942 resultados para errors and erasures decoding
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Water budget parameters are estimated for Shark River Slough (SRS), the main drainage within Everglades National Park (ENP) from 2002 to 2008. Inputs to the water budget include surface water inflows and precipitation while outputs consist of evapotranspiration, discharge to the Gulf of Mexico and seepage losses due to municipal wellfield extraction. The daily change in volume of SRS is equated to the difference between input and outputs yielding a residual term consisting of component errors and net groundwater exchange. Results predict significant net groundwater discharge to the SRS peaking in June and positively correlated with surface water salinity at the mangrove ecotone, lagging by 1 month. Precipitation, the largest input to the SRS, is offset by ET (the largest output); thereby highlighting the importance of increasing fresh water inflows into ENP for maintaining conditions in terrestrial, estuarine, and marine ecosystems of South Florida.
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The localization of mobile robots in indoor environments finds lots of problems such as accumulated errors and the constant changes that occur at these places. A technique called global vision intends to localize robots using images acquired by cameras placed in such a way that covers the place where the robots movement takes place. Localization is obtained by marks put on top of the robot. Algorithms applied to the images search for the mark on top of the robot and by finding the mark they are able to get the position and orientation of the robot. Such techniques used to face some difficulties related with the hardware capacity, fact that limited their execution in real time. However, the technological advances of the last years changed that situation and enabling the development and execution of such algorithms in plain capacity. The proposal specified here intends to develop a mobile robot localization system at indoor environments using a technique called global vision to track the robot and acquire the images, all in real time, intending to improve the robot localization process inside the environment. Being a localization method that takes just actual information in its calculations, the robot localization using images fit into the needs of this kind of place. Besides, it enables more accurate results and in real time, what is exactly the museum application needs.
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V článku sa analizujú ekvivalenty a rozdiely medzi priamym a nepriamym predmetom v španielčine a akuzatívom a datívom v slovenčine. Ďalej sa autorka snaží nájsť normy pre správne používanie zámen v datíve a akuzatíve v obidvoch jazykoch a sústredí sa na tretie osoby singuláru aj plurálu. Nakoniec sa vyvodia chyby a interferencie v používaní týchto zámen u slovensky hovoriacich.
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Salman, M. et al. (2016). Integrating Scientific Publication into an Applied Gaming Ecosystem. GSTF Journal on Computing (JoC), Volume 5 (Issue 1), pp. 45-51.
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Inverse simulations of musculoskeletal models computes the internal forces such as muscle and joint reaction forces, which are hard to measure, using the more easily measured motion and external forces as input data. Because of the difficulties of measuring muscle forces and joint reactions, simulations are hard to validate. One way of reducing errors for the simulations is to ensure that the mathematical problem is well-posed. This paper presents a study of regularity aspects for an inverse simulation method, often called forward dynamics or dynamical optimization, that takes into account both measurement errors and muscle dynamics. The simulation method is explained in detail. Regularity is examined for a test problem around the optimum using the approximated quadratic problem. The results shows improved rank by including a regularization term in the objective that handles the mechanical over-determinancy. Using the 3-element Hill muscle model the chosen regularization term is the norm of the activation. To make the problem full-rank only the excitation bounds should be included in the constraints. However, this results in small negative values of the activation which indicates that muscles are pushing and not pulling. Despite this unrealistic behavior the error maybe small enough to be accepted for specific applications. These results is a starting point start for achieving better results of inverse musculoskeletal simulations from a numerical point of view.
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Die Fähigkeit, geschriebene Texte zu verstehen, d.h. eine kohärente mentale Repräsentation von Textinhalten zu erstellen, ist eine notwendige Voraussetzung für eine erfolgreiche schulische und außerschulische Entwicklung. Es ist daher ein zentrales Anliegen des Bildungssystems Leseschwierigkeiten frühzeitig zu diagnostizieren und mithilfe zielgerichteter Interventionsprogramme zu fördern. Dies erfordert ein umfassendes Wissen über die kognitiven Teilprozesse, die dem Leseverstehen zugrunde liegen, ihre Zusammenhänge und ihre Entwicklung. Die vorliegende Dissertation soll zu einem umfassenden Verständnis über das Leseverstehen beitragen, indem sie eine Auswahl offener Fragestellungen experimentell untersucht. Studie 1 untersucht inwieweit phonologische Rekodier- und orthographische Dekodierfertigkeiten zum Satz- und Textverstehen beitragen und wie sich beide Fertigkeiten bei deutschen Grundschüler(inne)n von der 2. bis zur 4. Klasse entwickeln. Die Ergebnisse legen nahe, dass beide Fertigkeiten signifikante und eigenständige Beiträge zum Leseverstehen leisten und dass sich ihr relativer Beitrag über die Klassenstufen hinweg nicht verändert. Darüber hinaus zeigt sich, dass bereits deutsche Zweitklässler(innen) den Großteil geschriebener Wörter in altersgerechten Texten über orthographische Vergleichsprozesse erkennen. Nichtsdestotrotz nutzen deutsche Grundschulkinder offenbar kontinuierlich phonologische Informationen, um die visuelle Worterkennung zu optimieren. Studie 2 erweitert die bisherige empirische Forschung zu einem der bekanntesten Modelle des Leseverstehens—der Simple View of Reading (SVR, Gough & Tunmer, 1986). Die Studie überprüft die SVR (Reading comprehension = Decoding x Comprehension) mithilfe optimierter und methodisch stringenter Maße der Modellkonstituenten und überprüft ihre Generalisierbarkeit für deutsche Dritt- und Viertklässler(innen). Studie 2 zeigt, dass die SVR einer methodisch stringenten Überprüfung nicht standhält und nicht ohne Weiteres auf deutsche Dritt- und Viertklässler(innen) generalisiert werden kann. Es wurden nur schwache Belege für eine multiplikative Verknüpfung von Dekodier- (D) und Hörverstehensfertigkeiten (C) gefunden. Der Umstand, dass ein beachtlicher Teil der Varianz im Leseverstehen (R) nicht durch D und C aufgeklärt werden konnte, deutet darauf hin, dass das Modell nicht vollständig ist und ggf. durch weitere Komponenten ergänzt werden muss. Studie 3 untersucht die Verarbeitung positiv-kausaler und negativ-kausaler Kohärenzrelationen bei deutschen Erst- bis Viertklässler(inne)n und Erwachsenen im Lese- und Hörverstehen. In Übereinstimmung mit dem Cumulative Cognitive Complexity-Ansatz (Evers-Vermeul & Sanders, 2009; Spooren & Sanders, 2008) zeigt Studie 3, dass die Verarbeitung negativ-kausaler Kohärenzrelationen und Konnektoren kognitiv aufwändiger ist als die Verarbeitung positiv-kausaler Relationen. Darüber hinaus entwickelt sich das Verstehen beider Kohärenzrelationen noch über die Grundschulzeit hinweg und ist für negativ-kausale Relationen am Ende der vierten Klasse noch nicht abgeschlossen. Studie 4 zeigt und diskutiert die Nützlichkeit prozess-orientierter Lesetests wie ProDi- L (Richter et al., in press), die individuelle Unterschiede in den kognitiven Teilfertigkeiten des Leseverstehens selektiv erfassen. Hierzu wird exemplarisch die Konstruktvalidität des ProDi-L-Subtests ‚Syntaktische Integration’ nachgewiesen. Mittels explanatorischer Item- Repsonse-Modelle wird gezeigt, dass der Test Fertigkeiten syntaktischer Integration separat erfasst und Kinder mit defizitären syntaktischen Fertigkeiten identifizieren kann. Die berichteten Befunde tragen zu einem umfassenden Verständnis der kognitiven Teilfertigkeiten des Leseverstehens bei, das für eine optimale Gestaltung des Leseunterrichts, für das Erstellen von Lernmaterialien, Leseinstruktionen und Lehrbüchern unerlässlich ist. Darüber hinaus stellt es die Grundlage für eine sinnvolle Diagnose individueller Leseschwierigkeiten und für die Konzeption adaptiver und zielgerichteter Interventionsprogramme zur Förderung des Leseverstehens bei schwachen Leser(inne)n dar.
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Thesis (Ph.D.)--University of Washington, 2016-08
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Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.
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The main objective of this work was to develop an application capable of determining the diffusion times and diffusion coefficients of optical clearing agents and water inside a known type of muscle. Different types of chemical agents can also be used with the method implemented, such as medications or metabolic products. Since the diffusion times can be calculated, it is possible to describe the dehydration mechanism that occurs in the muscle. The calculation of the diffusion time of an optical clearing agent allows to characterize the refractive index matching mechanism of optical clearing. By using both the diffusion times and diffusion of water and clearing agents not only the optical clearing mechanisms are characterized, but also information about optical clearing effect duration and magnitude is obtained. Such information is crucial to plan a clinical intervention in cooperation with optical clearing. The experimental method and equations implemented in the developed application are described in throughout this document, demonstrating its effectiveness. The application was developed in MATLAB code, but the method was personalized so it better fits the application needs. This process significantly improved the processing efficiency, reduced the time to obtain he results, multiple validations prevents common errors and some extra functionalities were added such as saving application progress or export information in different formats. Tests were made using glucose measurements in muscle. Some of the data, for testing purposes, was also intentionally changed in order to obtain different simulations and results from the application. The entire project was validated by comparing the calculated results with the ones found in literature, which are also described in this document.
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The intensive character in knowledge of software production and its rising demand suggest the need to establish mechanisms to properly manage the knowledge involved in order to meet the requirements of deadline, costs and quality. The knowledge capitalization is a process that involves from identification to evaluation of the knowledge produced and used. Specifically, for software development, capitalization enables easier access, minimize the loss of knowledge, reducing the learning curve, avoid repeating errors and rework. Thus, this thesis presents the know-Cap, a method developed to organize and guide the capitalization of knowledge in software development. The Know-Cap facilitates the location, preservation, value addition and updating of knowledge, in order to use it in the execution of new tasks. The method was proposed from a set of methodological procedures: literature review, systematic review and analysis of related work. The feasibility and appropriateness of Know-Cap were analyzed from an application study, conducted in a real case, and an analytical study of software development companies. The results obtained indicate the Know- Cap supports the capitalization of knowledge in software development.
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Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.
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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2015.
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En la última década de este siglo se ha presenciado un salto cualitativo en el mundo comercial enfatizado a la calidad total, permitiendo a las empresas la máxima efectividad y productividad, colocándose así en niveles de competitividad en esta aldea global.Para entrar en este ámbito de calidad es necesario que las empresas asuman normas de aceptación internacional, tal es el caso de la serie de normas ISO 9000, cuyo objetivo es el establecimiento de sistemas de aseguramiento de la calidad que garantizan la buena marcha de las empresas y su relación con los clientes.A medida que se desarrolla el trabajo de investigación, se pretende dar una visión general sobre la serie de normas ISO 9000, incluyendo precisamente las normas que la componen, sus beneficios, sus ventajas, sus barreras, la forma de aplicación y la implementación de éstas, por otro lado se muestra la importancia de la base documental compuesta por tres niveles en forma de pirámide, que conforma el primer paso que debe dar una empresa para iniciar el proceso de asimilación de las normas ISO 9000 y así contar a corto o mediano plazo con un aumento en su productividad, la eliminación de errores y un mejoramiento en la calidad del producto, redundando en una importante mejoría tanto para la empresa y sus clientes.
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Dissertação de Mestrado apresentada ao Instituto Superior de Psicologia Aplicada para obtenção de grau de Mestre na especialidade de Psicologia Educacional.
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In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR.