arXiv subjects

Qiang Wu

Publications and source records attributed to Qiang Wu.

At least 163 records · Page 9Linked to original sources

arXiv2018

Dynamic Security Analysis of Power Systems by a Sampling-Based Algorithm

Dynamic security analysis is an important problem of power systems on ensuring safe operation and stable power supply even when certain faults occur. No matter such faults are caused by vulnerabilities of system components, physical attacks, or cyber-attacks that are more related to cyber-security, they eventually affect the physical stability of a power system. Examples of the loss of physical stability include the Northeast blackout of 2003 in North America and the 2015 system-wide blackout in Ukraine. The nonlinear hybrid nature, that is, nonlinear continuous dynamics integrated with discrete switching, and the high degree of freedom property of power system dynamics make it challenging to conduct the dynamic security analysis. In this paper, we use the hybrid automaton model to describe the dynamics of a power system and mainly deal with the index-1 differential-algebraic equation models regarding the continuous dynamics in different discrete states. The analysis problem is formulated as a reachability problem of the associated hybrid model. A sampling-based algorithm is then proposed by integrating modeling and randomized simulation of the hybrid dynamics to search for a feasible execution connecting an initial state of the post-fault system and a target set in the desired operation mode. The proposed method enables the use of existing power system simulators for the synthesis of discrete switching and control strategies through randomized simulation. The effectiveness and performance of the proposed approach are demonstrated with an application to the dynamic security analysis of the New England 39-bus benchmark power system exhibiting hybrid dynamics. In addition to evaluating the dynamic security, the proposed method searches for a feasible strategy to ensure the dynamic security of the system in face of disruptions.

eess.SY↗

arXiv2018

Multi-pseudo Regularized Label for Generated Data in Person Re-Identification

Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling large number of images, the amount of available training data is always limited. To produce more data for training a deep network, Generative Adversarial Network (GAN) can be used to generate artificial sample data. However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to pre-defined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0,1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets respectively, and outperforms state-of-the-art methods.

cs.CV↗

arXiv2018

Homogeneous Nucleation and Forest Hardening Result in Thermal Hardening Phenomenon in Shock Loaded BCC Metals

Interpretation of thermal hardening phenomenon at high strain rate has recently become a critical problem in shock wave physics. In this letter, this problem is addressed from a viewpoint of dislocation generation, and a novel conclusion is gained that forest hardening induced by homogeneous nucleation (HN) results in thermal hardening behavior in a BCC metal significantly, apart from phonon drag mechanism. Through numerical simulations with a dislocation based crystal plasticity model, we have reproduced the experimental results quantitatively and predicted a thermal hardening behavior in other BCC metals, i.e., Mo, at higher temperature.

cond-mat.mtrl-sci↗

arXiv2018

Mean Values for a Class of Arithmetic Functions in Short Intervals

In this paper, we shall establish a rather general asymptotic formula in short intervals for a classe of arithmetic functions and announce two applications about the distribution of divisors of square-full numbers and integers representable as sums of two squares.

math.NT↗

arXiv2018

An Alternative Channel For High-Mass Binary Black Holes-----Dark Matter Accretion Onto Black Hole

By a method of population synthesis we construct a model of dark matter (DM) accretion onto binary black holes (BHs), and investigate the merger rate of the binary BHs. We find that the merger rate can weakly increase (less than 10\%). However, the DM accretion can efficiently enhance the masses of binary BHs. In our model, the result for $Z = 0.01$ without the DM accretion can not explain the GW170104, GW170814, and GW150914, while with the DM accretion, it can cover all observations well. For the higher metallicity ($Z = 0.02$), our model can not explain the mergers of high-mass binary BHs like GW170104, GW170814, and GW150914. We estimate that the merger rate of binary BHs lies between $\rm55Gpc^{-3}yr^{-1}$ to $\rm197Gpc^{-3}yr^{-1}$.

astro-ph.HE↗

arXiv2018

Overlapping Sliced Inverse Regression for Dimension Reduction

Sliced inverse regression (SIR) is a pioneer tool for supervised dimension reduction. It identifies the effective dimension reduction space, the subspace of significant factors with intrinsic lower dimensionality. In this paper, we propose to refine the SIR algorithm through an overlapping slicing scheme. The new algorithm, called overlapping sliced inverse regression (OSIR), is able to estimate the effective dimension reduction space and determine the number of effective factors more accurately. We show that such overlapping procedure has the potential to identify the information contained in the derivatives of the inverse regression curve, which helps to explain the superiority of OSIR. We also prove that OSIR algorithm is $\sqrt n $-consistent and verify its effectiveness by simulations and real applications.

stat.ML↗

arXiv2018

Hartree-Fock Gamow basis from realistic nuclear forces

We present a simplified method to generate the Hartree-Fock Gamow basis from realistic nuclear forces. The Hartree-Fock iteration in the harmonic-oscillator basis is first performed, and then the obtained HF potential is analytically continued to the complex-k plane, finally by solving the Schrödinger equation in the complex-k plane the Gamow basis is obtained. As examples, the method is applied to 4He and 22O with the renormalized chiral N3LO potential. The basis obtained which includes bound, resonant and scattering states can be further used in many-body calculations to study weakly bound nuclei.

nucl-th↗

arXiv2018

Schwinger Pair Production by Electric Field Coupled to Inflaton

We analytically investigate the Schwinger pair production in the de Sitter background by using {\em the uniform asymptotic approximation method}, and show that the equation of motion in general has two turning points, and the nature of these points could be single, double, real or complex, depending on the choice of the free parameters involved in the theory. Different natures of these points lead to different electric currents. In particular, when $β\equiv m^2/H^2-9/4$ is positive, both turning points are complex, and the electric current due to the Schwinger process is highly suppressed, where $m$ and $H$ denote, respectively, the mass of the particle and the Hubble parameter. For the turning points to be real, it is necessary to have $β< 0$, and the more negative of $β$, the easier to produce particles. In addition, when $β< 0$, we also study the particle production when the electric field $E$ is very weak. We find that the electric current in this case is proportional to $E^{1/2 - \sqrt{|β|}}$, which is strongly enhanced in the weak electric field limit when $m < \sqrt{2} H$.

gr-qc↗

arXiv2018

Deterministic generation of single soliton Kerr frequency comb in microresonators by a single shot pulsed trigger

Kerr soliton frequency comb generation in monolithic microresonators recently attracted great interests as it enables chip-scale few-cycle pulse generation at microwave rates with smooth octave-spanning spectra for self-referencing. Such versatile platform finds significant applications in dual-comb spectroscopy, low-noise optical frequency synthesis, coherent communication systems, etc. However, it still remains challenging to straightforwardly and deterministically generate and sustain the single-soliton state in microresonators. In this paper, we propose and theoretically demonstrate the excitation of single-soliton Kerr frequency comb by seeding the continuous-wave driven nonlinear microcavity with a pulsed trigger. Unlike the mostly adopted frequency tuning scheme reported so far, we show that an energetic single shot pulse can trigger the single-soliton state deterministically without experiencing any unstable or chaotic states. Neither the pump frequency nor the cavity resonance is required to be tuned. The generated mode-locked single-soliton Kerr comb is robust and insensitive to perturbations. Even when the thermal effect induced by the absorption of the intracavity light is taken into account, the proposed single pulse trigger approach remains valid without requiring any thermal compensation means.

physics.optics↗

arXiv2017

Pre-inflationary dynamics in loop quantum cosmology: Power-law potentials

In this paper, we study the pre-inflationary dynamics for the power-law potential $(V(ϕ) \propto ϕ^n)$ with $n<2$ in the framework of loop quantum cosmology. In the case where the kinetic energy of the inflaton dominates at the initial, the evolution of the universe can always be divided into three different phases prior to preheating: {\em bouncing, transition and slow-roll inflation}. During the bouncing phase, the evolution of the expansion factor is independent not only on the initial conditions but also the inflationary potentials, and is given explicitly by an analytical solution. In contrast, for the potential energy dominated initial conditions, this universality is lost. We also obtain total number of e-folds during the slow-roll inflation, whereby physically viable models are identified. In addition, we present phase space analysis for the inflationary potentials under consideration and compare our results with the ones obtained previously for different potentials.

gr-qc↗

arXiv2017

Primordial Spectra of slow-roll inflation at second-order with the Gauss-Bonnet correction

The slow-roll inflation for a single scalar field that couples to the Gauss-Bonnet (GB) term represents an important higher-order curvature correction inspired by string theory. With the arrival of the era of precision cosmology, it is expected that the high-order corrections become more and more important. In this paper we study the observational predictions of the slow-roll inflation with the GB term by using the third-order uniform asymptotic approximation method. We calculate explicitly the primordial power spectra, spectral indices, running of the spectral indices for both scalar and tensor perturbations, and the ratio between tensor and scalar spectra. These expressions are all written in terms of the Hubble and GB coupling flow parameters and expanded up to the next-to-leading order in the slow-roll expansions. The upper bounds of errors of the approximations at the third-order are $0.15\%$, so they represent the most accurate results obtained so far in the literature. We expect that the understanding of the GB corrections in the primordial spectra and their constraints by forthcoming observational data will provide clues for the UV complete theory of quantum gravity, such as the string/M-theory.

gr-qc↗

arXiv2017

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network

Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression algorithm on each of them, and then simply average the output functions learnt from these blocks. Since the average process will decrease the variance, not the bias, bias correction is expected to improve the learning performance if the base regression algorithm is a biased one. Regularization kernel network is an effective and widely used method for nonlinear regression analysis. In this paper we will investigate a bias corrected version of regularization kernel network. We derive the error bounds when it is applied to a single data set and when it is applied as a base algorithm in distributed regression. We show that, under certain appropriate conditions, the optimal learning rates can be reached in both situations.

cs.LG↗

arXiv2017

Prediction of Stable Ground-State Lithium Polyhydrides under High Pressures

Hydrogen-rich compounds are important for understanding the dissociation of dense molecular hydrogen, as well as searching for room temperature Bardeen-Cooper-Schrieffer (BCS) superconductors. A recent high pressure experiment reported the successful synthesis of novel insulating lithium polyhydrides when above 130 GPa. However, the results are in sharp contrast to previous theoretical prediction by PBE functional that around this pressure range all lithium polyhydrides (LiHn (n = 2-8)) should be metallic. In order to address this discrepancy, we perform unbiased structure search with first principles calculation by including the van der Waals interaction that was ignored in previous prediction to predict the high pressure stable structures of LiHn (n = 2-11, 13) up to 200 GPa. We reproduce the previously predicted structures, and further find novel compositions that adopt more stable structures. The van der Waals functional (vdW-DF) significantly alters the relative stability of lithium polyhydrides, and predicts that the stable stoichiometries for the ground-state should be LiH2 and LiH9 at 130-170 GPa, and LiH2, LiH8 and LiH10 at 180-200 GPa. Accurate electronic structure calculation with GW approximation indicates that LiH, LiH2, LiH7, and LiH9 are insulative up to at least 208 GPa, and all other lithium polyhydrides are metallic. The calculated vibron frequencies of these insulating phases are also in accordance with the experimental infrared (IR) data. This reconciliation with the experimental observation suggests that LiH2, LiH7, and LiH9 are the possible candidates for lithium polyhydrides synthesized in that experiment. Our results reinstate the credibility of density functional theory in description H-rich compounds, and demonstrate the importance of considering van der Waals interaction in this class of materials.

cond-mat.mtrl-sci↗

arXiv2017

Predicted novel insulating electride compound between alkali metals lithium and sodium under high pressure

The application of high pressure can fundamentally modify the crystalline and electronic structures of elements as well as their chemical reactivity, which could lead to the formation of novel materials. Here, we explore the reactivity of lithium with sodium under high pressure, using a swarm structure searching techniques combined with first-principles calculations, which identify a thermodynamically stable LiNa compound adopting an orthorhombic oP8 phase at pressure above 355 GPa. The formation of LiNa may be a consequence of strong concentration of electrons transfer from the lithium and the sodium atoms into the interstitial sites, which also leads to opening a relatively wide band gap for LiNa-op8. This is substantially different from the picture that share or exchange electrons in common compounds and alloys. In addition, lattice-dynamic calculations indicate that LiNa-op8 remains dynamically stable when pressure decompresses down to 70 GPa.

physics.chem-ph↗

arXiv2017

Robust Guided Image Filtering

The process of using one image to guide the filtering process of another one is called Guided Image Filtering (GIF). The main challenge of GIF is the structure inconsistency between the guidance image and the target image. Besides, noise in the target image is also a challenging issue especially when it is heavy. In this paper, we propose a general framework for Robust Guided Image Filtering (RGIF), which contains a data term and a smoothness term, to solve the two issues mentioned above. The data term makes our model simultaneously denoise the target image and perform GIF which is robust against the heavy noise. The smoothness term is able to make use of the property of both the guidance image and the target image which is robust against the structure inconsistency. While the resulting model is highly non-convex, it can be solved through the proposed Iteratively Re-weighted Least Squares (IRLS) in an efficient manner. For challenging applications such as guided depth map upsampling, we further develop a data-driven parameter optimization scheme to properly determine the parameter in our model. This optimization scheme can help to preserve small structures and sharp depth edges even for a large upsampling factor (8x for example). Moreover, the specially designed structure of the data term and the smoothness term makes our model perform well in edge-preserving smoothing for single-image tasks (i.e., the guidance image is the target image itself). This paper is an extension of our previous work [1], [2].

cs.CV↗

arXiv2017

Citation personal display: A case study of personal websites by physicists in 11 well-known universities

This paper aims to investigate the extent to which researchers display citation, and wants to examine whether there are researcher differences in citation personal display at the level of university, country, and academic rank. Physicists in 11 well-known universities in USA, Britain, and China were chosen as the object of study. It was manually identified if physicists had mentioned citation counts, citation-based indices, or a link to Google Scholar Citations (GSC) on the personal websites. A chi-square test is constructed to test researcher differences in citation personal display. Results showed that the overall proportion of citation personal display is not high (14.8%), with 129 of 870 physicists displaying citation. And physicists from different well-known universities indeed had a significant difference in citation personal display. Moreover, at the national level, it was noticed that physicists in well-known Chinese universities had the highest level of citation personal display, followed by Britain and the USA. Further, this study also found that researchers who had the academic rank of professor had the highest citation personal display. In addition, the differences in h-index personal display by university, country or academic rank were analyzed, and the results showed that they were not statistically significant.

cs.DL↗

arXiv2016

A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds

With the development of numerous 3D sensing technologies, object registration on cross-source point cloud has aroused researchers' interests. When the point clouds are captured from different kinds of sensors, there are large and different kinds of variations. In this study, we address an even more challenging case in which the differently-source point clouds are acquired from a real street view. One is produced directly by the LiDAR system and the other is generated by using VSFM software on image sequence captured from RGB cameras. When it confronts to large scale point clouds, previous methods mostly focus on point-to-point level registration, and the methods have many limitations.The reason is that the least mean error strategy shows poor ability in registering large variable cross-source point clouds. In this paper, different from previous ICP-based methods, and from a statistic view, we propose a effective coarse-to-fine algorithm to detect and register a small scale SFM point cloud in a large scale Lidar point cloud. Seen from the experimental results, the model can successfully run on LiDAR and SFM point clouds, hence it can make a contribution to many applications, such as robotics and smart city development.

cs.CV↗

arXiv2016

A dislocation-based explanation of quasi-elastic release in shock-loaded aluminum

A novel explanation of the quasielastic release phenomenon in shock compressed aluminum is presented. A dislocation-based model, taking into account dislocation substructures and evolution, is applied to simulate the elastic plastic response of both single crystal and poly crystalline aluminum. The calculated results are in good agreement with experimental results from not only the velocity profiles but also the shear strength and dislocation density, which demonstrates the accuracy of our simulations. Simulated results indicate that dislocation immobilization during dynamic deformation results in a smooth increase of yield stress, which leads to the quasi-elastic release, while the generation of dislocations caused by the plastic release wave results in the appearance of a transition point between the quasielastic release and the plastic release in the profile.

cond-mat.mtrl-sci↗