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1

Granular representation of the information potentialof variables –application example

Kiersztyn Adam, Gandzel Agnieszka, Celiński Maciej, Koczan Leopold

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2021

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T. 11, nr 3

40--44

EN

With the introduction to the science paradigm of Granular Computing, in particular, information granules, the way of thinking about data has changed gradually. Both specialists and scientists stopped focusing on the single data records themselves, but began to look at the analyzed data in a broader context, closer to the way people think. This kind of knowledge representation is expressed, in particular, in approaches based on linguistic modelling or fuzzy techniques such as fuzzy clustering. Therefore, especially important from the point of view of the methodology of data research, is an attempt to understand their potential as information granules. In this study, we will present special cases of using the innovative method of representing the information potential of variables with the use of information granules. In a series of numerical experiments based on both artificially generated data and ecological data on changes in bird arrival dates in the context of climate change, we demonstrate the effectiveness of the proposed approach using classic, not fuzzy measures building information granules.

PL

Wraz z wprowadzeniem do nauki paradygmatu obliczeń ziarnistych, w szczególności ziaren informacji, sposób myślenia o danych stopniowo się zmieniał. Zarówno specjaliści, jak i naukowcy przestali skupiać się na samych rekordach pojedynczych danych, ale zaczęli patrzeć na analizowane dane w szerszym kontekście, bliższym ludzkiemu myśleniu. Ten rodzaj reprezentacji wiedzy wyraża się w szczególności w podejściach opartych na modelowaniu językowym lub technikach rozmytych, takich jak klasteryzacja rozmyta. Dlatego szczególnie ważna z punktu widzenia metodologii badania danych jest próba zrozumienia ich potencjału jako ziaren informacji. W niniejszym opracowaniu przedstawimy szczególne przypadki wykorzystania innowacyjnej metody reprezentacji potencjału informacyjnego zmiennych za pomocą ziaren informacji. W serii eksperymentów numerycznych opartych zarówno na danych generowanych sztucznie, jak i danych ekologicznych dotyczących zmian dat przylotów ptaków w kontekście zmian klimatycznych, demonstrujemy skuteczność proponowanego podejścia przy użyciu klasycznych, a nie rozmytych miar budujących ziarna informacji.

2

Modeling cognitive functionalities of prosthetic arm using conceptual spaces

Ishwarya M.S., Aswani Kumar Ch.

Journal of Automation Mobile Robotics and Intelligent Systems

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2019

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Vol. 13, No. 4

11--21

EN

Conceptual space framework is used for representing knowledge in cognitive systems. In this paper, we have adapted conceptual space framework for prosthetic arm considering its cognitive abilities such as receiving signals, recognizing and decoding the signal and responding with the corresponding action in order to develop a conceptual space of the prosthetic arm. Cognitive functionalities such as learning, memorizing and distinguishing configurations of prosthetic arm are achieved via its conceptual space. To our knowledge, this work is the first attempt to adapt the conceptual spaces to model cognitive functionalities of prosthetic arm. Adding to this, we have made use of different notion of concept that reflects the topological structure in concepts. To model the actions of the prosthetic arm functionalities, we have made use of force patterns to represent action. Similarly, to model the distinguishing ability, we make use of the relationship between the attributes conveyed by adapted different notion of concept.

3

Koncepcja obliczeń ziarnistych przy realizacji inteligentnego systemu interaktywnego

Rylski Ł.

Logistyka

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2011

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nr 6

PL

Artykuł jest wprowadzeniem do koncepcji obliczeń ziarnistych. Omówiono w nim fundamentalne pojęcia związane z ziarnami informacji. Przedstawiono równie? ogólny schemat koncepcji, który posłu?ył to do implementacji inteligentnego systemu interaktywnego. System w całości bazuje na koncepcji obliczeń z wykorzystaniem ziaren informacji, przez co stanowi praktyczny przykład jej zastosowania w inteligentnych systemach technicznych, ekonomicznych i innych.

EN

The article is an introduction to the concept of granular computing. It highlights fundamental terms connected with information granules. There is also presented a general flow chart of this concept which was used towards implementation of an intelligent interactive system. The entire system is based on the concept of granular computing, consequently it is a practical example of its usage in technical, economical and other intelligent systems.

4

New frontiers of analysis, interpretation and classification of biomedical signals: a computational intelligence framework

Gacek A.

Journal of Medical Informatics & Technologies

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2011

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Vol. 17

23--36

EN

The methods of Computational Intelligence (CI) including a framework of Granular Computing, open promising research avenues in the realm of processing, analysis and interpretation of biomedical signals. Similarly, they augment the existing plethora of "classic" techniques of signal processing. CI comes as a highly synergistic environment in which learning abilities, knowledge representation, and global optimization mechanisms and this essential feature is of paramount interest when processing biomedical signals. We discuss the main technologies of Computational Intelligence (namely, neural networks, fuzzy sets, and evolutionary optimization), identify their focal points and elaborate on possible limitations, and stress an overall synergistic character, which ultimately gives rise to the highly symbiotic CI environment. The direct impact of the CI technology on ECG signal processing and classification is studied with a discussion on the main directions present in the literature. The design of information granules is elaborated on; their design realized on a basis of numeric data as well as pieces of domain knowledge is considered. Examples of the CI-based ECG signal processing problems are presented. We show how the concepts and algorithms of CI augment the existing classification methods used so far in the domain of ECG signal processing. A detailed construction of granular prototypes of ECG signals being more in rapport with the diversity of signals analyzed is discussed as well. ECG signals, Computational Intelligence, neurocomputing, fuzzy sets, information granules, Granular Computing, interpretation, classification, interpretability.

5

Szereg czasowy zbiorów rozmytych w opisie i analizie sygnałów elektrokardiograficznych

Henzel N., Gacek A., Łęski J.

Acta Bio-Optica et Informatica Medica. Inżynieria Biomedyczna

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2011

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Vol. 17, nr 4

313-316

PL

W pracy przedstawiono ujednolicenie metod opisu sygnałów elektrokardiograficznych za pomocą koncepcji szeregu czasowego zbiorów rozmytych. Sygnał elektrokardiograficzny jest przetwarzany w "ruchomym oknie" czasowym i przekształcany na szereg czasowy funkcji przynależności zbiorów rozmytych. Jako szczególne przypadki wprowadzonej metody można rozważać koncepcję sygnału rozmytego oraz ciąg ziaren informacyjnych.

EN

The unification of the electrocardiography signals description methods by means of time series of fuzzy sets, is presented. The electrocardiographic signal is processed in a “moving window” and transformed into a time series of fuzzy sets membership functions. The idea of fuzzy signal and the sequence of information granules can be considered as special case of the presented method.

6

Models of computational intelligence in bioinformatics

Pedrycz W.

Journal of Medical Informatics & Technologies

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2003

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Vol. 5

IP13--23

EN

Computational Intelligence has emerged as a synergistic environment of Granular Computing (including fuzzy sets, rough sets, interval analysis), neural networks and evolutionary optimisation. This symbiotic framework addresses the needs of system modelling with regard to its transparency, accuracy and user friendliness. This becomes of paramount interest in various modelling in bioinformatics especially when we are concerned with decision-making processes. The objective of this study is to elaborate on the two essential features of CI that is Granular Computing and the resulting aspects of logic-oriented processing and its transparency. As the name stipulates, Granular Computing is concerned with processing carried out at a level of coherent conceptual entities - information granules. Such granules are viewed as inherently conceptual entities formed at some level of abstraction whose processing is rooted in the language of logic (especially, many valued or fuzzy logic). The logic facet of processing is cast in the realm of fuzzy logic and fuzzy sets that construct a consistent processing background necessary for operating on information granules. Several main categories of logic processing units (logic neurons) are discussed that support aggregative (and-like and or-like operators) and referential logic mechanisms (dominance, inclusion, and matching). We show how the logic neurons contribute to high functional transparency of granular processing, help capture prior domain knowledge and give rise to a diversity of the resulting models.

7

Upper and Lower Set Formulas: Restriction and Modification of the Dempster-Pawlak Formalism

Turksen I. B.

International Journal of Applied Mathematics and Computer Science

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2002

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Vol. 12, no 3

359-369

EN

A modification of Dempster's and Pawlak's constructs forms a new foundation for the identification of upper and lower sets formulas. Also, in this modified Dempster-Pawlak construct we require that subsets of the power set be restricted to the well-known information granules of the power set. An aggregation of upper information granules amongst each other and lower information granules amongst each other determine upper and lower set formulas for both crisp and fuzzy sets. The results are equivalent to the Truth Table derivation of FDCF and FCCF, Fuzzy Disjunctive Canonical Forms and Fuzzy Conjunctive Canonical Forms, respectively. Furthermore, they collapse to DNF \equiv CNF, i.e., the equivalence of Disjunctive Normal Forms and Conjunctive Normal Forms, in the combination of concepts once the LEM, LC and absorption, idempotency and distributivity axioms are admitted into the framework. Finally, a proof of the containment is obtained between FDCF and FCCF for the particular class of strict and nilpotent Archimedian t-norms and t-conorms.

8

Granular computing as an abstraction of data aggragation - a view on optical music recognition

Homenda W.

Archives of Control Sciences

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2002

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Vol. 12, no. 4

433-455

EN

In the paper optical music recognition (OMR) is considered as an example of paper-to-compute-memory data flow. This specific area of interest forces specific methods to be applied in data processing, but in principle, gives a perspective on the merit of the subject of data aggregation. The process of paper-to-computer-memory music data flow is presented from the perspective of the process of acquiring information from plain low-level data. The discussion outlines an interpretation of this process as a metaphor of granular computing. The stages of data aggregation and data abstraction are shown as steps leading to the formation of knowledge granules and to recovering dependencies between knowledge granules and between the information included in knowledge granules. An influence of the granular world of music notation on the design of a computer program is presented. the presentation is related to a real computer program of music notation recognition and music knowledge representation and processing. The relationship between the granular structure of music knowledge and user interface of the program is outlined.