9 resultados para Telecommunication switching systems equipment industry

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo


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This new and general method here called overflow current switching allows a fast, continuous, and smooth transition between scales in wide-range current measurement systems, like electrometers. This is achieved, using a hydraulic analogy, by diverting only the overflow current, such that no slow element is forced to change its state during the switching. As a result, this approach practically eliminates the long dead time in low-current (picoamperes) switching. Similar to a logarithmic scale, a composition of n adjacent linear scales, like a segmented ruler, measures the current. The use of a linear wide-range system based on this technique assures fast and continuous measurement in the entire range, without blind regions during transitions and still holding suitable accuracy for many applications. A full mathematical development of the method is given. Several computer realistic simulations demonstrated the viability of the technique.

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This work addresses the solution to the problem of robust model predictive control (MPC) of systems with model uncertainty. The case of zone control of multi-variable stable systems with multiple time delays is considered. The usual approach of dealing with this kind of problem is through the inclusion of non-linear cost constraint in the control problem. The control action is then obtained at each sampling time as the solution to a non-linear programming (NLP) problem that for high-order systems can be computationally expensive. Here, the robust MPC problem is formulated as a linear matrix inequality problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of a reactor system of the process industry.

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A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.

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Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system. (C) 2012 Elsevier Ltd. All rights reserved.

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In this paper, we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noises under two criteria. The first one is an unconstrained mean-variance trade-off performance criterion along the time, and the second one is a minimum variance criterion along the time with constraints on the expected output. We present explicit conditions for the existence of an optimal control strategy for the problems, generalizing previous results in the literature. We conclude the paper by presenting a numerical example of a multi-period portfolio selection problem with regime switching in which it is desired to minimize the sum of the variances of the portfolio along the time under the restriction of keeping the expected value of the portfolio greater than some minimum values specified by the investor. (C) 2011 Elsevier Ltd. All rights reserved.

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In this paper we point some aspects of workers activities in offshore units in the oil industry. These units became more verticalized and have a greater number of operating systems. Our goal is to present the main difficulties that workers face in these units.

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Objective: The aim of this study was to evaluate, ex vivo, the precision of five electronic root canal length measurement devices (ERCLMDs) with different operating systems: the Root ZX, Mini Apex Locator, Propex II, iPex, and RomiApex A-15, and the possible influence of the positioning of the instrument tips short of the apical foramen. Material and Methods: Forty-two mandibular bicuspids had their real canal lengths (RL) previously determined. Electronic measurements were performed 1.0 mm short of the apical foramen (-1.0), followed by measurements at the apical foramen (0.0). The data resulting from the comparison of the ERCLMD measurements and the RL were evaluated by the Wilcoxon and Friedman tests at a significance level of 5%. Results: Considering the measurements performed at 0.0 and -1.0, the precision rates for the ERCLMDs were: 73.5% and 47.1% (Root ZX), 73.5% and 55.9% (Mini Apex Locator), 67.6% and 41.1% (Propex II), 61.7% and 44.1% (iPex), and 79.4% and 44.1% (RomiApex A-15), respectively, considering ±0.5 mm of tolerance. Regarding the mean discrepancies, no differences were observed at 0.0; however, in the measurements at -1.0, the iPex, a multi-frequency ERCLMD, had significantly more discrepant readings short of the apical foramen than the other devices, except for the Propex II, which had intermediate results. When the ERCLMDs measurements at -1.0 were compared with those at 0.0, the Propex II, iPex and RomiApex A-15 presented significantly higher discrepancies in their readings. Conclusions: Under the conditions of the present study, all the ERCLMDs provided acceptable measurements at the 0.0 position. However, at the -1.0 position, the ERCLMDs had a lower precision, with statistically significant differences for the Propex II, iPex, and RomiApex A-15.

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The growing complexity of supply chains poses new challenges for Agricultural Research Centers and statistical agencies. The aim of this perspective paper is to discuss the role of empirical research in understanding the complex forms of governance in agribusiness. The authors argue that there are three fundamental levels of analysis: (i) the basic structure of the market, (ii) the formal contractual arrangements that govern relations within the agroindustrial system and (iii) the transactional dimensions governed by non-contractual means. The case of the agrochemical industry in Brazil illustrates how traditional analyses that only address market structure are insufficient to fully explain the agricultural sector and its supply chain. The article concludes by suggesting some indicators which could be collected by statistical agencies to improve understanding of the complex relationships among agribusiness segments. In doing so, the paper seeks to minimize costs and to enable a better formulation of public and private policies.

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Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two Multi-Objective Evolutionary Algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with NDE, named NSGA-N; (ii) the other is a Multi-Objective Evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults.