Geochemistry and petrogenesis of volcanic rocks from Daimao Seamount (South China Sea) and their tectonic implications

The South China Sea (SCS) experienced three episodes of seafloor spreading and left three fossil spreading centers presently located at 18°N, 17°N and 15.5°N. Spreading ceased at these three locations during magnetic anomaly 10, 8, and 5c, respectively. Daimao Seamount (16.6. Ma) was formed 10. my after the cessation of the 17°N spreading center. Volcaniclastic rocks and shallow-water carbonate facies near the summit of Daimao Seamount provide key information on the seamount's geologic history. New major and trace element and Sr-Nd-Pb isotopic compositions of basaltic breccia clasts in the volcaniclastics suggest that Daimao and other SCS seamounts have typical ocean island basalt-like composition and possess a 'Dupal' isotopic signature. Our new analyses, combined with available data, indicate that the basaltic foundation of Daimao Seamount was formed through subaqueous explosive volcanic eruptions at 16.6. Ma. The seamount subsided rapidly (>. 0.12. mm/y) at first, allowing the deposition of shallow-water, coral-bearing carbonates around its summit and, then, at a slower rate (<. 0.12. mm/y). We propose that the parental magmas of SCS seamount lavas originated from the Hainan mantle plume. In contrast, lavas from contemporaneous seamounts in other marginal basins in the western Pacific are subduction-related

Analysis of some interior point continuous trajectories for convex programming

In this paper, we analyse three interior point continuous trajectories for convex programming with general linear constraints. The three continuous trajectories are derived from the primal–dual path-following method, the primal–dual affine scaling method and the central path, respectively. Theoretical properties of the three interior point continuous trajectories are fully studied. The optimality and convergence of all three interior point continuous trajectories are obtained for any interior feasible point under some mild conditions. In particular, with proper choice of some parameters, the convergence for all three interior point continuous trajectories does not require the strict complementarity or the analyticity of the objective function. These results are new in the literature

Fast voltage detection method for grid-tied renewable energy generation systems under distorted grid voltage conditions

© The Institution of Engineering and Technology. A fast voltage detection method to assist with the low-voltage ride-through operation of grid-tied renewable energy generation systems is proposed in this study. It is designed to detect every phase voltage, so that it can be applied in both three-phase and single-phase applications. The whole voltage detection approach consists of two stages, the pre-filtering stage and the voltage detection stage. In the pre-filtering stage, a cascaded delayed signal cancellation (CDSC) module and a low-pass filter are connected in series to filter low-order harmonics and high-frequency noises. For eliminating the low-order harmonics of interest, different types of CDSC methods are studied in detail. Subsequently, a new orthogonal signal generator is built to calculate the voltage amplitude in the voltage detection stage. Finally, the proposed voltage detection method is verified by experimental results

A Novel Open-Loop Frequency Estimation Method for Single-Phase Grid Synchronization under Distorted Conditions

© 2013 IEEE. In this paper, a new open-loop architecture with good dynamic performance and strong harmonic rejection capability is proposed for single-phase grid synchronization under distorted conditions. Different from previous single-phase grid synchronization algorithms based on the phase-locked loop technique, the proposed method is to estimate the frequency and phase angle of the grid voltage in an open-loop manner so that fast dynamic response and enhanced system stability can be achieved. First, an open-loop frequency estimation algorithm is introduced under ideal grid condition. Then, it is extended to distorted grid voltages through the combination of the developed frequency estimation unit and a prefiltering stage consisting of a second-order low-pass filter and a cascaded delayed signal cancellation (DSC) module. In addition, a transient process smoothing unit is designed to achieve smooth frequency transients in cases where the grid voltage experiences fast and large changes. The working principle of the new frequency estimation algorithm and the developed single-phase grid synchronization approach is given in detail, together with some simulation and experiment results for verifying their performance

A Frequency-Fixed SOGI-Based PLL for Single-Phase Grid-Connected Converters

© 1986-2012 IEEE. Second-order generalized integrator (SOGI) based phase-locked loops (PLLs) are widely used for grid synchronization in single-phase grid-connected power converters. Previously, the estimated frequency of the PLL stage is fed back to the front-end SOGI block to make SOGI-PLLs frequency-Adaptive, which increases the implementation complexity, and makes the tuning sensitive, thus reducing stability margins. Alternatively, a frequency-fixed SOGI-based PLL (briefly called FFSOGI-PLL) is proposed to ensure stability and simple implementation in this letter. It is commonly known that the in-phase and quadrature-phase signals generated by the frequency-fixed SOGI are of different amplitudes in the presence of frequency drifts, which causes second-harmonic ripples in the estimated parameters of the PLL loop. To deal with this issue, a simple yet effective method is developed in FFSOGI-PLL. The standard SOGI-PLL is first introduced, followed by the working principle and small-signal model of FFSOGI-PLL. The FFSOGI-PLL is then compared with the SOGI-PLL in terms of stability and transient performance. Finally, experimental results are presented to demonstrate the effectiveness of FFSOGI-PLL

Cross-Domain Multitask Learning with Latent Probit Models

Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain transforms, and the shared probit classifier in the common domain. To learn meaningful task relatedness and avoid over-fitting in classification, we introduce sparsity in the domain transforms matrices, as well as in the common classifier. We derive theoretical bounds for the estimation error of the classifier in terms of the sparsity of domain transforms. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets

On nonlinear multiarmed spiral waves in slowly rotating systems

Stable nonlinear equilibria of convection in the form of quasistationary, multiarmed spiral waves, up to a maximum of six spiral arms, are found in a slowly rotating fluid confined within a thin spherical shell governed by the three-dimensional Navier–Stokes equation, driven by a radial unstable temperature gradient and affected by a weak Coriolis force. It is shown that three essential ingredients are generally required for the formation of the multiarmed spirals: the influence of slow rotation, large-aspect-ratio geometry and the effect of weak nonlinearity.published_or_final_versio

Supervised classification via constrained subspace and tensor sparse representation

SRC, a supervised classifier via sparse representation, has rapidly gained popularity in recent years and can be adapted to a wide range of applications based on the sparse solution of a linear system. First, we offer an intuitive geometric model called constrained subspace to explain the mechanism of SRC. The constrained subspace model connects the dots of NN, NFL, NS, NM. Then, inspired from the constrained subspace model, we extend SRC to its tensor-based variant, which takes as input samples of high-order tensors which are elements of an algebraic ring. A tensor sparse representation is used for query tensors. We verify in our experiments on several publicly available databases that the tensor-based SRC called tSRC outperforms traditional SRC in classification accuracy. Although demonstrated for image recognition, tSRC is easily adapted to other applications involving underdetermined linear systems

The Convergent Generalized Central Paths for Linearly Constrained Convex Programming

The convergence of central paths has been a focal point of research on interior point methods. Quite detailed analyses have been made for the linear case. However, when it comes to the convex case, even if the constraints remain linear, the problem is unsettled. In [Math. Program., 103 (2005), pp. 63–94], Gilbert, Gonzaga, and Karas presented some examples in convex optimization, where the central path fails to converge. In this paper, we aim at finding some continuous trajectories which can converge for all linearly constrained convex optimization problems under some mild assumptions. We design and analyze a class of continuous trajectories, which are the solutions of certain ordinary differential equation (ODE) systems for solving linearly constrained smooth convex programming. The solutions of these ODE systems are named generalized central paths. By only assuming the existence of a finite optimal solution, we are able to show that, starting from any interior feasible point, (i) all of the generalized central paths are convergent, and (ii) the limit point(s) are indeed the optimal solution(s) of the original optimization problem. Furthermore, we illustrate that for the key example of Gilbert, Gonzaga, and Karas, our generalized central paths converge to the optimal solutions