W.M. Caminhas

W.M. Caminhas
Federal University of Minas Gerais | UFMG · Departamento de Engenharia Eletrônica (DELT)

About

140
Publications
21,795
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
2,458
Citations
Introduction

Publications

Publications (140)
Article
Full-text available
The traditional Interacting Multiple Model (IMM) filters usually consider that the Transition Probability Matrix (TPM) is known, however, when the IMM is associated with time-varying or inaccurate transition probabilities the estimation of system states may not be predicted adequately. The main methodological contribution of this paper is an approa...
Chapter
Evolving systems emerge from the synergy between systems with adaptive structures, and the recursive methods of machine learning. Evolving algorithms construct models and derive decision patterns from stream data produced by dynamically changing environments. Different components can be chosen to assemble the system structure, rules, trees, and neu...
Conference Paper
Full-text available
Nas últimas décadas houve um crescimento expressivo do grau de complexidadee dimensionalidade dos processos nas indústrias, em parte, devido à revolução tecnológica que vem ocorrendo em igual período. Por consequência, surgiu a necessidade de ferramentas computacionais capazes de lidar com essa nova dinâmica e contribuir positivamente para a superv...
Conference Paper
Os sistemas inteligentes em prognóstico de falhas nas indústrias têm trazido importantes contribuições em termos de segurança e economia, tornando-os indispensáveis, e motivado cada vez mais pesquisas na área. Este artigo aborda e avalia métodos de análise de dados históricos para estimar o tempo de vida útil remanescente em problemas de prognóstic...
Article
Full-text available
The Online Neuro-Fuzzy Controller (ONFC) is a fuzzy-based adaptive control that uses a very simple structure and can control nonlinear, time-varying and uncertain systems. Its efficiency and low computational cost allowed applications in several industrial plants successfully. However, none of the previous works on the ONFC provided a design proced...
Conference Paper
Full-text available
A registration process generally uses two images at a time-the source S and target T images. The goal is to compute a spatial transformation between corresponding structures in S and T. If u is a function that represents a geometric deformation or displacement field to be performed on image S towards T, then the transformation function h at pixel p...
Article
This work presents a hybrid model of Case-based Reasoning (CBR) and artificial immune systems (AIS), which is able to manage the processes of recovery, adaptation (reuse and revision) and retention of cases. The developed model also provides an alternative way of clustering cases, identifying high density areas, improve search efficiency in the cas...
Article
The growing demand for high-speed transmission rates in recent years attracted research in new mechanisms for network traffic characterization and classification. Their inadequate treatment degrades the performance of important operational schemes, such as Network Survivability, Traffic Engineering, Quality of Service (QoS), and Dynamic Access Cont...
Conference Paper
Full-text available
This paper describes an ONFC (OnLine Neurofuzzy Controller) application with a dynamic learning rate to control the water flow of a real plant. A revision of ONFC is presented and the ONFCDw version is used, which has an action that minimizes the increase in the difference between the controller weights. The dynamic learning rate used to update the...
Article
The Interacting Multiple Model (IMM) filter is a recognized method for adaptive estimation of states which is often necessary to characterize the behavior of dynamic systems with multiple mode operation. The traditional IMM filter adopts the measurement set to update the information about the active models. However, when this approach is adopted fo...
Article
Full-text available
This paper proposes an improved fault prognostic approach based on a modified particle filter with a built-in differential evolution characteristic. The main methodological contribution of this study is to handle the problem of sample impoverishment faced by particle filters when only a few particles are resampled. This is done by incorporating mod...
Article
A multiple model recursive least squares algorithm combined with a first-order low-pass filter transformation method, named λ-transform, is proposed for the simultaneous identification of multiple model orders continuous transfer functions from non-uniformly sampled input–output data. The λ-transformation is shown to be equivalent to a canonical tr...
Article
The dendritic cell algorithm is an immune inspired method based on the danger model, which relies on cell interactions to antigens and signals, considering the correlation between both events, to solve anomaly detection problems. Starting with new datasets, comprising ping scans and file transfers in computer networks, this paper proposes improveme...
Article
Full-text available
This paper aims to document the application of a new generation of Artificial Immune Systems (AIS) in fault detection and isolation problems. These kind of algorithms are able to explore normal and anomalous behavior evidences, however, they may often require a more explicit prior knowledge provided by experts, usually difficult to obtain in some p...
Article
After a great advance by the industry on processes automation, an important challenge still remains: the automation under abnormal situations. The first step towards solving this challenge is the Fault Detection and Diagnosis (FDD). This work proposes a batch-incremental adaptive methodology for fault detection and diagnosis based on mixture models...
Article
Full-text available
This paper suggests an evolving approach to develop neural fuzzy networks for system modeling. The approach uses an incremental learning procedure to simultaneously select the model inputs, to choose the neural network structure, and to update the network weights. Candidate models with larger and smaller number of input variables than the current m...
Conference Paper
Full-text available
This paper addresses the problem of predicting faults and the tool wear condition of a computer numerical control. To handle this problem, an approach based on a particle filter is used and compared with traditional approaches for prediction. The contribution of the paper is to assess a particle filter robustness with respect to noise and uncertain...
Article
Full-text available
The emergence of complex machinery and equipment in several areas demands efficient fault diagnosis methods. Several fault diagnosis methods based on different theories and approaches have been proposed in the literature. According to the concept of intelligent maintenance, the application of intelligent systems to accomplish fault diagnosis from p...
Article
Full-text available
In this paper, a method that combines image analysis techniques, such as segmentation and registration, is proposed for an advanced and progressive evaluation of thermograms. The method is applied for the prevention of muscle injury in high-performance athletes, in collaboration with a Brazilian professional soccer club. The goal is to produce info...
Conference Paper
Nowadays, in several areas, efficient fault diagnosis methods for complex machinery and equipments are required. Several fault diagnosis methods based on different theories and approaches have been proposed in the literature. In general, these methods use mathematical/statistical models, accumulated experience, or even process historical data to pe...
Conference Paper
Full-text available
This paper reports how OptBees, an algorithm inspired by the collective decision-making of bee colonies, per- formed in the test bed developed for the Special Session & Competition on Real-Parameter Single Objective Optimization at CEC-2014. The test bed includes 30 scalable functions, many of which are both non-separable and highly multi-modal. Re...
Article
Full-text available
This paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulate the input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the spa...
Conference Paper
Full-text available
OptBees is an algorithm inspired by the processes of collective decision-making by bee colonies designed with the objective of generating and maintaining diversity, trading off ex-ploitation (diversification) and exploration (intensification) and promoting a multimodal search, so that a broader coverage of promising regions of the search space can...
Article
The transmission line is the most vulnerable element of any electrical power system due to its large physical dimension. As a consequence, many fault diagnosis algorithms have been proposed in the literature. In general, most proposals use signal-processing analysis and computational intelligence. In this paper, a new model to functionally represen...
Article
Full-text available
This paper presents OptBees, a new bee-inspired algorithm for solving continuous optimization problems. Two key mechanisms for OptBees are introduced: 1) a local search step; and 2) a process of dynamic variation of the number of active bees that helps the algorithm to regulate the computational effort spent in the search and to achieve improved re...
Conference Paper
This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechan...
Chapter
Many of the activities associated with the systems planning and operation require forecasts of future events. For instance, thermal models of distribution transformers with core immersed in oil are of utmost importance for power systems operation and safety. Its hot spot temperature determines the degradation speed of the insulation material and pa...
Article
Evolving intelligent systems (EIS) are highly adaptive systems able to update its own parameters and structure based on a date stream. These systems have been developed to address problems of modeling, control, prediction, classification and data processing in a nonstationary, dynamic changing environment. Pioneers works in this area are dated from...
Conference Paper
This paper introduces a neural fuzzy network approach for evolving system modeling. The approach uses neofuzzy neurons and a neural fuzzy structure monished with an incremental learning algorithm that includes adaptive feature selection. The feature selection mechanism starts considering one or more input variables from a given set of variables, an...
Article
Full-text available
This paper describes an immune-inspired system based on an alternate theory about the self–nonself distinction theory, which defines the negative selection process as a mechanism of a fuzzy system based on the affinity between antigen and T-cells. This theory may provide a decision making tool which improves the generation of detectors or even defi...
Conference Paper
Full-text available
The use of Immune Inspired approaches for anomaly detection have been adopted in the literature because of its analogy with body resistance in the human immune system provided against agents which causes diseases. There are many models in biology that attempt to explain the immune system behavior, as well some engineering systems inspired on these...
Conference Paper
Nowadays, online and real-time pattern classification applications are required in many areas. Most classification algorithms are suitable only for off-line applications. Using the concept of evolving intelligent systems, this paper proposes an evolving fuzzy classifier capable of creating the rule base in online mode and real-time. The proposed ev...
Article
Thermal models for distribution transformers with core immersed in oil are of utmost importance for transformers lifetime study. The hot spot temperature determines the degradation speed of the insulating paper. High temperatures cause loss of mechanical stiffness, generating failures. Since the paper is the most fragile component of the transforme...
Conference Paper
This paper suggests a fast learning algorithm for weighted uninorm-based neural networks. Fuzzy neural networks are models capable to approximate functions with high accuracy and to generate transparent models through extraction of linguistic information from the resulting topology. A fuzzy neural network model based on weighted uninorms has been d...
Conference Paper
The 2012 FUZZ-IEEE conference competition “Learning Fuzzy Systems from Data” aims to establish the empirical accuracy of fuzzy forecasting algorithms in the domain of prediction of the sales volume of petroleum products. Currently, there are no guidelines or consensus on a best practice methodology. This paper proposes evolving fuzzy linear regress...
Conference Paper
This paper presents the OptBees, an optimization algorithm inspired by the processes of collective decision-making by bee colonies. The algorithm was designed with the objective of generating and maintaining diversity, promoting a multimodal search and obtaining multiple local optima without losing the ability of global optimization, thus represent...
Conference Paper
Full-text available
The study of infectious diseases opened a whole new area of science, the mathematical epidemi- ology, that considers models that can aid in the study of the spreading of these diseases. These models include the SIR model (Susceptible - Infected - Removed) and the IBM (Individuals Based Model). The SIR model does not consider the spatial distributio...
Article
This paper presents an error detection methodology to enable fault detection inspired on recent immune theory. The fault detection problem is a challenging problem due to processes increasing complexity and agility necessary to avoid malfunction or accidents. The key challenge is determining the difference between normal and potential harmful activ...
Article
A large percentage of the total induction motor failures are due to mechanical faults. It is well known that, machine’s vibration is the best indicator of its overall mechanical condition, and an earliest indicator of arising defects. Support vector machines (SVM) is also well known as intelligent classifier with strong generalization ability. In t...
Conference Paper
This paper presents an innovative method to solve the reconfiguration problem in a distribution network. The main motivation of this work is to take advantage of the power flow analysis repetition when reconfiguration leads the network to a previous configuration due to cyclical loading pattern. The developed methodology combines an optimization te...
Article
This paper introduces an approach to evolve fuzzy modeling that simultaneously performs adaptive feature selec- tion. The model is a fuzzy linear regression tree whose topology can be continuously updated using statistical tests. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. The number of tree nodes and the number o...
Article
Full-text available
This paper introduces a class of evolving fuzzy rule-based system as an approach for multivariable Gaussian adaptive fuzzy modeling. The system is an evolving Takagi-Sugeno (eTS) functional fuzzy model, whose rule base can be continuously updated using a new recursive clustering algorithm based on participatory learning. The fuzzy sets of the rule...
Article
Full-text available
Fuzzy neural networks are hybrid models capable to approximate functions with high precision and to generate transparent models, enabling the extraction of valuable in-formation from the resulting topology. In this paper we will show that the recently proposed fuzzy neural network based on weighted uninorms aggregations uniformly approximates any r...
Article
In this paper the incipient fault detection problem in induction machine stator-winding is considered. The problem is solved using a new technique of change point detection in time series, based on a two-step formulation. The first step consists of a fuzzy clustering to transform the initial data, with arbitrary distribution, into a new one that ca...
Article
Pipeline leakage is a demand from governmental and environmental associations that companies need to comply with. Due the high accuracy on detecting leakage, it is necessary to set procedures that will achieve the leading performance. This paper describes a methodology to set instrumentations systems to accomplish with the legal requirement keeping...
Article
This paper suggests an approach for adaptive fault detection and diagnosis. The proposed approach detects new operation modes of a process such as operation point changes and faults, and incorporates information about operation modes in an evolving fuzzy classifier used for diagnosis. The approach relies upon an incremental clustering procedure to...
Conference Paper
Full-text available
In this paper the incipient fault detection problem in induction machine stator-winding is considered. The problem is solved using a new technique of change point detection in time series, based on a three-step formulation. The technique can detect up to two change points in the time series. The first step consists of a Kohonen neural network class...
Article
The major task for any monitoring system is to detect upcoming faults as early as possible. Rotor failures are responsible for a large percentage of total induction motor failures. Thus, a new nonintrusive and in-service approach has been proposed in this paper to detect one broken rotor bar in induction motor using only input quantities informatio...
Conference Paper
This paper introduces a new approach for evolving fuzzy modeling based on a tree structure. The system is a fuzzy linear regression tree whose topology can be continuously updated using a statistical model selection test. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. The evolving linear regression approach is evalua...
Conference Paper
This paper suggests a uninorm-based neuron model and a neural network architecture using unineurons. The unineuron generalizes logical and/or neurons using weighted uninorms. Previous works have addressed fuzzy neurons within the framework of uninorms. This paper introduces a new unineuron model that uses weighted aggregation of the inputs, and com...
Article
This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is determining the difference between normal and potent...