11 resultados para self adaptive modified teacher learning optimization (SAMTLO) algorithm

em Universidade Federal do Rio Grande do Norte(UFRN)


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This paper presents an evaluative study about the effects of using a machine learning technique on the main features of a self-organizing and multiobjective genetic algorithm (GA). A typical GA can be seen as a search technique which is usually applied in problems involving no polynomial complexity. Originally, these algorithms were designed to create methods that seek acceptable solutions to problems where the global optimum is inaccessible or difficult to obtain. At first, the GAs considered only one evaluation function and a single objective optimization. Today, however, implementations that consider several optimization objectives simultaneously (multiobjective algorithms) are common, besides allowing the change of many components of the algorithm dynamically (self-organizing algorithms). At the same time, they are also common combinations of GAs with machine learning techniques to improve some of its characteristics of performance and use. In this work, a GA with a machine learning technique was analyzed and applied in a antenna design. We used a variant of bicubic interpolation technique, called 2D Spline, as machine learning technique to estimate the behavior of a dynamic fitness function, based on the knowledge obtained from a set of laboratory experiments. This fitness function is also called evaluation function and, it is responsible for determining the fitness degree of a candidate solution (individual), in relation to others in the same population. The algorithm can be applied in many areas, including in the field of telecommunications, as projects of antennas and frequency selective surfaces. In this particular work, the presented algorithm was developed to optimize the design of a microstrip antenna, usually used in wireless communication systems for application in Ultra-Wideband (UWB). The algorithm allowed the optimization of two variables of geometry antenna - the length (Ls) and width (Ws) a slit in the ground plane with respect to three objectives: radiated signal bandwidth, return loss and central frequency deviation. These two dimensions (Ws and Ls) are used as variables in three different interpolation functions, one Spline for each optimization objective, to compose a multiobjective and aggregate fitness function. The final result proposed by the algorithm was compared with the simulation program result and the measured result of a physical prototype of the antenna built in the laboratory. In the present study, the algorithm was analyzed with respect to their success degree in relation to four important characteristics of a self-organizing multiobjective GA: performance, flexibility, scalability and accuracy. At the end of the study, it was observed a time increase in algorithm execution in comparison to a common GA, due to the time required for the machine learning process. On the plus side, we notice a sensitive gain with respect to flexibility and accuracy of results, and a prosperous path that indicates directions to the algorithm to allow the optimization problems with "η" variables

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We understand that the successful old age is not confined only in the analysis of quantitative aspects concerning the economic situation of the subject that gets old, but this special way of aging is related to other values, such as dignity, happiness, self-esteem, willingness, autonomy, independence, social involvement with family and friends, among others. Thus, this study aimed to investigate the human aging process, considering the history of life of ten seniors who fit the profile of successful old age, seeking to identify elements that contribute to educational thinking in order to get a successful aging. In this perspective, we argue in this study, the idea that we need to learn to engage in experiences that more than providing satisfaction and well-being in the moment in which we conducted, serve as a potentiating to a successful old age. Thus, throughout this thesis we question: what are the present indicative in the histories of life of elderly people, considered successful, which may contribute to an education that people experiencing their age more satisfactorily. This is a qualitative study, that took as universe a methodological oral history, in which we used as a technique of research a semistructured interview as a part of their life history, with emphasis on consideration of the following categories: diary routine, with emphasis on social harmony in the family, at work and in friendship groups of leisure and physical activity, self-knowledge and the learning experiences throughout life. To get the objectives outlined, as well as lectured on the categories analyzed, we support our reflection on the theory of the course of life, which includes human aging as a historical and cultural contextual reality. Our research revealed, among other things, that successful aging is coupled to an active lifestyle, where the involvement in physical activities, recreational and social experiences throughout life is important for increasing self-esteem, autonomy and joy of living, conditions that enable successful old age. Our study also revealed that the educationfor successful old age is linked with the consumption along the life, educational activities which broaden the possibilities of social interaction between people, even among different generations, since the interaction is important to learn and accept our possibilities and limits.

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The present work concerns an auto-ethnographic study based on life experiences and reflections of an educator at Escola Viva Preschool and Elementary-Middle School, located in the city center of Natal, Rio Grande do Norte. As a cognitive model of operation, we use the metaphor of the Circle Dance. The objective of this study is to identify, interpret and describe the ludopoetics that are achieved through a Musical Education program, which we denominate, Humanescent. The data of this investigation was derived from the music making by Preschool and Elementary-Middle School students at Escola Viva during 2007, 2008 and 2009, from which 20 learners were selected to form the corpus, along with the description and interpretation of photos of their experiences and sand tray scenes. We justify the methodological systemization of the research based on our own pedagogical practice, which supports Musical Education in the schools based on the principals of Embodiment, Autopoesis and Flow. The methodological systemization was developed through an Action Research model and on the concepts of Systemic Development, with the goal of re-reading the context investigated through the structuring of categories of Ludopoesis: Self-esteem, Self-territory, Self-connectivity, Self-realization and Selfworth. We used an observant-participant research approach with regard to the perception of emergent knowledge, the surroundings, the experience lived and the contextual and vibration of the circumstances. Besides this, we used projection to interpret the experiences lived, in the form of drawings, short poems, letters or sand tray scenes as symbolic interpretations of experience. In the unfolding of the Ludopoetic Process (Selfesteem, Self-territory, Self-connectivity, Self-realization and Selfworth) we draw conclusions about the relevance of the ludic musical experience, which foments the formation of the self based on music learning, and which is demonstrated in the Embodiment of the learners. In the auto-formative process (of learners and educators) we observe the importance of pedagogical work based on Musical Humanescent Education that gives value to the music making path to the construction of music and performance in play, creativity, and sensibility. The experience of making music in a playful way allows for organization of the self and its autonomous production in the joy of living within a ludopoetic process. These findings highlight the educator as in a permanent state of selfformation, which generates moments of flow. However, in Musical Humanescent Education, music is learned collectively, doing a circle dance, experiencing love, fostering an expansion of the creative spirit, and giving recognition to playfulness as a necessary condition for education and to the value of music made with the true nature and sensibilities of the educators

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We propose a multi-resolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen s self-organizing map. Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multi-resolution, iterative scheme. Reconstruction was experimented with several point sets, induding different shapes and sizes. Results show generated meshes very dose to object final shapes. We include measures of performance and discuss robustness.

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Image compress consists in represent by small amount of data, without loss a visual quality. Data compression is important when large images are used, for example satellite image. Full color digital images typically use 24 bits to specify the color of each pixel of the Images with 8 bits for each of the primary components, red, green and blue (RGB). Compress an image with three or more bands (multispectral) is fundamental to reduce the transmission time, process time and record time. Because many applications need images, that compression image data is important: medical image, satellite image, sensor etc. In this work a new compression color images method is proposed. This method is based in measure of information of each band. This technique is called by Self-Adaptive Compression (S.A.C.) and each band of image is compressed with a different threshold, for preserve information with better result. SAC do a large compression in large redundancy bands, that is, lower information and soft compression to bands with bigger amount of information. Two image transforms are used in this technique: Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). Primary step is convert data to new bands without relationship, with PCA. Later Apply DCT in each band. Data Loss is doing when a threshold discarding any coefficients. This threshold is calculated with two elements: PCA result and a parameter user. Parameters user define a compression tax. The system produce three different thresholds, one to each band of image, that is proportional of amount information. For image reconstruction is realized DCT and PCA inverse. SAC was compared with JPEG (Joint Photographic Experts Group) standard and YIQ compression and better results are obtain, in MSE (Mean Square Root). Tests shown that SAC has better quality in hard compressions. With two advantages: (a) like is adaptive is sensible to image type, that is, presents good results to divers images kinds (synthetic, landscapes, people etc., and, (b) it need only one parameters user, that is, just letter human intervention is required

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The use of middleware technology in various types of systems, in order to abstract low-level details related to the distribution of application logic, is increasingly common. Among several systems that can be benefited from using these components, we highlight the distributed systems, where it is necessary to allow communications between software components located on different physical machines. An important issue related to the communication between distributed components is the provision of mechanisms for managing the quality of service. This work presents a metamodel for modeling middlewares based on components in order to provide to an application the abstraction of a communication between components involved in a data stream, regardless their location. Another feature of the metamodel is the possibility of self-adaptation related to the communication mechanism, either by updating the values of its configuration parameters, or by its replacement by another mechanism, in case of the restrictions of quality of service specified are not being guaranteed. In this respect, it is planned the monitoring of the communication state (application of techniques like feedback control loop), analyzing performance metrics related. The paradigm of Model Driven Development was used to generate the implementation of a middleware that will serve as proof of concept of the metamodel, and the configuration and reconfiguration policies related to the dynamic adaptation processes. In this sense was defined the metamodel associated to the process of a communication configuration. The MDD application also corresponds to the definition of the following transformations: the architectural model of the middleware in Java code, and the configuration model to XML

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Distributed multimedia systems have highly variable characteristics, resulting in new requirements while new technologies become available or in the need for adequacy in accordance with the amount of available resources. So, these systems should provide support for dynamic adaptations in order to adjust their structures and behaviors at runtime. This paper presents an approach to adaptation model-based and proposes a reflective and component-based framework for construction and support of self-adaptive distributed multimedia systems, providing many facilities for the development and evolution of such systems, such as dynamic adaptation. The propose is to keep one or more models to represent the system at runtime, so some external entity can perform an analysis of these models by identifying problems and trying to solve them. These models integrate the reflective meta-level, acting as a system self-representation. The framework defines a meta-model for description of self-adaptive distributed multimedia applications, which can represent components and their relationships, policies for QoS specification and adaptation actions. Additionally, this paper proposes an ADL and architecture for model-based adaptation. As a case study, this paper presents some scenarios to demonstrate the application of the framework in practice, with and without the use of ADL, as well as check some characteristics related to dynamic adaptation

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Self-adaptive software system is able to change its structure and/or behavior at runtime due to changes in their requirements, environment or components. One way to archieve self-adaptation is the use a sequence of actions (known as adaptation plans) which are typically defined at design time. This is the approach adopted by Cosmos - a Framework to support the configuration and management of resources in distributed environments. In order to deal with the variability inherent of self-adaptive systems, such as, the appearance of new components that allow the establishment of configurations that were not envisioned at development time, this dissertation aims to give Cosmos the capability of generating adaptation plans of runtime. In this way, it was necessary to perform a reengineering of the Cosmos Framework in order to allow its integration with a mechanism for the dynamic generation of adaptation plans. In this context, our work has been focused on conducting a reengineering of Cosmos. Among the changes made to in the Cosmos, we can highlight: changes in the metamodel used to represent components and applications, which has been redefined based on an architectural description language. These changes were propagated to the implementation of a new Cosmos prototype, which was then used for developing a case study application for purpose of proof of concept. Another effort undertaken was to make Cosmos more attractive by integrating it with another platform, in the case of this dissertation, the OSGi platform, which is well-known and accepted by the industry

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One way to deal with the high complexity of current software systems is through selfadaptive systems. Self-adaptive system must be able to monitor themselves and their environment, analyzing the monitored data to determine the need for adaptation, decide how the adaptation will be performed, and finally, make the necessary adjustments. One way to perform the adaptation of a system is generating, at runtime, the process that will perform the adaptation. One advantage of this approach is the possibility to take into account features that can only be evaluated at runtime, such as the emergence of new components that allow new architectural arrangements which were not foreseen at design time. In this work we have as main objective the use of a framework for dynamic generation of processes to generate architectural adaptation plans on OSGi environment. Our main interest is evaluate how this framework for dynamic generation of processes behave in new environments

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This dissertation describes the construction of a alternative didactic incorporating a historical approach with the use of the Roman abacus for teaching multiplication to students of 2nd year of elementary school, through activities ranging from the representation of numbers to multiplying with the Roman abacus, for learning the multiplication algorithm. Qualitative research was used as a methodological approach since the research object fits the goals of this research mode. Concerning the procedures, the research can be seen as a teaching experiment developed within the school environment. The instruments used for data collection were: observation, logbook, questionnaires, interviews and document analysis. The processing and analysis of data collected through the activities were classified and quantified in tables for easy viewing, interpretation, understanding, analysis of data and then transposed to charts. The analysis confirmed the research objectives and contributed to indicate the pedagogical use of the Roman abacus for teaching multiplication algorithm through several activities. Thus, it can be considered that this educational product will have important contributions for the teaching of this mathematical content, in Basic Education, particularly regarding to the multiplication process

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Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process