The dynamics and control of large flexible space structures - 12, supplement 11

The rapid 2-D slewing and vibrational control of the unsymmetrical flexible SCOLE (Spacecraft Control Laboratory Experiment) with multi-bounded controls is considered. Pontryagin's Maximum Principle is applied to the nonlinear equations of the system to derive the necessary conditions for the optimal control. The resulting two point boundary value problem is then solved by using the quasilinearization technique, and the near minimum time is obtained by sequentially shortening the slewing time until the controls are near the bang-bang type. The tradeoff between the minimum time and the minimum flexible amplitude requirements is discussed. The numerical results show that the responses of the nonlinear system are significantly different from those of the linearized system for rapid slewing. The SCOLE station-keeping closed loop dynamics are re-examined by employing a slightly different method for developing the equations of motion in which higher order terms in the expressions for the mast modal shape functions are now included. A preliminary study on the effect of actuator mass on the closed loop dynamics of large space systems is conducted. A numerical example based on a coupled two-mass two-spring system illustrates the effect of changes caused in the mass and stiffness matrices on the closed loop system eigenvalues. In certain cases the need for redesigning control laws previously synthesized, but not accounting for actuator masses, is indicated

A Fast Forward and Inversion Strategy for Three-Dimensional Gravity Field

Obtaining a three-dimensional (3D) density distribution within a reasonable time is one of the most critical problems in gravity exploration. In this paper, we present an efficient 3D forward modeling and inversion method for gravity data. In forward modeling, the 3D model is discretized into multiple horizontal layers, with the gravity field at a point on the surface being the sum of the gravity fields from all layers. To calculate the gravity field from each horizontal layer, we use the fast Fourier transform (FFT) method and the Block Toeplitz with Toeplitz Blocks (BTTB) matrix, which dramatically reduces both the computation time and storage requirement. In the inversion, the observed gravity data are separated into multiple gravity components of different depths using the cutting separation method. An iterative method is used to adjust the model to fit the above gravity component for each cutting radius. The initial model is constructed from the transformation of gravity components. These methods were applied to both synthetic data and field data. The numerical simulation validated the proposed methods, and the inversion results of field data were consistent with information obtained from well logging. The computational time and memory usage were also reasonable

Maximum Margin based Semi-supervised Spectral Kernel Learning

Abstract — Semi-supervised kernel learning is attracting increasing research interests recently. It works by learning an embedding of data from the input space to a Hilbert space using both labeled data and unlabeled data, and then searching for relations among the embedded data points. One of the most well-known semi-supervised kernel learning approaches is the spectral kernel learning methodology which usually tunes the spectral empirically or through optimizing some generalized performance measures. However, the kernel designing process does not involve the bias of a kernel-based learning algorithm, the deduced kernel matrix cannot necessarily facilitate a specific learning algorithm. To supplement the spectral kernel learning methods, this paper proposes a novel approach, which not only learns a kernel matrix by maximizing another generalized performance measure, the margin between two classes of data, but also leads directly to a convex optimization method for learning the margin parameters in support vector machines. Moreover, experimental results demonstrate that our proposed spectral kernel learning method achieves promising results against other spectral kernel learning methods. I

Identifying climatic factors and circulation indices related to apple yield variation in main production areas of China

Assessing the impacts of future climate change on crop yields requires basic research into both the major meteorological factors that affect crop yield and the influence that changes to global circulation patterns have on local meterological parameters. For example, the atmospheric circulation and crop “meteorological yield” and the response of meteorological yield to climate change are positively correlated. Using apple (Malus domestica) production as an example, the present work investigated the link between fruit yield and global circulation factors by using yield data from 28 apple-producing counties in Shaanxi province, China, from 1980 to 2015. We used four methods to isolate apple production and applied a Grey relational analysis of 88 meteorological factors and the associated sequential correlation. We extracted the major climate factors based on the high consistency of climatic factors and the variation trend of meteorological yield of apple, and analyzed the main meteorological factors for the meteorological apple yield of three major apple producing areas by analyzing high-impact atmospheric circulation indices. The climate factors included solar radiation, pan evaporation, accumulated temperature, precipitation, wind speed, maximum temperature, minimum temperature, mean temperature, heat injury over several days, number of frost days during flowering, number of freezing days during dormancy, and annual temperature range. The main meteorological factors affecting annual apple meteorological yield were total solar radiation from April to October, evaporation from April to September, precipitation in April and June to August, and minimum temperature in mid-April. The Southern Oscillation Index (SOI) was the circulation index most closely related to total summer solar radiation, pan evaporation, and precipitation. Correlation analysis showed that the polar vortex area index and sea surface temperature index were important circulation indices affecting climate yield in apple regions. The meteorological yields of apples in zones Ⅰ and Ⅲ were significantly correlated with the ENSO period index, mainly in the autumn and winter. Similar to the conclusions of previous studies, the uncertainty of meteorological yield in the Shaanxi fruit region is predicted to increase under future climate change. Keywords: Apple yield, Shaanxi province, Grey relational analysis, Climate factors, Circulation inde

Efficient Convex Relaxation for Transductive Support Vector Machine

We consider the problem of Support Vector Machine transduction, which involves a combinatorial problem with exponential computational complexity in the number of unlabeled examples. Although several studies are devoted to Transductive SVM, they suffer either from the high computation complexity or from the solutions of local optimum. To address this problem, we propose solving Transductive SVM via a convex relaxation, which converts the NP-hard problem to a semi-definite programming. Compared with the other SDP relaxation for Transductive SVM, the proposed algorithm is computationally more efficient with the number of free parameters reduced from O(n 2) to O(n) where n is the number of examples. Empirical study with several benchmark data sets shows the promising performance of the proposed algorithm in comparison with other state-of-the-art implementations of Transductive SVM.