Luiz Biondi Neto

Avaliação do tamanho de aeroportos portugueses com relações multicritério de superação

In airport studies it is quite common to define categories of airports according to their sizes. Regarding size, each airport is usually classified according to only one criterion, although the problem of determining the airport size is clearly a multicriteria one. This paper employs a new approach for classifying airports according to their sizes by making use of a variant of the multicriteria method ELECTRE I. The new approach is applied to evaluate the size of Portuguese airports and to allocate these airport to well-defined size categories. The amount of cargo and the number of passengers, as well as number of aircraft landing in the airport, are the used criteria. The obtained results are compared with two additional models based on weighted sum and data envelopment analysis.

Flow estimation using an Elman networks

This paper investigates the application of partially recurrent artificial neural networks (ANN) in the flow estimation for Sao Francisco river that feeds the hydroelectric power plant of Sobradinho. An Elman neural network was used, suitably arranged to receive samples of the flow time series data available for Sao Francisco river shifted by one month. The data used in the application concern to the measured Sao Francisco river flow time series from 1931 to 1996, in a total of 65 years from what 60 were used for training and 5 for testing. The obtained results indicate that the Elman neural network is suitable to estimate the river flow for 5 year periods monthly. The average estimation error was less than 0.2%.

EVALUACIÓN DE LA CONCENTRACIÓN EN UNA RUTA AÉREA BRASILERA CON MODELO DEA Y FRONTERA INVERTIDA

El objetivo de este articulo es promover un primer analisis de las consecuencias para el pasajero del acuerdo de cooperacion entre las empresas aereas Varig y Tam, en una de las principales rutas nacionales. Se utiliza la tecnica del Analisis Envolvente de Datos (Data Envelopment Analysis - DEA) con dos fronteras (opticas del pasajero y del empresario, o frontera tradicional y frontera invertida), para comparar la situacion en octubre de 2002 (anterior al acuerdo) y abril de 2003 (posterior). Como los modelos DEA son comparativos, se necesita incluir en el analisis todas las otras opciones (otras companias aereas) de conexiones en la ruta Rio de Janeiro-Sao Paulo. Los resultados muestran que dicho acuerdo no beneficia a los pasajeros

Modelling with Self-Organising Maps and Data Envelopment Analysis: a Case Study in Educational Evaluation

In this chapter we deal with a problem of educational evaluation. We deal with an organization for distance education in the State of Rio de Janeiro, Brazil. This organization is the centre for distance undergraduate education in the Rio de Janeiro State (CEDERJ for the name in Portuguese). Although CEDERJ provides a wide set of undergraduate courses we focus ourselves on the Mathematics undergraduate course. The choice of this course is due to the fact that it exists since the very beginning of the CEDERJ. We do not intend to evaluate distance undergraduate education itself. That is, we will not compare results from distance undergraduate education with results from in situ undergraduate education. Instead, we will compare distance education with itself, thus meaning we will evaluate some thirteen centres of distance education, all of them belonging to the CEDERJ. We want to determine the best managerial practices and the most favourable regions to inaugurate new CEDERJ centres. The comparison hereabove mentioned takes into account how many students finish the course in each centre, how many students have began the course and the proxy for the resources employed in each centre. In the present chapter, we only consider graduates as outputs because graduating students is the main target of CEDERJ, while producing researches have low priority. In order to perform this evaluation, we will use a non parametric technique known as Data Envelopment Analysis – DEA. Initially developed by Charnes et al (1978), this technique deals with productive units, called Decision Making Units (DMUs). The DMUs use the same inputs to produce the same outputs and the DMUs set must be homogenous, i.e. they must work in similar environmental conditions. It is important to notice that these DMUs are not necessarily units involved in a productive or manufacture process, but they can be entity using resources (inputs) to generate some kind of products (outputs). In our case, the homogenous conditions are not verified since CEDERJ centres are located in different regions of the Rio de Janeiro State with different socio economical conditions that cannot be considered in the evaluation. So, in order to perform a DEA evaluation, we need

Application of Simulated Annealing and Hybrid Methods in the Solution of Inverse Heat and Mass Transfer Problems

Antonio Jose da Silva Neto1, Jader Lugon Junior2,5, Francisco Jose da Cunha Pires Soeiro1, Luiz Biondi Neto1, Cesar Costapinto Santana3, Fran Sergio Lobato4 and Valder Steffen Junior4 Universidade do Estado do Rio de Janeiro1, Instituto Federal de Educacao, Ciencia e Tecnologia Fluminense2, Universidade Estadual de Campinas3, Universidade Federal de Uberlândia4, Centro de Tecnologia SENAI-RJ Ambiental5 Brazil

Further results on the EKF-CRTRL equalizer for fast fading and frequency selective channels

This paper shows further results on the EKF-RTRL (extended Kalman filter-real time recurrent learning) equalizer comparing its performance with the PSP-LMS (per survivor processing-least mean squares) equalizer for fast fading selective frequency channels using the WSS_US (wide sense stationary-uncorrelated scattering) model. The EKF-RTRL is a symbol by symbol neural equalizer and the PSP-LMS equalizer uses the maximum likelihood criterion for symbol sequence estimation and the per survivor processing principle. The performance here presented depicts several scenarios regarding the channel variation speed. The performance considered in this paper is the symbol error rate (SER). A comparison involving the computational complexity of both equalizers is also carried out.

Ex-Post Clustering of Brazilian Beef Cattle Farms Using Soms and Cross-Evaluation Dea Models

The beef cattle production system is the set of technologies and management practices, ani‐ mal type, purpose of breeding, breed group and the eco-region where the activity is devel‐ oped. The central structure in the beef cattle production chain is the biological system of beef production, including the stages of creation (cow-calf production, stocker production, feedlot beef production) and their combinations. The cow-calf phase is the less profitable ac‐ tivity and the one that has the higher risk. However, it supports the entire structure of the production system.

ESTIMATING GREENHOUSE GAS EMISSIONS USING COMPUTATIONAL INTELLIGENCE

This work proposes a Neuro-Fuzzy Intelligent System – ANFIS (Adaptive Network based Fuzzy Inference System) for the annual forecast of greenhouse gases emissions (GHG) into the atmosphere. The purpose of this work is to apply a Neuro-Fuzzy System for annual GHG forecasting based on existing emissions data including the last 37 years in Brazil. Such emissions concern tCO2 (tons of carbon dioxide) resulting from fossil fuels consumption for energetic purposes, as well as those related to changes in the use of land, obtained from deforestation indexes. Economical and population growth index have been considered too. The system modeling took into account the definition of the input parameters for the forecast of the GHG measured in terms of tons of CO2. Three input variables have been used to estimate the total tCO2 one year ahead emissions. The ANFIS Neuro-Fuzzy Intelligent System is a hybrid system that enables learning capability in a Fuzzy inference system to model non-linear and complex processes in a vague information environment. The results indicate the Neural-Fuzzy System produces consistent estimates validated by actual test data.

Complex Kalman Filter Trained Recurrent Neural Network Based Equalizer for Mobile Channels

This paper presents a two state neural equalizer for mobile channels. The channel is modeled by the wide sense stationary - uncorrelated scattering (WSS-US) channel which is known to be an adequate model for wireless applications. The neural equalizer is trained by an extended Kalman filter in order to speed up the equalizer training. Simulation results are also shown in the paper for several scenarios indicating a good trade-off in performance and computational complexity. Comparisons involving traditional equalizers such as decision feedback equalizers (DFE) are also shown indicating that the proposed equalizer outperforms DFE equalizers. On the other hand, the proposed neural equalizer is outperformed by per survivor processing the (PSP) class of equalizers which are much more computational complex than the neural class of equalizers proposed in this paper.

Some Remarks About Negative Efficiencies in DEA Models

Data Envelopment Analysis appeared in 1978 when the first model, known as CCR was proposed by Charnes et al. (1978). This model calculates the efficiency of productive units, known as DMUs Decision Making Units, by comparing the use of resources (inputs) and the production (outputs) obtained. This model considers Constant Returns to Scale (CRS), i.e., an increase in resources generates a proportional increment to products. This proportion is constant for all DMUs. An important issue regarding the original CCR model is that all data and variables must be non-negative.