Plataforma computacional fuzzy para avaliação nos estágios do tomateiro dos efeitos da irrigação e salinidade da água

Pós-graduação em Agronegócio e Desenvolvimento - Tupã

Riverbed: A Novel User-Steered Image Segmentation Method Based on Optimum Boundary Tracking

This paper presents an optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed. The riverbed approach was devised using the image foresting transform with a never-exploited connectivity function. We analyze its properties in the derived image graphs and discuss its theoretical relation with other popular methods such as live wire and graph cuts. Several experiments show that riverbed can significantly reduce the number of user interactions (anchor points), as compared to live wire for objects with complex shapes. This paper also includes a discussion about how to combine different methods in order to take advantage of their complementary strengths.

Studies of Heavy Baryons in DELPHI and Radiation Monitoring in DØ

Between the years 1992 and 1995 about 3.5 million hadronic Z decays were collected by the DELPHI detector at CERN. This data has been used to measure the production and lifetime of the beauty strange baryon Ξb, in the inclusive decay channel Ξb →Ξ-ℓ- X. The Ξ- baryon was reconstructed through the decay Ξ- → Λ π-, using a constrained fit method for cascade decays. An iterative discriminant analysis was used for the Ξb selection. A search for the Ξb baryon was also performed using an alternative method of reconstructing the Ξ- baryon. A measurement of the production of the charmed strange baryon Ξc in the decay channel Ξc → Ξ-π+ using the same data is also presented. The radiation monitoring system of the Silicon Microstrip Tracker in the DØ detector is studied and used to estimate the radiation dose received by the Silicon detector during normal running conditions of the TeVatron accelerator.

Quantificazione del rischio occupazionale: Indicatori, indici e metodologia fuzzy

La tesi affronta il concetto di esposizione al rischio occupazionale e il suo scopo è quello di indagare l’ambiente di lavoro e il comportamento dei lavoratori, con l'obiettivo di ridurre il tasso di incidenza degli infortuni sul lavoro ed eseguire la riduzione dei rischi. In primo luogo, è proposta una nuova metodologia denominata MIMOSA (Methodology for the Implementation and Monitoring of Occupational SAfety), che quantifica il livello di "salute e sicurezza" di una qualsiasi impresa. Al fine di raggiungere l’obiettivo si è reso necessario un approccio multidisciplinare in cui concetti d’ingegneria e di psicologia sono stati combinati per sviluppare una metodologia di previsione degli incidenti e di miglioramento della sicurezza sul lavoro. I risultati della sperimentazione di MIMOSA hanno spinto all'uso della Logica Fuzzy nel settore della sicurezza occupazionale per migliorare la metodologia stessa e per superare i problemi riscontrati nell’incertezza della raccolta dei dati. La letteratura mostra che i fattori umani, la percezione del rischio e il comportamento dei lavoratori in relazione al rischio percepito, hanno un ruolo molto importante nella comparsa degli incidenti. Questa considerazione ha portato ad un nuovo approccio e ad una seconda metodologia che consiste nella prevenzione di incidenti, non solo sulla base dell'analisi delle loro dinamiche passate. Infatti la metodologia considera la valutazione di un indice basato sui comportamenti proattivi dei lavoratori e sui danni potenziali degli eventi incidentali evitati. L'innovazione consiste nell'applicazione della Logica Fuzzy per tener conto dell’"indeterminatezza" del comportamento umano e del suo linguaggio naturale. In particolare l’applicazione è incentrata sulla proattività dei lavoratori e si prefigge di impedire l'evento "infortunio", grazie alla generazione di una sorta d’indicatore di anticipo. Questa procedura è stata testata su un’azienda petrolchimica italiana.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.

A fuzzy risk attitude classification based on prospect theory

Traditional methods do not actually measure peoples’ risk attitude naturally and precisely. Therefore, a fuzzy risk attitude classification method is developed. Since the prospect theory is usually considered as an effective model of decision making, the personalized parameters in prospect theory are firstly fuzzified to distinguish people with different risk attitudes, and then a fuzzy classification database schema is applied to calculate the exact value of risk value attitude and risk be- havior attitude. Finally, by applying a two-hierarchical clas- sification model, the precise value of synthetical risk attitude can be acquired.

Fuzzy Online Reputation Analysis Framework

The fuzzy online reputation analysis framework, or “foRa” (plural of forum, the Latin word for marketplace) framework, is a method for searching the Social Web to find meaningful information about reputation. Based on an automatic, fuzzy-built ontology, this framework queries the social marketplaces of the Web for reputation, combines the retrieved results, and generates navigable Topic Maps. Using these interactive maps, communications operatives can zero in on precisely what they are looking for and discover unforeseen relationships between topics and tags. Thus, using this framework, it is possible to scan the Social Web for a name, product, brand, or combination thereof and determine query-related topic classes with related terms and thus identify hidden sources. This chapter also briefly describes the youReputation prototype (www.youreputation.org), a free web-based application for reputation analysis. In the course of this, a small example will explain the benefits of the prototype.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Applying the Fuzzy Analytical Hierarchy Process in Cognitive Cities

This paper introduces a mobile application (app) as the first part of an interactive framework. The framework enhances the inter-action between cities and their citizens, introducing the Fuzzy Analytical Hierarchy Process (FAHP) as a potential information acquisition method to improve existing citizen management en-deavors for cognitive cities. Citizen management is enhanced by advanced visualization using Fuzzy Cognitive Maps (FCM). The presented app takes fuzziness into account in the constant inter-action and continuous development of communication between cities or between certain of their entities (e.g., the tax authority) and their citizens. A transportation use case is implemented for didactical reasons.

Applying the fuzzy analytical hierarchy process in digital marketing

The fuzzy analytical network process (FANP) is introduced as a potential multi-criteria-decision-making (MCDM) method to improve digital marketing management endeavors. Today’s information overload makes digital marketing optimization, which is needed to continuously improve one’s business, increasingly difficult. The proposed FANP framework is a method for enhancing the interaction between customers and marketers (i.e., involved stakeholders) and thus for reducing the challenges of big data. The presented implementation takes realities’ fuzziness into account to manage the constant interaction and continuous development of communication between marketers and customers on the Web. Using this FANP framework, the marketers are able to increasingly meet the varying requirements of their customers. To improve the understanding of the implementation, advanced visualization methods (e.g., wireframes) are used.

Use of a modified Delphi panel to identify and weight criteria for prioritization of zoonotic diseases in Switzerland

Zoonotic diseases have a significant impact on public health globally. To prevent or reduce future zoonotic outbreaks, there is a constant need to invest in research and surveillance programs while updating risk management strategies. However, given the limited resources available, disease prioritization based on the need for their control and surveillance is important. This study was performed to identify and weight disease criteria for the prioritization of zoonotic diseases in Switzerland using a semi-quantitative research method based on expert opinion. Twenty-eight criteria relevant for disease control and surveillance, classified under five domains, were selected following a thorough literature review, and these were evaluated and weighted by seven experts from the Swiss Federal Veterinary Office using a modified Delphi panel. The median scores assigned to each criterion were then used to rank 16 notifiable and/or emerging zoonoses in Switzerland. The experts weighted the majority of the criteria similarly, and the top three criteria were Severity of disease in humans, incidence and prevalence of the disease in humans and treatment in humans. Based on these weightings, the three highest ranked diseases were Avian Influenza, Bovine Spongiform Encephalitis, and Bovine Tuberculosis. Overall, this study provided a preliminary list of criteria relevant for disease prioritization in Switzerland. These were further evaluated in a companion study which involved a quantitative prioritization method and multiple stakeholders.

Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.

Aerial Object Following Using Visual Fuzzy Servoing

This article presents a visual servoing system to follow a 3D moving object by a Micro Unmanned Aerial Vehicle (MUAV). The presented control strategy is based only on the visual information given by an adaptive tracking method based on the colour information. A visual fuzzy system has been developed for servoing the camera situated on a rotary wing MAUV, that also considers its own dynamics. This system is focused on continuously following of an aerial moving target object, maintaining it with a fixed safe distance and centred on the image plane. The algorithm is validated on real flights on outdoors scenarios, showing the robustness of the proposed systems against winds perturbations, illumination and weather changes among others. The obtained results indicate that the proposed algorithms is suitable for complex controls task, such object following and pursuit, flying in formation, as well as their use for indoor navigation

Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs

There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson’s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.

Contribución al estudio de dos conceptos básicos de la lógica fuzzy

La tesis doctoral CONTRIBUCIÓN AL ESTUDIO DE DOS CONCEPTOS BÁSICOS DE LA LÓGICA FUZZY constituye un conjunto de nuevas aportaciones al análisis de dos elementos básicos de la lógica fuzzy: los mecanismos de inferencia y la representación de predicados vagos. La memoria se encuentra dividida en dos partes que corresponden a los dos aspectos señalados. En la Parte I se estudia el concepto básico de «estado lógico borroso». Un estado lógico borroso es un punto fijo de la aplicación generada a partir de la regla de inferencia conocida como modus ponens generalizado. Además, un preorden borroso puede ser representado mediante los preórdenes elementales generados por el conjunto de sus estados lógicos borrosos. El Capítulo 1 está dedicado a caracterizar cuándo dos estados lógicos dan lugar al mismo preorden elemental, obteniéndose también un representante de la clase de todos los estados lógicos que generan el mismo preorden elemental. El Capítulo finaliza con la caracterización del conjunto de estados lógicos borrosos de un preorden elemental. En el Capítulo 2 se obtiene un subconjunto borroso trapezoidal como una clase de una relación de indistinguibilidad. Finalmente, el Capítulo 3 se dedica a estudiar dos tipos de estados lógicos clásicos: los irreducibles y los minimales. En el Capítulo 4, que inicia la Parte II de la memoria, se aborda el problema de obtener la función de compatibilidad de un predicado vago. Se propone un método, basado en el conocimiento del uso del predicado mediante un conjunto de reglas y de ciertos elementos distinguidos, que permite obtener una expresión general de la función de pertenencia generalizada de un subconjunto borroso que realice la función de extensión del predicado borroso. Dicho método permite, en ciertos casos, definir un conjunto de conectivas multivaluadas asociadas al predicado. En el último capítulo se estudia la representación de antónimos y sinónimos en lógica fuzzy a través de auto-morfismos. Se caracterizan los automorfismos sobre el intervalo unidad cuando sobre él se consideran dos operaciones: una t-norma y una t-conorma ambas arquimedianas. The PhD Thesis CONTRIBUCIÓN AL ESTUDIO DE DOS CONCEPTOS BÁSICOS DE LA LÓGICA FUZZY is a contribution to two basic concepts of the Fuzzy Logic. It is divided in two parts, the first is devoted to a mechanism of inference in Fuzzy Logic, and the second to the representation of vague predicates. «Fuzzy Logic State» is the basic concept in Part I. A Fuzzy Logic State is a fixed-point for the mapping giving the Generalized Modus Ponens Rule of inference. Moreover, a fuzzy preordering can be represented by the elementary preorderings generated by its Fuzzy Logic States. Chapter 1 contemplates the identity of elementary preorderings and the selection of representatives for the classes modulo this identity. This chapter finishes with the characterization of the set of Fuzzy Logic States of an elementary preordering. In Chapter 2 a Trapezoidal Fuzzy Set as a class of a relation of Indistinguishability is obtained. Finally, Chapter 3 is devoted to study two types of Classical Logic States: irreducible and minimal. Part II begins with Chapter 4 dealing with the problem of obtaining a Compa¬tibility Function for a vague predicate. When the use of a predicate is known by means of a set of rules and some distinguished elements, a method to obtain the general expression of the Membership Function is presented. This method allows, in some cases, to reach a set of multivalued connectives associated to the predicate. Last Chapter is devoted to the representation of antonyms and synonyms in Fuzzy Logic. When the unit interval [0,1] is endowed with both an archimedean t-norm and a an archi-medean t-conorm, it is showed that the automorphisms' group is just reduced to the identity function.