Fuzzy rule-based system for evaluation of uncertainty transaction in cassava chain

A fuzzy ruled-based system was developed in this study and resulted in an index indicating the level of uncertainty related to commercial transactions between cassava growers and their dealers. The fuzzy system was developed based on Transaction Cost Economics approach. The fuzzy system was developed from input variables regarding information sharing between grower and dealer on “Demand/purchase Forecasting”, “Production Forecasting” and “Production Innovation”. The output variable is the level of uncertainty regarding the transaction between seller and buyer agent, which may serve as a system for detecting inefficiencies. Evidences from 27 cassava growers registered in the Regional Development Offices of Tupa and Assis, São Paulo, Brazil, and 48 of their dealers supported the development of the system. The mathematical model indicated that 55% of the growers present a Very High level of uncertainty, 33% present Medium or High. The others present Low or Very Low level of uncertainty. From the model, simulations of external interferences can be implemented in order to improve the degree of uncertainty and, thus, lower transaction costs.

Novel expert system for defining power quality compensators

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

Neural tool condition estimation in the grinding of advanced ceramics

Ceramic parts are increasingly replacing metal parts due to their excellent physical, chemical and mechanical properties, however they also make them difficult to manufacture by traditional machining methods. The developments carried out in this work are used to estimate tool wear during the grinding of advanced ceramics. The learning process was fed with data collected from a surface grinding machine with tangential diamond wheel and alumina ceramic test specimens, in three cutting configurations: with depths of cut of 120 mu m, 70 mu m and 20 mu m. The grinding wheel speed was 35m/s and the table speed 2.3m/s. Four neural models were evaluated, namely: Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Networks and the Adaptive Neuro-Fuzzy Inference System. The models'performance evaluation routines were executed automatically, testing all the possible combinations of inputs, number of neurons, number of layers, and spreading. The computational results reveal that the neural models were highly successful in estimating tool wear, since the errors were lower than 4%.

Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Switch allocation problems in power distribution systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Developing an optimization tool for evaluating the transmission system expansion

The transmission system is responsible for connecting the power generators to consumers safely and reliably, its constant expansion is necessary to transport increasing amounts of electricity. In order to help the power systems engineers, an optimization tool for optimize the expansion of the transmission system was developed using the modeling method of the linearized load flow and genetic. This tool was designed to simulate the impact of different scenarios on the cost of transmission expansion. The proposed tool was used to simulate the effects of the presence of distributed generation in the expansion of a fictitious transmission system, where it was found a clear downward trend in investment required for the expansion of the transmission system taking account of increasing levels of distributed generation.

Estimation of the diagnostic accuracy of the glyco-C and US9 gene-based polymerase chain reaction technique for the detection of bovine Herpesvirus type 5 DNA in decomposed brain suspension from a slaughter house using Bayesian analysis, Brazil

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

Estimation of chlorophyll-a concentration and the trophic state of the Barra Bonita hydroelectric reservoir using oli/landsat-8 images

Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.

Bio-energy conversion cost: a new index to evaluate the bioeconomic eficiency

This study evaluated a nonlinear programming excel workbook PPFR (http://www.fmva.unesp.br/ppfr) for determining the optimum nutrient density and maximize margins. Two experiments were conducted with 240 one-day-old female chicks and 240 one-day-old male chicks distributed in 48 pens (10 chicks per pen, 4 replicates) in a completely randomized design. The treatments include the average price history (2009s and 2010s) for broiler increased and decreased by 25% or 50% (5 treatments to nonlinear feed formulation) and 1 linear feed formulation. Body gain, feed intake, feed conversion were measured at 21, 42 and 56 d of age. Chicks had ad libitum access to feed and water in floor pens with wood shavings as litter. The bio-economic Energy Conversion [BEC= (Total energy intake*Feed weighted cost per kg)/ (Weight gain*kg live chicken cost)] was more sensitive for measuring the bio-economic performance for broilers, and especially with better magnitude. This allowed a better assessment of profitability, the rate of growth and not just energy consumption, the production of broilers, by incorporating energy consumption, allowing for more sensitivity to the new index (BEC). The BEC was demonstrated that the principle of nonlinear formulation minimizes losses significantly (P<0.05), especially under unfavorable conditions the price of chicken in the market. Thus, when considering that a diet of energy supply shows up as the most expensive item of a formulation, it should compose necessarily the formula proposed for a bio-economic index. Thus, there is need to evaluate more accurately, not only the ingredients of a ration, but the impact of nutrients on the stability of a solution, mainly due to the energy requirement. This strategy promotes better accuracy for decision making under conditions of uncertainty, to find alternative post-formulation. From the above, both weight gain and feed conversion, as traditional performance indicators, cannot finalize or predict a performance evaluation of an economic system creating increasingly intense and competitive. Thus, the energy concentration of the diet becomes more important definition to feed formulator, by directly impact profit activity by interactions with the density of nutrients. This allowed a better evaluation of profitability, the rate of energy performance for broilers, by incorporating the energy consumption formula, allowing more sensitivity to the new index (BEC). These data show that nonlinear feed formulation is a toll to offer new opportunities for poultry production to improved profitability.

Nonlinear state estimation and control for chaos suppression in MEMS resonator

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

Nonlinear state estimation and control applied to a manipulator robotic including drive motor

A robotic control design considering all the inherent nonlinearities of the robot engine configuration is developed. The interactions between the robot and joint motor drive mechanism are considered. The proposed control combines two strategies, one feedforward control in order to maintain the system in the desired coordinate, and feedback control system to take the system into a desired coordinate. The feedback control is obtained using State Dependent Riccati Equation (SDRE). For link positioning two cases are considered. Case 1: For control positioning, it is only used motor voltage; Case 2: For control positioning, it is used both motor voltage and torque between the links. Simulation results, including parametric uncertainties in control shows the feasibility of the proposed control for the considered system.