Minimization of Reactive Probabilistic Automata

The problem of finite automata minimization is important for software and hardware designing. Different types of automata are used for modeling systems or machines with finite number of states. The limitation of number of states gives savings in resources and time. In this article we show specific type of probabilistic automata: the reactive probabilistic finite automata with accepting states (in brief the reactive probabilistic automata), and definitions of languages accepted by it. We present definition of bisimulation relation for automata's states and define relation of indistinguishableness of automata states, on base of which we could effectuate automata minimization. Next we present detailed algorithm reactive probabilistic automata’s minimization with determination of its complexity and analyse example solved with help of this algorithm.

Hyperspectral unmixing based on mixtures of Dirichlet components

This paper introduces a new unsupervised hyperspectral unmixing method conceived to linear but highly mixed hyperspectral data sets, in which the simplex of minimum volume, usually estimated by the purely geometrically based algorithms, is far way from the true simplex associated with the endmembers. The proposed method, an extension of our previous studies, resorts to the statistical framework. The abundance fraction prior is a mixture of Dirichlet densities, thus automatically enforcing the constraints on the abundance fractions imposed by the acquisition process, namely, nonnegativity and sum-to-one. A cyclic minimization algorithm is developed where the following are observed: 1) The number of Dirichlet modes is inferred based on the minimum description length principle; 2) a generalized expectation maximization algorithm is derived to infer the model parameters; and 3) a sequence of augmented Lagrangian-based optimizations is used to compute the signatures of the endmembers. Experiments on simulated and real data are presented to show the effectiveness of the proposed algorithm in unmixing problems beyond the reach of the geometrically based state-of-the-art competitors.

Compressed sensing in digital tomosynthesis reconstruction

In this work we study a model for the breast image reconstruction in Digital Tomosynthesis, that is a non-invasive and non-destructive method for the three-dimensional visualization of the inner structures of an object, in which the data acquisition includes measuring a limited number of low-dose two-dimensional projections of an object by moving a detector and an X-ray tube around the object within a limited angular range. The problem of reconstructing 3D images from the projections provided in the Digital Tomosynthesis is an ill-posed inverse problem, that leads to a minimization problem with an object function that contains a data fitting term and a regularization term. The contribution of this thesis is to use the techniques of the compressed sensing, in particular replacing the standard least squares problem of data fitting with the problem of minimizing the 1-norm of the residuals, and using as regularization term the Total Variation (TV). We tested two different algorithms: a new alternating minimization algorithm (ADM), and a version of the more standard scaled projected gradient algorithm (SGP) that involves the 1-norm. We perform some experiments and analyse the performance of the two methods comparing relative errors, iterations number, times and the qualities of the reconstructed images. In conclusion we noticed that the use of the 1-norm and the Total Variation are valid tools in the formulation of the minimization problem for the image reconstruction resulting from Digital Tomosynthesis and the new algorithm ADM has reached a relative error comparable to a version of the classic algorithm SGP and proved best in speed and in the early appearance of the structures representing the masses.

Spectrally tunable light source based on light-emitting diodes for custom lighting solutions

This study describes a novel spectral LED-based tunable light source used for customized lighting solutions, especially for the reconstruction of CIE (Commission Internationale de l’Éclairage) standard illuminants. The light source comprises 31 spectral bands ranging from 400 to 700 nm, an integrating cube and a control board with a 16-bit resolution. A minimization algorithm to calculate the weighting values for each channel was applied to reproduce illuminants with precision. The differences in spectral fitting and colorimetric parameters showed that the reconstructed spectra were comparable to the standard, especially for the D65, D50, A and E illuminants. Accurate results were also obtained for illuminants with narrow peaks such as fluorescents (F2 and F11) and a high-pressure sodium lamp (HP1). In conclusion, the developed spectral LED-based light source and the minimization algorithm are able to reproduce any CIE standard illuminants with a high spectral and colorimetric accuracy able to advance available custom lighting systems useful in the industry and other fields such as museum lighting.

Estimation of concentration-dependent diffusion coefficient in drying process from the space-averaged concentration versus time with experimental data

The estimation of a concentration-dependent diffusion coefficient in a drying process is known as an inverse coefficient problem. The solution is sought wherein the space-average concentration is known as function of time (mass loss monitoring). The problem is stated as the minimization of a functional and gradient-based algorithms are used to solve it. Many numerical and experimental examples that demonstrate the effectiveness of the proposed approach are presented. Thin slab drying was carried out in an isothermal drying chamber built in our laboratory. The diffusion coefficients of fructose obtained with the present method are compared with existing literature results.

Desenvolvimento da célula base de microestruturas periódicas de compósitos sob otimização topológica

This thesis develops a new technique for composite microstructures projects by the Topology Optimization process, in order to maximize rigidity, making use of Deformation Energy Method and using a refining scheme h-adaptative to obtain a better defining the topological contours of the microstructure. This is done by distributing materials optimally in a region of pre-established project named as Cell Base. In this paper, the Finite Element Method is used to describe the field and for government equation solution. The mesh is refined iteratively refining so that the Finite Element Mesh is made on all the elements which represent solid materials, and all empty elements containing at least one node in a solid material region. The Finite Element Method chosen for the model is the linear triangular three nodes. As for the resolution of the nonlinear programming problem with constraints we were used Augmented Lagrangian method, and a minimization algorithm based on the direction of the Quasi-Newton type and Armijo-Wolfe conditions assisting in the lowering process. The Cell Base that represents the composite is found from the equivalence between a fictional material and a preescribe material, distributed optimally in the project area. The use of the strain energy method is justified for providing a lower computational cost due to a simpler formulation than traditional homogenization method. The results are presented prescription with change, in displacement with change, in volume restriction and from various initial values of relative densities.

Tensor-Train decomposition for image classification problems

In these last years a great effort has been put in the development of new techniques for automatic object classification, also due to the consequences in many applications such as medical imaging or driverless cars. To this end, several mathematical models have been developed from logistic regression to neural networks. A crucial aspect of these so called classification algorithms is the use of algebraic tools to represent and approximate the input data. In this thesis, we examine two different models for image classification based on a particular tensor decomposition named Tensor-Train (TT) decomposition. The use of tensor approaches preserves the multidimensional structure of the data and the neighboring relations among pixels. Furthermore the Tensor-Train, differently from other tensor decompositions, does not suffer from the curse of dimensionality making it an extremely powerful strategy when dealing with high-dimensional data. It also allows data compression when combined with truncation strategies that reduce memory requirements without spoiling classification performance. The first model we propose is based on a direct decomposition of the database by means of the TT decomposition to find basis vectors used to classify a new object. The second model is a tensor dictionary learning model, based on the TT decomposition where the terms of the decomposition are estimated using a proximal alternating linearized minimization algorithm with a spectral stepsize.

Computational tool for three-point hitch geometry optimization

The weight-transfer effect, consisting of the change in dynamic load distribution between the front and the rear tractor axles, is one of the most impairing phenomena for the performance, comfort, and safety of agricultural operations. Excessive weight transfer from the front to the rear tractor axle can occur during operation or maneuvering of implements connected to the tractor through the three-point hitch (TPH). In this respect, an optimal design of the TPH can ensure better dynamic load distribution and ultimately improve operational performance, comfort, and safety. In this study, a computational design tool (The Optimizer) for the determination of a TPH geometry that minimizes the weight-transfer effect is developed. The Optimizer is based on a constrained minimization algorithm. The objective function to be minimized is related to the tractor front-to-rear axle load transfer during a simulated reference maneuver performed with a reference implement on a reference soil. Simulations are based on a 3-degrees-of-freedom (DOF) dynamic model of the tractor-TPH-implement aggregate. The inertial, elastic, and viscous parameters of the dynamic model were successfully determined through a parameter identification algorithm. The geometry determined by the Optimizer complies with the ISO-730 Standard functional requirements and other design requirements. The interaction between the soil and the implement during the simulated reference maneuver was successfully validated against experimental data. Simulation results show that the adopted reference maneuver is effective in triggering the weight-transfer effect, with the front axle load exhibiting a peak-to-peak value of 27.1 kN during the maneuver. A benchmark test was conducted starting from four geometries of a commercially available TPH. As result, all the configurations were optimized by above 10%. The Optimizer, after 36 iterations, was able to find an optimized TPH geometry which allows to reduce the weight-transfer effect by 14.9%.

An algorithm for the minimization of two-level multiple-output networks.

Vita.

CVaR minimization by the SRA algorithm

Using the risk measure CV aR in �nancial analysis has become more and more popular recently. In this paper we apply CV aR for portfolio optimization. The problem is formulated as a two-stage stochastic programming model, and the SRA algorithm, a recently developed heuristic algorithm, is applied for minimizing CV aR.

CVaR számítás SRA algoritmussal (CVaR minimization by the SRA algorithm)

A CV aR kockázati mérték egyre nagyobb jelentőségre tesz szert portfóliók kockázatának megítélésekor. A portfolió egészére a CVaR kockázati mérték minimalizálását meg lehet fogalmazni kétlépcsős sztochasztikus feladatként. Az SRA algoritmus egy mostanában kifejlesztett megoldó algoritmus sztochasztikus programozási feladatok optimalizálására. Ebben a cikkben az SRA algoritmussal oldottam meg CV aR kockázati mérték minimalizálást. ___________ The risk measure CVaR is becoming more and more popular in recent years. In this paper we use CVaR for portfolio optimization. We formulate the problem as a two-stage stochastic programming model. We apply the SRA algorithm, which is a recently developed heuristic algorithm, to minimizing CVaR.

Loss minimization by the predictor-corrector modified barrier approach

This paper presents a new approach, predictor-corrector modified barrier approach (PCMBA), to minimize the active losses in power system planning studies. In the PCMBA, the inequality constraints are transformed into equalities by introducing positive auxiliary variables. which are perturbed by the barrier parameter, and treated by the modified barrier method. The first-order necessary conditions of the Lagrangian function are solved by predictor-corrector Newton`s method. The perturbation of the auxiliary variables results in an expansion of the feasible set of the original problem, reaching the limits of the inequality constraints. The feasibility of the proposed approach is demonstrated using various IEEE test systems and a realistic power system of 2256-bus corresponding to the Brazilian South-Southeastern interconnected system. The results show that the utilization of the predictor-corrector method with the pure modified barrier approach accelerates the convergence of the problem in terms of the number of iterations and computational time. (C) 2008 Elsevier B.V. All rights reserved.

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on in machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard`s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling; problems. (C) 2007 Elsevier Ltd. All rights reserved.

A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder-Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO(b)). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free-free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results. (C) 2009 Elsevier Ltd. All rights reserved

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies. [DOI:10.1115/1.4002740]