Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Numerical simulation on mining-induced water inrushes related to geologic structures using a damage-based hydromechanical model

Abstract: A large number of statistics indicate that water inrush has a direct relationship with geological structures such as fault and karst collapse columns. Understanding the mechanism of water inrushes controlled by geologic structures is of vital importance for adopting effective measures to prevent their occurrence. The work begins with formulization of a damage-based hydromechanical model based on elastic damage theory. Next, the model is numerically implemented with finite element method by employing a finite element package called COMSOL Multiphysics, and is also validated against some existing experimental observations. Finally, the model is used to simulate the mining-induced groundwater inrushes when the effect of faults and karst collapse columns is considered in the numerical simulation, and some suggestive conclusions for preventing water inrushes and optimizing underground mining operations are drawn.

Fault Estimation and Fault-Tolerant Control for Switched Fuzzy Stochastic Systems

Abstract: This paper addresses the problems of fault estimation and fault-tolerant control for switched fuzzy stochastic systems with actuator fault and sensor fault. A novel observer is proposed to estimate the system states, actuator, and sensor faults, simultaneously. The proposed observer can be treated as an extension of the traditional proportional-integral observer. The estimation information is utilized to design the fault-tolerant controller. Based on the piecewise Lyapunov function and the average dwell time, a set of linear matrix inequalities can be achieved, which ensure that the closed-loop system is mean-square exponentially stable with a weighted $H_\infty$ performance level. At last, two simulation examples are provided to illustrate the effectiveness of the proposed approach.

Fast Single Image Super-Resolution via Self-Example Learning and Sparse Representation

Abstract: In this paper, we propose a novel algorithm for fast single image super-resolution based on self-example learning and sparse representation. We propose an efficient implementation based on the K-singular value decomposition (SVD) algorithm, where we replace the exact SVD computation with a much faster approximation, and we employ the straightforward orthogonal matching pursuit algorithm, which is more suitable for our proposed self-example-learning-based sparse reconstruction with far fewer signals. The patches used for dictionary learning are efficiently sampled from the low-resolution input image itself using our proposed sample mean square error strategy, without an external training set containing a large collection of high- resolution images. Moreover, the l 0 -optimization-based criterion, which is much faster than l 1 -optimization-based relaxation, is applied to both the dictionary learning and reconstruction phases. Compared with other super-resolution reconstruction methods, our low- dimensional dictionary is a more compact representation of patch pairs and it is capable of learning global and local information jointly, thereby reducing the computational cost substantially. Our algorithm can generate high-resolution images that have similar quality to other methods but with an increase in the computational efficiency greater than hundredfold.

Planck constraints on holographic dark energy

Abstract: We perform a detailed investigation on the cosmological constraints on the holographic dark energy (HDE) model by using the Planck data. We find that HDE can provide a good fit to the Planck high-l (l greater than or similar to 40) temperature power spectrum, while the discrepancy at 20 40 found in the ACDM model remains unsolved in the HDE model. The Planck data alone can lead to strong and reliable constraint on the HDE parameter c. At the 68% confidence level (CL), we obtain c = 0.508 +/- 0.207 with Planck+WP-lensing, favoring the present phantom behavior of HDE at the more than 2 sigma CL. By combining Planck+WP with the external astrophysical data sets, i.e. the BAO measurements from 6dFGS-FSDSS DR7(R)+BOSS DR9, the direct Hubble constant measurement result (Ho = 73.8 +/- 2.4 km s(-1) Mpc(-1)) from the HST, the SNLS3 supernovae data set, and Union2.1 supernovae data set, we get the 68% CL constraint results c = 0.484 +/- 0.070, 0.474 +/- 0.049, 0.594 +/- 0.051, and 0.642 +/- 0.066, respectively. The constraints can be improved by 2%-15% if we further add the Planck lensing data into the analysis. Compared with the WMAP-9 results, the Planck results reduce the error by 30%-60%, and prefer a phantom-like HDE at higher significant level. We also investigate the tension between different data sets. We find no evident tension when we combine Planck data with BAO and HST. Especially, we find that the strong correlation between Omega(m)h(3) and dark energy parameters is helpful in relieving the tension between the Planck and HST measurements. The residual value of chi(2)Planck+WP+HST (-) chi(2)Planck+WP is 7.8 in the ACDM model, and is reduced to 1.0 or 0.3 if we switch the dark energy to w model or the holographic model. When we introduce supernovae data sets into the analysis, some tension appears. We find that the SNLS3 data set is in tension with all other data sets; for example, for the Planck+WP, WMAP-9 and BAO+HST, the corresponding Delta chi(2) is equal to 6.4, 3.5 and 4.1, respectively. As a comparison, the Union2.1 data set is consistent with these three data sets, but the combination Union2.1+BAO+HST is in tension with Pianck+WP-Flensing, corresponding to a large Delta chi(2) that is equal to 8.6 (1.4% probability). Thus, combining internal inconsistent data sets (SNIa+BAO+HST with Planck+WP+lensing) can lead to ambiguous results, and it is necessary to perform the HDE data analysis for each independent data sets. Our tightest self-consistent constraint is c = 0.495 0.039 obtained from Planck+WP+BAO-FHST-Flensing.