This literature review focuses on three important aspects of an autonomous car system: tracking (assessing the identity of the actors such as cars, pedestrians or obstacles in a sequence of observations), prediction (...
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The analysis of the photospheric velocity field is essential for understanding plasma turbulence in the solar surface, which may be responsible for driving processes such as magnetic reconnection, flares, wave propaga...
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The exploration and visualization of multidimensional medical images is an intricate and complicated problem, considering the amount and complexity of the structures and features within such data. A lot of such struct...
The exploration and visualization of multidimensional medical images is an intricate and complicated problem, considering the amount and complexity of the structures and features within such data. A lot of such structures are identifiable as specific isosurfaces. Consequently, we propose a method for the intuitive selection and representation of meaningful isosurfaces from CT medical volume data. We first employ an isosurface detection algorithm which is easy to incorporate within a raycasting-based traversal of the volume. We then render 2D projections of multiple detected isosurfaces and subject them to various image processing and information visualization techniques meant to assist with the selection of meaningful structures and features from the whole body of data. Subsequently, we use a shape similarity metric to build a map which allows for the efficient selection of isosurfaces based on the relative differences of their projected contours. We then cluster the isosurfaces considering these relative differences, allowing for the fast selection of isosurface clusters which form the geometry of key anatomical structures. Our approaches are interactive and allow a potential user to quickly and effectively traverse the isosurface space, focusing on the selection and representation of meaningful portions of the volume data.
Embedded Feature Selection (FS) ensures the selection of few, relevant features, by directly re-designing the classifier for subsets of features. Naturally, this problem is formulated as a multi-objective optimization...
Embedded Feature Selection (FS) ensures the selection of few, relevant features, by directly re-designing the classifier for subsets of features. Naturally, this problem is formulated as a multi-objective optimization (MOO) addressing to the accuracy of the classifier and the parsimony of the feature vector. In MOOs, common ranking techniques use dominance analysis for providing a partial sorting of the solutions. Unfortunately, dominance analysis can also promote solutions less useful for the application. In order to gradually guide the search towards a user-preferred area set around the middle of the best fronts, this paper proposes an adaptive ranking algorithm with insertion of elites (ARE), which could be integrated in any MOO genetic algorithm. ARE incorporates two new procedures proposed for labeling the preferred solutions and for inserting elites in the less populated areas, whenever a biased exploration is detected. The experimental investigations illustrate that GA with ARE offers better results than NSGAII, both for electroencephalogram (EEG) feature selection problem (which likely involves weakly conflicting objectives) and MOOs with strongly conflicting objectives.
The article proves first that the constant quality factor (Q) contours for passive circuits, while represented on a 2D Smith chart, form circle arcs on a coaxal circle family. Furthermore, these circle arcs represent ...
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A multi-agent system designed to achieve distance-based shape control with flocking behavior can be seen as a mechanical system described by a Lagrangian function and subject to additional external forces. Forced vari...
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A multi-agent system designed to achieve distance-based shape control with flocking behavior can be seen as a mechanical system described by a Lagrangian function and subject to additional external forces. Forced variational integrators are given by the discretization of Lagrange-d’Alembert principle for systems subject to external forces, and have proved useful for numerical simulation studies of complex dynamical systems. We derive forced variational integrators that can be employed in the context of control algorithms for distance-based shape with velocity consensus. In particular, we provide an accurate numerical integrator with a lower computational cost than traditional solutions, while preserving the configuration space and symmetries. We also provide an explicit expression for the integration scheme in the case of an arbitrary number of agents with double integrator dynamics. For a numerical comparison of the performances, we use a planar formation consisting of three autonomous agents.
The paper contains a proposition of the application of a SRM motor with a modified stator geometry for an electric drive, in particular in an electric bicycle. The study involved a comparison of the cyclicity of the e...
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Defect engineering in metal oxides presents a promising approach for tailoring material properties. This strategy enhances gas sorption, catalysis, and control over key physical characteristics such as bandgap, magnet...
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We investigate the existence of nonnegative solutions for a Riemann-Liouville fractional differential equation with integral terms, subject to boundary conditions which contain fractional derivatives and Riemann-Stiel...
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We investigate the existence of nonnegative solutions for a Riemann-Liouville fractional differential equation with integral terms, subject to boundary conditions which contain fractional derivatives and Riemann-Stieltjes integrals. In the proof of the main results, we use the Banach contraction mapping principle and the Krasnosel’skii fixed point theorem for the sum of two operators.
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