Dissertations / Theses on the topic 'Evolutionary computing; Fuzzy logic'

Thesis (Ph. D.)--Georgia State University, 2005.
1 electronic text (119 p. : ill.) : digital, PDF file. Title from title screen. Rajshekhar Sunderraman, committee chair; Yan-Qing Zhang, Anu Bourgeois, Lifeng Ding, committee members. Description based on contents viewed Apr. 3, 2007. Includes bibliographical references (p. 112-119).

A neutrosophic set is a part of neutrosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Here, we define the set-theoretic operators on an instance of a neutrosophic set, and call it an Interval Neutrosophic Set (INS). We prove various properties of INS, which are connected to operations and relations over INS. We also introduce a new logic system based on interval neutrosophic sets. We study the interval neutrosophic propositional calculus and interval neutrosophic predicate calculus. We also create a neutrosophic logic inference system based on interval neutrosophic logic. Under the framework of the interval neutrosophic set, we propose a data model based on the special case of the interval neutrosophic sets called Neutrosophic Data Model. This data model is the extension of fuzzy data model and paraconsistent data model. We generalize the set-theoretic operators and relation-theoretic operators of fuzzy relations and paraconsistent relations to neutrosophic relations. We propose the generalized SQL query constructs and tuple-relational calculus for Neutrosophic Data Model. We also design an architecture of Semantic Web Services agent based on the interval neutrosophic logic and do the simulation study.

Flexible manufacturing systems (FMS) are production systems consisting of identical multipurpose numerically controlled machines (workstations), automated material handling system, tools, load and unload stations, inspection stations, storage areas and a hierarchical control system. The latter has the task of coordinating and integrating all the components of the whole system for automatic operations. A particular characteristic of FMSs is their complexity along with the difficulties in building analytical models that capture the system in all its important aspects. Thus optimal control strategies, or at least good ones, are hard to find and the full potential of manufacturing systems is not completely exploited.

The complexity of these systems induces a division of the control approaches based on the time frame they are referred to: long, medium and short term. This thesis addresses the short-term control of a FMS. The objective is to define control strategies, based on system state feedback, that fully exploit the flexibility built into those systems. Difficulties arise since the metrics that have to be minimized are often conflicting and some kind of trade-offs must be made using "common sense". The problem constraints are often expressed in a rigid and "crisp" way while their nature is more "fuzzy" and the search for an analytical optimum does not always reflect production needs. Indeed, practical and production oriented approaches are more geared toward a good and robust solution.

This thesis addresses the above mentioned problems proposing a fuzzy scheduler and a reinforcement-learning approach to tune its parameters. The learning procedure is based on evolutionary programming techniques and uses a performance index that contains the degree of satisfaction of multiple and possibly conflicting objectives. This approach addresses the design of the controller by means of language directives coming from the management, thus not requiring any particular interface between management and designers.

The performances of the fuzzy scheduler are then compared to those of commonly used heuristic rules. The results show some improvement offered by fuzzy techniques in scheduling that, along with ease of design, make their applicability promising. Moreover, fuzzy techniques are effective in reducing system congestion as is also shown by slower performance degradation than heuristics for decreasing inter- arrival time of orders. Finally, the proposed paradigm could be extended for on-line adaptation of the scheduler, thus fully responding to the flexibility needs of FMSs.

Master of Science

The objective of the thesis is to introduce a new fuzzy logic control application, develop the associated mathematical theory and prove the concept and its advantages through comparative simulation with existing, classical, methods. A stable fuzzy logic controller for the master-slave current sharing loop of a paralleled DC-DC system is presented that exhibits a considerably improved large signal performance over the presently employed, small signal designed compensators, both in terms of system response and control effort. Because of high system complexity, the present small signal designs are unable to give a good response for large load changes and line transients. Fuzzy logic, by dealing naturally with nonlinearities, offers a superior controller type, for this type of applications. The design uses a PID expert to derive the fuzzy inference rules, and simulation results show a good parameter insensitive transient response over a wide range load-step responses, e.g., from 25% to 75% of the nominal load. Current sharing control is formulated as a tracking problem and stability is ensured through adaptation or supervisory control on a Lyapunov trajectory. The technique benefits also from the heuristic approach to the problem that overcomes the complexity in modeling such systems and, hence, offers a practical engineering tool, amenable to both analog and digital implementations.
Ph. D.

As cloud computing continues to gain popularity and its economies of scale continue to improve, stakeholders want to minimise the security risk, protect their data and other resources while maximising the gains of using any cloud resources and its application. It is predicted that by the end of 2017, bulk of spending on any IT infrastructure would be on cloud infrastructure and services as many critical applications – power, medical, finance among others continue to be migrated onto cloud platforms. For these sectors, the security challenges of cloud adoption continue to be of a great concern even with its benefits. The ability to trust and measure security levels of any cloud platform is paramount in the complete adoption and use of cloud computing in many mission critical sectors. In-depth study and analysis of the trustworthiness of various cloud based platforms/systems are often limited by the complex and dynamic nature of cloud and often do not correctly foresee or practically determine the varying trust relationship between and across the cloud layers, components (schedulers), algorithms and applications especially at a large scale. Tradition security and privacy controls continue to be implemented on cloud but due to its fluid and dynamic nature, research work in the area of end-user attestable trust evaluation of the cloud platform is limited. Most of the current simulation tools do not cater for modelling of Trust on scalable multi-layer cloud deployments (including workflow and infrastructure).Even as these tools continue to be implemented none has been used to cater for all the layers of the cloud platform. This research presents a deployment of trusted computing applied in cloud computing suited for mission critical applications. It attempts to simplify the integration of trusted platform module based integrity measurement into cloud infrastructure. Using Eucalyptus cloud software on server-grade hardware, a trusted community cloud platform was deployed on the Brunel Network as presented in Chapter 3. Security is enhanced by the integration of an end-user accessible TPM integrity measurement and verification process; this guarantees trusted ownership and integrity of the uploaded data and provides additional level of trust for the cloud platform. This research further presents a technique which allows data owners to first secure their data offline by inserting colour drops into the data using steganography. The colour drops are used to detect unauthorised modifications, verify data owner in the event the copyright of the data is in dispute and identify the path through which it was tampered with. This process ensures integrity and confidentiality of the resources. This thesis also presents a trust model using fuzzy logic which was simulated using Simulink in Matlab and subsequently evaluated on an experimental platform deployed on the Brunel network. Using this model, end-users can determine the trust values for a cloud platform or service, as well as, classify and compare various cloud platforms. The results obtained suggest that the outputs of this research work can improve end-user confidence when selecting or consuming cloud resources with enhanced data integrity and protection.

Increasing demands, shrinking financial and human resources, and increased infrastructure deterioration have made the task of maintaining the infrastructure systems more challenging than ever before. Life-cycle cost analysis (LCCA) is an important tool for transportation infrastructure management, which is used extensively to support project level decisions, and is increasingly being applied to enhance network level analysis. However, traditional LCCA tools cannot practically and effectively utilize expert knowledge and handle ambiguous uncertainties. The main objective of this dissertation was to develop enhanced LCCA models using soft computing (mainly fuzzy logic) techniques. The proposed models use available "real-world" information to forecast life-cycle costs of competing maintenance and rehabilitation strategies and support infrastructure management decisions. A critical review of available soft computing techniques and their applications in infrastructure management suggested that these techniques provide appealing alternatives for supporting many of the infrastructure management functions. In particular, LCCA often utilizes information that is uncertain, ambiguous and incomplete, which is obtained from both existing databases and expert opinion. Consequently, fuzzy logic techniques were selected to enhance life-cycle cost analysis of transportation infrastructure investments because they provide a formal approach for the effective treatment of these types of information. The dissertation first proposes a fuzzy-logic-based decision-support model, whose inference rules can be customized according to agency's management policies and expert opinion. The feasibility and practicality of the proposed model is illustrated by its implementation in a life-cycle cost analysis algorithm for comparing and selecting pavement maintenance, rehabilitation and reconstruction (MR&R) policies. To enhance the traditional probabilistic LCCA model, the fuzzy-logic-based model is then incorporated into the risk analysis process. A fuzzy logic approach for determining the timing of pavement MR&R treatments in a probabilistic LCCA model for selecting pavement MR&R strategies is proposed. The proposed approach uses performance curves and fuzzy-logic triggering models to determine the most effective timing of pavement MR&R activities. The application of the approach in a case study demonstrates that the fuzzy-logic-based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the ambiguous uncertainty inherent to the process. Finally, the research establishes a systematic method to calibrate the fuzzy-logic based rehabilitation decision model using real cases extracted from the Long Term Pavement Performance (LTPP) database. By reinterpreting the model in the form of a neuro-fuzzy system, the calibration algorithm takes advantage of the learning capabilities of artificial neural networks for tuning the fuzzy membership functions and rules. The practicality of the method is demonstrated by successfully tuning the treatment selection model to distinguish between rehabilitation (light overlay) and do-nothing cases.
Ph. D.

Karstic aquifers present a great extension throughout the world (12 % of the emerged grounds) and in particular in countries around the Mediterranean sea (from 20 to 90 % of the surface of the Mediterranean countries). These aquifers represent important groundwater potentialities. In both countries (France and Turkey), these karstic aquifers are exploited for drinking water supply and other economic activities (agriculture, pisciculture,. . . ) and constitute the single water resource in certain areas of these countries. The role of these karstic hydrosystems in the social and economical context of some areas is extremely crucial. These aquifers are very vulnerable to contaminations and are overexploited, taking into account the increase in water requirements. Karstic aquifers are very complex and show very particular characteristics (strong heterogeneity, anisotropy, discontinuity of the medium, hierarchization of the flows), which make difficult any classical approach of identification of these systems and management of their water resources. The difficulty of modeling comes owing to the fact that these karstic systems are highly non-linear and are little adapted to the classical methods of identification (flow and transport modeling based on Darcy law). Soft computing methods (Artificial neural networks and fuzzy logic) are playing a key role in the modelling of complex and nonlinear problems. The uncertainty associated with the data, the immense size of the data to deal with, the diversity of the data type and the associated scales are important factors to rely on unconventional mathematical tools such as soft computing. In this thesis, the three pilot experimental sites are: the karst of La Rochefoucauld in France which is used in particular for the supply of the regional capital Angouleme; Orbe karstic spring which supplies the rural city of Arette (Southwestern France) and the karst of Safranbolu in Turkey which supplies the town of Safranbolu. The principal objective of this work is to test the ability of soft computing methods for modeling of complex karstic systems and predicting the discharge rates and quality of water at the outlets of these systems. The results show the ability of Soft computing method to model these highly non linear systems. All the modelling work was carried out using the Matlab programming environment
Les aquifères karstiques présentent une grande extension à travers le monde (12 % des terres émergées) et notamment dans les pays du pourtour méditerranéen (de 20 à 90 % de la surface des pays méditerranéens). Ces aquifères représentent d’importantes potentialités en eau souterraine. Dans les deux pays concernés (France et Turquie), ces aquifères karstiques sont exploités pour l’alimentation en eau potable et pour d’autres activités économiques (agriculture, pisciculture, …) et constituent parfois l’unique ressource en eau dans certaines régions de ces pays. Le rôle des hydrosystèmes karstiques dans le développement social et économique de telles régions est de ce fait extrêmement crucial. Ces aquifères sont cependant très vulnérables aux contaminations et font l’objet de surexploitation, compte tenu de l’accroissement des besoins en eau. Les aquifères karstiques sont très complexes et présentent des caractéristiques très particulières (forte hétérogénéité, anisotropie, discontinuité du milieu, hiérarchisation des écoulements) qui rendent difficile toute approche classique d’identification de ces milieux et de gestion de leurs ressources en eau. La difficulté de modélisation provient du fait que ces systèmes karstiques sont hautement non-linéaires et sont peu adaptés aux méthodes d’identification classiques (modélisation des flux d’eau et de matière basée sur la loi de Darcy). L’objectif principal de cette thèse est la modélisation de ces systèmes à l’aide d’approches nouvelles (méthodes de ‘soft computing’) dans le but de prédire les flux et la qualité des eaux dans ces systèmes. Les systèmes retenus sont : le karst de La Rochefoucauld en France qui est utilisé notamment pour l’alimentation de la capitale régionale Angoulême ; Le karst de l’Orbe qui est utilisé pour la ville d’Arrete et le karst de Safranbolu en Turquie qui alimente la ville de Safranbolu. Dans cette thèse, les points suivants sont étudiés :Installation, calibrage des systèmes d'enregistrement des données (data logger) et contrôle des sondes, Recherche sur les différents types de systèmes karstiques (La Rochefoucauld et Orbe en France et Safranbolu en Turquie, Détermination de la pluie efficace sur le karst de Safranbolu en utilisant des données hydrométéorologiques (y compris la neige). Interprétation des données des enregistreurs automatiques et des analyses chimiques effectuées en laboratoire pour mieux comprendre le fonctionnement du karst, Développement des modèles ‘soft computing’ (réseaux de neurone et neuro-floue) concernant les 3 systèmes karstiques étudiés. Discussion sur les méthodes (réseaux de neurone et neuro–flou) et les calibrages des modèles. Comparaison des modèles avec entrée simple et entrées multiples. Détermination des propriétés faibles et fortes de ces modèles. Les conclusions obtenues sont les suivantes :Les corrélations entre débits simulés et débits observés sont élevées pour le karst de La Rochefoucauld. Le coefficient de détermination pour la phase d’apprentissage est élevé (R2=0. 90). Les hydrogrammes permettent de se rendre compte que l’apprentissage et la validation des modèles sont tout à fait opérationnels, puisqu’on remarque que les parties montantes des hydrogrammes simulés correspondent bien à de fortes pluies. De plus la forme des hydrogrammes simulés (montée rapide, suivie d’une décrue assez lente) est semblable à celle des hydrogrammes réels de sources d’aquifères karstiques (Voir Figure 4. 8). Les données de pluie utilisées concernent la pluie brute, sans transformation en pluie efficace, ce qui permet de s’affranchir de certaines hypothèses simplificatrices non vérifiables pour l’aquifère de La Rochefoucauld. Par contre nous avons retenu la pluie efficace comme entrée des modèles du karst de Safranbolu. L’effet de la fonte de neige et une correction de certaines données par rapport à l’altitude ont été intégrées dans l’évaluation de la pluie efficace. Les modèles ‘soft computing’ pluie - qualité de l’eau (Turbidité, Conductivité électrique) ont été développés. Pour les modèles neuro-flous la phase d’apprentissage est beaucoup plus lente et nécessite un moyen de calcul puissant. Les modèles hybrides (neuro-flous) sont plus efficaces que les modèles de réseaux de neurones. Les modèles neuro-flous ont un coefficient de détermination plus élevé que les modèles de réseaux de neurones (Voir Table 4. 5). Les variables d’entrée ont une très grande importance dans le développement des méthodes ‘soft computing’. En augmentant les données d’entrée, les modèles peuvent calculer de meilleurs résultats. Ainsi, les modèles avec deux variables d’entrées sont caractérisés par un coefficient de détermination plus élevé que les modèles à une variable d’entrée. En outre, pour la prédiction des valeurs extrêmes, les modèles avec entrées multiples sont plus efficaces que les modèles à entrée simple (Voir Table 4. 9). Pour les aquifères karstiques d’Orbe et de Safranbolu, la prédiction des paramètres hydrochimiques (conductivité électrique et turbidité) à été egalement modelisée à l’aide des méthodes ‘soft computing’. Les résultats montrent que la forme des chemogrammes simulés est semblable à celle des chemogrammes réels (Voir Figures 5. 19, 5. 22, 5. 25, 5. 27, 5. 28, 5. 29, 5. 30). Par contre, on constate aussi que pour obtenir des prédictions plus longues, le modèle aura besoin de séries de données plus longues. Ainsi, les résultats obtenus sont très encourageants et permettent d’envisager des perspectives intéressantes et nouvelles de modélisation des aquifères karstiques, qui sont des systèmes hautement non-linéaires

The Generalized Constraint Language (GCL), introduced by Zadeh, is the essence of Computing with Words (CW). It provides an genda to represent the meaning of imprecise words and phrases in natural language and introduces advanced techniques to perform reasoning on imprecise knowledge. Despite its fundamental role, the definition of GCL has remained informal since its introduction by Zadeh and, to our knowledge, no attempt has been made to formalize GCL or to build a working GCL deduction system. In this dissertation, two main interrelated objectives are pursued: First, the syntax and semantics of GCL are formalized in a logical setting. The notion of soundness of a GCL argument is defined and Zadeh's inference rules are proven sound in the defined language. Second, a CW Expert System Shell (CWSHELL) is implemented for the realization of a GCL deduction system. The CWSHELL software allows users to express their knowledge in terms of GCL formulas and pose queries to a GCL knowledge base. The richness of GCL language allows CWSHELL to greatly surpass current fuzzy logic expert systems both in its knowledge representation and reasoning capabilities. While many available fuzzy logic toolboxes can only represent knowledge in terms of fuzzy-if-then rules, CWShell goes beyond simple fuzzy conditional statements and performs a chain of reasoning on complex fuzzy propositions containing generalized constraints, fuzzy arithmetic expressions, fuzzy quantifiers, and fuzzy relations. To explore the application of CWSHELL, a realistic case study is developed to compute the auto insurance premium based on an imprecise knowledge base. The alpha version of CWSHELL along with the case study and documentation is available for download at http://cwjess.cs.siu.edu/.

Computing with words (CW) provides symbolic and semantic methodology to deal with imprecise information associated with natural language. The CW paradigm rooted in fuzzy logic, when coupled with an expert system, offers a general methodology for computation with fuzzy variables and a fusion of natural language propositions for this purpose. Fuzzy variables encode the semantic knowledge, and hence, the system can understand the meaning of the symbols. The use of words not only simplifies the knowledge acquisition process, but can also eliminate the need of a human knowledge engineer. CW encapsulates various fuzzy logic techniques developed in past decades and formalizes them. Z-number is an emerging paradigm that has been utilized in computing with words among other constructs. The concept of Z-number is intended to provide a basis for computation with numbers that deals with reliability and likelihood. Z-numbers are confluence of the two most prominent approaches to uncertainty, probability and possibility, that allow computations on complex statements. Certain computations related to Z-numbers are ambiguous and complicated leading to their slow adaptation into areas such as computing with words. Moreover, as acknowledged by Zadeh, there does not exist a unique solution to these problems. The biggest contributing factor to the complexity is the use of probability distributions in the computations. This dissertation seeks to provide an applied model of Z-number based on certain realistic assumptions regarding the probability distributions. Algorithms are presented to implement this model and integrate it into an expert system shell for computing with words called CWShell. CWShell is a software tool that abstracts the underlying computation required for computing with words and provides a convenient way to represent and reason on a unstructured natural language.

Real-world problems, especially those that involve natural systems, are complex and composed of many nondeterministic components having non-linear coupling. It turns out that in dealing with such systems, one has to face a high degree of uncertainty and tolerate imprecision. Classical system models based on numerical analysis, crisp logic or binary logic have characteristics of precision and categoricity and classified as hard computing approach. In contrast soft computing approaches like probabilistic reasoning, fuzzy logic, artificial neural nets etc have characteristics of approximation and dispositionality. Although in hard computing, imprecision and uncertainty are undesirable properties, in soft computing the tolerance for imprecision and uncertainty is exploited to achieve tractability, lower cost of computation, effective communication and high Machine Intelligence Quotient (MIQ). Proposed thesis has tried to explore use of different soft computing approaches to handle uncertainty in environmental risk management. The work has been divided into three parts consisting five papers.
In the first part of this thesis different uncertainty propagation methods have been investigated. The first methodology is generalized fuzzy α-cut based on the concept of transformation method. A case study of uncertainty analysis of pollutant transport in the subsurface has been used to show the utility of this approach. This approach shows superiority over conventional methods of uncertainty modelling. A Second method is proposed to manage uncertainty and variability together in risk models. The new hybrid approach combining probabilistic and fuzzy set theory is called Fuzzy Latin Hypercube Sampling (FLHS). An important property of this method is its ability to separate randomness and imprecision to increase the quality of information. A fuzzified statistical summary of the model results gives indices of sensitivity and uncertainty that relate the effects of variability and uncertainty of input variables to model predictions. The feasibility of the method is validated to analyze total variance in the calculation of incremental lifetime risks due to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) for the residents living in the surroundings of a municipal solid waste incinerator (MSWI) in Basque Country, Spain.
The second part of this thesis deals with the use of artificial intelligence technique for generating environmental indices. The first paper focused on the development of a Hazzard Index (HI) using persistence, bioaccumulation and toxicity properties of a large number of organic and inorganic pollutants. For deriving this index, Self-Organizing Maps (SOM) has been used which provided a hazard ranking for each compound. Subsequently, an Integral Risk Index was developed taking into account the HI and the concentrations of all pollutants in soil samples collected in the target area. Finally, a risk map was elaborated by representing the spatial distribution of the Integral Risk Index with a Geographic Information System (GIS). The second paper is an improvement of the first work. New approach called Neuro-Probabilistic HI was developed by combining SOM and Monte-Carlo analysis. It considers uncertainty associated with contaminants characteristic values. This new index seems to be an adequate tool to be taken into account in risk assessment processes. In both study, the methods have been validated through its implementation in the industrial chemical / petrochemical area of Tarragona.
The third part of this thesis deals with decision-making framework for environmental risk management. In this study, an integrated fuzzy relation analysis (IFRA) model is proposed for risk assessment involving multiple criteria. The fuzzy risk-analysis model is proposed to comprehensively evaluate all risks associated with contaminated systems resulting from more than one toxic chemical. The model is an integrated view on uncertainty techniques based on multi-valued mappings, fuzzy relations and fuzzy analytical hierarchical process. Integration of system simulation and risk analysis using fuzzy approach allowed to incorporate system modelling uncertainty and subjective risk criteria. In this study, it has been shown that a broad integration of fuzzy system simulation and fuzzy risk analysis is possible.
In conclusion, this study has broadly demonstrated the usefulness of soft computing approaches in environmental risk analysis. The proposed methods could significantly advance practice of risk analysis by effectively addressing critical issues of uncertainty propagation problem.
Los problemas del mundo real, especialmente aquellos que implican sistemas naturales, son complejos y se componen de muchos componentes indeterminados, que muestran en muchos casos una relación no lineal. Los modelos convencionales basados en técnicas analíticas que se utilizan actualmente para conocer y predecir el comportamiento de dichos sistemas pueden ser muy complicados e inflexibles cuando se quiere hacer frente a la imprecisión y la complejidad del sistema en un mundo real. El tratamiento de dichos sistemas, supone el enfrentarse a un elevado nivel de incertidumbre así como considerar la imprecisión. Los modelos clásicos basados en análisis numéricos, lógica de valores exactos o binarios, se caracterizan por su precisión y categorización y son clasificados como una aproximación al hard computing. Por el contrario, el soft computing tal como la lógica de razonamiento probabilístico, las redes neuronales artificiales, etc., tienen la característica de aproximación y disponibilidad. Aunque en la hard computing, la imprecisión y la incertidumbre son propiedades no deseadas, en el soft computing la tolerancia en la imprecisión y la incerteza se aprovechan para alcanzar tratabilidad, bajos costes de computación, una comunicación efectiva y un elevado Machine Intelligence Quotient (MIQ). La tesis propuesta intenta explorar el uso de las diferentes aproximaciones en la informática blanda para manipular la incertidumbre en la gestión del riesgo medioambiental. El trabajo se ha dividido en tres secciones que forman parte de cinco artículos.
En la primera parte de esta tesis, se han investigado diferentes métodos de propagación de la incertidumbre. El primer método es el generalizado fuzzy α-cut, el cual está basada en el método de transformación. Para demostrar la utilidad de esta aproximación, se ha utilizado un caso de estudio de análisis de incertidumbre en el transporte de la contaminación en suelo. Esta aproximación muestra una superioridad frente a los métodos convencionales de modelación de la incertidumbre. La segunda metodología propuesta trabaja conjuntamente la variabilidad y la incertidumbre en los modelos de evaluación de riesgo. Para ello, se ha elaborado una nueva aproximación híbrida denominada Fuzzy Latin Hypercube Sampling (FLHS), que combina los conjuntos de la teoría de probabilidad con la teoría de los conjuntos difusos. Una propiedad importante de esta teoría es su capacidad para separarse los aleatoriedad y imprecisión, lo que supone la obtención de una mayor calidad de la información. El resumen estadístico fuzzificado de los resultados del modelo generan índices de sensitividad e incertidumbre que relacionan los efectos de la variabilidad e incertidumbre de los parámetros de modelo con las predicciones de los modelos. La viabilidad del método se llevó a cabo mediante la aplicación de un caso a estudio donde se analizó la varianza total en la cálculo del incremento del riesgo sobre el tiempo de vida de los habitantes que habitan en los alrededores de una incineradora de residuos sólidos urbanos en Tarragona, España, debido a las emisiones de dioxinas y furanos (PCDD/Fs).
La segunda parte de la tesis consistió en la utilización de las técnicas de la inteligencia artificial para la generación de índices medioambientales. En el primer artículo se desarrolló un Índice de Peligrosidad a partir de los valores de persistencia, bioacumulación y toxicidad de un elevado número de contaminantes orgánicos e inorgánicos. Para su elaboración, se utilizaron los Mapas de Auto-Organizativos (SOM), que proporcionaron un ranking de peligrosidad para cada compuesto. A continuación, se elaboró un Índice de Riesgo Integral teniendo en cuenta el Índice de peligrosidad y las concentraciones de cada uno de los contaminantes en las muestras de suelo recogidas en la zona de estudio. Finalmente, se elaboró un mapa de la distribución espacial del Índice de Riesgo Integral mediante la representación en un Sistema de Información Geográfico (SIG). El segundo artículo es un mejoramiento del primer trabajo. En este estudio, se creó un método híbrido de los Mapas Auto-organizativos con los métodos probabilísticos, obteniéndose de esta forma un Índice de Riesgo Integrado. Mediante la combinación de SOM y el análisis de Monte-Carlo se desarrolló una nueva aproximación llamada Índice de Peligrosidad Neuro-Probabilística. Este nuevo índice es una herramienta adecuada para ser utilizada en los procesos de análisis. En ambos artículos, la viabilidad de los métodos han sido validados a través de su aplicación en el área de la industria química y petroquímica de Tarragona (Cataluña, España).
El tercer apartado de esta tesis está enfocado en la elaboración de una estructura metodológica de un sistema de ayuda en la toma de decisiones para la gestión del riesgo medioambiental. En este estudio, se presenta un modelo integrado de análisis de fuzzy (IFRA) para la evaluación del riesgo cuyo resultado depende de múltiples criterios. El modelo es una visión integrada de las técnicas de incertidumbre basadas en diseños de valoraciones múltiples, relaciones fuzzy y procesos analíticos jerárquicos inciertos. La integración de la simulación del sistema y el análisis del riesgo utilizando aproximaciones inciertas permitieron incorporar la incertidumbre procedente del modelo junto con la incertidumbre procedente de la subjetividad de los criterios. En este estudio, se ha demostrado que es posible crear una amplia integración entre la simulación de un sistema incierto y de un análisis de riesgo incierto.
En conclusión, este trabajo demuestra ampliamente la utilidad de aproximación Soft Computing en el análisis de riesgos ambientales. Los métodos propuestos podría avanzar significativamente la práctica de análisis de riesgos de abordar eficazmente el problema de propagación de incertidumbre.

Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.

Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of Multi-Objective Genetic Algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated Genetic Algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary Algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN's initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective.

Risk management is an essential tool for ensuring the safety and timeliness of maritime operations and transportation. Some of the many risk factors that can compromise the smooth operation of maritime activities include harsh weather and pirate activity. However, identifying and quantifying the extent of these risk factors for a particular vessel is not a trivial process. One challenge is that processing the vast amounts of automatic identification system (AIS) messages generated by the ships requires significant computational resources. Another is that the risk management process partially relies on human expertise, which can be timeconsuming and error-prone. In this thesis, an existing Risk Management Framework (RMF) is augmented to address these issues. A parallel/distributed version of the RMF is developed to e ciently process large volumes of AIS data and assess the risk levels of the corresponding vessels in near-real-time. A genetic fuzzy system is added to the RMF's Risk Assessment module in order to automatically learn the fuzzy rule base governing the risk assessment process, thereby reducing the reliance on human domain experts. A new weather risk feature is proposed, and an existing regional hostility feature is extended to automatically learn about pirate activity by ingesting unstructured news articles and incident reports. Finally, a geovisualization tool is developed to display the position and risk levels of ships at sea. Together, these contributions pave the way towards truly automatic risk management, a crucial component of modern maritime solutions. The outcomes of this thesis will contribute to enhance Larus Technologies' Total::Insight, a risk-aware decision support system successfully deployed in maritime scenarios.

Nous vivons aujourd’hui dans l'ère cybernétique où de nouvelles technologies naissent chaque jour avec la promesse de rendre la vie humaine plus confortable, pratique et sûre. Parmi ces nouvelles technologies, l'informatique mobile se développe en influençant la vie de l’utilisateur. En effet, les plates-formes mobiles (smartphone, tablette) sont devenues les meilleurs compagnons de l’utilisateur pour mener à bien ses activités quotidiennes, comme les activités commerciales ou de divertissement. En jouant ces rôles importants, les plates-formes mobiles doivent opérer dans des environnements de confiance. Dans cette thèse, nous étudions la sécurité des plates-formes mobiles en passant d’un niveau de sécurité primitif qui s’appuie sur les plates-formes de confiance, à un niveau plus sophistiqué qui se base sur de l’intelligence bio-inspirée. Plus précisément, après avoir abordé les défis du cloud computing mobile (MCC), nous développons une étude de cas appelée Droplock pour le cloud mobile et nous étudions son efficacité énergétique et ses performances pour illustrer le modèle MCC. En outre, en s’appuyant sur les plates-formes de confiance (comme les TPM), nous avons introduit un nouveau schéma d'attestation à distance pour sécuriser les plates-formes mobiles dans le contexte du cloud mobile. Pour améliorer le niveau de sécurité et être adaptatif au contexte, nous avons utilisé de la logique floue combinant un système de colonies de fourmis pour évaluer la confiance et la réputation du cloud mobile basé sur la notion de cloudlets
As living in the cyber era, we admit that a dozen of new technologies have been born every day with the promises that making a human life be more comfortable, convenient and safe. In the forest of new technologies, mobile computing is raise as an essential part of human life. Normally, mobile devices have become the best companions in daily activities. They have served us from the simple activities like entertainment to the complicated one as business operations. As playing the important roles, mobile devices deserve to work in the environment which they can trust for serving us better. In this thesis, we investigate the way to secure mobile devices from the primitive security level (Trusted Platforms) to the sophisticated one (bio-inspired intelligence). More precisely, after addressing the challenges of mobile cloud computing (MCC), we have studied the real-case of mobile cloud computing, in terms of energy efficiency and performance, as well as proposed a demonstration of particular MCC model, called Droplock system. Moreover, taking advantages of trusted platform module functionality, we introduced a novel schema of remote attestation to secure mobile devices in the context of Mobile-Cloud based solution. To enhance the security level, we used fuzzy logic combining with ant colony system to assess the trust and reputation for securing another mobile cloud computing model based on the cloudlet notion

Muitos problemas de otimização multi-objetivo utilizam os algoritmos evolutivos para encontrar as melhores soluções. Muitos desses algoritmos empregam as fronteiras de Pareto como estratégia para obter tais soluções. Entretando, conforme relatado na literatura, há a limitação da fronteira para problemas com até três objetivos, podendo tornar seu emprego insatisfatório para os problemas com quatro ou mais objetivos. Além disso, as propostas apresentadas muitas vezes eliminam o emprego dos algoritmos evolutivos, os quais utilizam tais fronteiras. Entretanto, as características dos algoritmos evolutivos os qualificam para ser empregados em problemas de otimização, como já vem sendo difundido pela literatura, evitando eliminá-lo por causa da limitação das fronteiras de Pareto. Assim sendo, neste trabalho se buscou eliminar as fronteiras de Pareto e para isso utilizou a lógica Fuzzy, mantendo-se assim o emprego dos algoritmos evolutivos. O problema escolhido para investigar essa substituição foi o problema de predição de estrutura terciária de proteínas, pois além de se encontrar em aberto é de suma relevância para a área de bioinformática.
Several multi-objective optimization problems utilize evolutionary algorithms to find the best solution. Some of these algoritms make use of the Pareto front as a strategy to find these solutions. However, according to the literature, the Pareto front limitation for problems with up to three objectives can make its employment unsatisfactory in problems with four or more objectives. Moreover, many authors, in most cases, propose to remove the evolutionay algorithms because of Pareto front limitation. Nevertheless, characteristics of evolutionay algorithms qualify them to be employed in optimization problems, as it has being spread out by literature, preventing to eliminate it because the Pareto front elimination. Thus being, this work investigated to remove the Pareto front and for this utilized the Fuzzy logic, remaining itself thus the employ of evolutionary algorithms. The choice problem to investigate this remove was the protein tertiary structure prediction, because it is a open problem and extremely relevance to bioinformatic area.

This study investigates E-banking Operational Risk Assessment (ORA) to enable the development of a new ORA framework and methodology. The general view is that E-banking systems have modified some of the traditional banking risks, particularly Operational Risk (OR) as suggested by the Basel Committee on Banking Supervision in 2003. In addition, recent E-banking financial losses together with risk management principles and standards raise the need for an effective ORA methodology and framework in the context of E-banking. Moreover, evaluation tools and / or methods for ORA are highly subjective, are still in their infant stages, and have not yet reached a consensus. Therefore, it is essential to develop valid and reliable methods for effective ORA and evaluations. The main contribution of this thesis is to apply Fuzzy Inference System (FIS) and Tree Augmented Naïve Bayes (TAN) classifier as standard tools for identifying OR, and measuring OR exposure level. In addition, a new ORA methodology is proposed which consists of four major steps: a risk model, assessment approach, analysis approach and a risk assessment process. Further, a new ORA framework and measurement metrics are proposed with six factors: frequency of triggering event, effectiveness of avoidance barriers, frequency of undesirable operational state, effectiveness of recovery barriers before the risk outcome, approximate cost for Undesirable Operational State (UOS) occurrence, and severity of the risk outcome. The study results were reported based on surveys conducted with Nigerian senior banking officers and banking customers. The study revealed that the framework and assessment tools gave good predictions for risk learning and inference in such systems. Thus, results obtained can be considered promising and useful for both E-banking system adopters and future researchers in this area.

This study investigates E-banking Operational Risk Assessment (ORA) to enable the development of a new ORA framework and methodology. The general view is that E-banking systems have modified some of the traditional banking risks, particularly Operational Risk (OR) as suggested by the Basel Committee on Banking Supervision in 2003. In addition, recent E-banking financial losses together with risk management principles and standards raise the need for an effective ORA methodology and framework in the context of E-banking. Moreover, evaluation tools and / or methods for ORA are highly subjective, are still in their infant stages, and have not yet reached a consensus. Therefore, it is essential to develop valid and reliable methods for effective ORA and evaluations. The main contribution of this thesis is to apply Fuzzy Inference System (FIS) and Tree Augmented Naïve Bayes (TAN) classifier as standard tools for identifying OR, and measuring OR exposure level. In addition, a new ORA methodology is proposed which consists of four major steps: a risk model, assessment approach, analysis approach and a risk assessment process. Further, a new ORA framework and measurement metrics are proposed with six factors: frequency of triggering event, effectiveness of avoidance barriers, frequency of undesirable operational state, effectiveness of recovery barriers before the risk outcome, approximate cost for Undesirable Operational State (UOS) occurrence, and severity of the risk outcome. The study results were reported based on surveys conducted with Nigerian senior banking officers and banking customers. The study revealed that the framework and assessment tools gave good predictions for risk learning and inference in such systems. Thus, results obtained can be considered promising and useful for both E-banking system adopters and future researchers in this area.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
One of the greatest limitations of Evolutionary Robotics is when transfering controllers evolved by simulation to real environments. This limitation is mainly caused by model simplifications and difficulties to represent dynamic characteristics, whether from the robot or the environment. And this results in performance degradation of the evolved controller after the transfer, a phenomenon called reality gap. Because this problem is a limitation for practical and complex applications of evolutionary robotics, many solutions have been proposed since the 90s. Until now, most of the research use control strategies based on artificial neural networks because they allow algorithms to be evolved with less designer influence. On the other hand, fuzzy logic can also be used for the development of controllers in the field of evolutionary robotics because it also allows emulating human intelligence. Therefore, this dissertation investigates whether fuzzy control systems are more robust than neural control systems, both optimized by a genetic algorithm in simulation and later transferred to a real robot in physical environment in the task of autonomous navigation while avoiding obstacles. The results show that in the analyzed conditions, fuzzy controllers present better transfer characteristics, mainly considering the smoothness of the executed trajectory, and an equivalent performance, when compared with neural controllers.
Uma das grandes limitações da Robótica Evolutiva diz respeito à transferência de controladores evoluídos por simulação e transferidos ao ambiente real. Tal limitação devese, sobretudo, a simplificações de modelo e dificuldades na representação de características dinâmicas, tanto do robô quanto do ambiente, e isso resulta na queda de desempenho do controlador evoluído após a transferência, fenômeno denominado de reality gap. Muitas soluções vêm sendo propostas desde a década de 90, em virtude deste problema ser uma limitação para aplicações práticas e complexas da robótica evolutiva. Até o momento, a maioria dos trabalhos de pesquisa desenvolvidos utiliza estratégias de controle baseadas em redes neurais artificiais por permitirem que algoritmos possam ser evoluídos com menor influência do projetista. Por outro lado, a lógica fuzzy também pode ser usada para o desenvolvimento de controladores no âmbito da robótica evolutiva, pois também permite emular a inteligência humana. Portanto, nesta dissertação é investigado se sistemas de controle fuzzy são mais robustos que sistemas de controle neurais, ambos otimizados por um algoritmo genético em simulação e posteriormente transferidos para um robô real em ambiente físico na tarefa de navegação autônoma evitando obstáculos. Como resultado, obteve-se que nas condições analisadas, os controladores fuzzy apresentaram uma melhor transferência, com destaque para a suavidade da trajetória executada, e um desempenho equivalente, quando comparados com controladores neurais.
São Cristóvão, SE

Neste trabalho foi realizada uma busca por relações, regras e transformações entre duas metodologias constituintes da Inteligência Computacional - a Computação Nebulosa e a Computação Evolutiva. Com a organização e sistematização da existência de tais transformações, obtém-se uma mudança na modelagem de soluções que as utilizam de forma conjunta, possibilitando que teorias e modelos bem estabelecidos em uma das metodologias possam ser aproveitados pela outra de uma forma mais robusta, correta por construção, intrínseca e transparente. Um modelo foi proposto para direcionar o trabalho de pesquisa. Através da análise desse modelo e de uma revisão bibliográfica realizada, transformações pontuais entre as metodologias foram elencadas, e posteriormente consolidadas por meio de experimentos práticos: uma Base de Conhecimento (BC) de um Controlador Lógico Nebuloso foi criada e modificada, conforme a necessidade, através de um Algoritmo Genético (AG). Com a abordagem desenvolvida, além da criação de BCs a partir de pouquíssimo conhecimento sobre o domínio do problema, tornou-se possível a inserção de novos \"comportamentos desejados\" em BCs já existentes, automaticamente, através de AGs. Os resultados desses experimentos, realizados sobre uma plataforma computacional especificada e implementada para este fim, foram apresentados e analisados.
This work addressed a search of relations, rules and transformations between two Computational Intelligence constituent methodologies - Fuzzy Computing and Evolutionary Computing. The existence of these relations changes the actual way of solutions modeling that uses these methodologies, allowing the utilization of well established theories and models of one technique by the other in a more robust, intrinsic and transparent way. Besides the research and systematization of points that indicate the existence of relations between the two methodologies, a model to guide these exploration was proposed. By this model analysis and by the bibliographic revision made, punctual transformations were pointed out, and further consolidated through practical experiments: a Knowledge Base (KB) of a Fuzzy Logic Controller was created and modified automatically by a Genetic Algorithm. With the developed approach, besides the creation of KBs, it became possible to automatically insert new \"desired behaviors\" to existent KBs. The results of such experiments, realized through a computational platform specified and implemented to this task, were presented and analyzed.

Ao considerar um sistema distribuído composto por um conjunto de servidores, clientes e recursos, que caracterizam ambientes como grades ou nuvens computacionais, que oferecem um grande número de recursos distribuídos como CPUs ou máquinas virtuais, os quais são utilizados conjuntamente por diferentes tipos de aplicações, tem-se a necessidade de se ter uma solução para alocação destes recursos. O apoio à alocação dos recursos fornecidos por tais ambientes deve satisfazer todas as solicitações de recursos das aplicações, e fornecer respostas afirmativas para alocação eficiente de recursos, fazer justiça na alocação no caso de pedidos simultâneos entre vários clientes de recursos e responder em um tempo finito a requisições. Considerando tal contexto de grande escala em sistemas distribuídos, este trabalho propõe um algoritmo distribuído para alocação de recursos. Este algoritmo explora a Lógica Fuzzy sempre que um servidor está impossibilitado de atender a uma solicitação feita por um cliente, encaminhando esta solicitação a um servidor remoto. O algoritmo utiliza o conceito de relógio lógico para garantir justiça no atendimento das solicitações feitas em todos os servidores que compartilham recursos. Este algoritmo segue o modelo distribuído, onde uma cópia do algoritmo é executada em cada servidor que compartilha recursos para seus clientes, e todos os servidores tomam parte das decisões com relação a alocação destes recursos. A estratégia desenvolvida tem como objetivo minimizar o tempo de resposta na alocação de recursos, funcionando como um balanceamento de carga em um ambiente cliente-servidor com alto índice de solicitações de recursos pelos clientes. A eficiência do algoritmo desenvolvido neste trabalho foi comprovada através da implementação e comparação com outros algoritmos tradicionais, mostrando a possibilidade de utilização de recursos que pertencem a distintos servidores por uma mesma solicitação de recursos, com a garantia de que esta requisição será atendida, e em um tempo finito.
When considering a distributed system composed of a set of servers, clients, and resources that characterize environments like computational grids or clouds that offer a large number of distributed resources such as CPUs or virtual machines, which are used jointly by different types of applications, there is the need to have a solution for allocating these resources. Support the allocation of resources provided by such environments must satisfy all Requests for resources such applications, and provide affirmative answers to the efficient allocation of resources, to do justice in this allocation in the case of simultaneous Requests from multiple clients and answer these resources in a finite time these Requests. Considering such a context of large- scale distributed systems, this paper proposes a distributed algorithm for resource allocation This algorithm exploits fuzzy logic whenever a server is unable to meet a request made by a client, forwarding this request to a remote server. The algorithm uses the concept of logical clock to ensure fairness in meeting the demands made on all servers that share resources. This algorithm follows a distributed model, where a copy of the algorithm runs on each server that shares resources for its clients and all servers take part in decisions regarding allocation of resources. The strategy developed aims to minimize the response time in allocating resources, functioning as a load-balancing in a client-server environment with high resource Requests by customers.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-04-06T14:32:21Z No. of bitstreams: 1 Ariel Soares.pdf: 3082206 bytes, checksum: 46f45860ff1dce76fc8e91ec4f3546f3 (MD5)
Made available in DSpace on 2017-04-06T14:32:21Z (GMT). No. of bitstreams: 1 Ariel Soares.pdf: 3082206 bytes, checksum: 46f45860ff1dce76fc8e91ec4f3546f3 (MD5) Previous issue date: 2017-02-10
FAPEMA, CNPQ
This research firstly investigates the privacy requirements of users in Mobile Social Networks (MSNs) through a study with 164 Brazilians, which indicated that their requirements are usually dynamic and contextual. Next, the research applies the Situational Computing paradigm to develop a solution to serve them. This solution is called SelPri, developed as proof of concept in the form of a mobile social application to autonomously adapt the privacy settings of posts in MSNs according to the user situation. SelPri uses a conceptual model with fuzzy logic as the basis for constructing an inference engine to identify mobile user situations from the following context information: location, time of the day, day of week, and co-location. SelPri is integrated with Facebook. Additionally, to show the flexibility of the conceptual model, it is also used to construct an inference engine to be used in a different application domain, the mental health. This second inference engine identifies user situations from different context information: it does not use co-location and uses the user activity. The solution originated in the mental health domain is called SituMan. Two experiments were carried out with both solutions, in order to verify the accuracy of the fuzzy inference engine to identify situations, and to evaluate the user satisfaction. The use experience evaluation with SelPri emphasized that the approach to meet the dynamic and contextdependent privacy requirements was well accepted by the participants and proved to be of practical use. The experiments also showed that both solutions were well evaluated with respect to usability. The accuracy evaluations showed a high hit rate of the inference engines to identify situations: ≈94.6% and ≈ 92.04%, for SelPri and SituMan, respectively.
Esta pesquisa primeiramente investiga os requisitos de privacidade de usuários em Redes Sociais Móveis (RSMs) através de um estudo com 164 brasileiros, o qual indicou que seus requisitos são normalmente dinâmicos e contextuais. Em seguida, a pesquisa aplica o paradigma de Computação Situacional para o desenvolvimento de uma solução para atendê-los. Esta solução é chamada de SelPri, desenvolvida como prova de conceito em forma de uma aplicação social móvel para adaptar com autonomia as configurações de privacidade de postagens em RSMs de acordo com a situação do usuário. O SelPri utiliza um modelo conceitual que faz uso de lógica nebulosa como base para a construção de um motor de inferência para identificar as situações de usuários móveis a partir das seguintes informações de contexto: localização, período do dia, dias da semana, e co-localização. O SelPri é implementado integrado ao Facebook. Adicionalmente, para mostrar a flexibilidade do modelo conceitual, ele é também usado para a construção de um motor de inferência para ser utilizado em um domínio de aplicação diferente, o de saúde mental. Esse motor de inferência identifica situações de usuários a partir de informações contextuais diferentes: não utiliza a co-localização e passa a usar a atividade do usuário. A solução originada no domínio de saúde mental é chamada de SituMan. Dois experimentos foram realizados com ambas soluções, em que objetivaram verificar a acurácia do motor de inferência nebulosa para identificação de situações, e avaliar a satisfação do usuário. A avaliação da experiência de uso realizada com o SelPri destacou que a abordagem para atender os requisitos dinâmicos e dependentes de contexto de privacidade teve uma boa aceitação pelos participantes e provou ser de uso prático. As avaliações de experiência de uso também mostraram que ambas soluções foram bem avaliadas com relação a usabilidade. As avaliações de acurácia mostraram uma taxa de acerto elevada dos motores de inferência para identificar situações: ≈94,6% e ≈92,04%, para o SelPri e SituMan, respectivamente

Some disciplines are inherently complex and challenging to learn. This research attempts to design an instructional strategy for CBL systems to simplify learning certain complex domains. Firstly, problem transformation, a constructionist instructional technique, is used to promote active learning by encouraging students to construct more complex artefacts based on less complex ones. Scaffolding is used at the initial learning stages to alleviate the difficulty associated with complex transformation processes. The proposed instructional strategy brings various techniques together to enhance the learning experience. A functional prototype is implemented with Object-Z as the exemplar subject. Both objective and subjective evaluations using the prototype indicate that the proposed CBL system has a statistically significant impact on learning a complex domain. CBL systems include Learner models to provide adaptable support tailored to individual learners. Bayesian theory is used in general to manage uncertainty in Learner models. In this research, a fuzzy logic based locally intelligent Learner model is utilized. The fuzzy model is simple to design and implement, and easy to understand and explain, as well as efficient. Bayesian theory is used to complement the fuzzy model. Evaluation shows that the accuracy of the proposed Learner model is statistically significant. Further, opening Learner model reduces uncertainty, and the fuzzy rules are simple and resemble human reasoning processes. Therefore, it is argued that opening a fuzzy Learner model is both easy and effective. Scaffolding requires formative assessments. In this research, a confidence based multiple test marking scheme is proposed as traditional schemes are not suitable for measuring partial knowledge. Subjective evaluation confirms that the proposed schema is effective. Finally, a step-by-step methodology to transform simple UML class diagrams to Object-Z schemas is designed in order to implement problem transformation. This methodology could be extended to implement a semi-automated translation system for UML to Object Models.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2016-04-27T13:37:50Z No. of bitstreams: 2 Dissertação - Francisco Calaça Xavier - 2012.pdf: 5019345 bytes, checksum: 0e64a53ebdeda990e6ef1175f1732c19 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)
Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-04-27T13:40:15Z (GMT) No. of bitstreams: 2 Dissertação - Francisco Calaça Xavier - 2012.pdf: 5019345 bytes, checksum: 0e64a53ebdeda990e6ef1175f1732c19 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)
Made available in DSpace on 2016-04-27T13:40:15Z (GMT). No. of bitstreams: 2 Dissertação - Francisco Calaça Xavier - 2012.pdf: 5019345 bytes, checksum: 0e64a53ebdeda990e6ef1175f1732c19 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2012-06-21
The problem of decision making about the allocation of resources is present in many areas of society. The allocation of ambulances to the occurrence of accidents with victims and the allocation of teams to solve problems in the supply of electricity are examples of situations where it is necessary to make this decision. We can also mention the problems that occur in the allocation of hardware resources when a system is running in a distributed form. In this context, this paper presents the system COGNARE, which brings together techniques such as Genetic Algorithms, Fuzzy Logic and Multiagent Systems in order to allocate tasks to resources dynamically. The COGNARE was used in two different situations. At first, the problem was to allocate vehicles to a distributor of electricity to occurrences of failures in supply. In the second situation, the problem was to allocate hardware resources in a distributed system. In both cases, the COGNARE presented as a system of allocating resources efficiently. Keywords
O problema da tomada de decisão quanto a alocação de recursos está presente em diversas áreas da sociedade. A alocação de ambulâncias à ocorrências de acidentes com vítimas e a alocação de equipes para solução de problemas no fornecimento de energia elétrica são exemplos de situações onde são necessárias tomadas de decisão. Os problemas que ocorrem na alocação de recursos de hardware quando um sistema é executado de forma distribuída também requerem decisões. Neste contexto, este trabalho apresenta o sistema COGNARE, que reúne a utililização de técnicas como Algoritmos Genéticos, Lógica Fuzzy e Sistemas Multiagentes com o objetivo de alocar dinamicaminte tarefas a recursos. O COGNARE foi utilizado em duas situações distintas. Na primeira, o problema consistia em alocar dinamicamente viaturas de uma empresa de distribuição de energia elétrica a ocorrências de falhas no fornecimento. Na segunda situação, o problema consistia em alocar dinamicamente recursos de hardware em um sistema distribuído. Nestes dois casos, o COGNARE apresentou-se como um sistema de alocação de recursos eficiente.

Made available in DSpace on 2016-04-04T18:31:34Z (GMT). No. of bitstreams: 1 Marcos Jesus dos Santos.pdf: 4921814 bytes, checksum: c8f0f107e2ca014d40151c8d1191abe2 (MD5) Previous issue date: 2012-12-18
Over the last years, Cloud Computing has brought about a paradigm shift regarding the deployment of computing services for corporations, small business and even end-users. This model of resource utilization has originated a whole new set of business options and business opportunities yet to be explored. The Hybrid Cloud model, which refers to the situation when the cloud is built using both internal and external resources, or by the combination of resources provided by more than one provider, presents a new challenge for the network management tools. The main difficult is on an efficient resource allocation that allows the network management to follow the system performance in real-time. In this work, it is proposed a Fuzzy Logic controller based on a Fuzzy Finite-State Machine as a tool to manage Hybrid Cloud deployments. This Fuzzy Finite-State Machine operates as in accordance with the service level agreements (SLA) and the quality of experience of the user. By comparing the results obtained by using the proposed controller with typical controllers based on Boolean Logic, savings ranging from 3% to up to 50% where achieved, depending on the number of servers and the demand. The prior use of Fuzzy Finite-State Machines to manage Cloud Computing systems has not been found in the literature until the present proposal.
A utiliza??o de sistemas de Computa??o em Nuvem tem se tornado quase que obrigat?ria na disponibiliza??o de solu??es e servi?os, seja para pequenas ou grandes corpora??es, ou mesmo para usu?rios finais. Tal modelo de consumo de recursos abre toda uma gama de oportunidades de neg?cio que est? sendo cada vez mais explorada. Em especial, o uso de Nuvens H?bridas, nas quais os recursos da pr?pria organiza??o usu?ria s?o combinados com recursos de provedores externos, representa um novo desafio para as ferramentas de gerenciamento. Estas ferramentas devem permitir a aloca??o dos recursos de forma eficiente e possibilitar ao gestor a visualiza??o do estado do sistema em tempo real. Com tal cen?rio em vista, neste trabalho apresenta-se e investiga-se um modelo de controlador para Computa??o em Nuvem baseado em uma m?quina de estados nebulosos. Esta m?quina de estados opera de acordo com crit?rios definidos nos contratos de n?vel de acordo (service level agreement SLA) e com a qualidade de experi?ncia do usu?rio (quality of experience QoE). Comparando-se o desempenho desta ferramenta com o de um controlador t?pico, baseado em ?lgebra Booleana, obteve-se uma economia entre 3% at? 50% de recursos, isto para um sistema operando com servidor ?nico, dependendo das solicita??es de demanda. Finalmente, observa-se que, at? o presente momento, a abordagem desenvolvida neste trabalho ? in?dita na literatura.

This research presents a solution to the problem of tuning a PID controller for a nonlinear system. Many systems in industrial applications use a PID controller to control a plant or the process. Conventional PID controllers work in linear systems but are less effective when the plant or the process is nonlinear because PID controllers cannot adapt the gain parameters as needed. In this research we design a Nonlinear PID (NPID) controller using a fuzzy logic system based on the Mamdani type Fuzzy Inference System to control three different DC motor systems. This fuzzy system is responsible for adapting the gain parameters of a conventional PID controller. This fuzzy system's rule base was heuristically evolved using an Evolutionary Algorithm (Differential Evolution). Our results show that a NPID controller can restore a moderately or a heavily under-damped DC motor system under consideration to a desired behavior (slightly under-damped).

Orientadores: Fernando Antonio Campos Gomide, Rosangela Ballini
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-20T17:20:07Z (GMT). No. of bitstreams: 1 Maciel_LeandrodosSantos_M.pdf: 2052895 bytes, checksum: a88ae55ebe5e6a0ea1053d3c5aef5f66 (MD5) Previous issue date: 2012
Resumo: Este trabalho propõe a utilização de técnicas de inteligência computacional para a estimação e previsão da estrutura a termo das taxas de juros, com base em dados dos mercados de renda fixa dos Estados Unidos e Brasil. Para o problema de estimação da curva de juros, as técnicas de computação evolucionária, Algoritmos Genéticos, Evolução Diferencial e Estratégias Evolutivas, foram comparadas com abordagens tradicionais da literatura, como mínimos quadrados não-lineares e programação quadrática sequencial. A motivação da aplicação de técnicas de computação evolucionária no problema de estimação da estrutura a termo busca superar limitações como não-convergência e elevada instabilidade dos parâmetros à inicialização. Além disso, recentemente, a literatura tem apontado o elevado desempenho dos algoritmos genéticos em problemas de modelagem da curva de rendimentos. Outra contribuição deste trabalho consiste no desenvolvimento de um modelo nebuloso evolutivo de aprendizado participativo estendido, denominado ePL+, que inclui em sua versão original, ePL, mecanismos para aumentar sua autonomia e adaptabilidade na modelagem de sistemas complexos. Dessa forma, o modelo ePL+ e outros modelos nebulosos funcionais evolutivos foram avaliados na questão da previsão das taxas futuras de juros, em contraposição com modelos econométricos baseados em processos autoregressivos e modelos de redes neurais artificiais multi-camadas, uma vez que a evolução das taxas de juros apresenta uma dinâmica altamente não-linear e variante no tempo, justificando a ideia de modelagem adaptativa. O desempenho dos métodos considerados foi avaliado em termos de métricas de erro, complexidade computacional e por meio de testes estatísticos paramétricos e não-paramétricos, MGN e SIGN, respectivamente. Os resultados evidenciaram o elevado potencial dos modelos de inteligência computacional na estimação e previsão da estrutura a termo em ambas economias consideradas, constatado pelo melhor desempenho, em termos de ajuste e significância estatística, em relação às técnicas de otimização de parâmetros e econométricas mais utilizadas na literatura
Abstract: This work proposes the term structure of interest rates modeling and forecasting using computational intelligence techniques, based on data from the US and Brazilian fixed income markets. The yield curve modeling includes the use of some evolutionary computation methods like Genetic Algorithms, Differential Evolution and Evolution Strategies in comparison with traditional optimization techniques such as nonlinear least squares and sequential quadratic programming. The motivation behind the use of evolutionary computation to yield curve estimation aims to overcome limitations like non-convergence and high parameters instability to initialization. Moreover, recently, the literature has been shown the higher performance of genetic algorithms in yield curve modeling problems. This work also contributes by developing an extended participatory learning fuzzy model, called ePL+, which includes on its original version, ePL, mechanisms to improve its autonomy and adaptability in complex systems modeling. Therefore, the ePL+ model and some evolving functional fuzzy approaches were evaluated in the future interest rates forecasting, as opposed to econometric models based on autoregressive processes and multilayer artificial neural networks methodologies, since interest rates evolution shows a high non-linear dynamics and also time-varying, justifying the idea of adaptive modeling. Models' performance were compared in terms of error measures, computational complexity and by parametric and non-parametric statistical tests, MGN and SIGN, respectively. The results reveal the high potential of computational intelligence methods to deal with the term structure modeling and forecasting for both economies considered, as pointed out by their adjustment and statistical superior performance then traditional optimization and econometrics techniques reported in the literature
Mestrado
Automação
Mestre em Engenharia Elétrica

O objetivo deste trabalho é avaliar os riscos de ocorrências de intrusos em um sistema de computação em nuvem para sistemas distribuídos utilizando lógica nebulosa. A computação em nuvem é um tema que vem sendo bastante abordado e vem alavancando discussões calorosas, tanto na comunidade acadêmica quanto em palestras profissionais. Embora essa tecnologia esteja ganhando mercado, alguns estudiosos encontram-se céticos afirmando que ainda é cedo para se tirar conclusões. Isto se deve principalmente por causa de um fator crítico, que é a segurança dos dados armazenados na nuvem. Para esta dissertação, foi elaborado um sistema distribuído escrito em Java com a finalidade de controlar um processo de desenvolvimento colaborativo de software na nuvem, o qual serviu de estudo de caso para avaliar a abordagem de detecção de intrusos proposta. Este ambiente foi construído com cinco máquinas (sendo quatro máquinas virtuais e uma máquina real). Foram criados dois sistemas de inferência nebulosos, para análise de problemas na rede de segurança implementados em Java, no ambiente distribuído. Foram realizados diversos testes com o intuito de verificar o funcionamento da aplicação, apresentando um resultado satisfatório dentro dessa metodologia.
The objective of this study is to evaluate the risk of occurrence of intruders in a system of cloud computing at distributed systems using fuzzy logic. Cloud computing is a topic that has been widely discussed and has been leveraging heated discussions, both in academic and in professional speaking. Although this technology is gaining market share, some academics are incredulous saying that is too early to draw conclusions. This is mainly because of a critical factor, which is the security of data stored in the cloud. For this thesis, we designed a distributed system written in Java, with the purpose of controlling a process of softwares development in the cloud, wich served as a case study to evaluate the approach proposed intrusion detection. This environment was build with five machines (being four virtual machines and one real machine). It was created two fuzzy inference systems for analysis of problems in network security implemented in Java, in the distributed environment. Several tests were performed in order to verify the functionality of the application, presenting a satisfactory outcome within this methodology.

Control theory of complex systems by utilization of artificial intelligent algorithms is relatively new science field and it can be used in many areas of technical practise. Best known algorithms to solved similar tasks are genetic algorithm, differential evolution, HC12 Nelder-Mead method, fuzzy logic and grammatical evolution. Complex solution is presented at selected examples from mathematical nonlinear systems to examples of anthems design and stabilization of deterministic chaos. The goal of this thesis is present examples of implementation and utilization of artificial algorithms by multi-objective optimization. To achieve optimal results is used designed software solution by multi-platform application, which used Matlab and Java interfaces. The software solution integrate every algorithms of this thesis to complex solution and it extends possible application of those approaches to real systems and practical world.

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support.

Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts.

We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases.

This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas.

Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc.

In this thesis, we aim at modeling some aspects of the functional role of emotions on an autonomous embodied agent. We begin by describing our robotic prototype, Cherry--a robot with the task of being a tour guide and an office assistant for the Computer Science Department at the University of Central Florida. Cherry did not have a formal emotion representation of internal states, but did have the ability to express emotions through her multimodal interface. The thesis presents the results of a survey we performed via our social informatics approach where we found that: (1) the idea of having emotions in a robot was warmly accepted by Cherry's users, and (2) the intended users were pleased with our initial interface design and functionalities. Guided by these results, we transferred our previous code to a human-height and more robust robot--Petra, the PeopleBot™--where we began to build a formal emotion mechanism and representation for internal states to correspond to the external expressions of Cherry's interface. We describe our overall three-layered architecture, and propose the design of the sensory motor level (the first layer of the three-layered architecture) inspired by the Multilevel Process Theory of Emotion on one hand, and hybrid robotic architecture on the other hand. The sensory-motor level receives and processes incoming stimuli with fuzzy logic and produces emotion-like states without any further willful planning or learning. We will discuss how Petra has been equipped with sonar and vision for obstacle avoidance as well as vision for face recognition, which are used when she roams around the hallway to engage in social interactions with humans. We hope that the sensory motor level in Petra could serve as a foundation for further works in modeling the three-layered architecture of the Emotion State Generator.
M.S.
School of Computer Science
Engineering and Computer Science
Computer Science

This thesis seeks to address current problems encountered when training classifiers within the framework of cascades of boosted ensembles (CoBE). At present, a signifi- cant challenge facing this framework are inordinate classifier training runtimes. In some cases, it can take days or weeks (Viola and Jones, 2004; Verschae et al., 2008) to train a classifier. The protracted training runtimes are an obstacle to the wider use of this framework (Brubaker et al., 2006). They also hinder the process of producing effective object detection applications and make the testing of new theories and algorithms, as well as verifications of others research, a considerable challenge (McCane and Novins, 2003). An additional shortcoming of the CoBE framework is its limited ability to train clas- sifiers incrementally. Presently, the most reliable method of integrating new dataset in- formation into an existing classifier, is to re-train a classifier from beginning using the combined new and old datasets. This process is inefficient. It lacks scalability and dis- cards valuable information learned in previous training. To deal with these challenges, this thesis extends on the research by Barczak et al. (2008), and presents alternative CoBE frameworks for training classifiers. The alterna- tive frameworks reduce training runtimes by an order of magnitude over common CoBE frameworks and introduce additional tractability to the process. They achieve this, while preserving the generalization ability of their classifiers. This research also introduces a new framework for incrementally training CoBE clas- sifiers and shows how this can be done without re-training classifiers from beginning. However, the incremental framework for CoBEs has some limitations. Although it is able to improve the positive detection rates of existing classifiers, currently it is unable to lower their false detection rates.

Rozšiřování moderních vestavěných a mobilních systémů napájených bateriemi zvyšuje požadavky na návrh těchto systémů s ohledem na příkon. Přestože moderní návrhové techniky optimalizují příkon, elektrická spotřeba těchto obvodů stále roste díky jejich složitosti. Nicméně existuje celá řada aplikací, kde nepotřebujeme získat úplně přesný výstup. Díky tomu se objevuje technika zvaná aproximativní (přibližné) počítání, která umožňuje za cenu zanesení malé chyby do výpočtu významně redukovat příkon obvodů. V práci se zaměřujeme na použití evolučních algoritmů v této oblasti. Ačkoliv již tyto algoritmy byly úspěšně použity v syntéze přesných i aproximativních obvodů, objevují se problémy škálovatelnosti - schopnosti aproximovat složité obvody. Cílem této disertační práce je ukázat, že aproximační logická syntéza založená na genetickém programování umožňuje dosáhnout vynikajícího kompromisu mezi spotřebou a chybou. Byla provedena analýza čtyř různých aplikacích na třech úrovních popisu. Pomocí kartézského genetického programování s modifikovanou reprezentací jsme snížili spotřebu malých obvodů popsaných na úrovni tranzistorů použitelných například v technologické knihovně. Dále jsme zavedli novou metodu pro aproximaci aritmetických obvodů, jako jsou sčítačky a násobičky, popsaných na úrovni hradel. S využitím metod formální verifikace navíc celý návrhový proces umožňuje garantovat stanovenou chybu aproximace. Tyto obvody byly využity pro významné snížení příkonu v neuronových sítích pro rozpoznávání obrázků a v diskrétní kosinově transformaci v HEVC kodéru. Pomocí nové chybové metriky nezávislé na rozložení vstupních dat jsme navrhli komplexní aproximativní mediánové filtry vhodné pro zpracování signálů. Disertační práce reprezentuje ucelenou metodiku pro návrh aproximativních obvodů na různých úrovních popisu, která navíc garantuje nepřekročení zadané chyby aproximace.