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Qian Xu

Publications and source records attributed to Qian Xu.

85 records · Page 5Linked to original sources
arXiv2018

Complete modes of star-shaped oscillating drops

The star-shaped oscillation of water drops has been observed in various physical situations with different sources of vertical excitation. Previous studies apply a quasi-2D model to analyze the resonance frequency and only consider the azimuthal oscillation mode. In this paper, we find that the upper surface of water drops also develop different motion patterns due to parametric instability and it is the coupling of surface motion and azimuthal oscillation that leads to star-shaped oscillations. We will introduce the analysis of the surface mode, combining with the azimuthal mode to give a complete description of the motion of water drops. We propose a new dispersion relation based on the complete description, which provides a significant increase of accuracy in predicting the oscillating frequencies.

physics.flu-dyn↗
arXiv2018

Neural network state estimation for full quantum state tomography

An efficient state estimation model, neural network estimation (NNE), empowered by machine learning techniques, is presented for full quantum state tomography (FQST). A parameterized function based on neural network is applied to map the measurement outcomes to the estimated quantum states. Parameters are updated with supervised learning procedures. From the computational complexity perspective our algorithm is the most efficient one among existing state estimation algorithms for full quantum state tomography. We perform numerical tests to prove both the accuracy and scalability of our model.

quant-ph↗
arXiv2018

Efficient Uncertainty Evaluation of Vector Network Analyser Measurements Using Two-Tier Bayesian Analysis and Monte Carlo Method

Antennas are a key element in any communication system and vector network analyser (VNA) is popular tool for charactering antenna impedance bandwidth. In this paper, an efficient uncertainty evaluation method is proposed for VNA measurement based on its uncertainty propagation mechanism using Bayesian analysis and Monte Carlo method. The proposed method is generic and can be applied to VNA with arbitrary number of ports. In order to obtain the complete information of measurement uncertainty distribution, a two-tier Bayesian analytic process is carried out. The proposed method contains three steps. In the first step, the posterior distribution of each uncertainty source of VNA calibrations is deduced by the use of prior and current sample information through the first-tier Bayesian analysis. In the second step, the obtained posterior distributions of uncertainty sources are taken into the Monte Carlo simulation of one-port VNA measurement uncertainties. In the last step, the results obtained in the second step are used as the prior distribution of the secondary Bayesian evaluation, then the evaluation results of the measurement uncertainty can be obtained with the means, variances and skewness of the probabilistic distribution. The numerical analysis using an antenna measurement results demonstrate the high-efficiency and reliability of this proposed method.

eess.SP↗
arXiv2018

Single Layer PCB Broadband Circular Polarisation Millimetre Wave Massive MIMO Array

A single layer broadband millimetre wave (mm-wave) array with circular polarisation is proposed for the fifth generation (5G) mm-wave communication applications. The antenna element is designed to integrate the matching circuit into a single layer. Hence no additional matching and feeding networks are required which makes the antenna element very easy to synthesise a massive array. The array has a 10 dB return loss bandwidth between 26 GHz and 32 GHz. The polar angle axial ratio is less than 3 within the beamwidth of -60 degree to 60 degree. The proposed antenna structure is simple, low cost and robust, which we envisage suitable for the massive multiple-input and multiple-output (MIMO) applications.

eess.SP↗
arXiv2018

Long Short-Term Memory Networks for CSI300 Volatility Prediction with Baidu Search Volume

Intense volatility in financial markets affect humans worldwide. Therefore, relatively accurate prediction of volatility is critical. We suggest that massive data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. First we select 28 key words, which are related to finance as indicators of the public mood and macroeconomic factors. Then those 28 words of the daily search volume based on Baidu index are collected manually, from June 1, 2006 to October 29, 2017. We apply a Long Short-Term Memory neural network to forecast CSI300 volatility using those search volume data. Compared to the benchmark GARCH model, our forecast is more accurate, which demonstrates the effectiveness of the LSTM neural network in volatility forecasting.

q-fin.ST↗
arXiv2018

Nonlinear effect of forced harmonic oscillator subject to sliding friction and simulation by a simple nonlinear circuit

We study the nonlinear behaviors of mass-spring systems damped by dry friction using simulation by a nonlinear LC circuit damped by anti-parallel diodes. We show that the differential equation for the electric oscillator is equivalent to the mechanical one's when a piecewise linear model is used to simplify the diodes I-V curve. We derive series solutions to the differential equation under weak nonlinear approximation which can describe the resonant response as well as amplitudes of superharmonic components. Experimental results are consistent with series solutions. We also present the phenomenon of hysteresis. Theoretical analysis along with numerical simulations are conducted to explore the stick-slip boundary. The correspondence between the mechanical and electric oscillators makes it easy to demonstrate the behaviors of this nonlinear oscillator on a digital oscilloscope. It can be used to extend the linear RLC experiment at the undergraduate level.

physics.class-ph↗
arXiv2017

A Generalized Short Circuit Ratio for Multi-Infeed LCC-HVDC System

The relationship between the short circuit ratio (SCR) and static voltage stability is analyzed in this paper. According to eigenvalue decomposition method, a novel concept named generalized short circuit ratio (gSCR) has been proposed for multi-infeed LCC-HVDC (MIDC) systems to mathematically measure the connect-ed AC strength from the point view of voltage stability, which can overcome the rule-of-thumb basis of existing multi-infeed short circuit ratio (MISCR) concept. In gSCR, two indices, the critical gSCR (CgSCR) and the boundary gSCR (BgSCR) are developed to quantitatively evaluate if the connected AC system is strong or weak, in which CgSCR=2 and BgSCR=3 are two critical values for strength evaluation. Finally, numerical simulations are conducted to validate the effectiveness of the proposed gSCR concept.

eess.SY↗
arXiv2016

Modeling The Adaption Rule in Context-aware Systems

Context awareness is increasingly gaining applicability in interactive ubiquitous mobile computing systems. Each context-aware application has its own set of behaviors to react to context modifications. This paper is concerned with the context modeling and the development methodology for context-aware systems. We proposed a rule-based approach and use the adaption tree to model the adaption rule of context-aware systems. We illustrate this idea in an arithmetic game application.

cs.HC↗
arXiv2014

Tuning the catalytic activity of graphene nanosheets for oxygen reduction reaction via size and thickness reduction

Currently, the fundamental factors that control the oxygen reduction reaction (ORR) activity of graphene itself, in particular the dependence of the ORR activity on the number of exposed edge sites remain elusive, mainly due to limited synthesis routes of achieving small size graphene. In this work, the synthesis of low oxygen content (< 2.5 +/-0.2 at %), few layer graphene nanosheets with lateral dimensions smaller than a few hundred nm was achieved using a combination of ionic liquid assisted grinding of high purity graphite coupled with sequential centrifugation. We show for the first time, that the graphene nanosheets possessing a plethora of edges exhibited considerably higher electron transfer numbers compared to the thicker graphene nanoplatelets. This enhanced ORR activity was accomplished by successfully exploiting the plethora of edges of the nanosized graphene as well as the efficient electron communication between the active edge sites and the electrode substrate. The graphene nanosheets were characterized by an onset potential of -0.13 V vs. Ag/AgCl and a current density of -3.85 mA/cm2 at -1 V, which represent the best ORR performance ever achieved from an undoped carbon based catalyst. This work demonstrates how low oxygen content nanosized graphene synthesized by a simple route can considerably impact the ORR catalytic activity and hence it is of significance in designing and optimizing advanced metal-free ORR electrocatalysts.

cond-mat.mtrl-sci↗
arXiv2012

Inversion mechanism of uncertain orders and parameters for the non-commensurate and hyper fractional order chaotic systems via differential evolution

In this paper, a novel uncertain fractional-orders and parameters' inversion mechanism via the differential evolution algorithms (DE) with a general mathematical model is proposed for non-commensurate and hyper fractional chaotic systems. The problems of fractional-order chaos' inversion estimation are converted into multiple modal non-negative objective functions' minimization, which takes fractional-orders and parameters as its particular independent variables. And the objective is to find optimal combinations of fractional-orders and systematic parameters by DE in the predefined intervals for fractional order chaotic systems such that the objective function is minimized. Simulations are done to estimate a series of non-commensurate and hyper fractional chaotic systems. The experiments' results show that the proposed inversion mechanism for fractional-order chaotic systems is a successful methods with the advantages of high precision and robustness.

nlin.CD↗
arXiv2012

Reconstruction mechanism with self-growing equations for hyper, improper and proper fractional chaotic systems through a novel non-Lyapunov approach

Identification of the unknown parameters and orders of fractional chaotic systems is of vital significance in controlling and synchronization of fractional-order chaotic systems. However there exist basic hypotheses in traditional estimation methods, that is, the parameters and fractional orders are partially known or the known data series coincide with definite forms of fractional chaotic differential equations except some uncertain parameters and fractional orders. What should I do when these hypotheses do not exist? In this paper, a non-Lyapunov novel approach with a novel united mathematical model is proposed to reconstruct fractional chaotic systems, through the fractional-order differential equations self-growing mechanism by some genetic operations ideas independent of these hypotheses. And the cases of identifying the unknown parameters and fractional orders of fractional chaotic systems can be thought as special cases of the proposed united mathematical reconstruction method in non-Lyapunov way. The problems of fractional-order chaos reconstruction are converted into a multiple modal non-negative special functions' minimization through a proper translation, which takes fractional-order differential equations as its particular independent variables instead of the unknown parameters and fractional orders. And the objective is to find best form of fractional-order differential equations such that the objective function is minimized. Simulations are done to reconstruct a series of hyper and normal fractional chaotic systems. The experiments' results show that the proposed self-growing mechanism of fractional-order differential equations with genetic operations is a successful methods for fractional-order chaotic systems' reconstruction, with the advantages of high precision and robustness.

nlin.CD↗
arXiv2012

Identification of unknown parameters and orders via cuckoo search oriented statistically by differential evolution for non-commensurate fractional order chaotic systems

Identification of the unknown parameters and orders of fractional chaotic systems is of vital significance in controlling and synchronization of fractional-order chaotic systems. In this paper, a non-Lyapunov novel approach is proposed to estimate the unknown parameters and orders together for non-commensurate and hyper fractional chaotic systems based on cuckoo search oriented statistically the differential evolution (CSODE). Firstly, a novel Gao's mathematical model is put and analysed in three sub-models, not only for the unknown orders and parameters' identification but also for systems' reconstruction of fractional chaos systems with time-delays or not. Then the problems of fractional-order chaos' identification are converted into a multiple modal non-negative functions' minimization through a proper translation, which takes fractional-orders and parameters as its particular independent variables. And the objective is to find best combinations of fractional-orders and systematic parameters of fractional order chaotic systems as special independent variables such that the objective function is minimized. Simulations are done to estimate a series of non-commensurate and hyper fractional chaotic systems with the new approaches based on CSODE, the cuckoo search and differential evolution respectively. The experiments' results show that the proposed identification mechanism based on CSODE for fractional-orders and parameters is a successful methods for fractional-order chaotic systems, with the advantages of high precision and robustness.

nlin.CD↗