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Detection of Anomalies in Operation of Continuous Plants and Systems
2020 IEEE 10th International Conference on Intelligent Systems (IS)
Year: 2020 | Conference Paper |
Anomaly detection during real time operation of continuous plants and systems is a key activity of the operator that aims at determining the health status of the system. For such detection the information from real time multidimensional data streams supplied by multiple sensors has to be analyzed and com-pared for similarity analysis. This paper presents a methodology and algorithm for developing ...Show More
Grid fuzzy models with variable boundaries and their application to monitoring of plant operations
2017 IEEE International Conference on Mechatronics and Automation (ICMA)
Year: 2017 | Conference Paper |
Cited by: Papers (4)
This paper describes a special type of fuzzy models with incomplete grid-type fuzzy rule base in the multidimensional input space. The number and locations of the fuzzy rules are automatically defined by the concrete distribution of the training data and by the assumptions for the size and the structure of the grid. A simplified iterative learning algorithm for calculating the singletons of such G...Show More
Partial fuzzy grid models and application to condition monitoring of photovoltaic systems
2016 IEEE 8th International Conference on Intelligent Systems (IS)
Year: 2016 | Conference Paper |
Cited by: Papers (2)
This paper presents a computational strategy for condition monitoring of multi-mode processes on the example of real data from a photovoltaic system. The concept uses a new type of fuzzy models with partially activated set of fuzzy rules on a pre-determined grid, called partial fuzzy grid models. Each such model represents one specific operating condition of the process, which is saved in the Mode...Show More
A random selection fuzzy rule base for robust target approaching behavior of a mobile robot
2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)
Year: 2015 | Conference Paper |
In this paper we present a simple control architecture of a mobile robot for the purpose of target approaching. The proposed architecture combines three separate control modules to achieve an overall robot behavior for robust target approaching in a constrained environment. The three modules are "target search", "target approach" and "fuzzy obstacle avoidance". The target search module applies a c...Show More
Incremental RBF network models for nonlinear approximation and classification
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2015 | Conference Paper |
In this paper a multistep learning algorithm for creating a novel incremental Radial Basis Function Network (RBFN) Model is presented and analyzed. The proposed incremental RBFN model has a composite structure that consists of one initial linear sub-model and a number of incremental sub-models, each of them being able to gradually decrease the overall approximation error of the model, until a desi...Show More
Optimization strategies for tuning the parameters of radial basis functions network models
2014 4th International Conference On Simulation And Modeling Methodologies, Technologies And Applications (SIMULTECH)
Year: 2014 | Conference Paper |
In this paper the problem of tuning the parameters of the RBF networks by using optimization methods is investigated. Two modifications of the classical RBFN, called Reduced and Simplified RBFN are introduced and analysed in the paper. They have a smaller number of parameters. Three optimization strategies that perform one or two steps for tuning the parameters of the RBFN models are explained and...Show More
Growing radial basis function network models
Asia-Pacific World Congress on Computer Science and Engineering
Year: 2014 | Conference Paper |
Cited by: Papers (2)
In this paper a learning algorithm for creating a Growing Radial Basis Function Network (RBFN) Model is presented and analyzed. The main concept of this algorithm is that the number of the Radial Basis Function (RBF) units is gradually increased at each learning step of the algorithm and the model is gradually improved, until a predetermined (desired) approximation error is achieved. The important...Show More
Vision based autonomous path tracking of a mobile robot using fuzzy logic
Asia-Pacific World Congress on Computer Science and Engineering
Year: 2014 | Conference Paper |
Cited by: Papers (12)
In this paper we present an algorithm for autonomous path tracking of a mobile robot to track straight and curved paths traced in the environment. The algorithm uses a fuzzy logic based approach for path tracking so that human driving behavior can be emulated in the mobile robot. The method combines a fuzzy steering controller, which controls the steering angle of the mobile robot for path trackin...Show More
Fuzzy controller tuning of a mobile robot for exploration and safe navigation in constrained environment
2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM)
Year: 2013 | Conference Paper |
Cited by: Papers (1)
In this paper we present a novel fuzzy controller structure for a mobile robot with the purpose for exploration of a constrained environment with obstacles. The proposed fuzzy rule base contains redundancy in some of the fuzzy rules, i.e., several consequents could be used. At each step we make random selection of one of these consequents. This is called in our paper Random Selection Fuzzy Rule Ba...Show More
Online detection of deviation in performance of multichannel dynamical processes
2013 IEEE International Conference on Mechatronics and Automation
Year: 2013 | Conference Paper |
In this paper a computational method for detection of deviation in performance of multiple parallel working channels in a dynamical process is proposed and discussed. The method consists of the following computation steps: one-dimensional multi-agent clustering of the outputs of the channels; similarity analysis of all pairs of channels; calculating the weighted global distance for each channel; s...Show More
Estimation of the Insulation Deterioration of Metallurgical Ladle by Use of RBFNN Models
2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation
Year: 2012 | Conference Paper |
A model-based approach for estimation and diagnosis of the deterioration in the metallurgical ladle insulation is proposed in this paper. It is based on using the diverse information that comes from the so called thermo vision analysis (thermographic images), which show the temperature profile on the surface of the ladle. A group of Radial Basis Function Neural Network (RBFNN) models with differen...Show More
Battery aging detection based on sequential clustering and similarity analysis
2012 6th IEEE International Conference Intelligent Systems
Year: 2012 | Conference Paper |
Cited by: Papers (3)
The battery cells are an important part of electric and hybrid vehicles and their deterioration due to aging directly affects the life cycle and performance of the whole battery system. Therefore an early aging detection of the battery cell is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for batte...Show More
Incremental classification of process data for anomaly detection based on similarity analysis
2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)
Year: 2011 | Conference Paper |
Performance evaluation and anomaly detection in complex systems are time consuming tasks based on analyzing, similarity analysis and classification of many different data sets from real operations. This paper presents an original computational technology for unsupervised incremental classification of large data sets by using a specially introduced similarity analysis method. First of all the so ca...Show More
Classification of visual information by structure based similarity analysis
2010 World Automation Congress
Year: 2010 | Conference Paper |
Two algorithms - for online and for offline classification of images are presented in this paper. They perform the classification by comparing the dissimilarity degrees between all pairs of available images. A special computational way is proposed to evaluate the normalized dissimilarity, based on the color RGB histograms, extracted from each image. The online classification algorithm creates a mu...Show More
Classification of visual information by structure based similarity analysis
Year: 2010 | Conference Paper |
This paper proposes a computational scheme of a novel Evolving Knowledge Base system that is able to gradually grow and update spatially and temporally. The main assumption is that the input information comes from the real environment in the form of chunks of data (not single data points). Therefore the whole system works in a quasi-real time. Each chunk of data is used for extraction of the so ca...Show More
Online incremental image classification by use of human assisted fuzzy similarity
The 2010 IEEE International Conference on Information and Automation
Year: 2010 | Conference Paper |
In this paper we propose a computational scheme for online incremental type classification of images, based on human assisted fuzzy similarity analysis. First of all, two main parameters from each image are extracted in the form of a center-of-gravity and a generalized volume of the image model.. Then their differences for each pair of images are taken as respective features F1 and F2, which serve...Show More
Classification of process data and images by human assisted fuzzy similarity analysis
2009 ICCAS-SICE
Year: 2009 | Conference Paper |
In this paper an incremental classification scheme for large data sets and images is proposed in the form of a two-stage computation scheme. First, information compression of the original data set or pixels is performed by a modification of the Neural-Gas unsupervised learning algorithms. Then two features are extracted from the obtained compressed information model, namely the center-of-gravity o...Show More
Image segmentation algorithms based on information compression and graph structures
2009 International Conference on Mechatronics and Automation
Year: 2009 | Conference Paper |
Cited by: Papers (1)
In this paper we propose a multistage computational procedure for segmentation of images that can also be used for partitioning of large process data sets. In the first step the original "raw" data set (e.g. the set of pixels from a given image) is compressed by use of the neural-gas unsupervised learning algorithm into compressed information model (CIM) that contains small predefined number of ne...Show More
Data Partitioning and Image Segmentation by Use of Information Compression and Graph Structures
2009 11th International Conference on Computer Modelling and Simulation
Year: 2009 | Conference Paper |
Cited by: Papers (2)
In this paper we propose a multistage computational procedure for partitioning of large data sets and for segmentation of images. In the first step the original ldquorawrdquo data set (or the set of pixels from a given image) is compressed by use of the neural-gas unsupervised learning algorithm into compressed information model (CIM) that contains small predefined number of neurons. In the second...Show More
Comparison, Segmentation and Analysis of Images by Use of Information Compression Algorithms
2008 International Conference on Computational Intelligence for Modelling Control & Automation
Year: 2008 | Conference Paper |
Computational scheme for comparison, color analysis and segmentation of images is proposed in this paper. First of all, two growing unsupervised learning algorithms are introduced. They create the so called compressed information model (CIM) of the image that replaces the original ldquoraw datardquo (the RGB pixels) with a smaller number of neurons. Then two main features are extracted from the CI...Show More
Unsupervised learning algorithms for comparison and analysis of images
2008 IEEE International Conference on Mechatronics and Automation
Year: 2008 | Conference Paper |
Cited by: Papers (1)
This paper proposes a computational scheme for comparison and color analysis of images by using unsupervised learning algorithms. As a first step, two special growing unsupervised learning algorithms are introduced and used to create the so called compressed information model (CIM) which replaces the original ldquoraw datardquo (the RGB pixels) of the image with a much smaller number of neurons. T...Show More
Similarity analysis of images based on information granulation and fuzzy decision
2008 4th International IEEE Conference Intelligent Systems
Year: 2008 | Conference Paper |
Cited by: Papers (2)
This paper proposes a computational scheme for fuzzy similarity analysis and classification of images that uses first an information granulation procedure followed by a subsequent fuzzy decision procedure. A special new version of the growing unsupervised learning algorithm is introduced in the paper for information granulation. It reduces the original ldquoraw datardquo (the RGB pixels) of the im...Show More
Classification of images based on information compression and fuzzy rule based similarity analysis
2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)
Year: 2008 | Conference Paper |
Cited by: Papers (9)
This paper proposes a computational scheme for fuzzy similarity analysis and classification of images by comparison of the new (unknown) images with a predetermined number of known (core) images, contained in an image base. As a first step, an unsupervised competitive learning algorithm is used to create the so called compressed information model (CIM) which replaces the original ldquoraw datardqu...Show More
On-Line Learning of Evolving Neural Models for Process Identification and Abnormality Detection
Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007)
Year: 2007 | Conference Paper |
In this paper a new algorithm for creating evolving neural models is proposed. Instead of repeating the "growing" and "pruning" steps during the learning, as in the most other known evolving algorithms, here we create a growing neural model from a fixed size data buffer and repeatedly check the model quality, in the sense of "average minimal distance" between the neurons and the data in this buffe...Show More
On-Line Unsupervised Learning for Information Compression and Similarity Analysis of Large Data Sets
2007 International Conference on Mechatronics and Automation
Year: 2007 | Conference Paper |
Cited by: Papers (6)
The growing huge amount of information from the operations of complex processes and systems requires suitable methods for information compression. Therefore in this paper three unsupervised learning algorithms for information compression are proposed and analysed, namely the fixed-model learning (FML), the growing-model learning (GML) and the on-line model learning (OML) algorithms. They convert t...Show More
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