10,406 research outputs found

    Dynamo effect in parity-invariant flow with large and moderate separation of scales

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    It is shown that non-helical (more precisely, parity-invariant) flows capable of sustaining a large-scale dynamo by the negative magnetic eddy diffusivity effect are quite common. This conclusion is based on numerical examination of a large number of randomly selected flows. Few outliers with strongly negative eddy diffusivities are also found, and they are interpreted in terms of the closeness of the control parameter to a critical value for generation of a small-scale magnetic field. Furthermore, it is shown that, for parity-invariant flows, a moderate separation of scales between the basic flow and the magnetic field often significantly reduces the critical magnetic Reynolds number for the onset of dynamo action.Comment: 44 pages,11 figures, significantly revised versio

    Computational methods and software systems for dynamics and control of large space structures

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    Two key areas of crucial importance to the computer-based simulation of large space structures are discussed. The first area involves multibody dynamics (MBD) of flexible space structures, with applications directed to deployment, construction, and maneuvering. The second area deals with advanced software systems, with emphasis on parallel processing. The latest research thrust in the second area involves massively parallel computers

    Extended Object Tracking: Introduction, Overview and Applications

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    This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

    Learning Task Priorities from Demonstrations

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    Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic

    A Review of Time Relaxation Methods

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    The time relaxation model has proven to be effective in regularization of Navier–Stokes Equations. This article reviews several published works discussing the development and implementations of time relaxation and time relaxation models (TRMs), and how such techniques are used to improve the accuracy and stability of fluid flow problems with higher Reynolds numbers. Several analyses and computational settings of TRMs are surveyed, along with parameter sensitivity studies and hybrid implementations of time relaxation operators with different regularization techniques

    Application of special-purpose digital computers to rotorcraft real-time simulation

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    The use of an array processor as a computational element in rotorcraft real-time simulation is studied. A multilooping scheme was considered in which the rotor would loop over its calculations a number of time while the remainder of the model cycled once on a host computer. To prove that such a method would realistically simulate rotorcraft, a FORTRAN program was constructed to emulate a typical host-array processor computing configuration. The multilooping of an expanded rotor model, which included appropriate kinematic equations, resulted in an accurate and stable simulation

    Three-Dimensional Multi-Relaxation Time (MRT) Lattice-Boltzmann Models for Multiphase Flow

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    In this paper, three-dimensional (3D) multi-relaxation time (MRT) lattice-Boltzmann (LB) models for multiphase flow are presented. In contrast to the Bhatnagar-Gross-Krook (BGK) model, a widely employed kinetic model, in MRT models the rates of relaxation processes owing to collisions of particle populations may be independently adjusted. As a result, the MRT models offer a significant improvement in numerical stability of the LB method for simulating fluids with lower viscosities. We show through the Chapman-Enskog multiscale analysis that the continuum limit behavior of 3D MRT LB models corresponds to that of the macroscopic dynamical equations for multiphase flow. We extend the 3D MRT LB models developed to represent multiphase flow with reduced compressibility effects. The multiphase models are evaluated by verifying the Laplace-Young relation for static drops and the frequency of oscillations of drops. The results show satisfactory agreement with available data and significant gains in numerical stability.Comment: Accepted for publication in the Journal of Computational Physic

    Advances in humanoid control and perception

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    One day there will be humanoid robots among us doing our boring, time-consuming, or dangerous tasks. They might cook a delicious meal for us or do the groceries. For this to become reality, many advances need to be made to the artificial intelligence of humanoid robots. The ever-increasing available computational processing power opens new doors for such advances. In this thesis we develop novel algorithms for humanoid control and vision that harness this power. We apply these methods on an iCub humanoid upper-body with 41 degrees of freedom. For control, we develop Natural Gradient Inverse Kinematics (NGIK), a sampling-based optimiser that applies natural evolution strategies to perform inverse kinematics. The resulting algorithm makes very few assumptions and gives much more freedom in definable constraints than its Jacobian-based counterparts. A special graph-building procedure is introduced to build Task-Relevant Roadmaps (TRM) by iteratively applying NGIK and storing the results. TRMs form searchable graphs of kinematic configurations on which a wide range of task-relevant humanoid movements can be planned. Through coordinating several instances of NGIK, a fast parallelised version of the TRM building algorithm is developed. To contrast the offline TRM algorithms, we also develop Natural Gradient Control which directly uses the optimisation pass in NGIK as an online control signal. For vision, we develop dynamic vision algorithms that form cyclic information flows that affect their own processing. Deep Attention Selective Networks (dasNet) implement feedback in convolutional neural networks through a gating mechanism that is steered by a policy. Through this feedback, dasNet can focus on different features in the image in light of previously gathered information and improve classification, with state-of-the- art results at the time of publication. Then, we develop PyraMiD-LSTM, which processes 3D volumetric data by employing a novel convolutional Long Short-Term Memory network (C-LSTM) to compute pyramidal contexts for every voxel, and combine them to perform segmentation. This resulted in state-of-the-art performance on a segmentation benchmark. The work on control and vision is integrated into an application on the iCub robot. A Fast-Weight PyraMiD-LSTM is developed that dynamically generates weights for a C-LSTM layer given actions of the robot. An explorative policy using NGC generates a stream of data, which the Fast-Weight PyraMiD-LSTM has to predict. The resulting integrated system learns to model the effects of head and hand movements and their effects on future visual input. To our knowledge, this is the first effective visual prediction system on an iCub
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