V. PETRIDIS

Aristotle University of Thessaloniki | AUTH · School of Electrical and Computer Engineering

Mobile robots need an environmental perception ability in order to interact with the surrounding environment. In this paper we present the G-SLAM method, where the map is consisted of a cloud of scattered points in the continuous space and each point is accompanied by an obstacle existence probability. On the other hand the robot's pose (and trajec...

Environment perception is a crucial ability for robot's interaction into an environment. One of the first steps in this direction is the combined problem of simultaneous localization and mapping (SLAM). A new method, called G-SLAM, is proposed, where the map is considered as a set of scattered points in the continuous space followed by a probabilit...

A workbench for intrinsic evolution of digital circuits is presented, based on a Cartesian Genetic Programming algorithm running on a personal computer and a reconfigurable platform suitable for run-time reconfiguration. Two types of Cartesian cell structures are proposed, based on a cylindrical interconnection grid. In addition to a feed-forward n...

This work presents a method for simulating asynchronous digital circuits, of both combinational and sequential logic, at the gate level. The simulator is going to serve as a fitness function of an Evolutionary Algorithm that will be used for optimal synthesis of digital circuits. Therefore the simulator needs to be simple, fast and reliable. The ci...

This work presents a method for simulating asynchronous digital circuits, of both combinational and sequential logic, at the gate level. The simulator is going to serve as a fitness function of an Evolutionary Algorithm that will be used for optimal synthesis of digital circuits. Therefore the simulator needs to be simple, fast and reliable. The ci...

Vascular tissue characterization is of great importance concerning the possibility of an Acute Cardiac Syndrome (ACS). Gray-scale intravascular ultrasound (IVUS) is a powerful tomographic modality providing a thorough visualization of coronary arteries. Among the existing methods, virtual histology (VH) is the most popular and clinically available...

In this paper, a new Simultaneous Localization and Mapping (SLAM) method is proposed, called L-SLAM, which is a Low dimension version of the FastSLAM family algorithms. L-SLAM uses a particle filter of lower dimensionality than FastSLAM and achieves better accuracy than FastSLAM 1.0 and 2.0 for a small number of particles. L-SLAM is suitable for hi...

Environment perception is a crucial ability for robot's interaction into an environment. One of the first steps in this direction is the combined problem of simultaneous localization and mapping (SLAM). A new method, called G-SLAM, is proposed, where the map is considered as a set of scattered points in the continuous space followed by a probabilit...

This paper proposes the application of a genetic fuzzy rule-based classification system (GFRBCS) for tissue characterization of intravascular ultrasound (IVUS) images. The presented approach follows the IVUS Virtual Histology (IVUS-VH) plaque characterization technique, whereby the plaque region is classified into four primary tissue types, namely,...

A novel fuzzy decision tree is proposed in this paper (the FDT-support vector machine (SVM) classifier), where the node discriminations are implemented via binary SVMs. The tree structure is determined via a class grouping algorithm, which forms the groups of classes to be separated at each internal node, based on the degree of fuzzy confusion betw...

The current paper focuses on the implementation of hybrid expert systems for audiovisual content description and management, by means of pattern analysis. The proposed methodology combines audio detection–segmentation, surveillance-video motion-detection and hierarchical audio pattern recognition, using neural networks, statistical clustering and s...

This work describes a computational approach for a typical machine-vision application, that of human action recognition from video streams. We present a method that has the following advantages: (a) no human intervention in pre-processing stages, (b) a reduced feature set, (c) modularity of the recognition system and (d) control of the model’s comp...

FastSLAM is one of the state-of-the-art approaches to the Simultaneous Localization and Mapping (SLAM) problem. In this paper, a new SLAM method is proposed, called L- SLAM, which is a low dimension version of the FastSLAM family algorithms. Dimensionality reduction of the particle filter is proposed, achieving better accuracy with less or the same...

In this paper, a new SLAM method is proposed, called L-SLAM. It is a low dimension version of the FastSLAM family algorithms. The proposed method reduces the dimensionality of the particle filter that FastSLAM algorithms use, while achieving better accuracy with less or the same number of particles. Dimensionality reduction of this problem is the k...

This paper deals with the issue of gradual classification of a multivariate sequence where the number of candidate time-series generators is significantly high. It proposes a prediction scheme that consists of two components: a hierarchical structure which organizes the time-series models and a decision maker tool that assigns and evolves a respect...

The objective of this study is to investigate alternative ways for representing suitably, with the fewest possible assumptions, the information derived from video recordings. It proposes a set of statistical descriptors capable of summarizing all the available information from each video frame. A sequence of such features expresses the object motio...

The aim of the present study is to validate a 2D kinematic model of human body in providing considerable features that they could be used for human actions classification. Human motion can be termed as a non-rigid, articulated motion, with body parts being piecewise rigid, held together by joints. The presented approach uses the fact that the human...

A novel self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) is suggested in this paper which is composed of small-scale interconnected fuzzy neuron classifiers (FNCs) arranged in layers. The model provides a different perspective for generating a new class of hierarchical classifiers with multilevel classifiers combination. At each layer...

We have presented a text segmentation algorithm following a supervised approach which we applied to the segmentation of Greek texts. On greek text collection our algorithm outperforms Choi's and Utiyama's algorithms. This is largely important particularly in the case of texts exhibiting strong variation as far as the average length is concerned. Le...

A novel formalism is presented, which enables the processing of heterogeneous data including numeric-, interval-valued-, or fuzzy-data. The data in question are represented herein as interval-supported fuzzy sets with suitable membership functions. The term Fuzzy Interval Number (FIN) denotes one of the aforementioned types of data. A FIN can have...

This work describes a two-mode clustering hierarchical model capable of dealing with high dimensional data spaces. The algorithm seeks a transformed subspace which can represent the initial data, simplify the problem and possibly lead to a better categorization level. We test the algorithm on two hard classification problems, the phoneme and the pe...

In this paper, we present a novel approach for 3D facial expression recognition which is inspired by the advances of ant colony and particle swarm optimization (ACO and PSO respectively) in the field of data mining. Anatomical correspondence between faces is first established using a generic 3D face model which is deformed elastically to match the...

This work focuses on the implementation of an autonomous system appropriate for long-term, unsupervised monitoring of bowel sounds, captured by means of abdominal surface vibrations. The autonomous intestinal motility analysis system (AIMAS) promises to deliver new potentials in gastrointestinal auscultation, towards the establishment of novel non-...

We introduce a dynamic programming algorithm to perform linear segmentation of concatenated texts by global minimization of a segmentation cost function which consists of: (a) within-segment word similarity (expressed in terms of the generalized density of the text dotplot) and (b) prior information regarding segment length. Our algorithm is evalua...

An enhanced memory TSK-type recurrent fuzzy network (EM-TRFN) is proposed in this paper, suitable for modeling complex dynamic systems. Feedback connections, formulated using finite impulse response (FIR) synaptic filters, are employed in the network architecture, serving as internal memories of multiple past firing values, used to determine the cu...

Summary. In this paper we investigate the issue of wind speed prediction at a particular location in the urban area of Thessaloniki, Greece, based on the historical data containing wind parameter values at two other di.erent locations. We evaluate the performance of two signi.cant machine learning methodologies, the Fuzzy Lattice Neurocomputing (FL...

The procedure of harmonic insertion is generalized in this paper. Analytical expressions of the voltage spectra are derived. The insertion of the 3rd harmonic to the modulating signal, which is of particular interest, is considered as a special case. It is demonstrated in this case that the fundamental harmonic component of the phase and the line-t...

Without Abstract

We present an evolutionary computational model for the emergence of binary words that denote shapes, positions and directions of camera-captured drawings. The emergence takes places in separate stages for each category. An additional evolutionary stage allows the evolution of a signal-ordering (parsing) convention among the agents

We present a procedure for the segmentation of hydrological and environmental time series. The procedure is based on the minimization of Hubert’s segmentation cost or various generalizations of this cost. This is achieved through a dynamic programming algorithm, which is guaranteed to find the globally optimal segmentations with K=1, 2, ..., K max...

We introduce a multi-model parameter estimation method for nonlinear dynamic systems. The method employs a genetic search with a recursive probability selection mechanism for parameter estimation. The method is applied to nonlinear systems with known structure and unknown parameters. A new technique is used to determine the selection probabilities....

Different optimisation methods developed for the solution of the nonlinear OPF problem with both continuous and discrete variables are compared. Two mathematical programming methods are compared with two metaheuristics, an enhanced genetic algorithm and a particle swarm optimisation implementation. Test results from the application of the methods t...

This paper addresses the problem of wind speed prediction at a particular location in the urban area of Thessaloniki, Greece, based on a data set containing wind parameter values at two other different locations. The problem is formulated and processed, and clusters in the data domain are identified by means of the Fuzzy Lattice Neurocomputing (FLN...

We present a methodology by which multiword communication could one day be realized in a population of evolving Artificial Life agents. The method which combines genetic algorithms and artificial neural networks has the prospect of a gradual increase of communication complexity (multi-word communication) through a series of evolutionary phases that...

A straightforward method for the construction of Lyapunov functions represented by neural networks is presented in this paper. The resulting neural networks are Lyapunov functions on the basis of which asymptotic stability or instability of a nonlinear system's equilibrium point can be mathematically proven. One of the main advantages of this metho...

This paper presents a comparison of different optimization methods developed for the solution of the nonlinear OPF problem with both continuous and discrete variables. Two mathematical programming methods are compared with two metaheuristics, a particle swarm optimization implementation and an enhanced genetic algorithm. Test results from the appli...

This paper presents an evolutionary communication model that makes use of neural networks and evolutionary computation. It is embedded in a simple two-dimensional artificial world. The work builds on the successful approach of Cangelosi (1997) but is designed with considerations of suitability for extensions of more elaborate communication.

In this paper, a genetic algorithm solution to the hydrothermal coordination problem is presented. The generation scheduling of the hydro production system is formulated as a mixed-integer, nonlinear optimization problem and solved with an enhanced genetic algorithm featuring a set of problem-specific genetic operators. The thermal subproblem is so...

In this paper, we use Barry and Hartigan's Product Partition Models to formulate text segmentation as an optimization problem, which we solve by a fast dynamic programming algorithm. We test the algorithm on Choi's segmentation benchmark and achieve the best segmentation results so far reported in the literature.

Most of the text categorization algorithms in the literature represent documents as collections of words. An alternative which has not been sufficiently explored is the use of word mcanin#s, also known as senses. In this paper, using several algorithms, we compare the categorization accuracy of classifiers based on words to that of classifiers base...

The authors reply to the comments made and the questions asked by Yog Raj Sood et al. (see ibid., vo.18, no.3, p.1219, 2003) relative to the original paper (see ibid., vol 17, p.229-36, 2002).

This paper presents the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for Short Term Load Forecasting (STLF) based on the combination of an artificial neural network (ANN) predictor and two linear regression (LR) predictors. The method is applied to STLF for the Greek Public Power Corporation dispatching center of the i...

In this paper we introduce a dynamic programming algorithm which performs linear text segmentation by global minimization of a segmentation cost function which incorporates two factors: (a) within-segment word similarity and (b) prior information about segment length. We evaluate segmentation accuracy of the algorithm by precision, recall and Beefe...

In this paper we introduce a dynamic programming algorithm to perform linear text segmentation by global minimization of a segmentation cost function which consists of: (a) within-segment word similarity and (b) prior information about segment length. The evaluation of the segmentation accuracy of the algorithm on Choi's text collection showed that...

This paper presents an interactive-, menu-driven prototype software platform, namely automatic control educational software (ACES), for self-instruction and self-evaluation in automatic control systems. ACES is used for enriching instruction in automatic control at Aristotle University of Thessaloniki, Greece, in the Department of Electrical and Co...

This work introduces FINkNN, a k-nearest-neighbor classifier operating over the metric lattice of conventional interval-supported convex fuzzy sets. We show that for problems involving populations of measurements, data can be represented by fuzzy interval numbers (FINs) and we present an algorithm for constructing FINs from such populations. We the...

The set F of Fuzzy Interval Numbers (FINs) is studied analytically in this work. A FIN is a set of "pulse-shaped" functions, namely generalized intervals. A FIN can be interpreted as a conventional convex fuzzy set; nevertheless a FIN can have either a positive or a negative membership function. The set of generalized intervals of height h is shown...

Fairly rapid environmental changes call for continuous surveillance and decision making, areas where IT technologies can be valuable. In the aforementioned context this work describes the application of a novel classifier, namely σ-FLNMAP, for estimating the ozone concentration level in the atmosphere. In a series of experiments on meteorological a...

In this paper we present a framework for modelling Simple and Parallel Evolutionary Algorithm implementations as Co-operating Populations. Using this framework, a method called Co-operating Populations with Different Evolution Behaviours (CoPDEB), for generalizing and improving the performance of Parallel Evolutionary Algorithms (PEAs) is also pres...

The accurate, and timely prediction of the annual sugar-beet crop yield is important to Sugar Industry because, based on it, the “harvest campaign” can be scheduled efficiently. This work presents intelligent clustering techniques for effecting efficient, small error prediction of the annual sugar-beet crop yield for the Hellenic Sugar Industry bas...

In this paper we present a promising technique that enhances the efficiency of GAs, when they are applied to constrained optimisation problems.

This paper presents an enhanced genetic algorithm for the solution of the optimal power flow with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of...

In this paper, we explore some aspects of the problem of online unsupervised learning of a switching time series, i.e., a time series which is generated by a combination of several alternately activated sources. This learning problem can be solved by a two-stage approach: 1) separating and assigning each incoming datum to a specific dataset (one da...

A novel corner detection algorithm is presented which can be used to camera calibration methods where square comers are used as control points. Corners are detected with sub-pixel accuracy, using a segmentation method for separation of each square, based on seeds. These are pixels with a predefined color or gray value. An 11×11 proper developed tem...

A novel theoretical framework is delineated for supervised and unsupervised learning. It is called framework of fuzzy lattices, or FL-framework for short, and it suggests mathematically sound tools for dealing separately and/or jointly with disparate types of data including vectors of numbers, fuzzy sets, symbols, etc. Specific schemes are proposed...

A switching dynamical system is a composite system comprising of a number of sub-systems, where, at every time step, there is a certain probability that a particular sub-system will be switched on. Identi cation of the composite system involves: (a) specifying the number of active sub-systems, (b) separating the observed data into groups, one group...

: This paper presents the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for Short Term Load Forecasting (STLF) based on the combination of an artificial neural network (ANN) predictor and two linear regression (LR) predictors. The method is applied to STLF for the Greek Public Power Corporation dispatching center of the...

In this paper we examine a problem which arises in connection with the application of the Lainiotis Partition Algorithm to tasks of signal classification, prediction and parameter estimation. We are particularly interested in tasks which involve composite systems, comprising of a finite number of switched sub-systems. The problem we consider arises...

Consider a switching time series, produced by several randomly activated sources. The separation of incoming data into distinct classes may be effected using predictive modular neural networks, where each module is trained on data from a particular source. We present a mathematical analysis regarding the convergence of a quite general class of comp...

We introduce a recurrent network architecture to solve a parameter estimation problem; namely we want to estimate the rotor resisatnce of an AC induction motor. A precise estimate of this parameter is very useful for accurate and economical control of the AC induction motor. We propose the Incremental CRedit Assignment (ICRA) method for testing onl...

Introduction Modular neural networks can be used for the solution of learning problems which involve data generated by several alternately activated sources. A well trained module models a particular source. A well trained modular neural network is characterized by a one-to-one association of sources to well trained modules (such that each module m...

In this paper we present an application of predictive modular neural networks (PREMONN) to short term load forecasting. PREMONNs are a family of probabilistically motivated algorithms which can be used for time series prediction, classification and identification. PREMONNs utilize local predictors of several types (e.g. linear predictors or artific...

We investigate the potential of a microgenetic algorithm (MGA) as a generalized hill-climbing operator. Combining a standard GA with the suggested MGA operator leads to a hybrid genetic scheme GA-MGA, with enhanced searching qualities. The main GA performs global search while the MGA explores a neighborhood of the current solution provided by the m...

In this paper we present the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for time series prediction. BCP utilizes local predictors of several types (e.g., linear predictors, artificial neural network predictors, polynomial predictors etc.) and produces a final prediction which is a weighted combination of the local pr...

A connectionist scheme, namely, σ-Fuzzy Lattice Neurocomputing scheme or σ-FLN for short, which has been introduced in the literature lately for clustering in a lattice data domain, is employed for computing clusters of directed graphs in a master-graph. New tools are presented and used, including a convenient inclusion measure function for cluster...

In this paper, an orthogonal least-squares (OLS) based modeling method is developed, named the constrained OLS (C-OLS), for generating simple and efficient TSK fuzzy models. The method is a two-stage model building technique, where both premise and consequent identification are simultaneously performed. The fuzzy system is considered as a linear re...

A neural network, namely the sigma fuzzy lattice neural network with mapping (σ-FLNMAP), is presented and applied to classification of text (documents) from the Brown Corpus benchmark collection of documents. The σ-FLNMAP is presented as an enhanced extension of the fuzzy-ARTMAP neural network in the framework of fuzzy lattices. An individual σ-FLN...

In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the capacity to deal rigorously with: disparate types of data such as numeric and linguistic data, intervals of values, 'missing' and 'don't care' data. A novel notation for t...

A novel theoretical framework is delineated for supervised and unsupervised learning. It is called framework of fuzzy lattices, or FLframework for short, and it suggests mathematically sound tools for dealing separately and/or jointly with disparate types of data including vectors of numbers, fuzzy sets, symbols, etc. Specific schemes are proposed...

This work reports results regarding the recognition of soft tissues in the Epidural surgical procedure by a novel scheme implementable on a surgical mechatronics tool. The data employed are Raman spectra of soft tissues. A whole Raman spectrum is treated here as a single datum. Soft tissue recognition is achieved using the novel "Voting s-FLNMAP" s...

Stapedotomy is a surgical procedure aimed at the treatment of hearing impairment due to otosclerosis. The treatment consists of drilling a hole through the stapes bone in the inner ear in order to insert a prosthesis. Safety precautions require knowledge of the nonmeasurable stapes thickness. The technical goal herein has been the design of high-le...

A basis for rigorous versatile learning is introduced theoretically, that is the framework of fuzzy lattices or FL-framework for short, which proposes a synergetic combination of fuzzy set theory and lattice theory. A fuzzy lattice emanates from a conventional mathematical lattice by fuzzifying the inclusion order relation. Learning in the FL-frame...

In this paper we present two advanced methods for evolutionary optimisation. One method is based on Parallel Genetic Algorithms. It is called Co-operating Populations with Different Evolution Behaviours (CoPDEB), and allows each population to exhibit a different evolution behaviour. Results from two problems show the advantage of using different ev...

This paper presents some experimental results on the realization of a parallel simulation of an Artificial Neural Network (ANN) on a Transputer based system. The ANN training method chosen is the BackPropagation algorithm and the parallelization technique utilized is the Processor Farm based on the partition of the training patterns between the ava...

A mathematically tractable methodology is proposed for system modeling based on heterogeneous data, these are jointly numeric and linguistic data. It is illustrated how the data in question can be represented as interval supported fuzzy sets, namely fuzzy interval numbers (FINs), with either positive or negative membership functions. Emphasis is gi...

Accurate short term load forecasting (STLF) is a necessary part of resource management for a power generation company. The more precise the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Significant forecasting errors can lead to either overly conservative or overly risky sc...

We present a specific varying fitness function technique in genetic algorithm (GA) constrained optimization. This technique incorporates the problem's constraints into the fitness function in a dynamic way. It consists of forming a fitness function with varying penalty terms. The resulting varying fitness function facilitates the GA search. The per...

A switching time series is the outcome of several different sources, the sources being activated in succession. The problem of on-line, unsupervised learning of switching time series, consists in separating incoming data and corresponding a separate class corresponding to each source; then developing a distinct model for each class of data. This is...

: In this paper we present several multiple model combination methods, utilizing neural as well as linear predictors, to predict sugar beet crop yield. The results are superior to previous prediction methods which used only neural network or only linear regresison predictors. Keywords: Prediction, Agriculture, Neural Networks. Abstract. Key Words....

In this paper we present a promising technique that enhances the efficiency of GAs, when they are a pplied to constrained optimisation p rob- lems. According to this technique, the problem constraints are included in the fitness function as penalty terms, that vary during the GA evolution, facilitat- ing thus the location of the global optimum and...

The two-level fuzzy-lattice (2L-FL) learning scheme is introduced for application on an intelligent surgical (mechatronic) drill in the stapedotomy surgical procedure in the ear. The 2L-FL scheme learned from past cases to evaluate pointedly the thickness of a stapes bone using a force/torque pair of drilling profiles. Hence it is possible, in prin...

We present a new on-line multi-model algorithm for parameter estimation of time-varying nonlinear systems. The time variation is captured by assuming that the system parameters change according to a Markovian mechanism. The algorithm postulates a finite number of possible values of the system parameter and computes recursively the credit function o...

A switching dynamical system is a composite system comprising of a number of subsystems, where, at every time step, there is a certain probability that a particular subsystem will be switched on. Identification of the composite system involves: (a) specifying the number of active subsystems, (b) separating the observed data into groups, one group c...

We introduce a hybrid neural-genetic multimodel parameter estimation algorithm. The algorithm is applied to structured system identification of nonlinear dynamical systems. The main components of the algorithm are 1) a recurrent incremental credit assignment (ICRA) neural network, which computes a credit function for each member of a generation of...