Daniel F. Leite

Universität Paderborn | UPB · Department of Computer Science

Unknown nonstationary processes require modeling and control design to be done in real time using streams of data collected from the process. The purpose is to stabilize the closed-loop system under changes of the operating conditions and process parameters. This paper introduces a model-based evolving granular fuzzy control approach as a step towa...

This paper presents an online-learning ensemble framework for nonstationary time series prediction. Optimal granular fuzzy rule-based models with different objective functions and constraints are evolved from data streams. Evolving optimal granular systems (eOGS) consider multiobjective optimization, the specificity of information, model compactnes...

Evolving systems unfolds from the interaction and cooperation between systems with adaptive structures, and recursive methods of machine learning. They construct models and derive decision patterns from stream data produced by dynamically changing environments. Different components that assemble the system structure can be chosen, being rules, tree...

We introduce an incremental learning method for the optimal construction of rule-based granular systems from numerical data streams. The method is developed within a multiobjective optimization framework considering the specificity of information, model compactness, and variability and granular coverage of the data. We use α-level sets over Gaussia...

This paper presents a method called Interval Incremental Learning (IIL) to capture spatial and temporal patterns in uncertain data streams. The patterns are represented by information granules and a granular rule base with the purpose of developing explainable human-centered computational models of virtual and physical systems. Fundamentally, inter...

2024 Explainable Online Learning for Uncertain Data Streams (OLUD 2024) within the IEEE International Conference on Evolving and Adaptive Intelligent Systems 2024 (IEEE EAIS 2024)Madrid, Spain, 23-24 May, 2024 https://sites.google.com/view/olud/home _______________________________________________________________________________ The key research qu...

Data driven modeling has been a major approach for learning and understanding systems, ranging from medical, biological, environmental, meteorological, transportation, and economic systems to complex dynamic information, engineering, and hybrid systems. Computational intelligence, rooted in fuzzy systems, neural networks, evolutionary computation a...

We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robust-ness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to cap...

Article available at the following link: https://www.amcs.uz.zgora.pl/?action=paper&paper=1714 ______________________________________________________ Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patien...

The paper addresses data driven fuzzy modeling and long-short term memory structures, and evaluate their performance in modeling nonlinear dynamic systems. These are representative and powerful modeling paradigms representative of the state of the art of fuzzy and deep neural modeling approaches. Models complexity and approximation capabilities are...

We present an approach for data-driven modeling and evolving control of unknown dynamic systems called State-Space Evolving Granular Control. The approach is based on elements of granular computing, discrete state-space systems, and online learning. First, the structure and parameters of a granular model is developed from a stream of state data. Th...

This editorial note provides an overview of the papers accepted to the First Workshop on Online Learning from Uncertain Data Streams (OLUD 2022) and related sub-areas. The OLUD workshop was intended to facilitate interdisciplinary discussion on recent advancements of state-of-the-art online machine learning and incremental pattern recognition metho...

Intelligent systems for the medical domain often require processing data streams that evolve over time and are only partially labeled. At the same time, the need for explanations is of utmost importance not only due to various regulations, but also to increase trust among systems’ users. In this work, an online data-driven learning method with focu...

Nowadays, applications in various domains (computer science, engineering, medicine, economy, etc.) are based on sensor data or depend on data transmission in the cloud. Effective modeling approaches to address such a massive amount of dynamically-changing data in a feasible period of time are of utmost importance. Traditional modeling approaches fo...

We introduce a method called evolving Log Parsing (eLP) to extract information granules and an interval rule-based classification model from streams of words in unstructured log files. Logs are elementary expressions of language that are used by computational systems to communicate with humans unidirectionally. The logs tell stories based on event...

The paper looks at the structure of fuzzy rule-based models from the point of view of a function relating membership grades of inputs with rule outputs. This view in turn is generalized by an approach that produces the output functions of the fuzzy rules using input and output data. In this view, a formulation to compute the output of the model con...

To adequately meet the nutritional needs of broilers, it is necessary to know the values of apparent metabolizable energy corrected by the nitrogen balance (AMEn) of the feedstuffs. To determine AMEn values, biological assays, feedstuff composition tables, or prediction equations are used as a function of the chemical composition of feedstuffs, the...

The 2022 IEEE Conference on Evolving and Adaptive Intelligent Systems (IEEE EAIS 2022) will be held in Larnaca (Cyprus), a picturesque sea side town. Larnaca is a small town combining old colonial architecture with modern buildings. The area has been inhabited for at least 3000 years and is framed in the east by the Larnaca Salt Lake and to the sou...

The Large Hadron Collider (LHC) demands a huge amount of computing resources to deal with petabytes of data generated from High Energy Physics (HEP) experiments and user logs, which report user activity within the supporting Worldwide LHC Computing Grid (WLCG). An outburst of data and information is expected due to the scheduled LHC upgrade, viz.,...

Emotion recognition has become a need for more realistic and interactive machines and computer systems. The greatest challenge is the availability of high-performance algorithms to effectively manage individual differences and nonstationarities in physiological data, i.e., algorithms that customize models to users with no subject-specific calibrati...

A doença de Parkinson é uma doença neurodegenerativa relacionada a idade. Cerca de 1% dos indivíduos com idade superior a 65 anos desenvolvem a doença. Pesquisas recentes em detecção incipiente da doença de Parkinson têm apontado como os primeiros indicadores da doença alterações sutis na voz, hiposmia e distúrbios do sono. Este trabalho leva em co...

Human emotion recognition has become a need for more realistic and interactive machines and computer systems. The greatest challenge is the availability of high-performance algorithms to effectively manage individual differences and nonstationarities in physiological data streams, i.e., algorithms that self-customize to a user with no subject-speci...

This paper addresses the use of data-driven evolving techniques applied to fault prognostics. In such problems, accurate predictions of multiple steps ahead are essential for the Remaining Useful Life (RUL) estimation of a given asset. The fault prognostics' solutions must be able to model the typical nonlinear behavior of the degradation processes...

We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based evolving Modeling (SS-FBeM) approach. The resulting fuzzy model is structurally and parametrically...

Descarga parcial é um tipo de falta comum em transformadores de potência. Ela indica o estado do material isolante dos equipamentos e sua identificação é uma tarefa fácil. Algumas técnicas como análise cromatográfica do óleo refrigerante têm sido utilizadas na detecção das descargas parciais off-line. Neste artigo é apresentada uma proposta de Sens...

We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based evolving Modeling (SS-FBeM) approach. The resulting fuzzy model is structurally and parametrically...

This paper addresses the use of data-driven evolving techniques applied to fault prognostics in Li-ion batteries. In such problems, accurate predictions of multiple steps ahead are essential for the Remaining Useful Life (RUL) estimation of a given asset. The fault prognostics’ solutions must be able to model the typical nonlinear behavior of the d...

Overhead cranes are widely used structures for lifting and conveying heavy loads. The development of feedback control systems for such equipment is important due to the large number of potential applications and advantages over manual operation concerning stability and robustness. This paper aims to represent the key nonlinear dynamics of crane sys...

Resumo O conceito de Qualidade da Energia está relacionado a um conjunto de alterações que podem ocorrer no sistema elétrico. Tais alterações (distúrbios/faltas) podem ocorrer em várias partes do sistema de energia, sejam nas instalações elétricas dos consumidores ou no sistema supridor da concessionária, causando prejuízos financeiros a ambas as p...

Distúrbios de qualidade de energia elétrica ocorrem em várias partes de um sistema de potência e podem causar prejuízos financeiros a todos que estão a ele conectado. Portanto, é de fundamental importância a classificação automática destes distúrbios, com alto nível de acurácia e baixo custo computacional. São consideradas as redes neuro-fuzzy gran...

Descrevemos um algoritmo de aprendizado de máquina online para construção de Classificadores Fuzzy Gaussianos (eGFC). Apresentamos um método de extração e seleção de atributos do espectro de Fourier de dados de eletro-encefalograma. Os dados são obtidos de 28 indivíduos expostos aos jogos de computador Train Sim World, Unravel, Slender The Arrival,...

A cama de galpões de confinamento de bovino leiteiro no modelo compost barn tem grande impacto na qualidade e produtividade animal. O objetivo deste estudo é desenvolver um modelo não-linear para estimar a quantidade de bactéria em camas de compostagem. A partir de variáveis de fácil mensuração, estimativas podem ser geradas pelo modelo e, conseque...

A detecção de anomalia de comportamento de sistemas é crucial para a manutenção preditiva e a segurança dos dados em centros de computação. Centro de computação é qualquer rede de computadores que permita aos usuários compartilhar dados e recursos computacionais. Em geral, logs são dados não-estruturados (arquivos) produzidos por processos estocást...

O objetivo desse artigo é avaliar o conforto térmico de bovinos leiteiros confinados em instalações Compost Barn com base em índices de conforto térmico, parâmetros fisiológicos, escore de higiene e claudicação, por meio de análises estatísticas. A pesquisa foi realizada, em uma propriedade comercial do município de Três Corações no estado de Minas...

This article aims to study the effect of different time intervals for bed maturation in compost sheds for dairy cattle, such as organic manure in maize crop for silage. The experimental design was a randomized block in split-plot with five treatments, six collection times, and five replicates. The data were submitted to variance analysis and compar...

This paper concerns the application of a neuro-fuzzy learning method based on data streams for high impedance fault (HIF) detection in medium-voltage power lines. A wavelet-packet-transform-based feature extraction method combined with a variation of evolving neuro-fuzzy network with fluctuating thresholds is considered for recognition of spatial–t...

Online monitoring systems have been developed for real-time detection of high impedance faults in power distribution networks. Sources of distributed generation are usually ignored in the analyses. Distributed generation imposes great challenges to monitoring systems. This paper proposes a wavelet transform-based feature-extraction method combined...

Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nuclear Research) physics experiments. A log, as event-oriented ad-hoc information, is quite often given as unstructured big data. Log data processing is a time-consuming computational task...

Detection of anomalous behaviors in data centers is crucial to predictive maintenance and data safety. With data centers, we mean any computer network that allows users to transmit and exchange data and information. In particular, we focus on the Tier-1 data center of the Italian Institute for Nuclear Physics (INFN), which supports the high-energy...

Detection of anomalous behaviors in data centers is crucial to predictive maintenance and data safety. With data centers, we mean any computer network that allows users to transmit and exchange data and information. In particular, we focus on the Tier-1 data center of the Italian Institute for Nuclear Physics (INFN), which supports the high-energy...

Power-quality disturbances lead to several drawbacks such as limitation of the production capacity, increased line and equipment currents, and consequent ohmic losses; higher operating temperatures, premature faults, reduction of life expectancy of machines, malfunction of equipment, and unplanned outages. Real-time detection and classification of...

Power-quality disturbances lead to several drawbacks such as limitation of the production capacity, increased line and equipment currents, and consequent ohmic losses; higher operating temperatures, premature faults, reduction of life expectancy of machines, malfunction of equipment, and unplanned outages. Real-time detection and classification of...

Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nuclear Research) physics experiments. A log, as event-oriented adhoc information, is quite often given as unstructured big data. Log data processing is a time-consuming computational task....

Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). We develop the notion of double-boundary fuzzy gr...

Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). We develop the notion of double-boundary fuzzy gr...

Resumo-Technological advancements has made individuals and organizations more dependent on e-mails to communicate and share information. The increasing use of e-mails has led to an increased production of unsolicited commercial messages, known as spam. Spam classification systems able to self-adapt over time, with no human intervention, are rare. A...

Partial discharge is a common type of fault in power transformers. It indicates the state of the insulation material. Identifying their existence is not an easy task. Some techniques such as chromatographic analysis of the refreshing oil have been used for offline detection of partial discharges. In this paper we present a Virtual Sensor scheme for...

We present a recursive machine learning method for the optimal construction of rule-based granular models from numerical data streams. We consider a multiobjective function, the specificity of information, model compactness, and data coverage. We use α-level sets over Gaussian membership functions to control the model granularity and operate with a...

Technological advancements have made individuals and organizations more dependent on e-mails to communicate and share information. The increasing use of e-mails has led to an increased production of unsolicited commercial messages, known as spam. Spam classification systems able to self-adapt over time, with no human intervention, are rare. Adaptat...

Electroencephalography (EEG) data analysis has been widely used in a variety of application domains. This study aims at detecting the state of the eyes from 14 EEG electrodes. Three artificial neural models are briefly presented, computationally realized, and compared for the task of classifying the state of the eyes. A feed-forward Multi-Layer Per...

In recent years, several advances in intelligent driving systems have been presented worldwide. One of the consequences of such developments is the use of intelligent vehicles to make transport more efficient, improve mobility in major centers, and increase the safety of drivers and pedestrians. However, a number of technical and non-technical chal...

This paper presents a state-space fuzzy model and the design of a fuzzy controller for a class of overhead cranes. The fuzzy model is obtained from the sector nonlinearity method and from the use of the original nonlinear differential equations. A method for approximation in fuzzy partition spaces is applied to give a compact fuzzy model, which avo...

Microgrids composes several distributed generation, storage and load systems. However, controllers and systems are complex, especially in island mode operation. In this condition, the grid forming converters are responsible for the proper functioning of the microgrid, controlling voltage amplitude and frequency according to given references. There...

Missing values are common in real-world data stream applications. This paper proposes a modified evolving granular fuzzy rule-based model for function approximation and time series prediction in an online context where values may be missing. The fuzzy model is equipped with an incremental learning algorithm that simultaneously imputes missing data...

The use of organic wastes from housing systems in a compost bedded pack barn model could supply much of the fertiliser demand in the farm. Moreover, with the increase in food consumption from sustainable livestock, the use of compost generated in the farm itself becomes economically viable. This study aimed to evaluate the effect of organic manure...

This paper presents a new idea for incremental clustering based on decomposed Cauchy-like (deCauchy) density distribution. The algorithm is based on the metrics where the data sample is written in the form of unity orientation vector multiplied by the scalar of the data vector length. This notation offers a very clear and transparent way to calcula...

We introduce an incremental learning method for the optimal construction of rule-based granular models from numerical data streams. We take into account a multiobjective function, the specificity of information, model compactness, and variability and coverage of the data. We use α-level sets over Gaussian membership functions to set model granulari...

The development of feedback control systems for overhead cranes is of great importance due to many potential applications and advantages over manual operation concerning stability and robustness. We represent the key nonlinear dynamics of cranes in a compact state-space fuzzy model. The fuzzy model assists the design of a fuzzy controller through p...

The success of confinement for dairy cattle in the compost barn model depends mainly on the management of the bed and consists of its turning. This paper characterises the spatial variability of the bed temperature in the compost barn confinement model, as well as verifying whether there was an effect on efficiency from turning the bed with differe...

Learning of neural network structure and parameters using genetic and incremental heuristic algorithms are potential approaches to address the local optima and design issues experienced when using conventional deterministic algorithms and arbitrarily chosen network structures. This chapter presents results on the development of an evolutionary (EAN...

Learning of neural network structure and parameters using genetic and incremental heuristic algorithms are potential approaches to address the local optima and design issues experienced when using conventional deterministic algorithms and arbitrarily chosen network structures. This chapter presents results on the development of an evolutionary (EAN...

O estudo a respeito do quanto os fatores ambientais e o microclima das instalações de bovinos leiteiros influenciam no desempenho da produção do animal tem ganhado espaço no meio científico e prioridade do ponto de vista dos produtores. Este estudo tem como objetivo utilizar a modelagem geoestatística para representar e localizar os pontos críticos...

Control and analysis of multivariable linear dynamic systems with unstructured parametric uncertainty are dealt with from the interval mathematics perspective. Guaranteed stability is achieved from the Kharitonov theorem. Observability and complete state controllability are also generalized for systems with uncertain parameters. Two controller desi...

This paper presents a new method to quantify inter-harmonics, caused by voltage fluctuations, in terms of frequency and amplitude. The frequencies of the fundamental and fluctuation component are also estimated, which allows the application of the algorithm in the presence of frequency and amplitude variations. The proposed methodology uses adaptiv...

Parkinson's disease is an age-related neurodegenerative disease. About 1% of individuals over the age of 65 develop the disease. Recent research on incipient Parkinson's disease detection has indicated subtle changes in voice, hyposmia and sleep disorders as the first indicators of the disease. This work considers analyses of speech amplitudes in c...

A posição angular do volante garante controle lateral de um veículo enquanto ele trafega em uma via. Antever os valores dessa variável pode sugerir uma boa forma de se analisar a dinâmica envolvida nesses sistemas e dos condutores, além de criar mecanismos para se obter controle autônomo de veículos. Este trabalho tem como objetivo realizar a predi...

This paper presents a Gaussian fuzzy set-based evolving modeling method, FBeM-G, to predict tropical cyclone tracks 6 hours in advance. FBeM-G summarizes similar data into Gaussian granules evolved from a sequence of data. It uses a recursive learning algorithm to update its parameters and structure over time and therefore is able to cope with nons...

Detection and location of high impedance faults in power distribution systems by means of online condition monitoring and protection devices is a challenge, especially in systems with distributed generation. This paper describes a wavelet transform feature extraction method combined with a pair of online adaptive neural networks to detect and locat...

This paper describes a variation of data cloud-based intelligent method known as typicality-and-eccentricity-based method for data analysis (TEDA). The objective is to develop data-centric nonlinearand time-varying models to predict mean monthly temperature. TEDA is an incremental algorithm thatconsiders the data density and scattering of clouds ov...

This paper concerns how to apply an incremental learning algorithm based on data streams to detect high impedance faults in power distribution systems. A feature extraction method, based on a discrete wavelet transform that is combined with an evolving neural network, is used to recognize spatial–temporal patterns of electrical current data. Differ...

Este artigo trata da aplicação de métodos de agrupamento fuzzy na avaliação de confinamentos Compost Barn (CB). Agrupamentos fuzzy são desenvolvidos para auxiliar a tomada de decisão no que diz respeito ao controle de variáveis, como a umidade, temperatura e aeração da cama de compostagem. A ideia é promover o bem-estar de bovinos leiteiros e melho...

Previsões de temperatura são importantes em muitas áreas e provêm fundamentos para vários empreendimentos humanos. Por exemplo, a agricultura é extremamente sensível à mudanças climáticas. Previsões dão suporte à produtores para que tomem decisões com relação a atividades e proteção de propriedade. Este artigo apresenta a aplicação de um método de...

Evolving intelligent systems are useful for processing online data streams. This paper presents an evolving granular neuro-fuzzy modeling framework and an application example on the modeling of the Rossler chaos. The evolving Granular Neural Network (eGNN) is able to deal with new events of nonstationary environments using fuzzy information granule...

This article provides definitions and principles of granular computing and discusses the generation and online adaptation of rule-based models from data streams. Essential notions of interval analysis and fuzzy sets are addressed from the granular computing point of view. The article also covers different types of aggregation operators which perfor...

Weather modeling and prediction has been quite a challenge over the years. Predictions based on climatic models whose dynamical behavior is nonlinear, nonstationary, and based on high order difference equations is a tough task and usually requires a demanding and non-intuitive tuning expertise. This paper suggests an ensemble of evolving fuzzy mode...

System modeling in dynamic environments needs processing of streams of sensor data and incremental learning algorithms. This paper suggests an incremental granular fuzzy rule-based modeling approach using streams of fuzzy interval data. Incremental granular modeling is an adaptivemodeling framework that uses fuzzy granular data that originate from...

In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of...

Recognition of patterns of disease progression from data requires the use of advanced computational methods. These methods should be able to group similar data together and dissimilar data into different clusters. Given the medical context, data are generally obtained under adverse conditions, such as uncertainties and nonstationarities, which can...

This work proposes the study of methods to model the behavior of the internal combustion engines using evolving algorithms, foccusing on embedded approach, seeking to replace static maps in control of eletroinjetores fuel. Evolving models can be continuously adapted the extent that detect new trends in the data. Such models are ideal for adapting t...

Resumo: Este trabalho propõe redes neuro-fuzzy evolutivas embarcadas para modelagem de motores de combustão interna. Redes neuro-fuzzy evolutivas são adaptadas conforme novos dados são apresentados. Em modo online, um modelo do processo é construído e ajustado em resposta a mudanças do processo e do ambiente. Motores de combustão interna são exempl...

This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) is able to handle gradual and abrupt parameter changes typical of nonstationary (online) environments. eGNN builds interpretable multi-sized local models using fuzzy neurons for informatio...

In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of...

Este trabalho propõe o estudo de modelos de inferência neuro-fuzzy evolutivos (DENFIS) para identificação de sistemas dinâmicos. O aspecto evolutivo da abordagem neuro-fuzzy proposta se refere a adaptação estrutural do modelo a partir de fluxos de dados. Particularmente, este trabalho enfatiza sistemas DENFIS embarcados em microcontroladores para o...

A primary requirement of a broad class of evolv- ing intelligent systems is to process a sequence of numeric data over time. This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) efficiently handles concept changes, distinctive events of nons...

Evolving granular modeling is an approach that considers online granular data stream processing and structurally adaptive rule-based models. As uncertain data prevail in stream applications, excessive data granularity becomes unnecessary and inefficient. This paper introduces an evolving fuzzy granular framework to learn from and model time-varying...

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...

Physical systems change over time and usually produce considerable amount of nonstationary data. Evolving modeling of time varying systems requires adaptive and flexible procedures to deal with heterogeneous data. Granular com- puting provides a rich framework for modeling time varying systems using non- stationary granular data streams. This work...

This work outlines a new approach for online learning from imprecise data, namely, fuzzy set based evolving modeling (FBeM) approach. FBeM is an adaptive modeling framework that uses fuzzy granular objects to enclose uncer- tainty in the data. The FBeM algorithm is data flow driven and supports learning on an instance-per-instance recursive basis b...

Modeling large volumes of flowing data from complex systems motivates rethinking several aspects of the machine learning theory. Data stream mining is concerned with extracting structured knowledge from spatio-temporally correlated data. A profusion of systems and algorithms devoted to this end has been constructed under the conceptual framework of...

Massive amounts of streaming data from complex systems motivate rethinking some aspects of the machine learning theory. Data stream mining is concerned with extracting structured knowledge from spatio-temporally correlated data. A profusion of systems and algorithms devoted to this end has been constructed under the conceptual framework of granular...