GPR applied to mapping utilities along the route of the Line 4 (yellow) subway tunnel construction in Sao Paulo City, Brazil

The rapid industrial development and disorganized population growth in huge cities bring about various urban problems due to intense use of physical space on and below the surface. Subsurface problems in metropolitan areas are caused by subway line construction, which often follows the routes of utility networks, such as electric and telephone cables, water and gas pipes, storm sewers, etc. Usually, the main problems are related to damage or destruction of preexisting utilities, often putting human lives at risk. With the purpose of minimizing risks. GPR-profiling with 200 MHz antennae was done at two sites, both located in downtown Sao Paulo, Brazil. The objectives of this work were to map utilities or existing infrastructure in the subsurface in order to orient the construction of the Line 4 (yellow) subway tunnel in Sao Paulo. GPR profiles can detect water pipes, utility networks in the subsurface, and concrete foundation columns or pilings in subsoil up to 2 m depth. In addition. the GPR profiles also provided details of the target shapes in the subsurface. GPR interpretations combined with lithological information from boreholes and trenches opened in the study areas were extremely important in mapping of the correct spatial distribution of buried utilities at these two sites in Sao Paulo. This information improves and updates maps of utility placement, serves as a basis for planning of the geotechnical excavation of the Line 4 (yellow) subway tunnel in Sao Paulo, helps minimize problems related to destruction of preexisting utilities in the subsoil, and avoids risk of dangerous accidents. (C) 2012 Elsevier B.V. All rights reserved.

TRANSVERSAL DISTRIBUTION COMPARISON OF SOLID FERTILIZERS APPLIED OVER ANNUAL CROPS

Centrifugal spreaders dominate the application of solid materials in agriculture offering expressive operational field capacity and extended range of applied rates. Field tests for characterization of theirperformance are conducted without any physical obstacles (such as the presence of plants) during the parabolic trajectory of the falling particles of fertilizer to the soil. The purpose of this study was to comparatively evaluate the transverse distribution of solid fertilizers applied on cropped corn, soybeans and cotton. Evaluations of the spreaders were designed according to ASAE S341.3/99 Standard. Tests consisted in aligning side by side collectors in-between the cropped rows and weighting the material deposited. The results showed that transverse distribution of solid fertilizers applied over the cotton and corn crops is affected by the crop height, interfering directly on the effective width of the spreader application, which was not observedin the soybean crop, once the fertilizer application is done when the crop was still below the collector's height. The results suggest that evaluation of effective width of the spreaders application need to be done under real crop environment.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.