FCTA 2020 Abstracts

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

Fuzzy Confidence Intervals by the Likelihood Ratio with Bootstrapped Distribution
Authors:

Rédina Berkachy and Laurent Donzé
Abstract: We propose a complete practical procedure to construct a fuzzy confidence interval by the likelihood method where the observations and the hypotheses are considered to be fuzzy. We use the bootstrap technique to estimate the distribution of the likelihood ratio. For this step of the process, we mainly expose two algorithms: the first one consists on simply randomly drawing the bootstrap samples, and the second one is based on drawing observations by preserving the location and dispersion measures of the primary data set. This is achieved in accordance with a new metric written as dθ? SGD. It is built on the basis of the known signed distance measure. We also provide a simulation study to measure the performance of both bootstrap algorithms and their influence on the constructed confidence intervals. We illustrate our method via a numerical application where we construct fuzzy confidence intervals by the traditional and the defended methods. The aim is to highlight important differences between them.
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Paper Nr: 8
Title:

Merging Partial Fuzzy Rule-bases
Authors:

Martina Daňková
Abstract: We propose two basic ways of merging various partial fuzzy rule-bases containing knowledge related to the same process or dependency in general. The knowledge that is not at the disposal is considered undefined and encoded using some dummy value. For simplicity, we use only one code for undefined membership value, and we handle the undefined membership values using operations of variable-domain fuzzy set theory, i.e., the theory that allows fuzzy sets to have undefined membership values. Moreover, we study one of the essential properties in fuzzy modeling–a graded property of functionality. We provide estimations for degrees of the functionality of input models and merged models of partial fuzzy rule-bases.
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Paper Nr: 14
Title:

The Implementation of a Product Fuzzy DPLL Solver
Authors:

Ivor Uhliarik
Abstract: The area of automated theorem proving in fuzzy logics has been revisited during the last decade with novel approaches, mathematical foundations, and software. However, only a few of these are capable of dealing with logics based on the product t-norm. The existing methods are usually based on (1) translations into satisfiability modulo theories, (2) evolutionary algorithms, and (3) fuzzy extensions of classical-logic procedures, such as the Davis-Putnam-Logemann-Loveland (DPLL) procedure. In this paper we present the results of our work on the first DPLL-based solver for product propositional logic extended with the Monteiro-Baaz ∆ operator and order (≺,≖) operators. Our contribution consists of the refinement and completion of our previously proposed deterministic algorithm and the working implementation of the solver. Comparing to other approaches, the essential difference of ours lies in its self-containment—it is not based on translations into other systems, which provides possibilities for feasible modifications or further optimizations. The solver yields answers to the 1-satisfiability and validity problems, and is available for download and use as free and open-source software.
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Paper Nr: 15
Title:

Rough Continuity Represented by Intuitionistic Fuzzy Sets
Authors:

Zoltán E. Csajbók
Abstract: Studying rough calculus was initiated by Z. Pawlak in his many papers. He originated the concept of rough real functions. Like the notion of continuity in classical analysis, the rough continuity is also a central notion in rough calculus. Relying on the Pawlak’s approximation spaces on the real closed bounded intervals, first, two intuitionistic fuzzy sets are established starting from rough functions. Then, based on them, some necessary and sufficient conditions for the rough continuity in terms of intuitionistic fuzzy set theory will be presented.
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Short Papers
Paper Nr: 4
Title:

Researching the Efficiency of Configurations of a Collective Decision-making System on the Basis of Fuzzy Logic
Authors:

Anastasiya Polyakova, Lipinskiy Leonid and Eugene Semenkin
Abstract: Collective decision-making systems (or ensembles) based on fuzzy logic have proven their effectiveness in a number of test and practical tasks. However, the problem of configuring the system and forming the main operators remains unsolved. In this paper is a study of the effectiveness of different sequences of applying optimization procedures for the formation of the main operators of a collective decision-making system based on fuzzy logic. The effectiveness of tuning schemes for a collective decision-making system is investigated using the problem of restoring the cryolite ratio and the content of calcium and magnesium fluorides. It is shown in the research that an effective choice of the sequence of applying optimization procedures for tuning and forming the main operators can significantly increase the overall efficiency of the system.
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Paper Nr: 11
Title:

A Diffusion Dimensionality Reduction Approach to Background Subtraction in Video Sequences
Authors:

Dina Dushnik, Alon Schclar, Amir Averbuch and Raid Saabni
Abstract: Identifying moving objects in a video sequence is a fundamental and critical task in many computer-vision applications. A common approach performs background subtraction, which identifies moving objects as the portion of a video frame that differs significantly from a background model. An effective background subtraction algorithm has to be robust to changes in the background and it should avoid detecting non-stationary background objects such as moving leaves, rain, snow, and shadows. In addition, the internal background model should quickly respond to changes in background such as objects that stop or start moving. We present a new algorithm for background subtraction in video sequences which are captured by a stationary camera. Our approach processes the video sequence as a 3D cube where time forms the third axis. The background is identified by first applying the Diffusion Bases (DB) dimensionality reduction algorithm to the time axis and then by applying an iterative method to extract the background.
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Paper Nr: 12
Title:

Using Fuzzy Inputs to Analyze Factors in the Adoption of Electric Vehicles (EVs)
Authors:

Arnab Sircar
Abstract: This research applies a set of mathematical techniques to a setting where precise values cannot be obtained for opinions from experts. In order to demonstrate the applicability of these techniques, a research study was designed to measure the importance of factors responsible for increased usage and adoption of electric vehicles (EVs). In the design, various factors were considered where their measured values were subjective since in such situations, the factors are not like typical variables that occur naturally. Further, these measured values may also be imprecise. So, the idea of fuzzy numbers and fuzzy sets were utilized to capture measured values of these factors. Twelve factors were identified under three different categories of environment and sustainability, performance and efficiency, and design and manufacture. Then, fuzzy inputs were sought from six experts as a means of measuring the importance of these twelve factors. The fuzzy numbers from the six experts were aggregated using a similarity-based method and ranked based on a concept of centroids of fuzzy numbers. Thus, the top three factors were determined by developing an adoption score and ranking them in order. The top three factors determined were battery recharge time, battery cost, and environmental pollution.
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Paper Nr: 13
Title:

Devising Asymmetric Linguistic Hedges to Enhance the Accuracy of NEFCLASS for Datasets with Highly Skewed Feature Values
Authors:

Jamileh Yousefi
Abstract: This paper presents a model to address the skewness problem in the NEFCLASS classifier by devising several novel asymmetric linguistic hedges within the classifier. NEFCLASS is a common example of the construction of a NEURO-FUZZY system. The NEFCLASS performs increasingly poorly as data skewness increases. This poses a challenge for the classification of biological data that commonly exhibits feature value skewness. The objective of this paper is to device several novel asymmetric linguistic hedges to modify the shape of membership functions, hence improving the accuracy of NEFCLASS. This study demonstrated that devising an appropriate asymmetric linguistic hedge significantly improves the accuracy of NEFCLASS for skewed data.
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Paper Nr: 10
Title:

A Neural-Fuzzy System for Predicting the Areal Surface Metrology Parameters
Authors:

Ronak Sharma, Mahdi Mahfouf and Olusayo Obajemu
Abstract: With the increasing demand for faster manufacturing, Industry 4.0 has now only started to contribute towards streamlining the manufacturing processes. Despite the availability of high dimensional manufacturing data, a significant amount of time is still spent on testing the end products. Therefore, with a drive to substitute these inspection processes with a “digital twin”, this paper presents a framework for predicting the optimal surface metrology parameters such as force and vibration, required to achieve the desired surface roughness of an end product. Firstly, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed to predict the surface roughness using vibration, force and temperature. A low RMSE of 0.07 was obtained between the predicted and desired surface roughness. This model was then reverse engineered to predict the optimal surface conditions (force, vibration and temperature) required to achieve the desired surface roughness. For this, optimisation was applied to minimise the error between the target and predicted surface roughness. This framework will help manufacturing industries to discard frequent in-depth product inspection processes in favour of this “digital twin” due to the possibility of achieving right-first-time production.
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