A similarity measure with uncertainty for incompletely known fuzzy sets

Stachowiak, Anna; Dyczkowski, Krzysztof

doi:10.1109/ifsa-nafips.2013.6608432

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…The system utilizes the IF-sets framework, hence it is possible to aggregate the results of many models into a single diagnosis represented in the form of bipolar information. Dealing with such information requires use of appropriate tools such as similarity measures for incompletely known fuzzy sets [29]. A gynecologist, at each stage of the diagnostic process, will receive a set of possible diagnoses for a particular type of tumor, with an indication of how probable it is that the diagnosis is correct and how probable that it is not.…”

Section: Decision Support In Making a Final Diagnosismentioning

confidence: 99%

An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis

Dyczkowski

Wójtowicz

Żywica

et al. 2015

Advances in Intelligent Systems and Computing

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Abstract. This article describes the fundamentals of an intelligent decision support system for the diagnosis of ovarian tumors. The system is designed to support diagnosis by less experienced gynecologists, and to gather data for continuous improvement of the quality of diagnosis. The theoretical basis for the construction of the system is the IF-sets framework, used to aggregate multiple decisionmaking methods, and simultaneously providing information about positive and negative diagnosis of a given tumor type.

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Section: Decision Support In Making a Final Diagnosismentioning

confidence: 99%

An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis

Dyczkowski

Wójtowicz

Żywica

et al. 2015

Advances in Intelligent Systems and Computing

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“…For example, the OFN describing set temperature T Z1 = [56,56,56,56] informs the controller to "hold temperature of 56" For the number T Z2 = [56,58,60,72] the directing means, "I prefer temperature 72, but may be no less than 56." For the reverse OFN number there is the statement, "I prefer 56, but nothing will happen if it is 72."…”

Section: Output Variables Arementioning

confidence: 99%

Fuzzy Numbers Applied to a Heat Furnace Control

Dobrosielski

Czerniak

Zarzycki

et al. 2017

Theory and Applications of Ordered Fuzzy Numbers

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This chapter presents a trend phenomenon and application of the fuzzy controller for Ordered Fuzzy Numbers (OFNs). The authors propose to use a trend in a combustion process for a simplified model of a solid fuel fired furnace. Better control over the process translates into reduced CO 2 emission as well as optimal use of the furnace. When carrying out the fuzzy observation of the efficiency of the furnace, the authors apply the OFN notation by connecting the trend of furnace temperature changes with the order appropriate for this notation. Thanks to this approach it is possible to enhance information without the additional need to multiply the transmitted data. It is particularly effective in the multidimensional fuzzy observation when monitoring not only the condition of the temperature in the furnace but also the ambient temperature and the temperatures in several rooms of the heated building. The chapter is a continuation of a series of papers published by the authors on multidimensional fuzzy observation using OFN notation. A controller in the conventional fuzzy logic approach is also presented in the chapter. The controller was built using jFuzzyLogic software. The fact that there are more and more OFN applications seems to be a good predictor of the development of this generalization, an example of which is the problem analyzed in this chapter.

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“…An algorithm has been proposed for OFN use for detecting future problems in the used connection [19,21,44,49,59]. For this purpose the algorithm should measure a TCP retransmission in all used channels during the transmission as a percentage value of transmitted packets (during the given timeslot).…”

Section: Ofn For Problem Detectionmentioning

confidence: 99%

Fuzzy Control for Secure TCP Transfer

Apiecionek

2017

Theory and Applications of Ordered Fuzzy Numbers

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This chapter presents the potential use of fuzzy observance implementation for detecting transmission problems that could appear in the near future. Using quick detection, appropriate action could be taken and the security and reliability of data transfer could be maintained at a high level. As a result the authors present a proposed solution for dividing a data stream between different data links and predicting transmission problems.Keywords TCP · Multipath TCP · Security · Fuzzy logic · Fuzzy observation IntroductionNowadays many networks require security such as data encryption and reliable transfer to the destination. Health care, rail, and power plant systems are some examples of such systems. When they are used an operator cannot lose a connection between his or her control and management applications and system sensors and actuating equipment. In such a system a lost connection could have tragic consequences. In the case of power plants this could also mean some huge disaster. That is why security regarding reliable transfer is very important. In the case of health systems it could be required for monitoring people but also for execution of medical operations through remote control of medical equipment such as a scalpel. Some problems with rail equipment could cause a train accident. There are also many other systems in which a lost connection would cause real damage. Such systems are called critical infrastructure. For this critical infrastructure more than one connection is usually prepared. For example, those systems can operate using cable and a 3G/4G/LTE connection concurrently. Unfortunately, in most cases, different kinds of used connections are not operated simultaneously. Systems switch between them when the operating connection collapses. When the systems switch connections they can lose some data.

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A similarity measure with uncertainty for incompletely known fuzzy sets

Cited by 18 publications

References 19 publications

An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis

An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis

Fuzzy Numbers Applied to a Heat Furnace Control

Fuzzy Control for Secure TCP Transfer

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