Mapping lightning processes using fuzzy inference system

This paper describes a novel approach for mapping lightning processes using fuzzy logic. The core regarding lightning process is to identify and to model those uncertain information on mathematical principles. In fact, the lightning process involves several nonlinear features that our current mathematical tools would not be able to model. The estimation process has been carried out using a fuzzy system based on Sugeno's architecture. Simulation results confirm that proposed approach can be efficiently used in these types of problem.

Psychophysical power functions for apparent area in perceptive, memory, and inference conditions for observers of different age groups

The purpose of this research was to verify the effect of age on the exponent of the power function in Perceptive, Memory, and Inference experimental conditions. In the Memory condition the intervals of 2 min., 8, 24, and 48 hr. and 1 wk. were used between acquisition of information and remembering. For each experimental condition the ages of observers ranged between 17 and 35 years (Group I), 40-55 years (Group II), and 60-77 years (Group III), and education ranged from high school to graduate school. The observers estimated the areas of the Brazilian states using the psychophysical method of magnitude estimation. No significant differences were obtained for Groups I, II, and III for each experimental condition, except in the Memory Condition with the 24-hr. interval. Analysis for experimental conditions and ages showed a significant difference between the Perceptive Condition and each of the others, but no difference between the Inference and Memory Conditions. These results indicated that in the remembering processes there is no loss of information as a function of age. From the small variability in the power function exponents for the three ages, we may assume that age could be related to amount of education of the observers, which suggests study is important.

Genetic parameters by Bayesian inference for dual purpose Jaffarabadi buffaloes

Knowledge of genetic parameters is essential for improved reproductive management and increased yield. Quantitative analysis of genetic parameters is lacking for many breeds of buffaloes. This article provides the first estimate of genetic parameters for dual purpose (meat and milk) Brazilian Jaffarabadi buffaloes, using Bayesian inference. Data on milk yield (MY), lactation length (LL), weight at 205 days (W205) and 365 (W365) days of age, and average daily gain (ADG) from 205 to 365 days of age were collected in two herds. Bivariate analyses (using the program MTGSAM) were performed with the Gibbs sampler to obtain estimates of variance and covariance. Average lactation milk yield and lactation length were 1 620.2 +/- 450.9 kg and 257.6 +/- 46.8 days, respectively, and the mean values for weight traits (kg) were 181.6 +/- 63.3 (W205), 298.04 +/- 116.1 (W365), and 0.73 +/- 0.35 (ADG). Heritability estimates (modes) were 0.16 for MY, 0.10 for LL, 0.43 for W205, 0.48 for W365 and 0.32 for ADG. There was a high genetic correlation (0.96) between milk yield and lactation length and very high genetic correlations (0.99) between the three growth traits. Our data suggest that both milk production and growth traits have clear potential for yield improvement through direct selection in this dual purpose breed. The selection for weight at an early age would be successful and selection for MY can be performed in the first lactation.

Optimum design of experiments for statistical inference

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

IEEE 1451-based multi-interface module (I2M) for industrial processes automation

This paper describes the implementation of a multi-interface module (I2M) for automation of industrial processes, based on the IEEE1451 standard. Process automation with I2M can communicate through either wires or using wireless communication, without any hardware or software changes. We used FPGA resources to implement the I2M functions FPGA, with a NIOS II processor and ZigBee communication system (IEEE802.15), as well as RS232 serial standard. Part of the project was done in the SOPC Builder environment, which gave the designer flexibility and speed to implement the NIOS II-based microprocessor system. To test the I2M implementation, a didactic Industrial Hydraulic Module (MHI-01) was used to simulate two industrial processes to be controlled by the system proposed.

Tuning of fuzzy inference systems through unconstrained optimization techniques

This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.

Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. © Springer-Verlag Berlin Heidelberg 2007.

Module for the analysis of growth in international commerce (MAGIC Plus): user guide (updated)

Includes bibliography

Use of bayesian inference to correlate in vitro embryo production and in vivo fertility in Zebu Bulls

The objective of this experiment was to test in vitro embryo production (IVP) as a tool to estimate fertility performance in zebu bulls using Bayesian inference statistics. Oocytes were matured and fertilized in vitro using sperm cells from three different Zebu bulls (V, T, and G). The three bulls presented similar results with regard to pronuclear formation and blastocyst formation rates. However, the cleavage rates were different between bulls. The estimated conception rates based on combined data of cleavage and blastocyst formation were very similar to the true conception rates observed for the same bulls after a fixed-time artificial insemination program. Moreover, even when we used cleavage rate data only or blastocyst formation data only, the estimated conception rates were still close to the true conception rates. We conclude that Bayesian inference is an effective statistical procedure to estimate in vivo bull fertility using data from IVP. © 2011 Mateus José Sudano et al.

A proposal for reliability evaluation of components on electric power distribution system integrating probabilistic models and fuzzy inference systems

The system reliability depends on the reliability of its components itself. Therefore, it is necessary a methodology capable of inferring the state of functionality of these components to establish reliable indices of quality. Allocation models for maintenance and protective devices, among others, have been used in order to improve the quality and availability of services on electric power distribution systems. This paper proposes a methodology for assessing the reliability of distribution system components in an integrated way, using probabilistic models and fuzzy inference systems to infer about the operation probability of each component. © 2012 IEEE.

Micro-inverter for integrated grid-tie PV module using resonant controller

Two-stage isolated converters for photovoltaic (PV) applications commonly employ a high-frequency transformer on the DC-DC side, submitting the DC-AC inverter switches to high voltages and forcing the use of IGBTs instead of low-voltage and low-loss MOSFETs. This paper shows the modeling, control and simulation of a single-phase full-bridge inverter with high-frequency transformer (HFT) that can be used as part of a two-stage converter with transformerless DC-DC side or as a single-stage converter (simple DC-AC inverter) for grid-connected PV applications. The inverter is modeled in order to obtain a small-signal transfer function used to design the PResonant current control regulator. A high-frequency step-up transformer results in reduced voltage switches and better efficiency compared with converters in which the transformer is used on the DC-DC side. Simulations and experimental results with a 200 W prototype are shown. © 2012 IEEE.

Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements

Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.

Formulação do controle preventivo em sistemas de distribuição de energia elétrica baseada na lógica fuzzy e redes neurais

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

Sistema para analizar el crecimiento del comercio internacional: Module to Analyse the Growth of International Commerce (MAGIC): manual de uso

Incluye Bibliografía