986 resultados para PROPOSED APPROACH


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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Today, the trend within the electronics industry is for the use of rapid and advanced simulation methodologies in association with synthesis toolsets. This paper presents an approach developed to support mixed-signal circuit design and analysis. The methodology proposed shows a novel approach to the problem of developing behvioural model descriptions of mixed-signal circuit topologies, by construction of a set of subsystems, that supports the automated mapping of MATLAB (R)/SINIULINK (R) models to structural VHDL-AMS descriptions. The tool developed, named (MSSV)-S-2, reads a SIMULINK (R) model file and translates it to a structural VHDL-AMS code. It also creates the file structure required to simulate the translated model in the SystemVision (TM). To validate the methodology and the developed program, the DAC08, AD7524 and AD5450 data converters were studied and initially modelled in MATLAB (R)/SIMULINK (R). The VHDL-AMS code generated automatically by (MSSV)-S-2, (MATLAB (R)/SIMULINK (R) to SystemVision (TM)), was then simulated in the SystemVision (TM). The simulation results show that the proposed approach, which is based on VHDL-AMS descriptions of the original model library elements, allows for the behavioural level simulation of complex mixed-signal circuits.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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This thesis proposes an architecture of a new multiagent system framework for hybridization of metaheuristics inspired on the general Particle Swarm Optimization framework (PSO). The main contribution is to propose an effective approach to solve hard combinatory optimization problems. The choice of PSO as inspiration was given because it is inherently multiagent, allowing explore the features of multiagent systems, such as learning and cooperation techniques. In the proposed architecture, particles are autonomous agents with memory and methods for learning and making decisions, using search strategies to move in the solution space. The concepts of position and velocity originally defined in PSO are redefined for this approach. The proposed architecture was applied to the Traveling Salesman Problem and to the Quadratic Assignment Problem, and computational experiments were performed for testing its effectiveness. The experimental results were promising, with satisfactory performance, whereas the potential of the proposed architecture has not been fully explored. For further researches, the proposed approach will be also applied to multiobjective combinatorial optimization problems, which are closer to real-world problems. In the context of applied research, we intend to work with both students at the undergraduate level and a technical level in the implementation of the proposed architecture in real-world problems

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Aspect Oriented approaches associated to different activities of the software development process are, in general, independent and their models and artifacts are not aligned and inserted in a coherent process. In the model driven development, the various models and the correspondence between them are rigorously specified. With the integration of aspect oriented software development (DSOA) and model driven development (MDD) it is possible to automatically propagate models from one activity to another, avoiding the loss of information and important decisions established in each activity. This work presents MARISA-MDD, a strategy based on models that integrate aspect-oriented requirements, architecture and detailed design, using the languages AOV-graph, AspectualACME and aSideML, respectively. MARISA-MDD defines, for each activity, representative models (and corresponding metamodels) and a number of transformations between the models of each language. These transformations have been specified and implemented in ATL (Atlas Definition Language), in the Eclipse environment. MARISA-MDD allows the automatic propagation between AOV-graph, AspectualACME, and aSideML models. To validate the proposed approach two case studies, the Health Watcher and the Mobile Media have been used in the MARISA-MDD environment for the automatic generation of AspectualACME and aSideML models, from the AOV-graph model

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This work proposes a model based approach for pointcut management in the presence of evolution in aspect oriented systems. The proposed approach, called conceptual visions based pointcuts, is motivated by the observation of the shortcomings in traditional approaches pointcuts definition, which generally refer directly to software structure and/or behavior, thereby creating a strong coupling between pointcut definition and the base code. This coupling causes the problem known as pointcut fragility problem and hinders the evolution of aspect-oriented systems. This problem occurs when all the pointcuts of each aspect should be reviewed due to any software changes/evolution, to ensure that they remain valid even after the changes made in the software. Our approach is focused on the pointcuts definition based on a conceptual model, which has definitions of the system's structure in a more abstract level. The conceptual model consists of classifications (called conceptual views) on entities of the business model elements based on common characteristics, and relationships between these views. Thus the pointcuts definitions are created based on the conceptual model rather than directly referencing the base model. Moreover, the conceptual model contains a set of relationships that allows it to be automatically verified if the classifications in the conceptual model remain valid even after a software change. To this end, all the development using the conceptual views based pointcuts approach is supported by a conceptual framework called CrossMDA2 and a development process based on MDA, both also proposed in this work. As proof of concept, we present two versions of a case study, setting up a scenario of evolution that shows how the use of conceptual visions based pointcuts helps detecting and minimizing the pointcuts fragility. For the proposal evaluation the Goal/Question/Metric (GQM) technique is used together with metrics for efficiency analysis in the pointcuts definition

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Through the adoption of the software product line (SPL) approach, several benefits are achieved when compared to the conventional development processes that are based on creating a single software system at a time. The process of developing a SPL differs from traditional software construction, since it has two essential phases: the domain engineering - when common and variables elements of the SPL are defined and implemented; and the application engineering - when one or more applications (specific products) are derived from the reuse of artifacts created in the domain engineering. The test activity is also fundamental and aims to detect defects in the artifacts produced in SPL development. However, the characteristics of an SPL bring new challenges to this activity that must be considered. Several approaches have been recently proposed for the testing process of product lines, but they have been shown limited and have only provided general guidelines. In addition, there is also a lack of tools to support the variability management and customization of automated case tests for SPLs. In this context, this dissertation has the goal of proposing a systematic approach to software product line testing. The approach offers: (i) automated SPL test strategies to be applied in the domain and application engineering, (ii) explicit guidelines to support the implementation and reuse of automated test cases at the unit, integration and system levels in domain and application engineering; and (iii) tooling support for automating the variability management and customization of test cases. The approach is evaluated through its application in a software product line for web systems. The results of this work have shown that the proposed approach can help the developers to deal with the challenges imposed by the characteristics of SPLs during the testing process

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Checking the conformity between implementation and design rules in a system is an important activity to try to ensure that no degradation occurs between architectural patterns defined for the system and what is actually implemented in the source code. Especially in the case of systems which require a high level of reliability is important to define specific design rules for exceptional behavior. Such rules describe how exceptions should flow through the system by defining what elements are responsible for catching exceptions thrown by other system elements. However, current approaches to automatically check design rules do not provide suitable mechanisms to define and verify design rules related to the exception handling policy of applications. This paper proposes a practical approach to preserve the exceptional behavior of an application or family of applications, based on the definition and runtime automatic checking of design rules for exception handling of systems developed in Java or AspectJ. To support this approach was developed, in the context of this work, a tool called VITTAE (Verification and Information Tool to Analyze Exceptions) that extends the JUnit framework and allows automating test activities to exceptional design rules. We conducted a case study with the primary objective of evaluating the effectiveness of the proposed approach on a software product line. Besides this, an experiment was conducted that aimed to realize a comparative analysis between the proposed approach and an approach based on a tool called JUnitE, which also proposes to test the exception handling code using JUnit tests. The results showed how the exception handling design rules evolve along different versions of a system and that VITTAE can aid in the detection of defects in exception handling code

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The Scientific Algorithms are a new metaheuristics inspired in the scientific research process. The new method introduces the idea of theme to search the solution space of hard problems. The inspiration for this class of algorithms comes from the act of researching that comprises thinking, knowledge sharing and disclosing new ideas. The ideas of the new method are illustrated in the Traveling Salesman Problem. A computational experiment applies the proposed approach to a new variant of the Traveling Salesman Problem named Car Renter Salesman Problem. The results are compared to state-of-the-art algorithms for the latter problem

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In Fazenda Belém oil field (Potiguar Basin, Ceará State, Brazil) occur frequently sinkholes and sudden terrain collapses associated to an unconsolidated sedimentary cap covering the Jandaíra karst. This research was carried out in order to understand the mechanisms of generation of these collapses. The main tool used was Ground Penetrating Radar (GPR). This work is developed twofold: one aspect concerns methodology improvements in GPR data processing whilst another aspect concerns the geological study of the Jandaíra karst. This second aspect was strongly supported both by the analysis of outcropping karst structures (in another regions of Potiguar Basin) and by the interpretation of radargrams from the subsurface karst in Fazenda Belém. It was designed and tested an adequate flux to process GPR data which was adapted from an usual flux to process seismic data. The changes were introduced to take into account important differences between GPR and Reflection Seismic methods, in particular: poor coupling between source and ground, mixed phase of the wavelet, low signal-to-noise ratio, monochannel acquisition, and high influence of wave propagation effects, notably dispersion. High frequency components of the GPR pulse suffer more pronounced effects of attenuation than low frequency components resulting in resolution losses in radargrams. In Fazenda Belém, there is a stronger need of an suitable flux to process GPR data because both the presence of a very high level of aerial events and the complexity of the imaged subsurface karst structures. The key point of the processing flux was an improvement in the correction of the attenuation effects on the GPR pulse based on their influence on the amplitude and phase spectra of GPR signals. In low and moderate losses dielectric media the propagated signal suffers significant changes only in its amplitude spectrum; that is, the phase spectrum of the propagated signal remains practically unaltered for the usual travel time ranges. Based on this fact, it is shown using real data that the judicious application of the well known tools of time gain and spectral balancing can efficiently correct the attenuation effects. The proposed approach can be applied in heterogeneous media and it does not require the precise knowledge of the attenuation parameters of the media. As an additional benefit, the judicious application of spectral balancing promotes a partial deconvolution of the data without changing its phase. In other words, the spectral balancing acts in a similar way to a zero phase deconvolution. In GPR data the resolution increase obtained with spectral balancing is greater than those obtained with spike and predictive deconvolutions. The evolution of the Jandaíra karst in Potiguar Basin is associated to at least three events of subaerial exposition of the carbonatic plataform during the Turonian, Santonian, and Campanian. In Fazenda Belém region, during the mid Miocene, the Jandaíra karst was covered by continental siliciclastic sediments. These sediments partially filled the void space associated to the dissolution structures and fractures. Therefore, the development of the karst in this region was attenuated in comparison to other places in Potiguar Basin where this karst is exposed. In Fazenda Belém, the generation of sinkholes and terrain collapses are controlled mainly by: (i) the presence of an unconsolidated sedimentary cap which is thick enough to cover completely the karst but with sediment volume lower than the available space associated to the dissolution structures in the karst; (ii) the existence of important structural of SW-NE and NW-SE alignments which promote a localized increase in the hydraulic connectivity allowing the channeling of underground water, thus facilitating the carbonatic dissolution; and (iii) the existence of a hydraulic barrier to the groundwater flow, associated to the Açu-4 Unity. The terrain collapse mechanisms in Fazenda Belém occur according to the following temporal evolution. The meteoric water infiltrates through the unconsolidated sedimentary cap and promotes its remobilization to the void space associated with the dissolution structures in Jandaíra Formation. This remobilization is initiated at the base of the sedimentary cap where the flow increases its abrasion due to a change from laminar to turbulent flow regime when the underground water flow reaches the open karst structures. The remobilized sediments progressively fill from bottom to top the void karst space. So, the void space is continuously migrated upwards ultimately reaching the surface and causing the sudden observed terrain collapses. This phenomenon is particularly active during the raining season, when the water table that normally is located in the karst may be temporarily located in the unconsolidated sedimentary cap

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In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison

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Multi-objective combinatorial optimization problems have peculiar characteristics that require optimization methods to adapt for this context. Since many of these problems are NP-Hard, the use of metaheuristics has grown over the last years. Particularly, many different approaches using Ant Colony Optimization (ACO) have been proposed. In this work, an ACO is proposed for the Multi-objective Shortest Path Problem, and is compared to two other optimizers found in the literature. A set of 18 instances from two distinct types of graphs are used, as well as a specific multiobjective performance assessment methodology. Initial experiments showed that the proposed algorithm is able to generate better approximation sets than the other optimizers for all instances. In the second part of this work, an experimental analysis is conducted, using several different multiobjective ACO proposals recently published and the same instances used in the first part. Results show each type of instance benefits a particular type of instance benefits a particular algorithmic approach. A new metaphor for the development of multiobjective ACOs is, then, proposed. Usually, ants share the same characteristics and only few works address multi-species approaches. This works proposes an approach where multi-species ants compete for food resources. Each specie has its own search strategy and different species do not access pheromone information of each other. As in nature, the successful ant populations are allowed to grow, whereas unsuccessful ones shrink. The approach introduced here shows to be able to inherit the behavior of strategies that are successful for different types of problems. Results of computational experiments are reported and show that the proposed approach is able to produce significantly better approximation sets than other methods