FCTA 2014 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 7
Title:

Fuzzy Signal Processing of Sound and Electromagnetic Environment by Introducing Probability Measure of Fuzzy Events
Authors:

Akira Ikuta and Hisako Orimoto
Abstract: The specific signal in real sound and electromagnetic waves frequently shows some very complex fluctuation forms of non-Gaussian type owing to natural, social and human factors. Furthermore, the observed data often contain fuzziness due to the existence of confidence limitation in measuring instruments, permissible error in experimental data, and the variety of human response to phenomena, etc. In this study, by introducing the probability measure of fuzzy events, static and dynamic signal processing methods based on fuzzy observations are proposed for specific signal in the sound and electromagnetic environment with complex probability distribution forms. The effectiveness of the proposed theoretical method is experimentally confirmed by applying it to estimation problems in the real sound and electromagnetic environment.
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Paper Nr: 11
Title:

Combination of Classifiers using the Fuzzy Integral for Uncertainty Identification and Subject Specific Optimization - Application to Brain-Computer Interface
Authors:

Francesco Cavrini, Lucia Rita Quitadamo, Luigi Bianchi and Giovanni Saggio
Abstract: In this paper we propose a framework for combination of classifiers using fuzzy measures and integrals that aims at providing researchers and practitioners with a simple and structured approach to deal with two issues that often arise in many pattern recognition applications: (i) the need for an automatic and user-specific selection of the best performing classifier or, better, ensemble of classifiers, out of the available ones; (ii) the need for uncertainty identification which should result in an abstention rather than an unreliable decision. We evaluate the framework within the context of Brain-Computer Interface, a field in which abstention and intersubject variability have a remarkable impact. Analysis of experimental data relative to five subjects shows that the proposed system is able to answer such needs.
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Paper Nr: 16
Title:

Fuzzy Inference System to Analyze Ordinal Variables - The Case of Evaluating Teaching Activity
Authors:

Michele Lalla, Davide Ferrari and Tommaso Pirotti
Abstract: The handling of ordinal variables presents many difficulties in both the measurements phase and the statistical data analysis. Many efforts have been made to overcome them. An alternative approach to traditional methods used to process ordinal data has been developed over the last two decades. It is based on a fuzzy inference system and is presented, here, applied to the student evaluations of teaching data collected via Internet in Modena, during the academic year 2009/10, by a questionnaire containing items with a four-point Likert scale. The scores emerging from the proposed fuzzy inference system proved to be approximately comparable to scores obtained through the practical, but questionable, procedure based on the average of the item value labels. The fuzzification using a number of membership functions smaller than the number of modalities of input variables yielded outputs that were closer to the average of the item value labels. The Center-of-Area defuzzification method showed good performances and lower dispersion around the mean of the value labels.
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Paper Nr: 18
Title:

An Order Hyperresolution Calculus for Gödel Logic with Truth Constants
Authors:

Dušan Guller
Abstract: We have generalised the well-known hyperresolution principle to the first-order G¨odel logic for the general case. This paper is a continuation of our work. We propose a modification of the hyperresolution calculus suitable for automated deduction with explicit partial truth. We expand the first-order G¨odel logic by a countable set of intermediate truth constants ¯ c, c 2 (0;1). Our approach is based on translation of a formula to an equivalent satisfiable finite order clausal theory, consisting of order clauses. An order clause is a finite set of order literals of the form e1  e2 where  is a connective either P or . P and  are interpreted by the equality and standard strict linear order on [0;1], respectively. We shall investigate the so-called canonical standard completeness, where the semantics of the first-order G¨odel logic is given by the standard G-algebra and truth constants are interpreted by themselves. The modified hyperresolution calculus is refutation sound and complete for a countable order clausal theory under certain condition for suprema and infima of sets of the truth constants occurring in the theory.
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Paper Nr: 23
Title:

A Performance Evaluation Model of a Job Title using Fuzzy Approach

Authors:

Hatice Esen, Tuğçen Hatipoğlu and Ali İhsan Boyacı
Abstract: Performance evaluation is described as comparing the performance of workers and the work standards and handling the necessary activities in a systematic way to attain these standards. What makes performance measure a necessity is its focus on performance of personnel as an objective measure of whether the company goes in the correct direction or not. This is because the most important problem encountered in organizations is the difficulty in the determination of how successful the personnel are in the satisfaction of their duties and what are their capabilities in their jobs. Besides performance evaluation is a decision making process which involves uncertainty. To overcome the uncertainty and evaluate the workers performance objectively, a performance evaluation model is developed of which the criteria are defined as the fuzzy numbers and the linguistic variables. The scope of the study is to determine the performance evaluation criteria of a purchasing specialist and weight for evaluating this job title.
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Paper Nr: 26
Title:

Measure of Roughness for Rough Approximation of Fuzzy Sets and Its Topological Interpretation
Authors:

Alexander Šostak
Abstract: We define the measure of upper and the measure of lower rough approximation for L-fuzzy subsets of a set equipped with a reflexive transitive fuzzy relation R. In case when the relation R is also symmetric, these measures coincide and we call their value by the measure of roughness of rough approximation. Basic properties of such measures are studied. A realization of measures of rough approximation in terms of L-fuzzy topologies is presented.
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Paper Nr: 27
Title:

Fuzzy Control of a Sintering Plant
Authors:

Marco Vannocci, Valentina Colla, Piero Pulito, Michele Zagaria, Vincenzo Di Mastromatteo and Marco Saccone
Abstract: Within an integrated steelwork, the industrial priorities in the automation of the sinter plant comprise stable production rate at the highest productivity level and classical control scheme may fail due to the complexity of the sinter process. The paper describes an approach exploiting a fuzzy rule-based expert system to control the charging gates of a sinter plant. Two different control strategies are presented and discussed within an innovative advisory system that supports the plant operators in the choice of the most promising action to do on each gate. Through the proposed approach the operators are supported by the system in the control of the plant: through a suitable exploitation of real-time data, the system suggests the most promising action to do, by reproducing the knowledge of the most expert operators. Thus, this approach can also be used to train new technicians before involving them in the actual plant operations. The performance of the strategies and the goodness of the system have been evaluated for long time in the sinter plant of one of the biggest integrated steelworks in Europe, namely the ILVA Taranto Works in Italy.
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Paper Nr: 30
Title:

Evaluating Relevant Opinions within a Large Group

Authors:

Ana Tapia-Rosero and Guy De Tré
Abstract: We propose to identify which opinions are relevant, from the decision-maker’s point of view, within a large group of opinions that could be collected using social media. Our approach considers that each participating person expresses his/her preferences over a criterion specification as a matter of degree. First, using a shape similarity method, we split a large group of opinions, where each opinion is represented through a membership function, into clusters —here, a cluster depicts a group of similar opinions over the criterion. Then, in order to evaluate the relevance of each cluster, we differentiate them based on some characteristics like the cohesion, the number of membership functions and the number of noticeable opinions. Within this paper, the cohesion of the cluster is a measure that takes into account the level of togetherness among its contained membership functions; and the representativeness of the cluster is obtained by combining the number of membership functions and the number of noticeable represented opinions (i.e., considered as more important or worthy of notice among other opinions). Moreover, relevant clusters result in the evaluation of combining their cohesion measure and their representativeness according to the decision-maker’s point of view. Finally, as a part of the evaluation, this proposal includes the steps describing the process through an illustrative example.
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Short Papers
Paper Nr: 10
Title:

Self-Organising Fuzzy Logic Control with a New On-Line Particle Swarm Optimisation-based Supervisory Layer
Authors:

M. Ehtiawesh and M. Mahfouf
Abstract: The Self-Organising Fuzzy Logic Control (SOFLC) which is an extended version of the Fuzzy logic controller was designed to make Fuzzy controllers work with less dependency on previous knowledge. Since the introduction of the SOFLC, only a few attempts have been made to create a performance index table that is responsible for the corrections of the low-level control ‘adaptable’ according to the dynamics of the process under control. In this paper a new dynamic supervisory layer is proposed which enables the controller to adapt its structure on-line to any given certain performance criteria. In this mechanism, the controller starts from an empty rule-base and uses an on-line Particle Swarm Optimisation (PSO) algorithm to adapt the cells of the performance index (PI) table while issuing control actions to the low-level fuzzy rule-base. The Simulation results achieved when the proposed scheme was tested on a non-linear muscle relation process showed that it is superior to the standard SOFLC scheme in terms of accurate tracking and efficient fuzzy rule-base elicitation (a conservative number of fuzzy rules)
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Paper Nr: 13
Title:

A Fuzzy Cognitive Map System to Explore Certain Scenarios on the Cyprus Banking System
Authors:

Maria Papaioannou, Costas Neocleous, Charalambos Papageorgiou and Christos N. Schizas
Abstract: The model of Fuzzy Cognitive Maps (FCMs) allows a user to investigate how the influencing parameters of a cause-effect system behave under the implementation of desired scenario. Human knowledge and experience is used to define the structure and the parameters of a FCM model. This paper proposes a methodology which smoothly directs the steps that the experts should take in building an FCM system in order to reduce the subjectivity which characterizes such expert systems. Furthermore, an update rule for the parameters during the simulation phase is presented. Finally, the novel FCM construction methodology and the proposed FCM update rule are tested on a real-life system to investigate the repercussions of the combination of the Greek private sector involvement (PSI) with the decrease of people’s confidence towards the Cyprus banking system. The results resemble what actually happened to the Cyprus economy a short period after the implementation of the Greek PSI.
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Paper Nr: 19
Title:

Enriching Traditional Databases with Fuzzy Definitions to Allow Flexible and Expressive Searches
Authors:

Victor Pablos-Ceruelo and Susana Muñoz Hernández
Abstract: Although the relevance of fuzzy information to represent concepts of real life is evident, almost all databases contain just crisp information. The main reason for this, apart from the tradition, is that fuzzy information is most of the times subjective and storing all users points of view is unfeasible. Allowing fuzzy concepts in the queries increases the queries' expressiveness and asking for cheap products, big size, close hotels, etc is much more interesting that asking for products with a price under X, of the size Y, hotels at most X kilometers far, etc. The way we propose for achieving this more expressive databases' queries is adding to the basic knowledge offered by a database (e.g. distance to hotel is 5 km) the link between this crisp concept and multiple fuzzy concepts that we use in real life (e.g. close hotel). We present FleSe, a framework for searching databases in a flexible way, thanks to the fuzzy concepts that we can define. In this paper we describe the easy procedure that let us define fuzzy concepts and link them to crisp database fields.
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Paper Nr: 25
Title:

Connotation-differential Prints - Comparing What Is Connoted Through (Fuzzy) Evaluations
Authors:

Marcelo Loor and Guy De Tré
Abstract: To evaluate the level to which an object belongs (or not) to a particular set, say A, one could focus on some object’s features according to what one understands by A. With this consideration, using the evaluations of a group of objects given by two persons, we want to determine the level to which their individual understandings of A match. Therefore, hypothesizing that a difference in understandings (or connotations) of A could be marked by a difference in one or more of the evaluations, we propose a connotation-differential print (CDP). A CDP is a representation of any difference in connotations of A between two persons in a form that makes itself available to computation. Additionally, we study how to use a CDP to extend a similarity measure for intuitionistic fuzzy sets in order to reach a meaningful comparison between two of them.
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Paper Nr: 37
Title:

Twodimensional Visualization of Discrete Time Domain Intervals Subject to Uncertainty
Authors:

Christophe Billiet and Guy De Tré
Abstract: One of the most important purposes of information systems is to allow human users to retrieve their data or information or knowledge derived from their data. These data may be subject to imperfections and often represent time indications, as time is an important part of reality. Representations of time indications rely on the information system's time domain. Obviously, the effectiveness of an information system in retrieval context depends greatly on the interpretability of the presentation of its data, information or knowledge. For that reason, such data, information or knowledge is usually visualized. The work presented in this paper proposes a novel approach to visualize time domain intervals subject to uncertainty and also shows how temporal reasoning with these visualizations can be done. The presented novel approach considers gradual confidence in the context of uncertainty and is specifically designed for time domain intervals.
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Paper Nr: 38
Title:

Fuzzy Rule Based Quality Measures for Adaptive Multimodal Biometric Fusion at Operation Time
Authors:

Madeena Sultana, Marina Gavrilova and Svetlana Yanushkevich
Abstract: Sample quality variation at operation time is one of the major concerns of real time biometric authentication and surveillance systems. Quality deviations of samples affect the performance of many benchmark biometric trait recognition systems. Moreover, large variation between enrolled and probe samples is very uncertain since it may arise at operation time for various reasons. In this paper, a novel adaptive multimodal biometric system is presented that can adapt the uncertainty of the quality degradation during operation. Fuzzy rule based method is applied for the first time to calculate the quality score of template-probe pairs dynamically. Feature extraction is accomplished using a novel shift invariant multi-resolution fusion approach. Finally, face and ear modalities are fused adaptively at rank level based on the quality scores. Proposed method relies more on good quality samples and disregards misclassification of poor quality samples. Experimental results demonstrate significant performance improvement of the proposed adaptive multimodal approach over baseline i.e. non-adaptive method.
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Paper Nr: 41
Title:

Suppression of Building Vibrations Using PD+PI Type Fuzzy Logic Controller
Authors:

Yuksel Hacioglu
Abstract: In order to bring the useful properties of PD and PI type fuzzy logic controllers together, a PD+PI type fuzzy logic controller for vibration suppression of a building was presented in this study. The building has nine storeys and an active tuned mass damper was placed on the top floor. The building model was excited with a real earthquake ground motion. The results have shown that designed controller attenuated the building vibrations successfully.
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Paper Nr: 42
Title:

Rough Approximations in Algebras of a Non-associative Generalization of the Łukasiewicz Infinite Valued Logic
Authors:

Jiří Rachůnek and Dana Šalounová
Abstract: Commutative basic algebras are non-associative generalizations of MV-algebras. They are an algebraic counterpart of a non-associative propositional logic which generalizes the Łukasiewicz infinite valued logic and which is related to reasoning under uncertainty. The paper investigates approximation spaces in commutative basic algebras based on their ideals.
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Paper Nr: 44
Title:

Fuzzy Function and the Generalized Extension Principle
Authors:

Irina Perfilieva and Alexandr Šostak
Abstract: The aim of this contribution is to develop a theory of such concepts as fuzzy point, fuzzy set and fuzzy function in a similar style as is common in classical mathematical analysis. We recall some known notions and propose new ones with the purpose to show that, similarly to the classical case, a (fuzzy) set is a collection of (fuzzy) points or singletons. We show a relationship between a fuzzy function and its ordinary “skeleton” that can be naturally associated with the original function. We show that any fuzzy function can be extended to the domain of fuzzy subsets and this extension is analogous to the Extension Principle of L. A. Zadeh.
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Paper Nr: 47
Title:

ANFIS Traffic Signal Controller for an Isolated Intersection
Authors:

Sahar Araghi, Abbas Khosravi and Douglas Creighton
Abstract: Traffic signal controlling is one of the solutions to reduce the traffic congestion in cities. To set appropriate green times for traffic signal lights, we have applied Adaptive Neuro-Fuzzy Inference System (ANFIS) method in traffic signal controllers. ANFIS traffic signal controller is used for controlling traffic congestion of a single intersection with the purpose of minimizing travel delay time. The ANFIS traffic controller is an intelligent controller that learns to set an appropriate green time for each phase of traffic signal lights at the start of the phase and based on the traffic information. The controller uses genetic algorithm to tune ANFIS parameters during learning time. The results of the experiments show higher performance of the ANFIS traffic signal controller compared to three other traffic controllers that are developed as benchmarks. One of the benchmarks is GA-FLC (Araghi et al., 2014), next one is a fixed-FLC, and a fixed-time controller with three different values for green phase. Results show the higher performance of ANFIS controller.
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Paper Nr: 48
Title:

Towards Unsupervised Word Error Correction in Textual Big Data
Authors:

Joao Paulo Carvalho and Sérgio Curto
Abstract: Large unedited technical textual databases might contain information that cannot be properly extracted using Natural Language Processing (NLP) tools due to the many existent word errors. A good example is the MIMIC II database, where medical text reports are a direct representation of experts’ views on real time observable data. Such reports contain valuable information that can improve predictive medic decision making models based on physiological data, but have never been used with that goal so far. In this paper we propose a fuzzy based semi-automatic method to specifically address the large number of word errors contained in such databases that will allow the direct application of NLP techniques, such as Bag of Words, to the textual data.
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Paper Nr: 51
Title:

Aggregation of Descriptive Regularization and Fuzzy Logic Techniques for Enhanced Remote Sensing Imaging
Authors:

A. Castillo Atoche, O. Palma Marrufo and R. Peon Escalante
Abstract: In this paper, the aggregation of the descriptive regularization and Fuzzy-Logic techniques is proposed for the enhancement/reconstruction of the power spatial spectrum pattern (SSP) of the wave field scattered from remotely sensed scenes. In particular, the Weighted Constrain Least Square (WCLS) and the Fuzzy anisotropic diffusion techniques are algorithmically adapted and implemented in a parallel fashion using commodity graphic processor units (GPUs) improving the time performance of real-time remote sensing applications. Experimental results show the performance efficiency both in resolution enhancement and in computational complexity reduction metrics with the presented approach.
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Paper Nr: 53
Title:

An Operational Semantics for XML Fuzzy Queries
Authors:

Alessandro Campi, Sam Guinea and Paola Spoletini
Abstract: XML has become a widespread format for data exchange over the Internet. The current state of the art in querying XML data is represented by XPath and XQuery, both of which define binary predicates. In this paper, we advocate that binary selection can at times be restrictive due to very nature of XML, and to the uses that are made of it. We therefore suggest a querying framework, called FXPath, based on fuzzy logics. In particular, we propose the use of fuzzy predicates for the definition of more ``vague'' and softer queries. We also introduce a function called ``deep-similar'', which aims at substituting XPath's typical ``deep-equal'' function. Its goal is to provide a degree of similarity between two XML trees, assessing whether they are similar both structure-wise and content-wise. In this paper we present the formal syntax and semantics of Fuzzy XPath, and discuss implementation issues
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Paper Nr: 54
Title:

Bloodstain Pattern Analysis - A New Challenge for Computational Intelligence Community
Authors:

Giovanni Acampora, Autilia Vitiello, Ciro Di Nunzio, Maurizio Saliva and Luciano Garofano
Abstract: Bloodstain pattern analysis (BPA) is a forensic discipline that plays a key role in tracing events which caused a bloodshed at a crime scene. Indeed, BPA supports worldwide investigation agencies (US FBI, Italian Carabinieri and so on) in interpreting the morphology and distribution of bloodspots at a crime scene in order to enable a potentially complete reconstruction of the dynamics of the act of violence with a consequent identification of potential suspects for that crime. However, in spite of its importance, this forensic discipline is still based on completely manual approaches, making the analysis of a crime scene long, tedious and potentially imperfect. This position paper is aimed at proving that computational intelligence methodologies can be efficiently integrated with image processing techniques to support forensic investigators in increasing their performance in examining bloodstains, both in terms of time and accuracy of analysis. A preliminary study involving the application of fuzzy clustering has been carried out in order to validate our opinion and stimulate computational intelligence community to face this new challenge towards a formal definition of Forensic Intelligence.
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Paper Nr: 4
Title:

An Analytical Approach to Evaluating Bivariate Functions of Fuzzy Numbers with One Local Extremum
Authors:

Arthur Seibel and Josef Schlattmann
Abstract: This paper presents a novel analytical approach to evaluating continuous, bivariate functions of independent fuzzy numbers with one local extremum. The approach is based on a parametric a-cut representation of fuzzy numbers and allows for the inclusion of parameter uncertainties into mathematical models.
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Paper Nr: 24
Title:

Supplier Selection Using Fuzzy Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP)
Authors:

Ali İhsan Boyacı, Tuğçen Hatipoğlu and Hatice Esen
Abstract: The increasing competition forces companies to use the capital more effectively and using suppliers which operate cheaper and with higher quality. Due to that, it is crucial to select the right suppliers. Supplier selection is a decision making problem that involves quantitative and non-quantitative, conflicting criteria. In The Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), all the decision data are known precisely or given as crisp values. But the uncertainty in the real life problems makes decision making more difficult. İn these situations, the complex situation varying with respect to decision makers can be solved by Fuzzy logic. Because of that, Fuzzy LINMAP has been used to solve the problem. The main aim of this study is to provide an analytical approach to decision makers for them to make objective decisions. Thus, supplier alternatives and selection criteria are determined. And a fuzzy LINMAP model is developed for supplier evaluation and selection of a company in automotive sector.
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Paper Nr: 43
Title:

A New Adaptive Universal Fuzzy Inference System with Application
Authors:

Yuan Yuan Chai, Jun Chen, Wei Luo and Li Min Jia
Abstract: Through comprehensive study on existing fuzzy inference systems, this paper presents a Choquet integral-OWA operator based fuzzy inference system (AggFIS) in order to solve the traditional FIS disadvantages and its adaptive model which is named Choquet integral-OWA operator based adaptive universal fuzzy inference system (Agg-AUFIS). By considering the universal fuzzy inference operators and importance factor during reasoning process, Agg-AUFIS tries to express the essence of fuzzy logic and simulate human thinking pattern sufficiently, which could provide a new methodology for fuzzy modeling in future.
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Paper Nr: 50
Title:

Computational Intelligence in a Classification of Audio Recordings of Nature
Authors:

Krzysztof Tyburek, Piotr Prokopowicz and Piotr Kotlarz
Abstract: This paper presents different ways for a classification of sounds of birds using linguistic approach with a fuzzy system, neural network and WEKA system. Features of sounds of birds species are coded by the selected MPEG-7 descriptors. The models of classification system are based on the audio descriptors for a some chosen species of birds like: Corn Crake, Hawk, Blackbird, Cuckoo, Lesser Whitethroat, Chiffchaff, Eurasian Pygmy Owl, Meadow Pipit, House Sparrow, Firecrest. The paper proposes fuzzy models that definitely bases on the linguistic description. Moreover neural network for classification was proposed. As reference results WEKA system is used.
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Paper Nr: 52
Title:

Overall Equipment Effectiveness and Overall Line Efficiency Measurement using Fuzzy Inference Systems
Authors:

Hasan Moradizadeh and Rene V. Mayorga
Abstract: Increasingly, Intelligent Systems (IS) techniques are being used to solve both complex problems and industrial problems with uncertainty. They also can implement the operator’s knowledge (experience) into the system. This Paper aims to improve and compute the well-known manufacturing metrics: the Overall Equipment Effectiveness (OEE), and Overall Line Efficiency (OLE), using IS techniques. The proposed methodologies to improve the OEE and OLE weakness are based on Fuzzy Inference Systems. These techniques result in an effective way to measure OEE and OLE considering different weight of losses and also the difference in machine’s weight factors. Moreover, they allow the operator’s knowledge to be taken into account in the measurement using uncertain input and output with implementation of linguistic terms.
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