408 research outputs found
Limited Attention Allocation in a Stochastic Linear Quadratic System with Multiplicative Noise
This study addresses limited attention allocation in a stochastic linear
quadratic system with multiplicative noise. Our approach enables strategic
resource allocation to enhance noise estimation and improve control decisions.
We provide analytical optimal control and propose a numerical method for
optimal attention allocation. Additionally, we apply our ffndings to dynamic
mean-variance portfolio selection, showing effective resource allocation across
time periods and factors, providing valuable insights for investors
Research on homogeneous deformation of electromagnetic incremental tube bulging
The electromagnetic incremental forming (EMIF) method is used for tube forming process.
Suitable 2D FE models are designed to predict the forming process with a moving coil. In
comparison with experimental values, simulation method can obtain accurate results. Then,
effect factors named overlapping ration of adjacent discharge positions, discharge voltage,
forming sequence and die dimension on tube homogeneous deformation are discussed. The
result demonstrates that it is feasible to produce long-straight wall tubes using a small coil by
electromagnetic incremental tube bulging
Kinematic Absolute Positioning with Quad-Constellation GNSS
The absolute positioning technique is based on a point positioning mode with a single Global Navigation Satellite System (GNSS) receiver, which has been widely used in many fields such as vehicle navigation and kinematic surveying. For a long period, this positioning technique mainly relies on a single GPS system. With the revitalization of Global Navigation Satellite System (GLONASS) constellation and two newly emerging constellations of BeiDou Navigation Satellite System (BDS) and Galileo, it is now feasible to carry out the absolute positioning with quad-constellation of GPS, GLONASS, BDS, and Galileo. A combination of multi-constellation observations can offer improved reliability, availability, and accuracy for position solutions. In this chapter, combined GPS/GLONASS/BDS/Galileo point positioning models for both traditional single point positioning (SPP) and precise point positioning (PPP) are presented, including their functional and stochastic components. The traditional SPP technique has a positioning accuracy at a meter level, whereas the PPP technique can reach an accuracy of a centimeter level. However, the later relies on the availability of precise ephemeris and needs a long convergence time. Experiments were carried out to assess the kinematic positioning performance in the two different modes. The positioning results are compared among different constellation combinations to demonstrate the advantages of quad-constellation GNSS
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression
Federated learning has become a popular tool in the big data era nowadays. It
trains a centralized model based on data from different clients while keeping
data decentralized. In this paper, we propose a federated sparse sliced inverse
regression algorithm for the first time. Our method can simultaneously estimate
the central dimension reduction subspace and perform variable selection in a
federated setting. We transform this federated high-dimensional sparse sliced
inverse regression problem into a convex optimization problem by constructing
the covariance matrix safely and losslessly. We then use a linearized
alternating direction method of multipliers algorithm to estimate the central
subspace. We also give approaches of Bayesian information criterion and
hold-out validation to ascertain the dimension of the central subspace and the
hyper-parameter of the algorithm. We establish an upper bound of the
statistical error rate of our estimator under the heterogeneous setting. We
demonstrate the effectiveness of our method through simulations and real world
applications
Online Kernel Sliced Inverse Regression
Online dimension reduction is a common method for high-dimensional streaming
data processing. Online principal component analysis, online sliced inverse
regression, online kernel principal component analysis and other methods have
been studied in depth, but as far as we know, online supervised nonlinear
dimension reduction methods have not been fully studied. In this article, an
online kernel sliced inverse regression method is proposed. By introducing the
approximate linear dependence condition and dictionary variable sets, we
address the problem of increasing variable dimensions with the sample size in
the online kernel sliced inverse regression method, and propose a reduced-order
method for updating variables online. We then transform the problem into an
online generalized eigen-decomposition problem, and use the stochastic
optimization method to update the centered dimension reduction directions.
Simulations and the real data analysis show that our method can achieve close
performance to batch processing kernel sliced inverse regression
A New Method for Superresolution Image Reconstruction Based on Surveying Adjustment
A new method for superresolution image reconstruction based on surveying adjustment method is described in this paper. The main idea of such new method is that a sequence of low-resolution images are taken firstly as observations, and then observation equations are established for the superresolution image reconstruction. The gray function of the object surface can be found by using surveying adjustment method from the observation equations. High-resolution pixel value of the corresponding area can be calculated by using the gray function. The results show that the proposed algorithm converges much faster than that of conventional superresolution image reconstruction method. By using the new method, the visual feeling of reconstructed image can be greatly improved compared to that of iterative back projection algorithm, and its peak signal-to-noise ratio can also be improved by nearly 1 dB higher than the projection onto convex sets algorithm. Furthermore, this method can successfully avoid the ill-posed problems in reconstruction process
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