Wyniki wyszukiwania - BazTech

1

A multi-case-based assembly management method for the shipbuilding industry

Iwańkowicz Remigiusz

Polish Maritime Research

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2021

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nr 2

27--35

EN

This article describes a method for planning the assembly of ship hulls that focuses on a welding sequence, takes into account subassembly processes and makes use of a previously built database of structures. Different degrees of similarity between structures are taken into account. The described research led to the development of an intelligent hybrid sequencing method for structure assembly that uses fuzzy clustering, case-based reasoning and evolutionary optimization. The method is called ‘Multi-case-Based Assembly Planning (MBAP)’. The method is developed to provide satisfactory solutions with low user effort. The analyses carried out show that the calculations are highly timeefficient. The developed evolutionary algorithm converges on sub-optimal solutions. The MBAP method can be directly implemented by any shipbuilder that assembles hulls. Apart from this, fuzzy clustering integrated with case-based reasoning can be applied in practice. The integration of fuzzy clustering and case-based reasoning has been taken to a level higher than previously described in the literature.

2

On wavelet based enhancing possibilities of fuzzy classification methods

Lilik Ferenc, Solecki Levente, Sziová Brigita, Kóczy László T., Nagy Szilvia

Journal of Automation Mobile Robotics and Intelligent Systems

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2020

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Vol. 14, No. 2

32--41

EN

If the antecedents of a fuzzy classification method are derived from pictures or measured data, it might have too many dimensions to handle. A classification scheme based on such data has to apply a careful selection or processing of the measured results: either a sampling, re‐ sampling is necessary. or the usage of functions, transfor‐ mations that reduce the long, high dimensional observed data vector or matrix into a single point or to a low num‐ ber of points. Wavelet analysis can be useful in such cases in two ways. As the number of resulting points of the wavelet ana‐ lysis is approximately half at each filters, a consecutive application of wavelet transform can compress the me‐ asurement data, thus reducing the dimensionality of the signal, i.e., the antecedent. An SHDSL telecommunication line evaluation is used to demonstrate this type of appli‐ cability, wavelets help in this case to overcome the pro‐ blem of a one dimensional signal sampling. In the case of using statistical functions, like mean, variance, gradient, edge density, Shannon or Rényi en‐ tropies for the extraction of the information from a pic‐ ture or a measured data set, and they don not produce enough information for performing the classification well enough, one or two consecutive steps of wavelet analy‐ sis and applying the same functions for the thus resulting data can extend the number of antecedents, and can dis‐ till such parameters that were invisible for these functi‐ ons in the original data set. We give two examples, two fuzzy classification schemes to show the improvement caused by wavelet analysis: a measured surface of a com‐ bustion engine cylinder and a colonoscopy picture. In the case of the first example the wear degree is to be deter‐ mine, in the case of the second one, the roundish polyp content of the picture. In the first case the applied statisti‐ cal functions are Rényi entropy differences, the structural entropies, in the second case mean, standard deviation, Canny filtered edge density, gradients and the entropies. In all the examples stabilized KH rule interpolation was used to treat sparse rulebases.

3

A framework of a ship domain-based near-miss detection method using Mamdani neuro-fuzzy classification

Niksa-Rynkiewicz T., Szłapczyński R.

Polish Maritime Research

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2018

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S 1

14--21

EN

Safety analysis of navigation over a given area may cover application of various risk measures for ship collisions. One of them is percentage of the so called near- miss situations (potential collision situations). In this article a method of automatic detection of such situations based on the data from Automatic Identification System (AIS), is proposed. The method utilizes input parameters such as: collision risk measure based on ship’s domain concept, relative speed between ships as well as their course difference. For classification of ships encounters, there is used a neuro-fuzzy network which estimates a degree of collision hazard on the basis of a set of rules. The worked out method makes it possibile to apply an arbitrary ship’s domain as well as to learn the classifier on the basis of opinions of experts interpreting the data from the AIS.

4

Design of fuzzy rule-based classifiers through granulation and consolidation

Riid A., Preden J.-S.

Journal of Artificial Intelligence and Soft Computing Research

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2017

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Vol. 7, No. 2

137--147

EN

This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

5

Fuzzy classification of medical data derived from diagnostic devices

Byczkowska-Lipińska L., Wosiak A.

Przegląd Elektrotechniczny

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2016

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R. 92, nr 12

85--88

EN

The research described in this paper concerns fuzzy classification of medical datasets obtained from diagnostic devices. Experimental studies were performed with use of fuzzy c-means algorithm. It was shown that despite the low accuracy of the results, fuzzy classification reduce the risks associated with the loss of internal relationships in the characteristics of the data, and thus increases the chances of finding the pathological cases, as well as taking preventive actions or therapy.

PL

W ramach niniejszej pracy przeprowadzona została klasyfikacja rozmyta w odniesieniu do medycznych zbiorów danych pozyskanych z urządzeń diagnostycznych. Zastosowana została rozmyta metoda k-średnich. Badania wykazały, że pomimo niskiej dokładności rezultatów, klasyfikacja rozmyta zmniejsza ryzyko związane z utratą wewnętrznych zależności w charakterystyce danych, a tym samym zwiększa szanse na stwierdzenie ryzyka patologii i tym samym szybsze podjęcie działań zapobiegawczych lub terapeutycznych.

6

Self-configuring hybrid evolutionary algorithm for fuzzy imbalanced classification with adaptive instance selection

Stanovov V., Semenkin E., Semenkina O.

Journal of Artificial Intelligence and Soft Computing Research

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2016

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Vol. 6, No. 3

173--188

EN

A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

7

Actualization of the distributed knowledge base of ergatic system using the method of fuzzy classification

Perederii V., Borchik E.

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2014

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nr 3

3--5

EN

In the article a method of actualization the distributed knowledge base of ergatic system using the method of fuzzy classification is proposed. As an example we consider the request choice formation of an alternative of decision-making from the knowledge base, according to the values of the input parameters. Genetic algorithm is used for finding optimal solutions. For automation of calculations MATLAB software package was used.

PL

W pracy zaproponowano metodę aktualizacji rozproszonej bazy wiedzy systemu ergatycznego (system maszyna-człowiek) używając rozmytej klasyfikacji. Rozważono przykłady formułowania zapytań, wybór alternatywnych decyzji z bazy wiedzy, zgodnie z wartościami parametrów wejściowych. Celem znalezienia optymalnych rozwiązań zastosowano algorytmy genetyczne. Do automatyzacji obliczeń zastosowano pakiet MATLAB.

8

Use of granulands for analysis of social class

Canal L., Ostasiewicz W.

Operations Research and Decisions

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2010

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Vol. 20, No. 1

25-40

EN

In this paper, an analytical tool enabling the analysis of social stratification is proposed. The classical scheme for scaling consisting of two stages, conceptualisation and operationalization, is modified by the use of the concept of granulation introduced by L. Zadeh. The essential step in the modified scheme for the quantification of vague concepts concerning social class is realized using linguistic variables. The essential part of the methodology presented is illustrated by a simple hypothetical example. However, the methodology is suitable for any classification problem when classes are defined verbally.

9

Fuzzy soft modeling of environmental data. A study of the imp act of a phosphatic fertilizer plant on the adjacent environment in Gdańsk

Sârbu C., Szczepaniak K., Astel A., Biziuk M.

Chemia i Inżynieria Ekologiczna

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2005

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Vol. 12, nr 5-6

587-601

EN

Cluster analysis is a large field, both within fuzzy sets and beyond it. Many algorithms have been developed to obtain bard clusters from a given data set. Among those, the c-means algorithms are probably the most widely used. Hard c-means execute a sharp classification, in which each object is either assigned to a class or not. The membership to a class of objects therefore amounts to either 1 or 0. The application of fuzzy sets in a classification function causes this class membership to become a relative one and consequently an object can belong to several classes at the same time but with different degrees. The c-means algorithms are prototype-based procedures, which minimize the total of the distances between the prototypes and the objects by the construction of a target function. Fuzzy generalized n-means is easy and well improved tool, which have been applied in many fields of chemistry. In this paper, different fuzzy classification algorithms of the 35 grass and soil samples based on the 37 chemical element concentrations have been allowing an objective interpretation of their similarities and differences, respectively. Much more, the results ob13ined can be very useful in their reclassification. The new fuzzy approach namely, fuzzy cross-classification algorithm, (FHCsC) allows the qualitative and quantitative identification of the characteristics (chemical elements) responsible for the observed similarities and dissimilarities between grass and soil samples. In addition, the fuzzy hierarchical characteristics clustering (FHiCC) and fuzzy horizon13l characteristics clustering (FHoCC) procedures revealed a high similarity between some chemical elements concentrations in grass and soil samples.

PL

Analiza podobieństwa obejmuje nie tylko zastosowanie logiki rozmytej, ale również wiele innych podejść matematycznych. Opracowano wiele algorytmów, których celem jest wyodrębnienie wyraźnych skupień (hard clusters) z danego zbioru danych. Prawdopodobnie najczęściej stosowanymi algorytmami są tzw. algorytmy c-średnie (c-means algorithms). Twarde c-średnie (hard c-means) służy do ostrej klasyfikacji, podczas której obiekt jest przypisany do danej klasy lub do niej nie należy. W takim przypadku przynależność obiektu do klasy wynosi 1 lub O. Zastosowanie układów rozmytych lfuzzy sets) w obliczaniu funkcji klasyfikującej powoduje, że dany obiekt może należeć do kilku klas równocześnie, ale w różnym stopniu przynależności. Algorytmy c-średnie są procedurami określanymi jako procedury prototyp-zależne (prototype-based procedures), które minimalizują odległości między prototypami a obiektami dzięki odpowiedniej formie funkcji docelowej. Algorytm rozmytych uoglnionych n-średnich (fuzzy generalized n-means) jest łatwym i dobrze opracowanym narzędziem, które wykorzystuje się w wielu dziedzinach chemii. W niniejszym opracowaniu różne algorytmy klasyfikacji rozmytej zostały zastosowane do wyników oznaczeń 37 stężeń pierwiastków w 35 próbkach trawy i gleby, co pozwoliło obiektywnie zinterpretować podobieństwa i różnice między danymi. Nowe podejście algorytm rozmytej klasyfikacji krzyżowej (fuzzy cross-classijication algorithm, FHCsC) pozwala jakościowo i ilościowo zidentyfikować zmienne (pierwiastki chemiczne) odpowiedzialne za obserwowane podobieństwa i różnice między próbkami trawy i gleby. Dodatkowo procedury: rozmyta hierarchiczna analiza wiązkowa (fuzzy hierarchical characteristics clustering, FHiCC) i rozmyta pozioma analiza wiązkowa (fuzzy horizontal characteristics clustering, FHoCC) wykazały znaczne podobieństwo między stężeniami pewnych pierwiastków w próbkach trawy i gleby.

10

Dynamika sprzedaży energii elektrycznej gospodarstwom domowym i rolnym

Trojanowska M.

Inżynieria Rolnicza

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2003

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R. 7, nr 7

215--220

PL

W pracy analizowano dynamikę sprzedaży energii elektrycznej gospodarstwom położonym na obszarach wiejskich Polski. W szczególności analizowano zróżnicowanie wielkości i zmian sprzedaży energii elektrycznej gospodarstwom domowym i rolnym znajdującym się w obszarze obsługi energetycznej poszczególnych przedsiębiorstw dystrybucyjnych. Do analizy wykorzystano elementy teorii zbiorów rozmytych.

EN

Paper analyzed the dynamics of selling electric energy to the consumers on rural areas in Poland. Differentiation of the amounts and changes in selling electric energy to the households and farms localized on the areas of energetic service by particular distributing enterprises were especially considered. Some elements of fuzzy sets’ theory were applied to analysis. The results revealed quite dependence of the dynamics in selling of electric energy to the farms and rural households on their geographic localization.

11

Practical approaches to statistical pattern recognition

Jóźwik A.

Biocybernetics and Biomedical Engineering

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2002

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Vol. 22, no. 2-3

207-221

EN

The paper describes new approaches to statistical pattern recognition. All presented methods are based on a distance function. The properties of these methods and their usefulness are illustrated on real problems. Some tasks with small and very large training sets are described to shown an effectiveness of the proposed approaches. There is no one universal method that would be satisfactory for all object classification problems. That's why several methods have been demonstrated.