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.

Modeling and identification of fertility maps using artificial neural networks

The application of agricultural fertilizers using variable rates along the field can be made through fertility maps previously elaborated or through real-time sensors. In most of the cases applies maps previously elaborated. These maps are identified from analyzes done in soil samples collected regularly (a sample for each field cell) or irregularly along the field. At the moment, mathematical interpolation methods such as nearest neighbor, local average, weighted inverse distance, contouring and kriging are used for predicting the variables involved with elaboration of fertility maps. However, some of these methods present deficiencies that can generate different fertility maps for a same data set. Moreover, such methods can generate inprecise maps to be used in precision farming. In this paper, artificial neural networks have been applied for elaboration and identification of precise fertility maps which can reduce the production costs and environmental impacts.

Influence of artificial reefs on fish assemblage of the Barra Bonita Reservoir (São Paulo, Brazil)

This paper deals with the effects of introduced artificial reefs on the diversity of freshwater fish communities in lentic and lotic zones of a very impacted river in southeastern Brazil. To accomplish this goal, artificial reefs were introduced, in December 1997, in the Barra Bonita reservoir and in the lotic zone immediately below the dam. Fish diversity was always higher in the lotic zone than in the reservoir. Accordingly, fish diversity near the artificial reefs was consistently higher than in the control areas. We propose that the higher environmental complexity in the lotic zone, as compared to the reservoir, is incremented in areas where artificial reefs were introduced; also, we present that, in both areas, diversity is mainly affected by the introduction of artificial reefs.

Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes

This work presents an investigation into the use of the finite element method and artificial neural networks in the identification of defects in industrial plants metallic tubes, due to the aggressive actions of the fluids contained by them, and/or atmospheric agents. The methodology used in this study consists of simulating a very large number of defects in a metallic tube, using the finite element method. Both variations in width and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of a perceptron multilayer artificial neural network. Finally, the obtained neural network is used to classify a group of new defects, simulated by the finite element method, but that do not belong to the original dataset. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject.

Using artificial intelligence for the control of medium voltage substations

This paper introduces a method for the supervision and control of devices in electric substations using fuzzy logic and artificial neural networks. An automatic knowledge acquisition process is included which allows the on-line processing of operator actions and the extraction of control rules to replace gradually the human operator. Some experimental results obtained by the application of the implemented software in a simulated environment with random signal generators are presented.

Artificial reefs and their effects on fish assemblages in a Brazilian reservoir and Tailrace

In this paper, we evaluate the effects of artificial reefs on fish assemblages in a hypereutrophic reservoir and in the lotic zone immediately below dam. Fish diversity was highest in the lotic zone relative to the reservoir. We also found an inverse relationship between diversity and distance from the river margin. Catches near the artificial reefs were more diverse than in control areas. A seasonal effect, possibly caused by variation in temperature, was significant in all comparisons. We argue that, in a scale of local effects, the ecological function of these structures would be similar to refuges.

The influence of fruit morphology and habitat structure on ant-seed interactions: A study with artificial fruits

Artificial fruits designed to simulate lipid-rich non-myrecochorous diaspores were used to test for the effect of fruit morphology and habitat structure on ant-seed interactions in an Atlantic Forest site in SE Brazil. The outcome of the interaction (i.e., if the fruit was removed, cleaned by ants on the spot or had no interaction with ants) and the time of ant response were the investigated variables. Models simulating drupes and arilate diaspores were used to test for morphological effects and four habitat attributes (litter depth, number of logs, number of trees, and percentage of bromeliad coverage on the forest floor), likely to be correlated with the ant diversity and abundance in the study site, were measured to test for the effect of habitat structure. The proportion of fruits removed or cleaned did not differ between the two morphological models. Sites in which fruits were cleaned had more trees than those in which no interaction occurred. This may be a result of the foraging behavior of arboreal ants that frequently descend to the forest floor to exploit fleshy diaspores. Sites in which model removal occurred had lower litter depth than both those in which models were cleaned and those in which no interaction occurred. A negative correlation was observed between litter depth and ant response time. Accumulation of leaf litter at a given point may have constrained the movements of large ants in general, and ponerine ants (that are important seed removers) in particular. We conclude that that local pattern in litter depth and tree density influence the frequency and outcome of interactions between ants and non-myrmecochorous, fleshy diaspores.

Determination of resonant frequencies of triangular and rectangular microstrip antennas, using artificial neural networks

This paper presents models that can be used in the design of microstrip antennas for mobile communications. The antennas can be triangular or rectangular. The presented models are compared with deterministic and empirical models based on artificial neural networks (ANN) presented in the literature. The models are based on Perceptron Multilayer (PML) and Radial Basis Function (RBF) ANN. RBF based models presented the best results. Also, the models can be embedded in CAD systems, in order to design microstrip antennas for mobile communications.

Surface electromyography of facial muscles during natural and artificial feeding of infants

Objective: To measure and compare the activity of the masseter, temporalis and buccinator muscles in different infant feeding methods. Method: Cross-sectional study of 60 full-term infants with no intercurrent diseases, aged between two and three months, classified into the following groups: 1) exclusive breastfeeding; 2) breastfeeding plus bottle-feeding; and 3) exclusive breastfeeding plus cup feeding. Surface electromyography was performed during infant feeding. The Krushal-Wallis test was used, complemented by multiple paired comparisons of the groups. A 5% significance level was chosen for the tests. Results: Statistically higher results were verified in the breastfeeding group in relation to the bottle-feeding one, both in the range of movement and the mean contraction of the masseter. With regard to the temporalis muscle, statistically higher results were found in the breastfeeding group comparatively to the bottle-feeding one. As to the buccinator muscle, statistically higher results were observed in the breastfeeding group in relation to the bottle-feeding one, although in this case, the difference concerned only the range of contraction. Conclusion: The similarities between the muscle activity in the breastfeeding and in the cup-feeding groups suggests that cup-feeding can be used as an alternative infant feeding method, being better than bottle-feeding, due to the hyperactivity of the buccinator muscle, which could result in changes to the structural growth and development of the stomatognathic system functions. Copyright © 2006 by Sociedade Brasileira de Pediatria.

Artificial immune systems applied to optimal capacitor placement in radial distribution networks

The capacitor placement problem for radial distribution networks aims to determine capacitor types, sizes, locations and control scheme. This is a combinatorial problem that can be formulated as a mixed integer nonlinear program. The paper presents an algorithm inspired in artificial immune systems and developed for this specific problem. A good performance was obtained through experimental tests applied to known systems. © 2006 IEEE.

Análise da incerteza na representação de classes de cobertura do solo urbano resultantes da aplicação de uma rede neural artificial

The great diversity of materials that characterizes the urban environment determines a structure of mixed classes in a classification of multiespectral images. In that sense, it is important to define an appropriate classification system using a non parametric classifier, that allows incorporating non spectral (such as texture) data to the process. They also allow analyzing the uncertainty associated to each class from the output alues of the network calculated in relation to each class. Considering these properties, an experiment was carried out. This experiment consisted in the application of an Artificial Neural Network aiming at the classification of the urban land cover of Presidente Prudente and the analysis of the uncertainty in the representation of the mapped thematic classes. The results showed that it is possible to discriminate the variations in the urban land cover through the application of an Artificial Neural Network. It was also possible to visualize the spatial variation of the uncertainty in the attribution of classes of urban land cover from the generated representations. The class characterized by a defined pattern as intermediary related to the impermeability of the urban soil presented larger ambiguity degree and, therefore, larger mixture.

Cryopreservation of dog spermatozoa: Effect of bovine serum albumin on acrosomal integrity and pregnancy rates after artificial insemination

The objective of the present study was to compare the in vitro and in vivo profile of frozen dog semen with Tris-bovine serum albumin (TB) and Tris-egg yolk (TE) extenders. Twenty dogs were used as donors. Each dog was stimulated by penile massage and only the sperm-rich fraction was collected weekly until 40 ejaculates were obtained. After macroscopic and microscopic analysis, equal parts of each ejaculate were diluted with TB and TE by the one-step method at 37 °C. The semen was added to 0.5-mL French straws which presented normal characteristics before freezing and after thawing. Acrosomal integrity was evaluated by double Trypan blue-Giemsa staining, in which alive intact (LI), alive reacted (LR), dead intact (DI) and dead reacted (DR) spermatozoa, were identified by the time of thawing and up to 4 h of incubation at 39 °C, the TE being significantly superior to TB (P<0,01) in the LI and LR variables. The TB being significantly superior to TE (P<0,01) in the DR variable. Female dogs in natural heat were submitted to artificial insemination, 20 receiving TE-semen and 20 receiving TB-semen with the Osiris probe (IMV, L'Aigle, France) and the numbers indicate that TE was significantly better than TB (P<0,01) to pregnancy rate and number of puppies/delivery. We concluded from this study, that TE was better than TB, because this, induced an eady acrossome reaction in dog's sperm.

Relação peso-comprimento e fator de condição de Cichla kelberi (Perciformes, Cichlidae) introduzidos em um lago artificial no sudeste Brasileiro

The length-weight relationship and condition factor for Cichla kelberi introduced in an artificial lake in southeastern Brazil were analyzed. By studying separately the male, female and immature individuals, different length-weight patterns were found to happen among them. The same happened when distinct seasonal variations were compared. They are related to feeding alterations and me reproductive periods. The length-weight relationship and condition factor for these fishes vary in accordance with the time of introduction, population size, the characteristics of the ecosystem, sexual maturity, as well as the intra- and interspecific interactions.

Weight vector estimation of circular arrays of antennas using Artificial Neural Networks

This paper presents a model for the control of the radiation pattern of a circular array of antennas, shaping it to address the radiation beam in the direction of the user, in order to reduce the transmitted power and to attenuate interference. The control of the array is based on Artificial Neural Networks (ANN) of the type RBF (Radial Basis Functions), trained from samples generated by the Wiener equation. The obtained results suggest that the objective was reached.