27 resultados para Dynamic Features
em Universidade do Minho
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Dissertação de mestrado integrado em Engenharia Biomédica
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Electric Vehicles (EVs) have limited energy storage capacity and the maximum autonomy range is strongly dependent of the driver's behaviour. Due to the fact of that batteries cannot be recharged quickly during a journey, it is essential that a precise range prediction is available to the driver of the EV. With this information, it is possible to check if the desirable destination is achievable without a stop to charge the batteries, or even, if to reach the destination it is necessary to perform an optimized driving (e.g., cutting the air-conditioning, among others EV parameters). The outcome of this research work is the development of an Electric Vehicle Assistant (EVA). This is an application for mobile devices that will help users to take efficient decisions about route planning, charging management and energy efficiency. Therefore, it will contribute to foster EVs adoption as a new paradigm in the transportation sector.
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The Our Lady of Conception church is located in village of Monforte (Portugal) and is not in use nowadays. The church presents structural damage and, consequently, a study was carried out. The study involved the survey of the damage, dynamic identification tests under ambient vibration and the numerical analysis. The church is constituted by the central nave, the chancel, the sacristy and the corridor to access the pulpit. The masonry walls present different thickness, namely 0.65 m in the chancel, 0.70 m in the sacristy, 0.92 in the central nave and 0.65 m in the corridor. The masonry walls present 8 buttresses with different dimensions. The total longitudinal and transversal dimensions of the church are equal to 21.10 m and 14.26 m, respectively. The survey of the damage showed that, in general, the masonry walls are in good conditions, with exception of the transversal walls of the nave, which present severe cracks. The arches of the vault presents also severe cracks along the central nave. As consequence, the infiltrations have increased the degradation of the vault and paintings. Furthermore, the foundations present settlements in the Southwest direction. The dynamic identification test were carried out under the action of ambient excitation of the wind and using 12 piezoelectric accelerometers of high sensitivity. The dynamic identification tests allowed to estimate the dynamic properties of the church, namely frequencies, mode shapes and damping ratios. A FEM numerical model was prepared and calibrated, based on the first four experimental modes estimated in the dynamic identification tests. The average error between the experimental and numerical frequencies of the first four modes is equal to 5%. After calibration of the numerical model, pushover analyses with a load pattern proportional to the mass, in the transversal and longitudinal direction of the church, were performed. The results of the analysis numerical allow to conclude that the most vulnerable direction of the church is in the transversal one and the maximum load factor is equal to 0.35.
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Existing masonry structures are usually associated to a high seismic vulnerability, mainly due to the properties of the materials, weak connections between floors and load-bearing walls, high mass of the masonry walls and flexibility of the floors. For these reasons, the seismic performance of existing masonry structures has received much attention in the last decades. This study presents the parametric analysis taking into account the deviations on features of the gaioleiro buildings - Portuguese building typology. The main objective of the parametric analysis is to compare the seismic performance of the structure as a function of the variations of its properties with respect to the response of a reference model. The parametric analysis was carried out for two types of structural analysis, namely for the non-linear dynamic analysis with time integration and for the pushover analysis with distribution of forces proportional to the inertial forces of the structure. The Young's modulus of the masonry walls, Young's modulus of the timber floors, the compressive and tensile non-linear properties (strength and fracture energy) were the properties considered in both type of analysis. Additionally, in the dynamic analysis, the influences of the vis-cous damping and of the vertical component of the earthquake were evaluated. A pushover analysis proportional to the modal displacement of the first mode in each direction was also carried out. The results shows that the Young's modulus of the masonry walls, the Young's modulus of the timber floors and the compressive non-linear properties are the pa-rameters that most influence the seismic performance of this type of tall and weak existing masonry structures. Furthermore, it is concluded that that the stiffness of the floors influences significantly the strength capacity and the collapse mecha-nism of the numerical model. Thus, a study on the strengthening of the floors was also carried out. The increase of the thickness of the timber floors was the strengthening technique that presented the best seismic performance, in which the reduction of the out-of-plane displacements of the masonry walls is highlighted.
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Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.
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Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.
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The usual high cost of commercial codes, and some technical limitations, clearly limits the employment of numerical modelling tools in both industry and academia. Consequently, the number of companies that use numerical code is limited and there a lot of effort put on the development and maintenance of in-house academic based codes. Having in mind the potential of using numerical modelling tools as a design aid, of both products and processes, different research teams have been contributing to the development of open source codes/libraries. In this framework, any individual can take advantage of the available code capabilities and/or implement additional features based on his specific needs. These type of codes are usually developed by large communities, which provide improvements and new features in their specific fields of research, thus increasing significantly the code development process. Among others, OpenFOAM® multi-physics computational library, developed by a very large and dynamic community, nowadays comprises several features usually only available in their commercial counterparts; e.g. dynamic meshes, large diversity of complex physical models, parallelization, multiphase models, to name just a few. This computational library is developed in C++ and makes use of most of all language capabilities to facilitate the implementation of new functionalities. Concerning the field of computational rheology, OpenFOAM® solvers were recently developed to deal with the most relevant differential viscoelastic rheological models, and stabilization techniques are currently being verified. This work describes the implementation of a new solver in OpenFOAM® library, able to cope with integral viscoelastic models based on the deformation field method. The implemented solver is verified through the comparison of the predicted results with analytical solutions, results published in the literature and by using the Method of Manufactured Solutions.
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The usual high cost of commercial codes, and some technical limitations, clearly limits the employment of numerical modelling tools in both industry and academia. Consequently, the number of companies that use numerical code is limited and there a lot of effort put on the development and maintenance of in-house academic based codes . Having in mind the potential of using numerical modelling tools as a design aid, of both products and processes, different research teams have been contributing to the development of open source codes/libraries. In this framework, any individual can take advantage of the available code capabilities and/or implement additional features based on his specific needs. These type of codes are usually developed by large communities, which provide improvements and new features in their specific fields of research, thus increasing significantly the code development process. Among others, OpenFOAM® multi-physics computational library, developed by a very large and dynamic community, nowadays comprises several features usually only available in their commercial counterparts; e.g. dynamic meshes, large diversity of complex physical models, parallelization, multiphase models, to name just a few. This computational library is developed in C++ and makes use of most of all language capabilities to facilitate the implementation of new functionalities. Concerning the field of computational rheology, OpenFOAM® solvers were recently developed to deal with the most relevant differential viscoelastic rheological models, and stabilization techniques are currently being verified. This work describes the implementation of a new solver in OpenFOAM® library, able to cope with integral viscoelastic models based on the deformation field method. The implemented solver is verified through the comparison of the predicted results with analytical solutions, results published in the literature and by using the Method of Manufactured Solutions
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Due to the fact that different injection molding conditions tailor the mechanical response of the thermoplastic material, such effect must be considered earlier in the product development process. The existing approaches implemented in different commercial software solutions are very limited in their capabilities to estimate the influence of processing conditions on the mechanical properties. Thus, the accuracy of predictive simulations could be improved. In this study, we demonstrate how to establish straightforward processing-impact property relationships of talc-filled injection-molded polypropylene disc-shaped parts by assessing the thermomechanical environment (TME). To investigate the relationship between impact properties and the key operative variables (flow rate, melt and mold temperature, and holding pressure), the design of experiments approach was applied to systematically vary the TME of molded samples. The TME is characterized on computer flow simulation outputsanddefined bytwo thermomechanical indices (TMI): the cooling index (CI; associated to the core features) and the thermo-stress index (TSI; related to the skin features). The TMI methodology coupled to an integrated simulation program has been developed as a tool to predict the impact response. The dynamic impact properties (peak force, peak energy, and puncture energy) were evaluated using instrumented falling weight impact tests and were all found to be similarly affected by the imposed TME. The most important molding parameters affecting the impact properties were found to be the processing temperatures (melt andmold). CI revealed greater importance for the impact response than TSI. The developed integrative tool provided truthful predictions for the envisaged impact properties.
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In this study, a high-performance composite was prepared from jute fabrics and polypropylene (PP). In order to improve the compatibility of the polar fibers and the non-polar matrix, alkyl gallates with different hydrophobic groups were enzymatically grafted onto jute fabric by laccase to increase the surface hydrophobicity of the fiber. The grafting products were characterized by FTIR. The results of contact angle and wetting time showed that the hydrophobicity of the jute fabrics was improved after the surface modification. The effect of the enzymatic graft modification on the properties of the jute/PP composites was evaluated. Results showed that after the modification, tensile and dynamic mechanical properties of composites improved, and water absorption and thickness swelling clearly decreased. However, tensile properties drastically decreased after a long period of water immersion. The thermal behavior of the composites was evaluated by TGA/DTG. The fiber-matrix morphology in the modified jute/PP composites was confirmed by SEM analysis of the tensile fractured specimens.
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This work reports on the influence of the substrate polarization of electroactive β-PVDF on human adipose stem cells (hASCs) differentiation under static and dynamic conditions. hASCs were cultured on different β-PVDF surfaces (non-poled and “poled -”) adsorbed with fibronectin and osteogenic differentiation was determined using a quantitative alkaline phosphatase assay. “Poled -” β-PVDF samples promote higher osteogenic differentiation, which is even higher under dynamic conditions. It is thus demonstrated that electroactive membranes can provide the necessary electromechanical stimuli for the differentiation of specific cells and therefore will support the design of suitable tissue engineering strategies, such as bone tissue engineering.
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Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.
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Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.
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[Excerpt] We read with interest the case report by Ismael et al1 describing a patient with Sjo¨gren’s syndrome and cystic lung disease who could not be weaned from a ventilator due to severe central excessive dynamic airway collapse (EDAC) of the lower part of the trachea and proximal bronchi. EDAC corresponds to the expiratory bulging of the tracheobronchial wall without known airway structural abnormalities, leading to a decrease of at least 50% in internal diameter.2 It is a rare and underdiagnosed entity, commonly confused with other respiratory diseases such as asthma and COPD. Although noninvasive procedures such as cervicothoracic computed tomography scan on inspiration and expiration may suggest the disorder, the accepted standard method for diagnosis is bronchoscopy.3-7 (...).
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Dissertação de mestrado integrado em Arquitectura