Influência do relevo na produtividade e custos do harvester

Among the several variables that influence timber harvesting is the slope, which influences the productivity of forest machines. In this experiment the harvester was evaluated technically and economically while cutting and processing eucalyptus activity on different slope classes. The technical analysis included a study of time and movements by the method of continuous time; productivity was determined by the volume in cubic meters of wood processing. The economic analysis included the parameters of operational cost, production cost and energy consumption. The analysis of the data showed that productivity decreased according to the increase of the percent slope inclination, resulting in an effective work hour productivity increase from 18.72 to 39.71 m(3)sc, with a mean of operating cost of US$ 78.78 per work hour.

Infuěncia do relevo na produtividade e custos do harvester

Among the several variables that influence timber harvesting is the slope, which influences the productivity of forest machines. In this experiment the harvester was evaluated technically and economically while cutting and processing eucalyptus activity on different slope classes. The technical analysis included a study of time and movements by the method of continuous time; productivity was determined by the volume in cubic meters of wood processing. The economic analysis included the parameters of operational cost, production cost and energy consumption. The analysis of the data showed that productivity decreased according to the increase of the percent slope inclination, resulting in an effective work hour productivity increase from 18.72 to 39.71 m 3sc, with a mean of operating cost of US$ 78.78 per work hour.

Atributos físicos do solo em áreas de plantio direto com e sem escarificação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação econômica de dois sistemas de colheita florestal mecanizada de Eucalipto

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Compactação do solo causada pelo tráfego de máquinas na colheita de madeira de Eucalyptus grandis Hill ex Maiden

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

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

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

An Optimum-Path Forest framework for intrusion detection in computer networks

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

Efficient supervised optimum-path forest classification for large datasets

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

Diagnosis of a compacted area for mining through the mechanical resistance to the penetration using geostatistical methods in the Amazon Forest

The mining process promotes land modification and complete landscape alteration. Those alterations in the surface are shown more obviously in the aesthetical aspect as the visual elements of form, texture, climbs, complexity and color which composes the landscape. As a consequence, mining has impacts on the topography, in the soil, in the vegetation and in the area's drainage, with a direct influence on the enterprise. A quite common problem in the recovery of degraded areas in mineral exploration is the compaction of the soil due to the intense traffic of machines and earth movement. The most common problem of the compaction of a degraded surface is an increase of the mechanical resistance to the penetration of plant roots, a reduction of the aeration, an alteration of the flow of water and heat, also in the availability of water and nutrients. Thus, the present work had the basic objective of diagnosing the compaction of an area degraded by mining in a spacial way, through the mechanical resistance and the penetration, to guide the future subsoiling in the area requiring recovery. Through the studies, it was concluded that the krigagem method in agreement with the space variation allows the division of the area under study into sub areas facilitating a future work to reduce costs and unnecessary interference to the atmosphere. The method was shown to be quite appropriate and it can be used in the diagnosis of compaction in a degraded area by mining, foreseeing the subsoiling requirement.

Oropharyngeal dysphagia identification using wavelets and optimum path forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.

Infrared face recogniton by optimum-path forest

This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial Neural Networks and Support Vector Machines. The achieved performance asserts the promise of the proposed framework. ©2009 IEEE.

Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.