Kinematic Design of a 6-DOF Parallel Manipulator with Decoupled Translation and Rotation

A new three-limb, six-degree-of-freedom (DOF) parallel manipulator (PM), termed a selectively actuated PM (SA-PM), is proposed. The end-effector of the manipulator can produce 3-DOF spherical motion, 3-DOF translation, 3-DOF hybrid motion, or complete 6-DOF spatial motion, depending on the types of the actuation (rotary or linear) chosen for the actuators. The manipulator architecture completely decouples translation and rotation of the end-effector for individual control. The structure synthesis of SA-PM is achieved using the line geometry. Singularity analysis shows that the SA-PM is an isotropic translation PM when all the actuators are in linear mode. Because of the decoupled motion structure, a decomposition method is applied for both the displacement analysis and dimension optimization. With the index of maximal workspace satisfying given global conditioning requirements, the geometrical parameters are optimized. As a result, the translational workspace is a cube, and the orientation workspace is nearly unlimited.

Observations of Sausage Modes in Magnetic Pores

We present here evidence for the observation of the magnetohydrodynamic (MHD) sausage modes in magnetic pores in the solar photosphere. Further evidence for the omnipresent nature of acoustic global modes is also found. The empirical decomposition method of wave analysis is used to identify the oscillations detected through a 4170 Å "blue continuum" filter observed with the Rapid Oscillations in the Solar Atmosphere (ROSA) instrument. Out of phase, periodic behavior in pore size and intensity is used as an indicator of the presence of magnetoacoustic sausage oscillations. Multiple signatures of the magnetoacoustic sausage mode are found in a number of pores. The periods range from as short as 30 s up to 450 s. A number of the magnetoacoustic sausage mode oscillations found have periods of 3 and 5 minutes, similar to the acoustic global modes of the solar interior. It is proposed that these global oscillations could be the driver of the sausage-type magnetoacoustic MHD wave modes in pores.

A hardware solution on multi-field packet classification

In this paper, a hardware solution for packet classification based on multi-fields is presented. The proposed scheme focuses on a new architecture based on the decomposition method. A hash circuit is used in order to reduce the memory space required for the Recursive Flow Classification (RFC) algorithm. The implementation results show that the proposed architecture achieves significant performance advantage that is comparable to that of some well-known algorithms. The solution is based on Altera Stratix III FPGA technology.

Influence of surface structures, subsurface carbon and hydrogen, and surface alloying on the activity and selectivity of acetylene hydrogenation on Pd surfaces: A density functional theory study

The selective hydrogenation of acetylene to ethylene on several Pd surfaces (Pd(111), Pd(100), Pd(211), and Pd(211)-defect) and Pd surfaces with subsurface species (carbon and hydrogen) as well as a number of Pd-based alloys (Pd-M/Pd(111) and Pd-M/Pd(211) (M = Cu, Ag and Au)) are investigated using density functional theory calculations to understand both the acetylene hydrogenation activity and the selectivity of ethylene formation. All the hydrogenation barriers are calculated, and the reaction rates on these surfaces are obtained using a two-step model. Pd(211) is found to have the highest activity for acetylene hydrogenation while Pd(100) gives rise to the lowest activity. In addition, more open surfaces result in over-hydrogenation to form ethane, while the close-packed surface (Pd(111)) is the most selective. However, we also find that the presence of subsurface carbon and hydrogen significantly changes the reactivity and selectivity of acetylene toward hydrogenation on Pd surfaces. On forming surface alloys of Pd with Cu, Ag and Au, the selectivity for ethylene is also found to be changed. A new energy decomposition method is used to quantitatively analyze the factors in determining the changes in selectivity. These surface modifiers are found to block low coordination unselective sites, leading to a decreased ethane production. (C) 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Acrolein hydrogenation on Pt(211) and Au(211) surfaces:a density functional theory study

Partial hydrogenation of acrolein, the simplest alpha, beta-unsaturated aldehyde, is not only a model system to understand the selectivity in heterogeneous catalysis, but also technologically an important reaction. In this work, the reaction on Pt(211) and Au(211) surfaces is thoroughly investigated using density functional theory calculations. The formation routes of three partial hydrogenation products, namely propenol, propanal and enol, on both metals are studied. It is found that the pathway to produce enol is kinetically favoured on Pt while on Au the route of forming propenol is preferred. Our calculations also show that the propanal formation follows an indirect pathway on Pt(211). An energy decomposition method to analyze the barrier is utilized to understand the selectivities at Pt(211) and Au(211), which reveals that the interaction energies between the reactants involved in the transition states play a key role in determining the selectivity difference.

Non-Markovian decoherence: complete positivity and decomposition

In this work, the non-Markovian decoherence is considered in two ways. Firstly, an effective Hamiltonian approach is demonstrated to investigate the decoherence of a quantum system in a non-Markovian environment, in which complete positivity of the reduced dynamics is achieved. This method uses the notion of an effective environment, that is a subsystem of the environment that causes the decoherence. Secondly, the evolution of the system and environment is decomposed, thus partially illuminating how they would interact given that memory effects are allowed. It should be noted that beam splitters and rotators are sufficient to explain this decomposition.

A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Cobalt-doped ZnO nanowires on quartz: Synthesis by simple chemical method and characterization

The synthesis of cobalt-doped ZnO nanowires is achieved using a simple, metal salt decomposition growth technique. A sequence of drop casting on a quartz substrate held at 100 degrees C and annealing results in the growth of nanowires of average (modal) length similar to 200 nm and diameter of 15 +/- 4 nm and consequently an aspect ratio of similar to 13. A variation in the synthesis process, where the solution of mixed salts is deposited on the substrate at 25 degrees C, yields a grainy film structure which constitutes a useful comparator case. X-ray diffraction shows a preferred [0001] growth direction for the nanowires while a small unit cell volume contraction for Co-doped samples and data from Raman spectroscopy indicate incorporation of the Co dopant into the lattice; neither technique shows explicit evidence of cobalt oxides. Also the nanowire samples display excellent optical transmission across the entire visible range, as well as strong photoluminescence (exciton emission) in the near UV, centered at 3.25 eV. (C) 2012 Elsevier B.V. All rights reserved.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

Model reduction for nonlinear aerodynamics and aeroelasticity using a Discrete Empirical Interpolation Method

A novel surrogate model is proposed in lieu of Computational Fluid Dynamics (CFD) solvers, for fast nonlinear aerodynamic and aeroelastic modeling. A nonlinear function is identified on selected interpolation points by
a discrete empirical interpolation method (DEIM). The flow field is then reconstructed using a least square approximation of the flow modes extracted
by proper orthogonal decomposition (POD). The aeroelastic reduce order
model (ROM) is completed by introducing a nonlinear mapping function
between displacements and the DEIM points. The proposed model is investigated to predict the aerodynamic forces due to forced motions using
a N ACA 0012 airfoil undergoing a prescribed pitching oscillation. To investigate aeroelastic problems at transonic conditions, a pitch/plunge airfoil
and a cropped delta wing aeroelastic models are built using linear structural models. The presence of shock-waves triggers the appearance of limit
cycle oscillations (LCO), which the model is able to predict. For all cases
tested, the new ROM shows the ability to replicate the nonlinear aerodynamic forces, structural displacements and reconstruct the complete flow
field with sufficient accuracy at a fraction of the cost of full order CFD
model.

Model reduction for nonlinear aeroelasticity using the Discrete Empirical Interpolation Method

A novel surrogate model is proposed in lieu of computational fluid dynamic (CFD) code for fast nonlinear aerodynamic modeling. First, a nonlinear function is identified on selected interpolation points defined by discrete empirical interpolation method (DEIM). The flow field is then reconstructed by a least square approximation of flow modes extracted by proper orthogonal decomposition (POD). The proposed model is applied in the prediction of limit cycle oscillation for a plunge/pitch airfoil and a delta wing with linear structural model, results are validate against a time accurate CFD-FEM code. The results show the model is able to replicate the aerodynamic forces and flow fields with sufficient accuracy while requiring a fraction of CFD cost.