This diploma thesis is focused on risk analysis of dry dog food range of products sold in a retail chain named Zvěrokruh. The main goal of this work is creation of model with use of the fuzzy logic. The model helps with the dry dog food range risk evaluation. The model is created in MS Excel and MATLAB. Outcome of this work is a tool which evaluate quality of the dry dog food, which can be afterwards analysed in other point of view.

In the Master’s thesis I am deal with the proposal of sophisticated method for suppliers rating. The thesis analyses the supply environment of business company and characterizes the problems related to suppliers rating and choosing. The object of my Master’s thesis is the elaboration of the models for suppliers rating on the basis of fuzzy logic. These models should successfully solve the questions of suppliers rating as suitable method for minimizing of risk of suppliers.

The diploma thesis deals with the design of models for evaluation of suppliers and their possible risks for a retail food business. Supplier risk is evaluated using fuzzy logic. Models are created in Microsoft Excel and MATLAB. At the beginning of the thesis is the problem defined in the theoretical framework, which is followed by the analytical and practical part of the thesis with proposals for solution of the problem.

Orientador: José Arnaldo Frutuoso Roveda / Resumo: No Brasil e no mundo é latente a necessidade de fazer a proteção, a ampliação e a promoção do patrimônio geológico, com a finalidade de garantir as próximas gerações acesso a este tipo de patrimônio natural, de modo econômica e ecologicamente sustentável. Diante disso, a Organização das Nações Unidas para a Educação, a Ciência e a Cultura - UNESCO criou a GGN - Global Geoparks Network, ou em sua tradução Rede Global de Geoparques - RGG. A Rede Global de Geoparques tem por função fazer a inclusão de novos parques membros, bem como, fazer a manutenção dos parques membros já existentes. Para que um parque seja postulante a membro do RGG, este deve atender uma série de requisitos, dentre as quais a Geoeducação, além disso, os parques membros de tempos em tempos devem passar por validação destes requisitos. Os parques candidatos possuem a dificuldade de elencar quais são as ações prioritárias num esforço de se tornarem membros da RGG. Como estes parques podem se tornar membros da RGG com o menor esforço. O presente trabalho se tem como objetivo apresenta uma proposta de criação um Sistema de Inferência Fuzzy (SIF) para tomada de decisões em Geoeducação, por parte dos gestores de áreas passíveis de se tornarem membros da RGG. A metodologia utilizada foi a teoria de conjuntos Fuzzy, através da criação de um sistema de inferência para geração de índices de adequação em Geoeducação das áreas candidatas. Foram criados 155 cenários para testar e validar o comportamento do sistema, o mes... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

We propose to replace Zadeh's DeMorgan-type negation in fuzzy logic by a Heyting-type negation which, unlike the former, preserves the law of the excluded contradiction and is more in line with negation in databases and logic programs. We show that the resulting system can be used for obtaining conservative extensions of relational and deductive databases (resp. normal logic programs).

<p>Doktorska disertacija &ldquo;Model procene kvaliteta podzemne vode sa<br />povećanim sadržajem arsena primenom fazi logike&rdquo; urađena je kao rezultat<br />potrebe za istraživanjem mogućnosti primene fazi logike u savremenom<br />pristupu procene kvaliteta podzemnih voda. Imajući u vidu da ova tematika<br />nije dovoljno istražena u na&scaron;em okruženju i da joj nije dat odgovarajući<br />akcenat i težina, osnovni cilj disertacije je da se razrade optimalni modeli za<br />procenu kvaliteta podzemnih voda sa povećanim sadržajem arsena.<br />Disertacija pored teorijske dimenzije prezentuje i primenu evaluiranih fazi<br />modela na primeru eksperimenatlno odabranog lokaliteta grada Zrenjanina.<br />Razvijene modele je moguće transponovati na urbanim i ruralnim područjima<br />uz odgovarajuće iteracije.</p> / <p>PhD Thesis &ldquo;Model assessment for groundwater quality with elevated arsenic<br />content with application of fuzzy logic&rdquo; was elaborated as a result of the need<br />to explore the possibilities of application of fuzzy logic in the modern<br />approach of assessing the groundwater quality. Because this issue has not<br />been sufficiently explored and it was not given proper emphasis and weight,<br />the main aim of the dissertation is to develop optimal models for assessing<br />the groundwater quality with elevated arsenic content. Dissertation, besides<br />theoretical dimensions, presente and evaluate the implementation of the<br />created models to the experimentaly selected locality of the city of Zrenjanin.<br />The developed models can be transposed to the urban and rural areas with<br />appropriate iteration.</p>

Wood-framed buildings have generally performed well during earthquake events, resulting in low fatality levels. However, various degrees of damage is still observed in these buildings during previous earthquakes. Lessons learned from the performance of wood frame construction in these earthquakes is led to an improvement in the design codes and construction practices over the past decades. But, the existing buildings are still vulnerable, since they were designed based on the older codes or constructed using old construction practices. Wood frame construction is the most common construction type in Canada, especially for single family dwellings. Most of these buildings are old, built prior to any modern seismic requirement, and have not been retrofitted against the damaging effects of earthquakes. Therefore, with a number of Canadians living in areas of high or moderate earthquake risk, there is a need to develop tools to assess the seismic vulnerability of the exiting wood-framed buildings in Canada. In the following thesis, a risk-based visual seismic assessment model and a screening tool (CanRisk) is developed, to assess the seismic vulnerability of existing wood frame construction in Canada. The model is dependent on the seismic hazard, building vulnerability, and building importance/exposure, which are integrated using hierarchical fuzzy rule based modeling. In the proposed seismic assessment model, fuzzy logic is used as a computing technique to capture the vagueness and uncertainty of a seismic vulnerability assessment, caused by subjectivity involved in the evaluation process. The hierarchical fuzzy rule based modeling used in this seismic assessment method is implemented in a prototype Matlab based program (CanRisk), which incorporates the Canadian seismic design practice based on the National Building Code of Canada (NBCC) and the Canadian site seismic hazard. A sensitivity analysis is conducted to test and verify the seismic assessment model and investigate the effects of various parameters on the outcome of the assessment. Also, in a case study, selected wood-framed buildings located in the city of Ottawa are evaluated using CanRisk, to demonstrate the applicability of the program.

Energy consumption in Wireless Sensor Networks (WSN) involving multiple sensor nodes is a crucial parameter in many applications like smart healthcare systems, home automation, environmental monitoring, and industrial use. Hence, an energy-efficient cluster-head (CH) selection strategy is imperative in a WSN to improve network performance. So to balance the harsh conditions in the network with fast changes in the energy dynamics, a novel energy-efficient adaptive fuzzy-based CH selection approach is projected. Extensive simulations exploited various real-time scenarios, such as varying the optimal position of the location of the base station and network energy. Additionally, the results showed an improved performance in the throughput (46%) and energy consumption (66%), which demonstrated the robustness and efficacy of the proposed model for the future designs of WSN applications.

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.

A module-level DC optimization proof of concept architecture is proposed to increase the efficiency of photovoltaic (PV) strings by minimizing the negative effects of shading caused by intermittent cloud cover while reducing cloud induced fast frequency fluctuations. The decentralized inverter approach combines the benefits of string and micro-inverter technology. This device can be affixed to pre-existing or new systems and operates in compliance with IEEE 1547 and California rule 21 standards by operating in maximum power point tracking (MPPT) or curtailment mode whenever necessary. The modular level device encapsulates three individual processes: an optimization engine to determine minimum power requirements, a fuzzy logic controller (FLC) to eliminate the effect of passing cloud cover, and a voltage regulation stage to monitor and appropriately adjust the output voltage of the device. Ramp rate reduction was accomplished using adaptive fuzzy logic control with a heuristic rule base inference engine. The modular design can be affixed to grid connected or islanded systems allowing for operation in regulated and variable load conditions. Matlab/Simulink 2019a was used to design and simulate the proof of concept model to verify the resiliency to partial shading, reduction of ramp rates during passing cloud coverage, and optimal output voltage for each panel while maintaining a constant DC link voltage of 120 V. This proof of concept has been successfully validated therefore further testing will be performed for various irradiance conditions.