Submitted in complete fulfillment for the degree of Doctor of Philosophy (Biotechnology), Durban University of Technology, Durban, South Africa, 2016. / Seasonal nitrification breakdown is a major problem in wastewater treatment plants which makes it difficult for the plant operators to meet discharge limits. The present study focused on understanding the seasonal impact of environmental and operational parameters on nitrifiers and nitrification, in a biological nutrient removal wastewater treatment works situated in the midlands of KwaZulu Natal. Composite sludge samples (from the aeration tank), influent and effluent water samples were collected twice a month for 237 days. A combination of fluorescent in-situ hybridization, polymerase chain reaction (PCR)-clone library, quantitative polymerase chain reaction (qPCR) were employed for characterizing and quantifying the dominant nitrifiers in the plant. In order to have more insight into the activated sludge community structure, pyrosequencing was used in profiling the amoA locus of ammonia oxidizing bacteria (AOB) community whilst Illumina sequencing was used in characterising the plant’s total bacterial community. The nonlinear effect of operating parameters and environmental conditions on nitrification was also investigated using an adaptive neuro-fuzzy inference system (ANFIS), Pearson’s correlation coefficient and quadratic models. The plant operated with higher MLSS of 6157±783 mg/L during the first phase (winter) whilst it was 4728±1282 mg/L in summer. The temperature recorded in the aeration tanks ranged from 14.2oC to 25.1oC during the period. The average ammonia removal during winter was 60.0±18% whereas it was 83±13% during summer and this was found to correlate with temperature (r = 0.7671; P = 0.0008). A significant correlation was also found between the AOB (amoA gene) copy numbers and temperature in the reactors (α= 0.05; P=0.05), with the lowest AOB abundance recorded during winter. Sanger sequencing analysis indicated that the dominant nitrifiers were Nitrosomonas spp. Nitrobacter spp. and Nitrospira spp. Pyrosequencing revealed significant differences in the AOB population which was 6 times higher during summer compared to winter. The AOB sequences related to uncultured bacterium and uncultured AOB also showed an increase of 133% and 360% respectively when the season changed from winter to summer. This study suggests that vast population of novel, ecologically significant AOB species, which remain unexploited, still inhabit the complex activated sludge communities. Based on ANFIS model, AOB increased during summer season, when temperature was 1.4-fold higher than winter (r 0.517, p 0.048), and HRT decreased by 31% as a result of rainfall (r - 0.741, p 0.002). Food: microorganism ratio (F/M) and HRT formed the optimal combination of two inputs affecting the plant’s specific nitrification (qN), and their quadratic equation showed r2-value of 0.50. This study has significantly contributed towards understanding the complex relationship between the microbial population dynamics, wastewater composition and nitrification performance in a full-scale treatment plant situated in the subtropical region. This is the first study applying ANFIS technique to describe the nitrification performance at a full-scale WWTP, subjected to dynamic operational parameters. The study also demonstrated the successful application of ANFIS for determining and ranking the impact of various operating parameters on plant’s nitrification performance, which could not be achieved by the conventional spearman correlation due to the non-linearity of the interactions during wastewater treatment. Moreover, this study also represents the first-time amoA gene targeted pyrosequencing of AOB in a full-scale activated sludge is being done. / D

[pt] Esta tese apresenta uma nova metodologia para valoração dinâmica do Capital Intelectual, aplicada ao Capital Humano. Trata-se de oferecer, ao tomador de decisão, uma ferramenta capaz de calcular e comparar o retorno do investimento em ativos intangíveis, como ocorre com outros ativos tangíveis. Através da metodologia proposta, denominada KVA-ACHE, é possível estimar a quantidade potencial de conhecimento humano, utilizado na geração do resultado financeiro da empresa. Essa metodologia também permite medir variações de desempenho nos processos-chave que compõem a cadeia de valor da empresa e o impacto do investimento em educação em um determinado processo. O método KVA-ACHE é composto de cinco módulos, que são executados em três fases. Na primeira fase se avalia a empresa de forma agregada, segundo seu modelo estratégico e, na segunda fase, avalia-se a quantidade de conhecimento potencial e disponível, associado a cada processo-chave. A terceira fase é aplicado o método KVA e obtido o indicador de desempenho ROI. Ao final da sua aplicação, essa metodologia permite: identificar os processos que estão drenando resultado da empresa, através da observação de indicador financeiro adaptado, como o ROIK (Return on Investment on Knowledg), identificar a necessidade individualizada de treinamento para se atingir o máximo de desempenho em um determinado processochave; analisar o impacto percebido em termos percentuais do investimento em educação, realizado em determinado processo-chave; e, finalmente, dar uma visão sobre os recursos de conhecimentos e habilidades disponíveis na equipe de colaboradores, os quais poderão ser aproveitados na avaliação de novos negócios e desafios para empresa. A principal inovação dessa metodologia está no fato de se utilizar a Teoria dos Conjuntos Fuzzy e de Sistemas de Inferência Fuzzy - SIF para transformar conceitos relacionados à disponibilidade e ao uso de conhecimento humano em valores que, dessa forma, permitem a comparação de ativos intangíveis com ativos tangíveis. / [en] This thesis presents a new methodology for dynamic valuation of Intellectual Capital, applied to the Human Capital. It offers, to the decision-maker, a computational tool able to quote and compare the return on investment in intangible assets, as with tangible assets. Through the proposed methodology, called KVAACHE, it is possible to estimate the potential amount of human knowledge, used in generating the company’s financial results. This approach also allows the measurement of variations in performance in the key processes that make up the value chain of the company and the impact of investment in education in a given process. The method KVA-ACHE is composed of five modules, which are executed in three phases. The first phase evaluates the company on an aggregate basis, according to its strategic model, and, in the second phase, the amount of potential and available knowledge, associated with each key process, is evaluated. The third phase applies KVA method. This methodology allows: the identification of the processes that are draining the company’s income by looking at the adapted financial indicators, such as ROIK (Return on Investment on Knowledge); the individualized need for training to achieve maximum performance in a particular key process; the analysis of the impact noticed in terms of percentage of the investment in education, held in a certain key process; and finally, an insight into the resources of knowledge and skills available in the team of collaborators, which may be used in the assessment of new challenges and business to the enterprise. The main innovation of this methodology lies in the use of Fuzzy Set Theory and Fuzzy Inference Systems - FIS to transform concepts related to the availability and use of human knowledge into values, and thus allow the comparison of intangible assets with tangible assets.

Os sistemas elétricos de potência modernos apresentam inúmeros desafios em sua operação. Nos sistemas de distribuição de energia elétrica, devido à grande ramificação, presença de extensos ramais monofásicos, à dinâmica das cargas e demais particularidades inerentes, a localização de faltas representa um dos maiores desafios. Das barreiras encontradas, a influência da impedância de falta é uma das maiores, afetando significativamente a aplicação dos métodos tradicionais na localização, visto que a magnitude das correntes de falta é similar à da corrente de carga. Neste sentido, esta tese objetivou desenvolver um sistema inteligente para localização de faltas de alta impedância, o qual foi embasado na aplicação da técnica de decomposição por componentes ortogonais no pré-processamento das variáveis e inferência fuzzy para interpretar as não-linearidades do Sistemas de Distribuição com presença de Geração Distribuída. Os dados para treinamento do sistema inteligente foram obtidos a partir de simulações computacionais de um alimentador real, considerando uma modelagem não-linear da falta de alta impedância. O sistema fuzzy resultante foi capaz de estimar as distâncias de falta com um erro absoluto médio inferior a 500 m e um erro absoluto máximo da ordem de 1,5 km, em um alimentador com cerca de 18 km de extensão. Tais resultados equivalem a um grau de exatidão, para a maior parte das ocorrências, dentro do intervalo de ±10%. / Modern electric power systems present numerous challenges in its operation. Fault location is a major challenge in Power Distribution Systems due to its large branching, presence of single-phase laterals and the dynamic loads. The influence of the fault impedance is one of the largest, significantly affecting the use of traditional methods for its location, since the magnitude of the fault currents is similar to the load current. In this sense, this thesis aimed to develop an intelligent system for location of high impedance faults, which was based on the application of the decomposition technique of orthogonal components in the pre-processing variables and fuzzy inference to interpret the nonlinearities of Power Distribution Systems with the presence of Distributed Generation. The data for training the intelligent system were obtained from computer simulations of an actual feeder, considering a non-linear modeling of the high impedance fault. The resulting fuzzy system was able to estimate distances to fault with an average absolute error of less than 500 m and a maximum absolute error of 1.5 km order, on a feeder about 18 km long. These results are equivalent to a degree of accuracy for the most occurrences within the ± 10% range.

O planejamento da operação de sistemas hidrotérmicos pode ser classificado como um problema de um sistema acoplado no tempo e no espaço, não linear, não convexo, estocástico e de grande porte. A complexidade do problema justifica a necessidade de utilização de diversas ferramentas computacionais com abordagens variadas. Este trabalho tem como objetivo a realização de estudos relacionados ao planejamento da operação energética de sistemas hidrotérmicos de geração, pela aplicação de componentes de software e de sistemas de inferência fuzzy. Pretende-se apresentar e aplicar um processo de desenvolvimento (UML Components), baseado em componentes de software, para a construção de modelos computacionais de simulação e otimização para servir de apoio ao planejamento da operação energética do sistema hidrotérmico brasileiro. O processo de desenvolvimento UML Components é aplicado de forma a nortear o desenvolvimento do software, para englobar as diferentes atividades realizadas nos fluxos de trabalho, além de incluir os vários artefatos produzidos. Como contribuição adicional, paralelamente ao uso dos componentes de software, este trabalho apresenta uma política de operação energética para reservatórios baseada em sistemas de inferência fuzzy Takagi-Sugeno. A política proposta é baseada na otimização da operação energética das usinas hidrelétricas, empregando o modelo de otimização desenvolvido. Com a operação energética otimizada, obtém-se as relações entre a energia armazenada do sistema e o volume útil operativo de cada usina a reservatório. A partir dessas relações são ajustados os parâmetros do modelo Takagi-Sugeno de ordem um. Ao optar-se por um sistema de inferência fuzzy para determinar a política de operação energética de um conjunto de reservatórios, obtém-se uma estratégia de ação/controle que pode ser monitorada e interpretada, inclusive do ponto de vista lingüístico. Outra vantagem na aplicação de sistemas fuzzy deve-se ao fato dos operadores humanos (especialistas) poderem traduzir, de forma consistente, e em termos de regras lingüísticas, o seu processo de tomada de decisões, fazendo com que a ação do sistema fuzzy seja tão fundamentada e consistente quanto a deles. / The operation planning of hydrothermal power systems can be classified as a nonseparable, nonlinear, nonconvex, stochastic and of large scale optimization problem. The complexity of this problem justifies the need for the use of various computational tools with different approaches. This work aims the accomplishment of studies related to the operation planning of hydrothermal power systems through the implementation of software components and fuzzy inference systems. It is intended to provide and implement a development process (UML Components) based on software components for building computational model of optimization and simulation to support the operation planning of the Brazilian hydrothermal power systems. The UML Components development process is a applied in a way to guide the software development to encompass different activities realized on workflows, as well as to include the various artifacts produced. As additional contribution, in parallel to the use of software components, it is intended to present an operational policy of reservoirs based on Takagi-Sugeno fuzzy inference systems. The proposed policy is based on optimization of hydropower operation, using the optimization model developed. Through the optimized operation, relations between system stored energy and the reservoir volume of each plat are obtained. With these relationships, the parameters of the Takagi-Sugeno model are adjusted. In choosing a fuzzy inference system for determining the operational policy of a set of reservoirs, it is obtained as strategy of action/control that can be monitored and interpreted including linguistic standpoint. Another benefit of the fuzzy system application refers to the fact that human specialists can consistently represent, through linguistic rules, their decision making process, making the fuzzy system action as consistent and sound as theirs.

Este trabalho tem por objetivo desenvolver uma abordagem fuzzy focando na estimação de curto prazo da demanda de potência ativa de um alimentador de sistema de distribuição de energia elétrica. A motivação para este trabalho encontra-se na redução do erro de estimação para que o sistema de distribuição como um todo seja corretamente operado. O destaque da abordagem desenvolvida é a metodologia de seleção de entradas para o sistema de estimação, que o treina fornecendo-lhe informações não redundantes e não desnecessárias sobre o comportamento da série temporal. Os resultados, obtidos com treinamento e teste de um sistema de inferência fuzzy multicamadas, mostram que as estimações realizadas selecionando as entradas do sistema de forma criteriosa apresentam menor erro que quando não há critério de seleção. Conclui-se então que a metodologia foi funcional e eficiente para o caso estudado, o que faz com que este trabalho resulte em válidas contribuições nas áreas de sistemas inteligentes, de sistemas dinâmicos e inclusive na forma metodológica de especificação de modelos de estimação de séries temporais. / This work aims to develop a fuzzy approach focusing on the short-term active power demand forecast in a feeder of an electrical energy distribution system. This work motivation lies on the reduction of the forecast error so that the whole distribution system can be correctly operated. The highlight of the developed approach is the methodology to select the inputs for the estimation system, which trains it giving to it non-redundant and non-unnecessary information about the time series behavior. The results, obtained by training and testing a multilayer fuzzy inference system, show that the estimations made by following a criterion to select the inputs have smaller error than when there is no selection criterion at all. It is therefore concluded that the methodology was functional and efficient for the case under study, what makes this work result in valid contributions for the fields of intelligent systems, dynamic systems and in the methodological way to specify models to estimate time series.

Les données manquantes constituent un challenge commun en reconnaissance de forme et traitement de signal. Une grande partie des techniques actuelles de ces domaines ne gère pas l'absence de données et devient inutilisable face à des jeux incomplets. L'absence de données conduit aussi à une perte d'information, des difficultés à interpréter correctement le reste des données présentes et des résultats biaisés notamment avec de larges sous-séquences absentes. Ainsi, ce travail de thèse se focalise sur la complétion de larges séquences manquantes dans les séries monovariées puis multivariées peu ou faiblement corrélées. Un premier axe de travail a été une recherche d'une requête similaire à la fenêtre englobant (avant/après) le trou. Cette approche est basée sur une comparaison de signaux à partir d'un algorithme d'extraction de caractéristiques géométriques (formes) et d'une mesure d'appariement élastique (DTW - Dynamic Time Warping). Un package R CRAN a été développé, DTWBI pour la complétion de série monovariée et DTWUMI pour des séries multidimensionnelles dont les signaux sont non ou faiblement corrélés. Ces deux approches ont été comparées aux approches classiques et récentes de la littérature et ont montré leur faculté de respecter la forme et la dynamique du signal. Concernant les signaux peu ou pas corrélés, un package DTWUMI a aussi été développé. Le second axe a été de construire une similarité floue capable de prender en compte les incertitudes de formes et d'amplitude du signal. Le système FSMUMI proposé est basé sur une combinaison floue de similarités classiques et un ensemble de règles floues. Ces approches ont été appliquées à des données marines et météorologiques dans plusieurs contextes : classification supervisée de cytogrammes phytoplanctoniques, segmentation non supervisée en états environnementaux d'un jeu de 19 capteurs issus d'une station marine MAREL CARNOT en France et la prédiction météorologique de données collectées au Vietnam. / Missing data are a prevalent problem in many domains of pattern recognition and signal processing. Most of the existing techniques in the literature suffer from one major drawback, which is their inability to process incomplete datasets. Missing data produce a loss of information and thus yield inaccurate data interpretation, biased results or unreliable analysis, especially for large missing sub-sequence(s). So, this thesis focuses on dealing with large consecutive missing values in univariate and low/un-correlated multivariate time series. We begin by investigating an imputation method to overcome these issues in univariate time series. This approach is based on the combination of shape-feature extraction algorithm and Dynamic Time Warping method. A new R-package, namely DTWBI, is then developed. In the following work, the DTWBI approach is extended to complete large successive missing data in low/un-correlated multivariate time series (called DTWUMI) and a DTWUMI R-package is also established. The key of these two proposed methods is that using the elastic matching to retrieving similar values in the series before and/or after the missing values. This optimizes as much as possible the dynamics and shape of knowledge data, and while applying the shape-feature extraction algorithm allows to reduce the computing time. Successively, we introduce a new method for filling large successive missing values in low/un-correlated multivariate time series, namely FSMUMI, which enables to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy grades of basic similarity measures and fuzzy logic rules. Finally, we employ the DTWBI to (i) complete the MAREL Carnot dataset and then we perform a detection of rare/extreme events in this database (ii) forecast various meteorological univariate time series collected in Vietnam

O planejamento da operação de sistemas hidrotérmicos pode ser classificado como um problema de um sistema acoplado no tempo e no espaço, não linear, não convexo, estocástico e de grande porte. A complexidade do problema justifica a necessidade de utilização de diversas ferramentas computacionais com abordagens variadas. Este trabalho tem como objetivo a realização de estudos relacionados ao planejamento da operação energética de sistemas hidrotérmicos de geração, pela aplicação de componentes de software e de sistemas de inferência fuzzy. Pretende-se apresentar e aplicar um processo de desenvolvimento (UML Components), baseado em componentes de software, para a construção de modelos computacionais de simulação e otimização para servir de apoio ao planejamento da operação energética do sistema hidrotérmico brasileiro. O processo de desenvolvimento UML Components é aplicado de forma a nortear o desenvolvimento do software, para englobar as diferentes atividades realizadas nos fluxos de trabalho, além de incluir os vários artefatos produzidos. Como contribuição adicional, paralelamente ao uso dos componentes de software, este trabalho apresenta uma política de operação energética para reservatórios baseada em sistemas de inferência fuzzy Takagi-Sugeno. A política proposta é baseada na otimização da operação energética das usinas hidrelétricas, empregando o modelo de otimização desenvolvido. Com a operação energética otimizada, obtém-se as relações entre a energia armazenada do sistema e o volume útil operativo de cada usina a reservatório. A partir dessas relações são ajustados os parâmetros do modelo Takagi-Sugeno de ordem um. Ao optar-se por um sistema de inferência fuzzy para determinar a política de operação energética de um conjunto de reservatórios, obtém-se uma estratégia de ação/controle que pode ser monitorada e interpretada, inclusive do ponto de vista lingüístico. Outra vantagem na aplicação de sistemas fuzzy deve-se ao fato dos operadores humanos (especialistas) poderem traduzir, de forma consistente, e em termos de regras lingüísticas, o seu processo de tomada de decisões, fazendo com que a ação do sistema fuzzy seja tão fundamentada e consistente quanto a deles. / The operation planning of hydrothermal power systems can be classified as a nonseparable, nonlinear, nonconvex, stochastic and of large scale optimization problem. The complexity of this problem justifies the need for the use of various computational tools with different approaches. This work aims the accomplishment of studies related to the operation planning of hydrothermal power systems through the implementation of software components and fuzzy inference systems. It is intended to provide and implement a development process (UML Components) based on software components for building computational model of optimization and simulation to support the operation planning of the Brazilian hydrothermal power systems. The UML Components development process is a applied in a way to guide the software development to encompass different activities realized on workflows, as well as to include the various artifacts produced. As additional contribution, in parallel to the use of software components, it is intended to present an operational policy of reservoirs based on Takagi-Sugeno fuzzy inference systems. The proposed policy is based on optimization of hydropower operation, using the optimization model developed. Through the optimized operation, relations between system stored energy and the reservoir volume of each plat are obtained. With these relationships, the parameters of the Takagi-Sugeno model are adjusted. In choosing a fuzzy inference system for determining the operational policy of a set of reservoirs, it is obtained as strategy of action/control that can be monitored and interpreted including linguistic standpoint. Another benefit of the fuzzy system application refers to the fact that human specialists can consistently represent, through linguistic rules, their decision making process, making the fuzzy system action as consistent and sound as theirs.

本研究發展可以在任意網頁上進行閱讀標註之合作式閱讀標註系統，並透過探勘集體智慧方式，在合作式閱讀標註系統上發展「優質標註萃取」及「達人標註萃取」機制，來輔助學習者進行數位文本閱讀學習，以達到提昇閱讀理解成效的目的。此外，本研究也進一步探討透過「優質標註萃取」及「達人標註萃取」機制過濾掉一部份品質較差的標註，是否可有效降低閱讀標註文本時產生的認知負荷。 本研究將學習者分成實驗組1（達人標註）、實驗組2（優質標註）與控制組（所有標註）三組，並分別進行約80分鐘的合作式閱讀標註學習活動。其中控制組的成員採用「呈現所有標註之合作式閱讀標系統」支援閱讀學習；而實驗組1的成員則透過「呈現達人標註之合作式閱讀標註系統」來進行閱讀學習；實驗組2則透過「呈現優質標註之合作式閱讀標註系統」來進行閱讀學習。合作式閱讀標註活動要求學習者在指定時間內閱讀本研究指定的文本（化學科普之文章），同時利用「合作式閱讀標註系統」進行閱讀標註撰寫與分享。閱讀標註活動結束後，學習者將進行所閱讀文本之閱讀理解評量以及認知負荷量表填寫，據此瞭解學習者的閱讀理解成效及認知負荷程度。 研究結果顯示，採用具有「優質標註萃取」機制所得標註支援閱讀學習，有助於過濾品質不佳的閱讀標註，並提供更簡潔易找尋之優質標註支援閱讀學習，進而提昇閱讀理解成效，由於閱讀時更容易找到所需的優質資訊，因此亦較有助於提昇學習者不同面向概念的閱讀理解成效；此外，本研究基於每位學習者的有效標註，在考量標註層次及標註數量下，評估每位學習者的“標註能力”，採用優質標註支援閱讀學習的實驗組2（優質標註）學習者中，標註能力越高的學習者，其閱讀理解成效也較佳；而本研究將學習者依照閱讀理解後測成績高低，分成高分組及低分組後顯示，控制組（所有標註）與實驗組2（優質標註）的組別中，均呈現出低分組學習者的認知負荷顯著高於高分組學習者的現象；除此之外，本研究比較三組採用不同標註呈現方式之合作式閱讀標註系統進行閱讀學習之學習者時，結果發現，採用三種不同閱讀標註呈現方式組別學習者之認知負荷無顯著差異。 最後，本研究歸納研究者在研究過程及結果中之發現，提出發展結合合作式閱讀標註的有效閱讀學習策略、探討各類型標註眼動行為對於閱讀理解成效影響與擴展合作式閱讀標註系統支援行動閱讀學習等未來研究議題之初步架構，供後續研究參考以進行更深入之探究。 / A Collaborative Reading Annotation System, which can be randomly proceeded reading annotations on any web pages, is developed in this study. Furthermore, Quality Annotation Extraction and Master Annotation Extraction are developed on the Collaborative Reading Annotation System by mining collective intelligence for assisting learners in proceeding reading digital texts and promoting the reading comprehension performance. The effect of removing some bad-quality annotations through Quality Annotation Extraction and Master Annotation Extraction on reducing the cognitive load when reading annotation texts is further discussed in this study. The learners are divided into Experiment Group 1 (Master Annotation), Experiment Group 2 (Quality Annotation), and Control Group (All Annotation) for 80-minute collaborative reading annotation learning. Control Group uses Collaborative Reading Annotation System with all annotations for promoting reading; Experiment Group 1 proceeds reading through Collaborative Reading Annotation System with master annotations; and, Experiment Group 2 applies Collaborative Reading Annotation System with quality annotations to reading. The learners are requested to read the assigned texts (articles of popular science in chemistry) in the assigned period and write and share the reading annotations with the Collaborative Reading Annotation System. Afterwards, the learners are evaluated the reading comprehension of the texts and fill in the cognitive load scale for understanding the reading comprehension performance and the cognitive load. The research results show that utilizing the annotations acquired by Quality Annotation Extraction for promoting reading could filter out unfavorable reading annotations and provide quality annotations, which are more easily searched for promoting reading, to further enhance the reading comprehension performance. Since the quality information can be more easily searched, it could better assist learners in promoting reading comprehension performance in various aspects. Moreover, based on the valid annotations of each learner, the annotation ability is evaluated the annotation level and quantity. Learners with higher annotation ability in Experiment Group 2 (Quality Annotation) present better reading comprehension performance. Based on the reading comprehension post-test results, the learners are divided into high-score and low-score groups. The cognitive load of low-score learners in both Control Group (All Annotation) and Experiment Group 2 (Quality Annotation) is higher than it of high-score learners. Besides, the cognitive load among the three groups applying the Collaborative Reading Annotation System with different annotations to reading does not appear significant differences. Finally, developing effective reading strategies with Collaborative Reading Annotation, discussing the effects of various annotations on reading comprehension performance, and expanding Collaborative Reading Annotation System for promoting mobile reading are proposed as the preliminary framework for future research, with which in-depth exploration could be preceded in successive research.

A estabilidade de taludes naturais é um tema de grande interesse ao engenheiro geotécnico, face às significativas perdas econômicas, e até mesmo humanas, resultantes da ruptura de taludes. Estima-se que a deflagração de escorregamentos já provocou milhares de mortes, e dezenas de bilhões de dólares em prejuízos anuais em todo o mundo. Os fenômenos de instabilização de encostas são condicionados por muitos fatores, como o clima, a litologia e as estruturas das rochas, a morfologia, a ação antrópica e outros. A análise dos condicionantes geológicos e geotécnicos de escorregamentos proporciona a apreciação de cada um dos fatores envolvidos nos processos de instabilização de encostas, permitindo a obtenção de resultados de interesse, no que diz respeito ao modo de atuação destes fatores. O presente trabalho tem como objetivo a utilização da Lógica Nebulosa (Fuzzy) para criação de um Modelo que, de forma qualitativa, forneça uma previsão do risco de escorregamento de taludes em solos residuais. Para o cumprimento deste objetivo, foram estudados os fatores envolvidos nos processos de instabilização de encostas, e a forma como estes fatores se interrelacionam. Como experiência do especialista para a elaboração do modelo, foi analisado um extenso banco de dados de escorregamentos na cidade do Rio de Janeiro, disponibilizado pela Fundação Geo-Rio. Apresenta-se, neste trabalho, um caso histórico bem documentado para a validação do Modelo Fuzzy e análises paramétricas, realizadas com o objetivo verificar a coerência do modelo e a influência de cada um dos fatores adotados na previsão do risco de escorregamento. Dentre as principais conclusões, destaca-se a potencialidade da lógica nebulosa na previsão de risco de escorregamentos de taludes em solo residual, aparecendo como uma ferramenta capaz de auxiliar na detecção de áreas de risco. / The stability of slopes is a topic of great interest to the geotechnical engineer, given the significant economic losses, and even human, resulting from the slopes collapse. Its estimated that the landslides outbreak has already caused thousands of deaths and tens of billions of dollars in annual losses worldwide. The phenomena of instability of slopes are conditioned by many factors, such as climate, the lithology and structures of rock, the morphology, the anthropic and others. The analysis of geological and geotechnical conditions of landslides provides an appraisal of each of the factors involved in the processes of instability of slopes, allowing the achievement of results of interest with regard to the mode of action of factors. The current work aims at the use of Fuzzy Logic to create a model that, in qualitative form, provide an estimate of the risk of landslides on the slope of residual soil. To fulfill this objective, we studied the factors involved in the processes of instability of slopes, and how these factors are interrelated. As experience of the expert to the development of the model was examined an extensive database of landslides in Rio de Janeiro, provided by the Geo-Rio Foundation. It is presented in this work, one history case well documented for the validation of the Fuzzy Model and parametric analysis, conducted with the objective to verify the consistency of the model and influence of each of the factors used to predict the risk of landslides. Among the main findings includes the capability of Fuzzy Logic in predicting risk of landslides on the slope of residual soil, appearing as a tool capable of assisting in the detection of areas of risk.

[pt] A maioria dos estudos existentes em controle de qualidade de processos focam no desempenho de máquinas e ferramentas. Assim, estes já contam com bons métodos para serem controlados. Contudo, erros humanos em potencial estão presentes em todos os processos industriais que contenham a relação homem-máquina, fazendo com que a necessidade de se avaliar a qualidade do desempenho humano seja de igual importância. A abordagem para se avaliar quão suscetível à falha humana estão tais processos baseiam-se em probabilidades de erro, supondo que o desempenho humano funciona da mesma maneira que o desempenho de máquinas, ou em PSFs (Performance Shaping Factors), variáveis representativas de características de desempenho humano. Embora esta última abordagem seja mais eficiente, ainda existem críticas a sua falta de contextualização: tais características são avaliadas separadamente uma das outras, e independentemente da tarefa que o operador esteja realizando. Sistemas de Inferência Fuzzy (SIFs) permitem que variáveis lingüísticas sejam avaliadas em conjunto, isto é, passa a ser possível criar um modelo que assimile as nuances da variação do comportamento de um PSF concomitantemente com a alteração de outro PSF. Dessa forma, a caracterização da confiabilidade humana, considerando que diversos PSFs afetam no desempenho dos demais, pode ser satisfeita ao se fazer uso de SIFs interligados seqüencialmente - SIFs hierárquicos. Para se contextualizar a caracterização da confiabilidade humana por tarefa realizada, necessita-se que os PSFs pertinentes a cada determinada tarefa sejam medidos novamente e realimentados ao sistema (desenvolvido nesta dissertação). O SIF geral (composto por nove camadas de SIFs hierárquicos) foi testado com dados hipotéticos e dados reais de operadores e tarefas de uma empresa do setor elétrico brasileiro. Os resultados encontrados foram satisfatórios e evidenciaram que a Lógica Fuzzy, na forma de SIFs hierárquicos, pode ser utilizada para caracterizar a confiabilidade humana, com a vantagem de fazê-lo enquanto seu contexto é considerado. / [en] Most of existing studies in quality control focus on machinery performance. There are effective and advanced control methods to deal with that. However, potential human errors are present in every industrial process operated by humans. Therefore, evaluating the quality of human performance becomes as important as evaluate machinery s. The approach to evaluate how much processes are susceptible to human error are based on error probabilities, by assuming that human performance is similar to machinery performance, or on PSFs (Performance Shaping Factors) – variables representing human features. Although this based approach is more efficient, there are still criticisms about its lack of context awareness: those features are evaluated separately from one another, and regardless of which task the employee is performing. Fuzzy Inference Systems (FISs) allow linguistic variables to be evaluated simultaneously, thus making it possible to develop a method that gathers the nuances of behavioral changes of a PSF whilst another PSF varies. With this method, and considering that different PSFs affect the performance of others, human reliability can be assessed through the use of sequentially interconnected FISs – Hierarchical Fuzzy Inference Systems. In order to contextualize this assessment by tasks, each of the PSFs that affects each task will have to be measured and fed into the system (as developed within this dissertation) once per task and per employee. The main FIS (which contains nine layers of hierarchical FISs) was tested by using both hypothetical and real data from operators and tasks of a Brazilian electricity company. Results were satisfactory and attested that Fuzzy Logic, in the form of hierarchical FISs, can be used to assess human reliability, with the advantage of also taking the context into account.