Reactive power dispatch and planning using a non-linear branch-and-bound algorithm

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Oncological Safety of Autologous Fat Grafting after Breast Conservative Treatment: A Prospective Evaluation

Autologous fat graft to the breast is a useful tool to correct defects after breast conservative treatment (BCT). Although this procedure gains popularity, little is known about the interaction between the fat graft and the prior oncological environment. Evidences of safety of this procedure in healthy breast and after post-mastectomy reconstruction exist. However, there is paucity of data among patients who underwent BCT which are hypothetically under a higher risk of local recurrence (LR). Fifty-nine patients, with prior BCT, underwent 75 autologous fat graft procedures using the Coleman's technique, between October 2005 and July 2008. Follow-up was made by clinical and radiologic examination at least once, after 6 months of the procedure. Mean age was 50 +/- 8.5 years, and mean follow-up was 34.4 +/- 15.3months. Mean time from oncological surgery to the first fat grafting procedure was 76.6 +/- 30.9months. Most of patients were at initial stage 0 (11.8%), I (33.8%), or IIA (23.7%). Immediate complication was observed in three cases (4%). Only three cases of true LR (4%) associated with the procedure were observed during the follow-up. Abnormal breast images were present in 20% of the postoperative mammograms, and in 8% of the cases, biopsy was warranted. Autologous fat graft is a safe procedure to correct breast defects after BCT, with low postoperative complications. Although it was not associated with increased risk of LR in the group of patients studied, prospective trials are needed to certify that it does not interfere in patient's oncological prognosis.

Planejamento de Fontes Reativas em Sistemas de Energia Elétrica utilizando a técnica de Decomposição de Benders e o algoritmo de Branch-and-Bound

This paper presents the Benders decomposition technique and Branch and Bound algorithm used in the reactive power planning in electric energy systems. The Benders decomposition separates the planning problem into two subproblems: an investment subproblem (master) and the operation subproblem (slave), which are solved alternately. The operation subproblem is solved using a successive linear programming (SLP) algorithm while the investment subproblem, which is an integer linear programming (ILP) problem with discrete variables, is resolved using a Branch and Bound algorithm especially developed to resolve this type of problem.

Avaliação do tecido mamário em mulheres na pós-menopausa usuárias de isoflavona da soja

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Interior point algorithm for linear programming used in transmission network synthesis

This article presents a well-known interior point method (IPM) used to solve problems of linear programming that appear as sub-problems in the solution of the long-term transmission network expansion planning problem. The linear programming problem appears when the transportation model is used, and when there is the intention to solve the planning problem using a constructive heuristic algorithm (CHA), ora branch-and-bound algorithm. This paper shows the application of the IPM in a CHA. A good performance of the IPM was obtained, and then it can be used as tool inside algorithm, used to solve the planning problem. Illustrative tests are shown, using electrical systems known in the specialized literature. (C) 2005 Elsevier B.V. All rights reserved.

Influence of Microwave Disinfection on the Linear Dimensional Stability of Complete Dentures: A Clinical Study

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

Efficient linear programming algorithm for the transmission network expansion planning problem

The transmission network planning problem is a non-linear integer mixed programming problem (NLIMP). Most of the algorithms used to solve this problem use a linear programming subroutine (LP) to solve LP problems resulting from planning algorithms. Sometimes the resolution of these LPs represents a major computational effort. The particularity of these LPs in the optimal solution is that only some inequality constraints are binding. This task transforms the LP into an equivalent problem with only one equality constraint (the power flow equation) and many inequality constraints, and uses a dual simplex algorithm and a relaxation strategy to solve the LPs. The optimisation process is started with only one equality constraint and, in each step, the most unfeasible constraint is added. The logic used is similar to a proposal for electric systems operation planning. The results show a higher performance of the algorithm when compared to primal simplex methods.

Damage Characterization Using Guided-Wave Linear Arrays and Image Compounding Techniques

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Three-dimensional reconstruction of fracture surfaces: Area matching algorithms for automatic parallax measurements

This paper describes two solutions for systematic measurement of surface elevation that can be used for both profile and surface reconstructions for quantitative fractography case studies. The first one is developed under Khoros graphical interface environment. It consists of an adaption of the almost classical area matching algorithm, that is based on cross-correlation operations, to the well-known method of parallax measurements from stereo pairs. A normalization function was created to avoid false cross-correlation peaks, driving to the true window best matching solution at each region analyzed on both stereo projections. Some limitations to the use of scanning electron microscopy and the types of surface patterns are also discussed. The second algorithm is based on a spatial correlation function. This solution is implemented under the NIH Image macro programming, combining a good representation for low contrast regions and many improvements on overall user interface and performance. Its advantages and limitations are also presented.

Qualitative analysis of two reconstruction techniques and their application in dentistry

Although visualization in the field of dentistry has some of the same requirements as the medicine field, the differences in goal demand specific approaches. This paper reports on the implementation of two fundamentally different approaches to reconstruction of structures from planar cross sections and their application to dentistry data. One of the approaches was an implementation of a distance-based sampling technique, and the other is a new algorithm, based on the Delaunay triangulation. Both were tested using contour data of teeth and the results are compared here in the light of the target applications, which are teaching and training dentistry, as well as simulation of dental procedures and illnesses. Widely mentioned problems encountered in local reconstruction methods such as marching cubes for these cases are clearly illustrated in this paper, and a very satisfactory alternative is given. © 2000 SPIE and IS&T.

VAr planning using genetic algorithm and linear programming

A combined methodology consisting of successive linear programming (SLP) and a simple genetic algorithm (SGA) solves the reactive planning problem. The problem is divided into operating and planning subproblems; the operating subproblem, which is a nonlinear, ill-conditioned and nonconvex problem, consists of determining the voltage control and the adjustment of reactive sources. The planning subproblem consists of obtaining the optimal reactive source expansion considering operational, economical and physical characteristics of the system. SLP solves the optimal reactive dispatch problem related to real variables, while SGA is used to determine the necessary adjustments of both the binary and discrete variables existing in the modelling problem. Once the set of candidate busbars has been defined, the program implemented gives the location and size of the reactive sources needed, if any, to maintain the operating and security constraints.

A hybrid image restoration algorithm based on Projections onto Convex Sets and Harmony Search

Image restoration is a research field that attempts to recover a blurred and noisy image. Since it can be modeled as a linear system, we propose in this paper to use the meta-heuristics optimization algorithm Harmony Search (HS) to find out near-optimal solutions in a Projections Onto Convex Sets-based formulation to solve this problem. The experiments using HS and four of its variants have shown that we can obtain near-optimal and faster restored images than other evolutionary optimization approach. © 2013 IEEE.

H-infinity control design for time-delay linear systems: a rational transfer function based approach

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

Computed tomographic-dacryocystography (CT-DCG) of the normal canine nasolacrimal drainage system with three-dimensional reconstruction

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)