STOCHASTIC ADDING MACHINE AND 2-DIMENSIONAL JULIA SETS

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Integrated cutting machine programming and lot sizing in furniture industry

In this paper a mathematical model that combines lot-sizing and cutting-stock problems applied to the furniture industry is presented. The model considers the usual decisions of the lot sizing problems, as well as operational decisions related to the cutting machine programming. Two sets of a priori generated cutting patterns are used, industry cutting patterns and a class of n-group cutting patterns. A strategy to improve the utilization of the cutting machine is also tested. An optimization package was used to solve the model and the computational results, using real data from a furniture factory, show that a small subset of n-group cutting patterns provides good results and that the cutting machine utilization can be improved by the proposed strategy.

Introducing the Discriminative Paraconsistent Machine (DPM)

This paper introduces a new tool for pattern recognition. Called the Discriminative Paraconsistent Machine (DPM), it is based on a supervised discriminative model training that incorporates paraconsistency criteria and allows an intelligent treatment of contradictions and uncertainties. DPMs can be applied to solve problems in many fields of science, using the tests and discussions presented here, which demonstrate their efficacy and usefulness. Major difficulties and challenges that were overcome consisted basically in establishing the proper model with which to represent the concept of paraconsistency.

lmmittance electroanalysis in diagnostics

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

Cardiopulmonary bypass: development of John Gibbon's heart-lung machine

To provide a brief review of the development of cardiopulmonary bypass. A review of the literature on the development of extracorporeal circulation techniques, their essential role in cardiovascular surgery, and the complications associated with their use, including hemolysis and inflammation. The advancement of extracorporeal circulation techniques has played an essential role in minimizing the complications of cardiopulmonary bypass, which can range from various degrees of tissue injury to multiple organ dysfunction syndrome. Investigators have long researched the ways in which cardiopulmonary bypass may insult the human body. Potential solutions arose and laid the groundwork for development of safer postoperative care strategies. Steady progress has been made in cardiopulmonary bypass in the decades since it was first conceived of by Gibbon. Despite the constant evolution of cardiopulmonary bypass techniques and attempts to minimize their complications, it is still essential that clinicians respect the particularities of each patient's physiological function.

Preliminary diagnosis of ophtalmological diseases through machine learning techniques

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

Machine vision and planning applied on assembby line of multiple products

This article describes the use of Artificial Intelligence (IA) techniques applied in cells of a manufacturing system. Machine Vision was used to identify pieces and their positions of two different products to be assembled in the same productive line. This information is given as input for an IA planner embedded in the manufacturing system. Therefore, initial and final states are sent automatically to the planner capable to generate assembly plans for a robotic cell, in real time.

Algoritmos de aprendizado semi-supervisionado baseados em grafos aplicados na bioinformática

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

Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.

SimSight: A Virtual Machine Based Dynamic Call Graph Generator

One problem with using component-based software development approach is that once software modules are reused over generations of products, they form legacy structures that can be challenging to understand, making validating these systems difficult. Therefore, tools and methodologies that enable engineers to see interactions of these software modules will enhance their ability to make these software systems more dependable. To address this need, we propose SimSight, a framework to capture dynamic call graphs in Simics, a widely adopted commercial full-system simulator. Simics is a software system that simulates complete computer systems. Thus, it performs nearly identical tasks to a real system but at a much lower speed while providing greater execution observability. We have implemented SimSight to generate dynamic call graphs of statically and dynamically linked functions in x86/Linux environment. A case study illustrates how we can use SimSight to identify sources of software errors. We then evaluate its performance using 12 integer programs from SPEC CPU2006 benchmark suite.

An ice machine adapted into an autonomous photovoltaic system without batteries using a variable-speed drive

The adaptation of a commercially available ice machine for autonomous photovoltaic operation without batteries is presented. In this adaptation a 1040 W(p) photovoltaic array directly feeds a variable-speed drive and a 24 V(dc) source. The drive runs an induction motor coupled by belt-and-pulley to an open reciprocating compressor, while the dc source supplies a solenoid valve and the control electronics. Motor speed and refrigerant evaporation pressure are set aiming at continuously matching system power demand to photovoltaic power availability. The resulting system is a simple integration of robust, standard, readily available parts. It produces 27 kg of ice in a clear-sky day and has ice production costs around US$0.30/kg. Although a few machine features might be specific to Brazil, its technical and economical guidelines are applicable elsewhere. Copyright (C); 2010 John Wiley & Sons, Ltd.

Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.

Optical transition radiation used in the diagnostic of low energy and low current electron beams in particle accelerators

Optical transition radiation (OTR) plays an important role in beam diagnostics for high energy particle accelerators. Its linear intensity with beam current is a great advantage as compared to fluorescent screens, which are subject to saturation. Moreover, the measurement of the angular distribution of the emitted radiation enables the determination of many beam parameters in a single observation point. However, few works deals with the application of OTR to monitor low energy beams. In this work we describe the design of an OTR based beam monitor used to measure the transverse beam charge distribution of the 1.9-MeV electron beam of the linac injector of the IFUSP microtron using a standard vision machine camera. The average beam current in pulsed operation mode is of the order of tens of nano-Amps. Low energy and low beam current make OTR observation difficult. To improve sensitivity, the beam incidence angle on the target was chosen to maximize the photon flux in the camera field-of-view. Measurements that assess OTR observation (linearity with beam current, polarization, and spectrum shape) are presented, as well as a typical 1.9-MeV electron beam charge distribution obtained from OTR. Some aspects of emittance measurement using this device are also discussed. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4748519]

Heuristic methods for the single machine scheduling problem with different ready times and a common due date

The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.

Safety illusion and error trap in a collectively-operated machine accident

Workplace accidents involving machines are relevant for their magnitude and their impacts on worker health. Despite consolidated critical statements, explanation centered on errors of operators remains predominant with industry professionals, hampering preventive measures and the improvement of production-system reliability. Several initiatives were adopted by enforcement agencies in partnership with universities to stimulate production and diffusion of analysis methodologies with a systemic approach. Starting from one accident case that occurred with a worker who operated a brake-clutch type mechanical press, the article explores cognitive aspects and the existence of traps in the operation of this machine. It deals with a large-sized press that, despite being endowed with a light curtain in areas of access to the pressing zone, did not meet legal requirements. The safety devices gave rise to an illusion of safety, permitting activation of the machine when a worker was still found within the operational zone. Preventive interventions must stimulate the tailoring of systems to the characteristics of workers, minimizing the creation of traps and encouraging safety policies and practices that replace judgments of behaviors that participate in accidents by analyses of reasons that lead workers to act in that manner.