarantees convergence to equilibrium points that represent a solution for the ED problem. Simulation results and a comparative analysis involving a 3-bus test system are presented to illustrate efficiency of the proposed approach.

Modeling thermal conductivity, specific heat, and density of milk: A neural network approach

The accurate determination of thermophysical properties of milk is very important for design, simulation, optimization, and control of food processing such as evaporation, heat exchanging, spray drying, and so forth. Generally, polynomial methods are used for prediction of these properties based on empirical correlation to experimental data. Artificial neural networks are better Suited for processing noisy and extensive knowledge indexing. This article proposed the application of neural networks for prediction of specific heat, thermal conductivity, and density of milk with temperature ranged from 2.0 to 71.0degreesC, 72.0 to 92.0% of water content (w/w), and 1.350 to 7.822% of fat content (w/w). Artificial neural networks presented a better prediction capability of specific heat, thermal conductivity, and density of milk than polynomial modeling. It showed a reasonable alternative to empirical modeling for thermophysical properties of foods.

A 2D coordination polymer with brick-wall network topology based on the [Cu(NCS)(2)(pn)] monomer

The new complex [Cu(NCS)(2)(pn)] (1) (pn = 1,3-propanediamine) has been synthesized and characterized by elemental analysis, infrared and electronic spectroscopy. Single crystal X-ray diffraction studies revealed that complex 1 is made up of neutral [Cu(NCS)(2)(pn)] units which are connected by mu-1,3,3-thiocyanato groups to yield a 2D metal-organic framework with a brick-wall network topology. Intermolecular hydrogen bonds of the type NH...SCN and NH...NCS are also responsible for the stabilization of the crystal structure. (c) 2007 Elsevier B.V. All rights reserved.

A W-matrix methodology for solving sparse network equations on multiprocessor computers

This paper describes a methodology for solving efficiently the sparse network equations on multiprocessor computers. The methodology is based on the matrix inverse factors (W-matrix) approach to the direct solution phase of A(x) = b systems. A partitioning scheme of W-matrix , based on the leaf-nodes of the factorization path tree, is proposed. The methodology allows the performance of all the updating operations on vector b in parallel, within each partition, using a row-oriented processing. The approach takes advantage of the processing power of the individual processors. Performance results are presented and discussed.

Comparative studies on non-convex optimization methods for transmission network expansion planning

We have investigated and extensively tested three families of non-convex optimization approaches for solving the transmission network expansion planning problem: simulated annealing (SA), genetic algorithms (GA), and tabu search algorithms (TS). The paper compares the main features of the three approaches and presents an integrated view of these methodologies. A hybrid approach is then proposed which presents performances which are far better than the ones obtained with any of these approaches individually. Results obtained in tests performed with large scale real-life networks are summarized.

Influence of the network former on the properties of magnesium spinels

Undoped and/or doped with 1 mol% of Co2+ Mg2TiO4 and Mg2SnO4 powders were synthesized by the polymeric precursor method. The influence of the network former (Sn4+ or Ti4+) on the thermal, structural and optical properties was investigated. The recorded mass losses are due to the escape of water and adsorbed gases and to the elimination of the organic matter. Mg2TiO4 crystallizes at lower temperatures and also presents more ordered structure with a smaller unit call and having more intense green color than Mg2SnO4 has.