Hybrid fuzzy analytical hierarchy process with fuzzy inference system on ranking stem approach towards blended learning in mathematics

In the era of Education 4.0, blended learning has been selected as one of the transformational pedagogies for the teaching and learning process that integrate Science, Technology, Engineering, and Mathematics (STEM), a new norm that needs to be adopted by Malaysia. Since the COVID-19 pandemic, the issue has been highlighted at most levels of study in the education field. However, limited knowledge of the implementation of 21st Century learning skills with Web 2.0 among teachers has made the students demotivated for their mathematics classroom. Moreover, dynamic changes in the standard curriculum have made the situation more challenging for teachers in selecting the appropriate STEM approach to ensure students are fully engaged. Inspired by the problem, this research used fuzzy multi-criteria decision-making (MCDM) concepts. A hybrid fuzzy MCDM model proposes a four stages process to rank and find the best implementation STEM approach in the mathematics classroom. The model is constructed by integrating the Fuzzy Analytical Hierarchy Process (FAHP) to determine the weights of STEM criteria and sub-criteria and the Fuzzy Inference System (FIS) to compute the best STEM approach in the mathematics classroom. The procedure involves exploring the issue associated with the selection problems, deriving decision criteria important weights, and ranking various alternatives with applied intuitive multiple centroids as a defuzzification method. The results showed hands-on activities as the best STEM approach while requisite knowledge is the important criterion with the greatest value of weights. Thus, the proposed model helps provide a clear picture for teachers in the implementation of STEM approach in Mathematics based on a comprehensive view and also lay a new foundation knowledge in fuzzy MCDM view, particularly in STEM education. Also, it helps the Ministry of Education (MoE) to achieve one of the initiatives in Wave 3 of the Malaysia Education Blueprint (2021-2025), which is to share the best practice in the classroom to cultivate a peer-led culture of professional excellence among teachers as the basis for improving the implementation and achievement of STEM at the national level

Development of a neuro fuzzy mathematical model for predicting the eletric power consumption from the baseline of a refrigerated distribution center

Orientador: Flávio Vasconcelos da SilvaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: O Protocolo Internacional de Medição e Verificação de Performance (PIMVP) é uma metodologia para apuração de resultados após a implementação de Ações de Eficiência Energética (AEEs). Para aplicação desta metodologia é necessário desenvolver um modelo matemático para ajuste da linha de base do consumo de energia conforme novas condições ambientais e operacionais. Neste trabalho foi desenvolvido um modelo Neuro Fuzzy para ajuste da linha de base para um centro de distribuição refrigerado. O modelo foi validado segundo os critérios do PIMVP (EVO, 2012). Para fins de comparação, outros modelos matemáticos da linha de base foram desenvolvidos utilizando a técnica tradicional regressão linear (RL). Os modelos foram comparados segundo os mesmos critérios e o modelo Neuro Fuzzy apresentou melhor resultado em termos de coeficiente de determinação (R2), atingiu 0,88 contra 0,72 do modelo RL; e de erro padrão (EP) da estimativa, alcançando 6,89 contra 10,19 do modelo RL. Os resultados obtidos mostram que com a aplicação da modelagem Neuro Fuzzy é possível reduzir a incerteza na previsão do consumo de energia total da planta, sendo possível calcular qual seria o consumo de energia se as AEEs não tivessem sido implementadas. O modelo foi desenvolvido para suportar a estratégia de Medição e Verificação (M&V) adotada para o projeto de eficiência energética que foi realizado em um Centro de Distribuição RefrigeradoAbstract: The International Performance Measurement and Verification Protocol (IPMVP) is a methodology for assessing results after the implementation of energy saving measures (ESMs). To apply this methodology it is necessary to develop a mathematical model to adjust the baseline of the energy consumption according to new environmental and operational conditions. In this work, a Neuro Fuzzy model was developed to adjust the baseline to a refrigerated warehouse. The model was validated according to IPMVP (EVO, 2012) criteria. For comparison purposes, other mathematical models of the baseline were developed using the traditional linear regression (RL) technique. The models were compared according to the same criteria and the Neuro Fuzzy model presented better result in terms of coefficient of determination (R²), reached 0.88 against 0.72 of the RL model; and standard error (EP) of the estimate, reaching 7.12 against 10.19 of the RL model. The results show that with the application of the Neuro Fuzzy model it is possible to reduce the uncertainty in the forecast of the total energy consumption of the plant, being possible to calculate what would be the energy consumption if the ESAs had not been implemented. The model was developed to support the Measurement and Verification (M&V) strategy adopted for the energy efficiency project that was carried out in a Refrigerated Distribution CenterMestradoEngenharia QuímicaMestra em Engenharia Químic

Optimización de la gestión de redes de riego a presión a diferentes escalas mediante Inteligencia Artificial

Factors such as climate change, world population growth or the competition for the water resources make freshwater availability become an increasingly large and complex global challenge. Under this scenario of reduced water availability, increasing droughts frequency and uncertainties associated with a changing climate, the irrigated agriculture sector, particularly in the Mediterranean region, will need to be even more efficient in the use of the water resources. In Spain, many irrigation districts have been modernized in recent years, replacing the obsolete open channels by pressurized water distribution networks towards improvements in water use efficiency. Thanks to this, water use has reduced but the energy demand and the water costs have dramatically increased. Thus, strategies to reduce simultaneously water and energy uses in irrigation districts are required. This thesis consists of nine chapters, which include several models to optimize the management of the irrigation districts and increase the efficiency of water and energy use.Factores tales como el cambio climático, el crecimiento de la población mundial o la competencia por los recursos hídricos hacen que la disponibilidad de agua se esté convirtiendo en un desafío global cada vez más grande y complejo. En este escenario de reducción de la disponibilidad de agua, aumento de la frecuencia de las sequías y de las incertidumbres asociadas a un cambio climático, el sector de la agricultura de regadío, en particular en la región mediterránea, tendrá que ser aún más eficiente en el uso de los recursos hídricos. En España, muchas comunidades de regantes se han modernizado en los últimos años, sustituyendo los obsoletos canales abiertos por redes de distribución de agua a presión con el objetivo de mejorar la eficiencia en el uso del agua. Gracias a esto, el uso del agua se ha reducido, pero la demanda de energía y los costos del agua se han incrementado drásticamente. Por lo tanto, se requieren estrategias para reducir simultáneamente el uso de agua y energía en las comunidades de regantes. Esta tesis consta de nueve capítulos que incluyen varios modelos para optimizar la gestión de las comunidades de regantes y aumentar la eficiencia en el uso del agua y la energía

TS fuzzy approach for modeling, analysis and design of non-smooth dynamical systems

There has been growing interest in the past two decades in studying the physical model of dynamical systems that can be described by nonlinear, non-smooth differential equations, i.e. non-smooth dynamical systems. These systems exhibit more colourful and complex dynamics compared to their smooth counterparts; however, their qualitative analysis and design are not yet fully developed and still open to exploration. At the same time, Takagi-Sugeno (TS) fuzzy systems have been shown to have a great ability to represent a large class of nonlinear systems and approximate their inherent uncertainties. This thesis explores an area of TS fuzzy systems that have not been considered before; that is, modelling, stability analysis and design for non-smooth dynamical systems. TS fuzzy model structures capable of representing or approximating the essential dis- continuous dynamics of non-smooth systems are proposed in this thesis. It is shown that by incorporating discrete event systems, the proposed structure for TS fuzzy models, which we will call non-smooth TS fuzzy models, can accurately represent the smooth (or contin- uous) as well as non-smooth (or discontinuous) dynamics of different classes of electrical and mechanical non-smooth systems including (sliding and non-sliding) Filippov's systems and impacting systems. The different properties of the TS fuzzy modelling (or formalism) are discussed. It is highlighted that the TS fuzzy formalism, taking advantage of its simple structure, does not need a special platform for its implementation. Stability in its new notion of structural stability (stability of a periodic solution) is one of the most important issues in the qualitative analysis of non-smooth systems. An important part of this thesis is focused on addressing stability issues by extending non- smooth Lyapunov theory for verifying the stability of local orbits, which the non-smooth TS fuzzy models can contain. Stability conditions are proposed for Filippov-type and impacting systems and it is shown that by formulating the conditions as Linear Matrix inequalities (LMIs), the onset of non-smooth bifurcations or chaotic phenomena can be detected by solving a feasibility problem. A number of examples are given to validate the proposed approach. Stability robustness of non-smooth TS fuzzy systems in the presence of model uncertainties is discussed in terms of non-smoothness rather than traditional observer design. The LMI stabilization problem is employed as a building block for devising design strategies to suppress the unwanted chaotic behaviour in non-smooth TS fuzzy models. There have been a large number of control applications in which the overall closed-loop sys tem can be stabilized by switching between pre-designed sub-controllers. Inspired by this idea, the design part of this thesis concentrates on fuzzy-chaos control strategies for Filippov-type systems. These strategies approach the design problem by switching be- tween local state-feedback controllers such that the closed-loop TS fuzzy system of interest rapidly converges to the stable periodic solution of the system. All control strategies are also automated as a design problem recast on linear matrix inequality conditions to be solved by modern optimization techniques. Keywords: Takagi-Sugeno fuzzy systems, non-smooth Lyapunov theory, non-smooth dy- namical systems, piecewise-smooth dynamical systems, structural stability, discontinuity- induced bifurcation, chaos controllers, dc-dc converters, Filippov's system, impacting system, linear matrix inequalities.EThOS - Electronic Theses Online ServiceGBUnited Kingdo