A 2-D delaunay refinement algorithm using an initial prerefinement from the boundary mesh

An improvement to the quality bidimensional Delaunay mesh generation algorithm, which combines the mesh refinement algorithms strategy of Ruppert and Shewchuk is proposed in this research. The developed technique uses diametral lenses criterion, introduced by L. P. Chew, with the purpose of eliminating the extremely obtuse triangles in the boundary mesh. This method splits the boundary segment and obtains an initial prerefinement, and thus reducing the number of necessary iterations to generate a high quality sequential triangulation. Moreover, it decreases the intensity of the communication and synchronization between subdomains in parallel mesh refinement. © 2008 IEEE.

Multiloop controller and reference generator for a dynamic voltage restorer implementation

This paper deals with the design and analysis of a Dynamic Voltage Restorer output voltage control. Such control is based on a multiloop strategy, with an inner current PID regulator and an outer P+Resonant voltage controller. The inner regulator is applied on the output inductor current. It will be also demonstrated how the load current behavior may influence in the DVR output voltage, which justifies the need for the resonant controller. Additionally, it will be discussed the application of a modified algorithm for the identification of the DVR voltage references, which is based on a previously presented positive sequence detector. Since the studied three-phase DVR is assumed to be based on three identical H-bridge converters, all the analysis and design procedures were realized by means of single-phase equivalent circuits. The discussions and conclusions are supported by theoretical calculations, nonlinear simulations and some experimental results. ©2008 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Distribution network planning using a constructive heuristic algorithm

An optimization technique to solve distribution network planning (DNP) problem is presented. This is a very complex mixed binary nonlinear programming problem. A constructive heuristic algorithm (CHA) aimed at obtaining an excellent quality solution for this problem is presented. In each step of the CHA, a sensitivity index is used to add a circuit or a substation to the distribution network. This sensitivity index is obtained solving the DNP problem considering the numbers of circuits and substations to be added as continuous variables (relaxed problem). The relaxed problem is a large and complex nonlinear programming and was solved through an efficient nonlinear optimization solver. A local improvement phase and a branching technique were implemented in the CHA. Results of two tests using a distribution network are presented in the paper in order to show the ability of the proposed algorithm. ©2009 IEEE.

A specialized genetic algorithm to solve the short term transmission network expansion planning

In this paper, the short term transmission network expansion planning (STTNEP) is solved through a specialized genetic algorithm (SGA). A complete AC model of the transmission network is used, which permits the formulation of an integrated power system transmission network expansion planning problem (real and reactive power planning). The characteristics of the proposed SGA to solve the STTNEP problem are detailed and an interior point method is employed to solve nonlinear programming problems during the solution steps of the SGA. Results of tests carried out with two electrical energy systems show the capabilities of the SGA and also the viability of using the AC model to solve the STTNEP problem. © 2009 IEEE.

Specialized genetic algorithm for transmission network expansion planning considering reliability

This paper presents a methodology to solve the transmission network expansion planning problem (TNEP) considering reliability and uncertainty in the demand. The proposed methodology provides an optimal expansion plan that allows the power system to operate adequately with an acceptable level of reliability and in an enviroment with uncertainness. The reliability criterion limits the expected value of the reliability index (LOLE - Loss Of Load Expectation) of the expanded system. The reliability is evaluated for the transmission system using an analytical technique based in enumeration. The mathematical model is solved, in a efficient way, using a specialized genetic algorithm of Chu-Beasley modified. Detailed results from an illustrative example are presented and discussed. © 2009 IEEE.

Multiarea optimal power flow using multiobjective evolutionary algorithm

In this work the multiarea optimal power flow (OPF) problem is decoupled into areas creating a set of regional OPF subproblems. The objective is to solve the optimal dispatch of active and reactive power for a determined area, without interfering in the neighboring areas. The regional OPF subproblems are modeled as a large-scale nonlinear constrained optimization problem, with both continuous and discrete variables. Constraints violated are handled as objective functions of the problem. In this way the original problem is converted to a multiobjective optimization problem, and a specifically-designed multiobjective evolutionary algorithm is proposed for solving the regional OPF subproblems. The proposed approach has been examined and tested on the RTS-96 and IEEE 354-bus test systems. Good quality suboptimal solutions were obtained, proving the effectiveness and robustness of the proposed approach. ©2009 IEEE.

Context-sensitive analysis of obfuscated x86 executables

A method for context-sensitive analysis of binaries that may have obfuscated procedure call and return operations is presented. Such binaries may use operators to directly manipulate stack instead of using native call and ret instructions to achieve equivalent behavior. Since definition of context-sensitivity and algorithms for context-sensitive analysis have thus far been based on the specific semantics associated to procedure call and return operations, classic interprocedural analyses cannot be used reliably for analyzing programs in which these operations cannot be discerned. A new notion of context-sensitivity is introduced that is based on the state of the stack at any instruction. While changes in 'calling'-context are associated with transfer of control, and hence can be reasoned in terms of paths in an interprocedural control flow graph (ICFG), the same is not true of changes in 'stack'-context. An abstract interpretation based framework is developed to reason about stack-contexts and to derive analogues of call-strings based methods for the context-sensitive analysis using stack-context. The method presented is used to create a context-sensitive version of Venable et al.'s algorithm for detecting obfuscated calls. Experimental results show that the context-sensitive version of the algorithm generates more precise results and is also computationally more efficient than its context-insensitive counterpart. Copyright © 2010 ACM.

On the determination of epsilon during discriminative GMM training

Discriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. In this letter, we derive an equation to adaptively determine ε, by showing that the second-order Newton-Raphson iterative method to find roots of equations is equivalent to the gradient descent algorithm. © 2010 IEEE.

Distribution system restoration in a DG environment using a heuristic constructive multi-start algorithm

This paper proposes a heuristic constructive multi-start algorithm (HCMA) to distribution system restoration in real time considering distributed generators installed in the system. The problem is modeled as nonlinear mixed integer and considers the two main goals of the restoration of distribution networks: minimizing the number of consumers without power and the number of switching. The proposed algorithm is implemented in C++ programming language and tested using a large real-life distribution system. The results show that the proposed algorithm is able to provide a set of feasible and good quality solutions in a suitable time for the problem. © 2011 IEEE.

An application of a modified constructive heuristic algorithm to transmission expansion planning

Transmission expansion planning (TEP) is a non-convex optimization problem that can be solved via different heuristic algorithms. A variety of classical as well as heuristic algorithms in literature are addressed to solve TEP problem. In this paper a modified constructive heuristic algorithm (CHA) is proposed for solving such a crucial problem. Most of research papers handle TEP problem by linearization of the non-linear mathematical model while in this research TEP problem is solved via CHA using non-linear model. The proposed methodology is based upon Garver's algorithm capable of applying to a DC model. Simulation studies and tests results on the well known transmission network such as: Garver and IEEE 24-bus systems are carried out to show the significant performance as well as the effectiveness of the proposed algorithm. © 2011 IEEE.

Application of ontologies in peer-to-peer database with routing queries based on ant colony algorithm

A significant set of information stored in different databases around the world, can be shared through peer-topeer databases. With that, is obtained a large base of knowledge, without the need for large investments because they are used existing databases, as well as the infrastructure in place. However, the structural characteristics of peer-topeer, makes complex the process of finding such information. On the other side, these databases are often heterogeneous in their schemas, but semantically similar in their content. A good peer-to-peer databases systems should allow the user access information from databases scattered across the network and receive only the information really relate to your topic of interest. This paper proposes to use ontologies in peer-to-peer database queries to represent the semantics inherent to the data. The main contribution of this work is enable integration between heterogeneous databases, improve the performance of such queries and use the algorithm of optimization Ant Colony to solve the problem of locating information on peer-to-peer networks, which presents an improve of 18% in results. © 2011 IEEE.

Architecture for peer-to-peer databases with routing queries using ant colony algorithm and semantic support

In a peer-to-peer network, the nodes interact with each other by sharing resources, services and information. Many applications have been developed using such networks, being a class of such applications are peer-to-peer databases. The peer-to-peer databases systems allow the sharing of unstructured data, being able to integrate data from several sources, without the need of large investments, because they are used existing repositories. However, the high flexibility and dynamicity of networks the network, as well as the absence of a centralized management of information, becomes complex the process of locating information among various participants in the network. In this context, this paper presents original contributions by a proposed architecture for a routing system that uses the Ant Colony algorithm to optimize the search for desired information supported by ontologies to add semantics to shared data, enabling integration among heterogeneous databases and the while seeking to reduce the message traffic on the network without causing losses in the amount of responses, confirmed by the improve of 22.5% in this amount. © 2011 IEEE.

Multi-relational algorithm for mining association rules in large databases

Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required exceeds the amount available. The proposed algorithm MRRadix presents a framework that promotes the optimization of memory usage. It also uses the concept of partitioning to handle large volumes of data. The original contribution of this proposal is enable a superior performance when compared to other related algorithms and moreover successfully concludes the task of mining association rules in large databases, bypass the problem of available memory. One of the tests showed that the MR-Radix presents fourteen times less memory usage than the GFP-growth. © 2011 IEEE.

Optimization of algorithm to identification of duplicate tuples through similarity phonetic based on multithreading

Aiming to ensure greater reliability and consistency of data stored in the database, the data cleaning stage is set early in the process of Knowledge Discovery in Databases (KDD) and is responsible for eliminating problems and adjust the data for the later stages, especially for the stage of data mining. Such problems occur in the instance level and schema, namely, missing values, null values, duplicate tuples, values outside the domain, among others. Several algorithms were developed to perform the cleaning step in databases, some of them were developed specifically to work with the phonetics of words, since a word can be written in different ways. Within this perspective, this work presents as original contribution an optimization of algorithm for the detection of duplicate tuples in databases through phonetic based on multithreading without the need for trained data, as well as an independent environment of language to be supported for this. © 2011 IEEE.