Artificial neural networks for machining processes surface roughness modeling

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

LH and FSH concentration and follicular development in Nellore heifers submitted to fixed-time artificial insemination protocols with different progesterone concentrations

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

Evaluation of the Possibility of Removing Staining by Repolishing Composite Resins Submitted to Artificial Aging

This in vitro research verified the possibility of eliminating staining caused by coffee and red wine in five composite resins, after being submitted to thermal cycling. Thirty-six specimens were prepared and immersed in water at 37 degrees C for 24 hours. After polishing, specimen color was measured in a spectrophotometer Cintra 10 UV (Visible Spectrometer, GBC, Braeside, VIC, Australia). All specimens were submitted to thermal cycling at temperatures of 5 and 55 degrees C with a dwell time of 1 minute, for 1,000 cycles in a 75% ethanol/water solution. After thermal cycling, the specimens were immersed in water at 37 degrees C until 7 days had elapsed from the time the specimens were prepared. All specimens were then taken to the spectrophotometer for color measurement. The specimens were divided into three groups (N = 12): distilled water (control), coffee, and red wine. For the staining process to occur on only one surface, all the sides, except one, of the surfaces were isolated with white wax. The specimens were immersed in one of the solutions at 37 degrees C for 14 days. The specimens were dried and taken to the spectrophotometer for color measurement. After this, the specimens were submitted to 20 mu m wear three times, and the color was measured after each one of the wear procedures. Calculation of the color difference was made using CIEDE2000 formula. According to the methodology used in this research, it was concluded that the staining caused by coffee and red wine was superficial and one wear of 20 mu m was sufficient to remove the discoloration.

Fuzzy distributed artificial intelligence systems

In this paper, we introduce a DAI approach called hereinafter Fuzzy Distributed Artificial Intelligence (FDAI). Through the use of fuzzy logic, we have been able to develop mechanisms that we feel may effectively improve current DAI systems, giving much more flexibility and providing the subsidies which a formal theory can bring. The appropriateness of the FDAI approach is explored in an important application, a fuzzy distributed traffic-light control system, where we have been able to aggregate and study several issues concerned with fuzzy and distributed artificial intelligence. We also present a number of current research directions necessary to develop the FDAI approach more fully.

Influence of Earth's shadow on the rotational motion of an artificial satellite perturbed by solar radiation torque

A semi-analytical approach is proposed to study the rotational motion of an artificial satellite under the influence of the torque due to the solar radiation pressure and taking into account the influence of Earth's shadow. The Earth's shadow is introduced in the equations for the rotational motion as a function depending on the longitude of the Sun, on the ecliptic's obliquity and on the orbital parameters of the satellite. By mapping and computing this function, we can get the periods in which the satellite is not illuminated and the torque due to the solar radiation pressure is zero. When the satellite is illuminated, a known analytical solution is used to predict the satellite's attitude. This analytical solution is expressed in terms of Andoyer's variables and depends on the physical and geometrical properties of the satellite and on the direction of the Sun radiation flux. By simulating a hypothetical circular cylindrical type satellite, an example is exhibited and the results agree quite well when compared with a numerical integration. © 1997 COSPAR. Published by Elsevier Science Ltd.

Comparison of artificial and natural, new and reused, brackish water for the larviculture of the freshwater prawn Macrobrachium rosenbergii in a recirculating system

Differences in culture duration, metamorphosis rate and the productivity in hatchery culture of M. rosenbergii using a closed system with natural and artificial brackish water were evaluated. Reuse of brackish water in more than one hatchery cycle was also evaluated. Natural and artificial brackish water constituted the two tested treatments, which were distributed in four independent recirculating systems (tank and respective biofilter). Four batches of cultures were conducted and the 2nd and 4th reused the water from the 1st and 3rd, respectively. Mean duration of the hatchery period was 28 d in natural brackish water and 31 d in artificial brackish water. The metamorphosis rate and the average productivity for the natural brackish water treatment were 74% and 60 postlarvae/ L. respectively, and values obtained with artificial brackish water were 55% and 44 postlarvae/L. The successful hatchery culture of M. rosenbergii in this specific artificial brackish water suggests its potential use in enterprises located far from the coast. Brackish water can be used in two consecutive cultures without a negative effect on productivity.

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Evaluation and identification of lightning models by artificial neural networks

This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.

Impedance-based structural health monitoring with artificial neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, multiple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.