Claudomiro Sales

Line Topology Identification Using Multiobjective Evolutionary Computation

The broadband capacity of the twisted-pair lines strongly varies within the copper access network. It is therefore important to assess the ability of a digital subscriber line (DSL) to support the DSL services prior to deployment. This task is handled by the line qualification procedures, where the identification of the line topology is an important part. This paper presents a new method, denoted topology identification via model-based evolutionary computation (TIMEC), for line topology identification, where either one-port measurements or both one- and two-port measurements are utilized. The measurements are input to a model-based multiobjective criterion that is minimized by a genetic algorithm to provide an estimate of the line topology. The inherent flexibility of TIMEC enables the incorporation of a priori information, e.g., the total line length. The performance of TIMEC is evaluated by computer simulations with varying degrees of information. Comparison with a state-of-art method indicates that TIMEC achieves better results for all the tested lines when only one-port measurements are used. The results are improved when employing both one- and two-port measurements. If a rough estimate of the total length is also used, near-perfect estimation is obtained for all the tested lines.

Multi-objective genetic algorithm for missing data imputation

The paper proposes a novel Multi-objective Genetic Algorithm for Data Imputation, called MOGAImp.This is the first method that applies a multi-objective approach to data imputation.MOGAImp presents a good tradeoff between the evaluation measures studied.The results confirm the MOGAImp prevalence for utilization over conflicting evaluation measures.MOGAImp codification scheme makes possible to adapt it to different application domains. A large number of techniques for data analyses have been developed in recent years, however most of them do not deal satisfactorily with a ubiquitous problem in the area: the missing data. In order to mitigate the bias imposed by this problem, several treatment methods have been proposed, highlighting the data imputation methods, which can be viewed as an optimization problem where the goal is to reduce the bias caused by the absence of information. Although most imputation methods are restricted to one type of variable whether categorical or continuous. To fill these gaps, this paper presents the multi-objective genetic algorithm for data imputation called MOGAImp, based on the NSGA-II, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and the modeling task. A set of tests for evaluating the performance of the algorithm were applied using 30 datasets with induced missing values; five classifiers divided into three classes: rule induction learning, lazy learning and approximate models; and were compared with three techniques presented in the literature. The results obtained confirm the MOGAImp outperforms some well-established missing data treatment methods. Furthermore, the proposed method proved to be flexible since it is possible to adapt it to different application domains.

A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges

This paper proposes a novel unsupervised and nonparametric genetic algorithm for decision boundary analysis (GADBA) to support the structural damage detection process, even in the presence of linear and nonlinear effects caused by operational and environmental variability. This approach is rooted in the search of an optimal number of clusters in the feature space, representing the main state conditions of a structural system, also known as the main structural components. This genetic-based clustering approach is supported by a novel concentric hypersphere algorithm to regularize the number of clusters and mitigate the cluster redundancy. The superiority of the GADBA is compared to state-of-the-art approaches based on the Gaussian mixture models and the Mahalanobis squared distance, on data sets from monitoring systems installed on two bridges: the Z-24 Bridge and the Tamar Bridge. The results demonstrate that the proposed approach is more efficient in the task of fitting the normal condition and its structural components. This technique also revealed to have better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, suggesting its applicability for real-world structural health monitoring applications.

A global expectation-maximization based on memetic swarm optimization for structural damage detection

During the service life of engineering structures, structural management systems attempt to manage all the information derived from regular inspections, evaluations and maintenance activities. However, the structural management systems still rely deeply on qualitative and visual inspections, which may impact the structural evaluation and, consequently, the maintenance decisions as well as the avoidance of collapses. Meanwhile, structural health monitoring arises as an effective discipline to aid the structural management, providing more reliable and quantitative information; herein, the machine learning algorithms have been implemented to expose structural anomalies from monitoring data. In particular, the Gaussian mixture models, supported by the expectation-maximization (EM) algorithm for parameter estimation, have been proposed to model the main clusters that correspond to the normal and stable state conditions of a structure when influenced by several sources of operational and environmental variations. Unfortunately, the optimal parameters determined by the EM algorithm are heavily dependent on the choice of the initial parameters. Therefore, this paper proposes a memetic algorithm based on particle swarm optimization (PSO) to improve the stability and reliability of the EM algorithm, a global EM (GEM-PSO), in searching for the optimal number of components (or data clusters) and their parameters, which enhances the damage classification performance. The superiority of the GEM-PSO approach over the state-of-the-art ones is attested on damage detection strategies implemented through the Mahalanobis and Euclidean distances, which permit one to track the outlier formation in relation to the main clusters, using real-world data sets from the Z-24 Bridge (Switzerland) and Tamar Bridge (United Kingdom).

Transfer Function Estimation of Telephone Lines from Input Impedance Measurements

The ability of a specific telephone line to support a certain digital subscriber line (DSL) service is determined by its downstream and upstream data rates, which are mainly dependent on the lines transfer function. In this way, methods for transfer function estimation play an important role on proper DSL deployment. Most of the existing methods derive the transfer function via line topology identification (LTI) processes. This paper proposes a method which directly estimates the transfer function of telephone lines without any previous LTI process. The results obtained from both simulations and experimental procedure using twisted-pair cables indicate that the proposed method achieves accurate estimations even for lines with bridged-taps.

Applicability of linear and nonlinear principal component analysis for damage detection

The goal of this work is to detect structural damage using vibration-based damage identification approaches even when the damage-sensitive features are camouflaged by the presence of operational and environmental conditions. For feature classification purposes, four machine learning algorithms are applied based on the principal component analysis (PCA), nonlinear PCA, kernel PCA and greedy kernel PCA. Time-series data from an array of accelerometers under several structural state conditions were obtained from a well-known base-excited three-story frame structure. The main contribution of this work is the applicability of those PCA-based algorithms, for damage detection, in the presence of operational and environmental effects. For these specific data sets, one can infer that the greedy kernel PCA algorithm is more appropriate when one wants to minimize false-positive indications of damage without increasing, significantly, the false-negative indications of damage.

Computational parallelization strategy applied in full 3D ray-tracing wireless channel modeling

This paper presents a computational parallelization strategy applied in propagation models based on 3D ray-tracing techniques. This approach considers that the rays are independent from each other, what allows a uniform division of the tasks by equal and random distribution of rays among the parallel computer nodes. The strategy efficiency is proved by simulation where the results are discussed.

3D ray-tracing parallel model for radio-propagation prediction

A computational parallel model based on 3D ray- tracing for radio-propagation prediction is presented. This approach considers that the main tasks in a 3D ray-tracing technique can be evaluated in an independent and/or parallel way. The workload distribution among the participant nodes of the parallel architecture (cluster of PCs), is performed through a random assignment of the initial rays and the field points for them. Simulations are realized in order to validate and evaluate the performance of the proposed model.

Expert system based on wavelets and DELT measurements for VDSL systems

Dual-ended line testing (DELT) is a common capability in most current modems and can be used for digital subscriber line (DSL) qualification and monitoring purposes. In spite of that, this feature remains largely unexplored in the literature. This paper proposes a new method based on wavelets and DELT measurements for estimating the total length of the line under test and identification of bridged-taps, two important line parameters that affect the maximum bit rate reached by a DSL line. The proposed method was tested with measurements employing real twisted-pair cables and obtained reasonably accurate results for the analyzed cases.

Cable parameters identification for DSL systems

A method to identify some physical parameters of twisted-pair cables is presented in this paper. The parameters identification process is carried out from input impedance measurements, applying analytical approach and mean squared estimation. The obtained results indicate the accuracy and applicability of the proposed method.