947 resultados para Variable Duty Cycle Control
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Global Network for the Molecular Surveillance of Tuberculosis 2010: A. Miranda (Tuberculosis Laboratory of the National Institute of Health, Porto, Portugal)
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Miniaturization of power generators to the MEMS scale, based on the hydrogen-air fuel cell, is the object of this research. The micro fuel cell approach has been adopted for advantages of both high power and energy densities. On-board hydrogen production/storage and an efficient control scheme that facilitates integration with a fuel cell membrane electrode assembly (MEA) are key elements for micro energy conversion. Millimeter-scale reactors (ca. 10 µL) have been developed, for hydrogen production through hydrolysis of CaH2 and LiAlH4, to yield volumetric energy densities of the order of 200 Whr/L. Passive microfluidic control schemes have been implemented in order to facilitate delivery, self-regulation, and at the same time eliminate bulky auxiliaries that run on parasitic power. One technique uses surface tension to pump water in a microchannel for hydrolysis and is self-regulated, based on load, by back pressure from accumulated hydrogen acting on a gas-liquid microvalve. This control scheme improves uniformity of power delivery during long periods of lower power demand, with fast switching to mass transport regime on the order of seconds, thus providing peak power density of up to 391.85 W/L. Another method takes advantage of water recovery by backward transport through the MEA, of water vapor that is generated at the cathode half-cell reaction. This regulation-free scheme increases available reactor volume to yield energy density of 313 Whr/L, and provides peak power density of 104 W/L. Prototype devices have been tested for a range of duty periods from 2-24 hours, with multiple switching of power demand in order to establish operation across multiple regimes. Issues identified as critical to the realization of the integrated power MEMS include effects of water transport and byproduct hydrate swelling on hydrogen production in the micro reactor, and ambient relative humidity on fuel cell performance.
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La literatura científica advierte de la elevada presencia de trastornos mentales en el medio penitenciario. Por ello, en este trabajo nos planteamos evaluar la salud psicosocial y la autorregulación de reclusos en relación a un grupo control de participantes no reclusos; adicionalmente tratamos de conocer la incidencia de la variable tiempo de reclusión y analizar el efecto intragrupo pre-post entrenamiento en habilidades sociales y comunicativas. La muestra estuvo compuesta por 20 varones, 10 reclusos (cinco con más de un año de condena y cinco con menos) y 10 participantes sin antecedentes delictivos, a los que se les administró el cuestionario de salud GHQ-28 y la escala de autorregulación MAPA. Se aplicó la prueba no paramétrica Mann-Whitney (U) para el cálculo de probabilidades y el test de Cramer (V) como indicador del tamaño de efecto. Los resultados indicaron que la reclusión no implicó necesariamente peor salud y autorregulación, que el tiempo de condena no ejerció excesiva influencia sobre estas dos dimensiones y que el taller de habilidades sociales no pareció, según el análisis pre-postest, haber producido efectos sobre la salud y la autorregulación de los reclusos. Finalmente, se discuten algunas reflexiones generales así como nuevas propuestas para mejorar actuaciones futuras.
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The natural compliance and force generation properties of pneumatic artificial muscles (PAMs) allow them to operate like human muscles in anthropomorphic robotic manipulators. Traditionally, manipulators use a single PAM or multiple PAMs actuated in unison in place of a human muscle. However, these manipulators experience efficiency losses when operated outside their target performance ranges. The unidirectional actuation behavior of a miniature PAM bundle and bidirectional actuation behavior of an antagonistic pair of miniature PAM bundles are characterized and modeled. The results are used to motivate the application of a variable recruitment control strategy to a parallel bundle of miniature PAMs as an attempt to mimic the selective recruitment of motor units in a human muscle to improve the operating efficiency of the actuator. Additionally, the fabrication and quasi-static testing results for PAMs assembled from candidate space qualified bladder and braided sleeve materials for use in space robotics are assessed.
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The aims of this thesis were evaluation the type of wave channel, wave current, and effect of some parameters on them and identification and comparison between types of wave maker in laboratory situations. In this study, designing and making of two dimension channels (flume) and wave maker for experiment son the marine buoy, marine building and energy conversion systems were also investigated. In current research, the physical relation between pump and pumpage and the designing of current making in flume were evaluated. The related calculation for steel building, channels beside glasses and also equations of wave maker plate movement, power of motor and absorb wave(co astal slope) were calculated. In continue of this study, the servo motor was designed and applied for moving of wave maker’s plate. One Ball Screw Leaner was used for having better movement mechanisms of equipment and convert of the around movement to linear movement. The Programmable Logic Controller (PLC) was also used for control of wave maker system. The studies were explained type of ocean energies and energy conversion systems. In another part of this research, the systems of energy resistance in special way of Oscillating Water Column (OWC) were explained and one sample model was designed and applied in hydrolic channel at the Sheikh Bahaii building in Azad University, Science and Research Branch. The dimensions of designed flume was considered at 16 1.98 0. 57 m which had ability to provide regular waves as well as irregular waves with little changing on the control system. The ability of making waves was evaluated in our designed channel and the results were showed that all of the calculation in designed flume was correct. The mean of error between our results and theory calculation was conducted 7%, which was showed the well result in this situation. With evaluating of designed OWC model and considering of changes in the some part of system, one bigger sample of this model can be used for designing the energy conversion system model. The obtained results showed that the best form for chamber in exit position of system, were zero degree (0) in angle for moving below part, forty and five (45) degree in front wall of system and the moving forward of front wall keep in two times of height of wave.
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In the context of computer numerical control (CNC) and computer aided manufacturing (CAM), the capabilities of programming languages such as symbolic and intuitive programming, program portability and geometrical portfolio have special importance -- They allow to save time and to avoid errors during part programming and permit code re-usage -- Our updated literature review indicates that the current state of art presents voids in parametric programming, program portability and programming flexibility -- In response to this situation, this article presents a compiler implementation for EGCL (Extended G-code Language), a new, enriched CNC programming language which allows the use of descriptive variable names, geometrical functions and flow-control statements (if-then-else, while) -- Our compiler produces low-level generic, elementary ISO-compliant Gcode, thus allowing for flexibility in the choice of the executing CNC machine and in portability -- Our results show that readable variable names and flow control statements allow a simplified and intuitive part programming and permit re-usage of the programs -- Future work includes allowing the programmer to define own functions in terms of EGCL, in contrast to the current status of having them as library built-in functions
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© IMechE 2014. Controlled auto-ignition, also known as homogeneous charge compression ignition, has been the subject of extensive research because of their ability to provide simultaneous reductions in fuel consumption and NOx emissions from a gasoline engine. However, due to its limited operation range, switching between controlled auto-ignition and spark ignition combustion is needed to cover the complete operating range of a gasoline engine for passenger car applications. Previous research has shown that the spark ignition -controlled auto-ignition hybrid combustion (SCHC) has the potential to control the ignition timing and heat release process during the mode transition operations. However, it was found that the SCHC is often characterized with large cycle-to-cycle variations. The cyclic variations in the in-cylinder pressure are particularly noticeable in terms of both their peak values and timings while the coefficient of variation in the indicated mean effective pressure is much less. In this work, the cyclic variations in SCHC operations were analyzed by means of in-cylinder pressure and heat release analysis in a single-cylinder gasoline engine equipped with Variable Valve Actuation (VVA) systems. First, characteristics of the in-cylinder pressure traces during the spark ignition-controlled auto-ignition hybrid combustion operation are presented and their heat release processes analyzed. In order to clarify the contribution to heat release and cyclic variation in SCHC, a new method is introduced to identify the occurrence of auto-ignition combustion and its subsequent heat release process. Based on the new method developed, the characteristics of cyclic variations in the maximum rate of pressure rise and different stages of heat release process have been analyzed and discussed.
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Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.
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Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance.
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The Homogeneous Charge Compression Ignition (HCCI) engine is a promising combustion concept for reducing NOx and particulate matter (PM) emissions and providing a high thermal efficiency in internal combustion engines. This concept though has limitations in the areas of combustion control and achieving stable combustion at high loads. For HCCI to be a viable option for on-road vehicles, further understanding of its combustion phenomenon and its control are essential. Thus, this thesis has a focus on both the experimental setup of an HCCI engine at Michigan Technological University (MTU) and also developing a physical numerical simulation model called the Sequential Model for Residual Affected HCCI (SMRH) to investigate performance of HCCI engines. The primary focus is on understanding the effects of intake and exhaust valve timings on HCCI combustion. For the experimental studies, this thesis provided the contributions for development of HCCI setup at MTU. In particular, this thesis made contributions in the areas of measurement of valve profiles, measurement of piston to valve contact clearance for procuring new pistons for further studies of high geometric compression ratio HCCI engines. It also consists of developing and testing a supercharging station and the setup of an electrical air heater to extend the HCCI operating region. The HCCI engine setup is based on a GM 2.0 L LHU Gen 1 engine which is a direct injected engine with variable valve timing (VVT) capabilities. For the simulation studies, a computationally efficient modeling platform has been developed and validated against experimental data from a single cylinder HCCI engine. In-cylinder pressure trace, combustion phasing (CA10, CA50, BD) and performance metrics IMEP, thermal efficiency, and CO emission are found to be in good agreement with experimental data for different operating conditions. Effects of phasing intake and exhaust valves are analyzed using SMRH. In addition, a novel index called Fuel Efficiency and Emissions (FEE) index is defined and is used to determine the optimal valve timings for engine operation through the use of FEE contour maps.
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To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.
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The invention relates to a variable-spectrum solar simulator for characterising photovoltaic systems. The simulator can be used to obtain a spectrum adjusted to the solar spectrum, both for a standard spectrum or a real spectrum adjusted to local irradiation conditions. The simulator also allows the spatial-angular characteristics of the sun to be reproduced. The invention comprises: a broad-spectrum light source, the flux from which is emitted through an aperture; an optical system which collimates the primary source; a system which disperses the beam chromatically; an optical system which forms an image of the dispersed primary source at a given position, at which a spatial mask is placed in order to filter the received irradiance spectrally; an optical system which captures the filtered spectrum and returns, mixes and concentrates same in a secondary source with the desired spectral, angular, and spatial characteristics; an optical system which collimates the secondary source such that it reproduces the angular characteristics of the sun; and a control system.
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Esta tesis vincula el estudio de los sistemas dinámicos caóticos con la teoría del control para explorar la relación que existe entre los métodos de control del caos y las reglas de política monetaria. En ambos casos está presente un objetivo estabilizador; de una parte, los métodos de control del caos buscan corregir movimientos irregulares estabilizando alguna de las orbitas periódicas inestables que se encuentran en un atractor extraño, esto es, llevar al sistema de un comportamiento caótico a un comportamiento regular; mientras que en economía, los policy maker fijan una meta para las variables objetivo de política y buscan que el valor fijado coincida con el valor observado. La forma con la cual se estabiliza es a través del empleo de reglas de control realimentado que operan reduciendo la diferencia entre el valor observado para la variable y su valor fijado, empleando para ello un instrumento de control. Así, las reglas de control de sistemas dinámicos caóticos y las reglas de política tienen como objetivo que el sistema en el cual sean aplicadas tenga un comportamiento deseado. Buscamos aplicar en esta tesis las técnicas de control de los sistemas dinámicos caóticos, en particular, el método OGY de control del caos, al diseño de reglas de política monetaria para comprobar su potencial estabilizador en las variables económicas. Pretendemos mostrar que el caos se puede controlar y que los métodos desarrollados para su control pueden servir de herramientas prácticas para la elaboración de políticas de estabilización. El método que empleamos aquí se puede aplicar en cualquier sistema dinámico que presente comportamiento caótico...
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The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma and rhinitis and (3) to develop guidelines with all stakeholders that could be used globally for all countries and populations. ARIA—disseminated and implemented in over 70 countries globally—is now focusing on the implementation of emerging technologies for individualized and predictive medicine. MASK [MACVIA (Contre les Maladies Chroniques pour un Vieillissement Actif)-ARIA Sentinel NetworK] uses mobile technology to develop care pathways for the management of rhinitis and asthma by a multi-disciplinary group and by patients themselves. An app (Android and iOS) is available in 20 countries and 15 languages. It uses a visual analogue scale to assess symptom control and work productivity as well as a clinical decision support system. It is associated with an inter-operable tablet for physicians and other health care professionals. The scaling up strategy uses the recommendations of the European Innovation Partnership on Active and Healthy Ageing. The aim of the novel ARIA approach is to provide an active and healthy life to rhinitis sufferers, whatever their age, sex or socio-economic status, in order to reduce health and social inequalities incurred by the disease.
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In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.