Search results for "MIZA"

Hybrid prediction-optimization approaches for maximizing parts density in SLM of Ti6Al4V titanium alloy

2022

AbstractIt is well known that the processing parameters of selective laser melting (SLM) highly influence mechanical and physical properties of the manufactured parts. Also, the energy density is insufficient to detect the process window for producing full dense components. In fact, parts produced with the same energy density but different combinations of parameters may present different properties even under the microstructural viewpoint. In this context, the need to assess the influence of the process parameters and to select the best parameters set able to optimize the final properties of SLM parts has been capturing the attention of both academics and practitioners. In this paper differ…

A Memetic-Neural Approach to Discover Resources in P2P Networks

2008

This chapter proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing in training of the neural network. The neural network, which is a multi-layer perceptron neural network, allows the P2P nodes to efficiently locate resources desired by the user. The necessity of testing the network in various working conditions, aiming to obtain a robust neural network, introduces noise in the objective function. The AGLMA is a memetic algorithm which employs two local search algorithms adaptively activated by an evolutionary framework. These local searchers, having different fe…

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Human factor policy testing in the sequencing of manual mixed model assembly lines

2004

In this paper the human resource management in manual mixed model assembly U-lines is considered. The objective is to minimise the total conveyor stoppage time to achieve the full efficiency of the line. A model, that includes effects of the human resource, was developed in order to evaluate human factor policies impact on the optimal solution of this line sequencing problem. Different human resource management policies are introduced to cope with the particular layout of the proposed line. Several examples have been proposed to investigate the effects of line dimensions on the proposed management policies. The examples have been solved through a genetic algorithm. The obtained results conf…

Distributed Consensus in Networks of Dynamic Agents

2006

Stationary and distributed consensus protocols for a network of n dynamic agents under local information is considered. Consensus must be reached on a group decision value returned by a function of the agents' initial state values. As a main contribution we show that the agents can reach consensus if the value of such a function computed over the agents' state trajectories is time invariant. We use this basic result to introduce a protocol design rule allowing consensus on a quite general set of values. Such a set includes, e.g., any generalized mean of order p of the agents' initial states. We demonstrate that the asymptotical consensus is reached via a Lyapunov approach. Finally we perfor…

2017

Abstract. We present a Monte Carlo genetic algorithm (MCGA) for efficient, automated, and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find and characterize the solution of an optimization problem. It addresses an issue inherent to complex models whose extensive input parameter sets may not be uniquely determined from limited in…

Influences of atomization process and precursor material on the compaction mechanisms and metallurgical properties of parts developed by HIP

2022

Hot Isostatic pressing process is based on the densification of metals powders to near net shape parts with higher mechanicals properties than forged chemical equivalent. The precursor material can influence the parts properties. Thus the aims of this these is to understand the influence of the powder properties on the HIP parts. The particles characterization and the elements and compound monitoring during the HIP cycle allowed to understand the different oxidations phenomenons and their impact on the mechanical properties of parts. The comparison of differents powders batches with identical particles size repartition or identical chemical composition showed that the chemical composition a…

Robust H-Infinity Filter Design for Uncertain Linear Systems Over Network with Network-Induced Delays and Output Quantization

2009

This paper investigates a convex optimization approach to the problem of robust H-Infinity filtering for uncertain linear systems connected over a common digital communication network. We consider the case where quantizers are static and the parameter uncertainties are norm bounded. Firstly, we propose a new model to investigate the effect of both the output quantization levels and the network conditions. Secondly, by introducing a descriptor technique, using Lyapunov-Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities (LMIs) for the existence of the desired network-based quantize…

Hopcroft’s Algorithm and Tree-like Automata

2009

In order to analyze some extremal cases of Hopcroft’s algorithm we deepened the relationship between combinatorial properties of circular words and the ex- ecution of the algorithm on cyclic automata associated to such words.In this paper we highlight the notion of word tree and in particular, we char- acterize the word trees for which Hopcroft’s algorithm on the associated tree-like automata has a unique refinement process. Moreover, we show the relationship between the time complexity of the refinements process of the Hopcroft’s algo- rithm on unary cyclic automata and binary tree-like automata. Such a result allows to exhibit a family of tree-like automata representing the worst case of …

Operator splitting methods for American option pricing

2004

Abstract We propose operator splitting methods for solving the linear complementarity problems arising from the pricing of American options. The space discretization of the underlying Black-Scholes Scholes equation is done using a central finite-difference scheme. The time discretization as well as the operator splittings are based on the Crank-Nicolson method and the two-step backward differentiation formula. Numerical experiments show that the operator splitting methodology is much more efficient than the projected SOR, while the accuracy of both methods are similar.