Various established and emerging applications of RFID technology have been and are being implemented by companies in different parts of the world. However, RFID technology is susceptible to a variety of security and privacy concerns, as it is prone to attacks such as eavesdropping, denial of service, tag cloning and user tracking. This is mainly because RFID tags, specifically low-cost tags, have low computational capability to support complex cryptographic algorithms. Tag cloning is a key problem to be considered since it leads to severe economic losses. One of the possible approaches to address tag cloning is using an intrusion detection system. Intrusion detection systems in RFID networks, on top of the existing lightweight cryptographic algorithms, provide an additional layer of protection where other security mechanisms may fail. This thesis presents an intrusion detection mechanism that detects anomalies caused by one or more cloned RFID tags in the system. We make use of a Hybrid Fuzzy Genetics-Based Machine Learning algorithm to design an intrusion detection model from RFID system-generated event logs. For the purpose of training and evaluation of our proposed approach, part of the RFID system-generated dataset provided by the University of Tasmania’s School of Computing and Information Systems was used, in addition to simulated datasets. The results of our experiments show that the model can achieve high detection rates and low false positive rates when identifying anomalies caused by one or more cloned tags. In addition, the model yields linguistically interpretable rules that can be used to support decision making during the detection of anomaly caused by the cloned tags.

The aim of this study is to solve Fault Detection and Diagnosis (FDD) problems occurring in nonlinear dynamical systems by using model and knowledge-based FDD methods and to give a priority and a degree about faults. For this purpose, three model-based FDD approaches, called FDD by utilizing principal component analysis (PCA), system identification based FDD and inverse model based FDD are introduced. Performances of these approaches are tested on different nonlinear dynamical systems starting from simple to more complex. New fuzzy discrete event system (FDES) and fuzzy discrete event dynamical system (FDEDS) concepts are introduced and their applicability to an FDD problem is investigated. Two knowledge-based FDD methods based on FDES and FDEDS structures using a fuzzy rule-base are introduced and they are tested on nonlinear dynamical systems. New properties related to FDES and FDEDS such as fuzzy observability and diagnosibility concepts and a relation between them are illustrated. A dynamical rule-base extraction method with classification techniques and a dynamical and a static diagnoser design methods are also introduced. A nonlinear and event based extension of the Luenberger observer and its application as a diagnoser to isolate faults are illustrated. Finally, comparisons between the proposed model and knowledge-based FDD methods are made.

Cognitive Maps (CMs) have shown promise as tools for modelling and simulation of knowledge in computers as representation of real objects, concepts, perceptions or events and their relations. This thesis examines the application of fuzzy theory to the expression of these relations, and investigates the development of a framework to better manage the operations of these relations. The Fuzzy Cognitive Map (FCM) was introduced in 1986 but little progress has been made since. This is because of the difficulty of modifying or extending its reasoning mechanism from causality to relations other than causality, such as associative and deductive reasoning. The ability to express the complex relations between objects and concepts determines the usefulness of the maps. Structuring these concepts and relations in a model so that they can be consistently represented and quickly accessed and anipulated by a computer is the goal of knowledge representation. This forms the main motivation of this research. In this thesis, a novel framework is proposed whereby single-antecedent fuzzy rules can be applied to a directed graph, and reasoning ability is extended to include noncausality. The framework provides a hierarchical structure where a graph in a higher layer represents knowledge at a high level of abstraction, and graphs in a lower layer represent the knowledge in more detail. The framework allows a modular design of knowledge representation and facilitates the creation of a more complex structure for modelling and reasoning. The experiments conducted in this thesis show that the proposed framework is effective and useful for deriving inferences from input data, solving certain classification problems, and for prediction and decision-making.

- / -

The Presented Thesis is focused on exploitation of stochastic cyclical networks in project management during project planning. Particularly, it is focused on the GERT method, which enables to carry out both the probability analysis and the time analysis of projects with stochastic structure. We deal primarily with analysis of such networks where cyclical activities occur. As an integral part of the Thesis, derivation of simplified computing procedures for cyclical activities is included. We extend the possibilities of the GERT method with stochastic evaluation of time duration of activities using the fuzzy GERT method. This fuzzy GERT method is applied on the real project and its results are compared to results of Monte Carlo simulation.

Master’s thesis deals with designing decision making system based on fuzzy logic principles which evaluates suppliers offers. In this Master’s thesis there were created two decision making systems based on the demands of the company. First decision making system was designed within the environment of MS Excel, the second one was designed in MATLAB software. In addition of creating the decision making system the thesis puts emphasis on the analysis of the current decision making model in the company and this section will serve as a springboard to designing a decision making system based on fuzzy logic. At the beginning of the work there are included all the necessary theoretical framework on which I based the writing of this thesis.

Abstract The following thesis discusses the problems of apprising methods of real estate with regard to subjective factor which is inherited in the process by the appricing subject. It discusses methods, evaluations and points out possible disturbing effects and faults which could influence these methods. The example case study shows possibilities in using the power of fuzzy logic, which contributes in a significant way to higher transparency, reproducibility and portability of the whole appricing process. The main goal of the thesis is to introduce the advantages and power of a new evaluation method in the appricing process.

Tato disertační práce se zabývá metodami modelování elektronického zařízení letadel. První část je stručným přehledem klasických metod modelování systémů a adaptivních, fuzzy a hybridních metod používaných převážně k black-box modelování. Cílem práce je vytvořit algoritmus pro identifikaci a modelování obecného systému, který může být nelineární, dynamický a velmi složitý, například co do množství rozměrů. Předpokládá se, že model má několik vstupů a výstupů. V hlavní části práce je rozebrána metoda, která patří mezi hybridní systémy, protože kombinuje fuzzy systém s parametricky definovanými pravidly a regresní neuronovou síť. Nejprve je zmíněn základní princip regresní sítě a způsob určení jejího parametru strmosti, dále se kapitola zabývá zavedením fuzzy pravidel do této sítě. Třetí část se zabývá jedním z hlavních bodů práce, paralelními výpočty. Výsledný algoritmus je navržen pro paralelní zpracování, protože výpočetní čas může být v případě složitějších modelů příliš vysoký, případně nelze výsledky získané ze sítě vyhodnotit pomocí výpočtu v jednom vlákně. V závěru práce je metoda ověřena na datech získaných z měření zmenšeného modelu letadla. Ověření je provedeno pomocí střední kvadratické odchylky a srovnáním s odpovídajícím modelem vytvořeným pomocí vícevrstvé neuronové sítě trénované zpětným šířením chyby s algoritmem Levenberg-Marquardt.

The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases.

No description available.