arXiv subjects

Bin Wang

Publications and source records attributed to Bin Wang.

At least 847 records · Page 47Linked to original sources

arXiv2018

Work Distributions in 1-D Fermions and Bosons with Dual Contact Interactions

We extend the well-known static duality \cite{girardeau1960relationship, cheon1999fermion} between 1-D Bosons and 1-D Fermions to the dynamical version. By utilizing this dynamical duality we find the duality of non-equilibrium work distributions between interacting 1-D bosonic (Lieb-Liniger model) and 1-D fermionic (Cheon-Shigehara model) systems with dual contact interactions. As a special case, the work distribution of the Tonks-Girardeau (TG) gas is identical to that of 1-D free fermionic system even though their momentum distributions are significantly different. In the classical limit, the work distributions of Lieb-Liniger models (Cheon-Shigehara models) with arbitrary coupling strength converge to that of the 1-D noninteracting distinguishable particles, although their elemetary excitations (quasi-particles) obey different statistics, e.g. the Bose-Einstein, the Fermi-Dirac and the fractional statistics. We also present numerical results of the work distributions of Lieb-Liniger model with various coupling strengths, which demonstrate the convergence of work distributions in the classical limit.

quant-ph↗

arXiv2018

Long-time energy analysis of extended RKN integrators for muti-frequency highly oscillatory Hamiltonian systems

In this paper, we study the long-time near conservation of the total and oscillatory energies for extended RKN (ERKN) integrators when applied to muti-frequency highly oscillatory Hamiltonian systems. We consider one-stage explicit symmetric integrators and show their long-time behaviour of numerical energy conservations by using modulated multi-frequency Fourier expansions. Numerical experiments are carried out and the numerical results demonstrate the remarkable long-time near conservation of the energies for the ERKN integrators and support our theoretical analysis presented in this paper.

math.NA↗

arXiv2018

A Time-Power Series Based Semi-Analytical Approach for Power System Simulation

Time domain simulation is the basis of dynamic security assessment for power systems. Traditionally, numerical integration methods are adopted by simulation software to solve nonlinear power system differential-algebraic equations about any given contingency under a specific operating condition. An alternative approach promising for online simulation is to offline derive a semi-analytical solution (SAS) and then online evaluate the SAS over consecutive time windows regarding the operating condition and contingency until obtaining the simulation result over a desired period. This paper proposes a general semi-analytical approach that derives and evaluates an SAS in the form of power series in time to approximate the solutions of power system differential equations. An error-rate upper bound of the SAS is also proposed to guarantee the reliable use of adaptive time windows for evaluation of the SAS. A dynamic bus method is proposed to extend the semi-analytical approach for solving general power system DAEs by efficiently linking the SASs for dynamic components through the numerical solution of the network algebraic equations. Case studies performed on the New England 39-bus system and the Polish 2383-bus system test the performance of the proposed semi-analytical approach and compare to existing methods. The results show that the SAS based approach has potentials for online simulations.

eess.SY↗

arXiv2018

Holographic RG flow of thermo-electric transports with momentum dissipation

We construct the holographic renormalization group (RG) flow of thermo-electric conductivities when the translational symmetry is broken. The RG flow is probed by the intrinsic observers hovering on the sliding radial membranes. We obtain the RG flow by solving a matrix-form Riccati equation. The RG flow provides a high-efficient numerical method to calculate the thermo-electric conductivities of strongly coupled systems with momentum dissipation. As an illustration, we recover the AC thermo-electric conductivities in the Einstein-Maxwell-axion model. Moreover, in several homogeneous and isotropic holographic models which dissipate the momentum and have the finite density, it is found that the RG flow of a particular combination of DC thermo-electric conductivities does not run. As a result, the DC thermal conductivity on the boundary field theory can be derived analytically, without using the conserved thermal current.

hep-th↗

arXiv2018

Comprehensive Modeling of Electric Vehicles in California Demand Response Markets

Electric vehicle (EV) is a significant type of distributed energy resources (DERs), that provide flexibilities to grid operators to achieve a myriad of objectives. This paper presents a comprehensive modeling framework of EVs under multiple real-world demand response (DR) markets in California and provides combined strategies to maximize the revenues via unidirectional EV-Grid integrations (V1G). EV itinerary and usage information from a commercial demonstration site is utilized to model the EV flexibilities, based on which, we modeled the heterogeneous market rules using mixed-integer programming approaches. The system cost-saving performance is analyzed with respect to fleet properties and market constraints, including flexibility, participation threshold, and baseline calculaton, etc.

math.OC↗

arXiv2018

Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification

Convolutional neural networks (CNNs) are one of the most effective deep learning methods to solve image classification problems, but the best architecture of a CNN to solve a specific problem can be extremely complicated and hard to design. This paper focuses on utilising Particle Swarm Optimisation (PSO) to automatically search for the optimal architecture of CNNs without any manual work involved. In order to achieve the goal, three improvements are made based on traditional PSO. First, a novel encoding strategy inspired by computer networks which empowers particle vectors to easily encode CNN layers is proposed; Second, in order to allow the proposed method to learn variable-length CNN architectures, a Disabled layer is designed to hide some dimensions of the particle vector to achieve variable-length particles; Third, since the learning process on large data is slow, partial datasets are randomly picked for the evaluation to dramatically speed it up. The proposed algorithm is examined and compared with 12 existing algorithms including the state-of-art methods on three widely used image classification benchmark datasets. The experimental results show that the proposed algorithm is a strong competitor to the state-of-art algorithms in terms of classification error. This is the first work using PSO for automatically evolving the architectures of CNNs.

cs.NE↗

arXiv2018

Data fusion of multivariate time series: Application to noisy 12-lead ECG signals

12-lead ECG signals fusion is crucial for further ECG signal processing. In this paper, a novel fusion data algorithm is proposed. In the method, 12-lead ECG signals are appropriately converted to a single-lead physiological signal via the idea of the local weighted linear prediction algorithm. For effectively inheriting the quality characteristics of the 12-lead ECG signals, the fuzzy inference system is rationally designed to estimate the weighted coefficient in our algorithm. Experimental results indicate that the algorithm can obtain desirable results on synthetic ECG signals, noisy ECG signals and realistic ECG signals.

eess.SP↗

arXiv2018

On the thermodynamics of the black hole and hairy black hole transitions in the asymptotically flat spacetime with a box

We study the asymptotically flat quasi-local black hole/hairy black hole model with nonzero mass of the scalar filed. We disclose effects of the scalar mass on transitions in a grand canonical ensemble with condensation behaviors of a parameter $ψ_{2}$, which is similar to approaches in holographic theories. We find that more negative scalar mass makes the phase transition easier to happen. We also obtain an analytical relation $ψ_{2}\varpropto(T_{c}-T)^{1/2}$ around the critical phase transition points implying a second order phase transition. Besides the parameter $ψ_{2}$, we show that metric solutions can be used to disclose properties of transitions. In this work, we observe that phase transitions in a box are strikingly similar to holographic transitions in the AdS gravity and the similarity provides insights into holographic theories.

hep-th↗

arXiv2018

First principles investigation of pressure related quantum transport in pure black phosphorus devices

We propose a first-principles calculation to investigate the pressure-related transport properties of two kinds of pure monolayer black phosphorus (MBP) devices. Numerical results show that semi-conducting MBP can withstand a considerable compression pressure until it is transformed to be a conductor. The pure MBP devices can work as flexible electronic devices, "negative" pressure sensors, and "positive" pressure sensors depending on the chirality of BP and the magnitude of vertical pressure. When pressure is relatively small, the conductance is robust against the stress for zigzag MBP devices, while shows pressure-sensitive properties for armchair MBP devices. The pressure-stable property of zigzag MBP devices implies a good application prospects as flexible electronic devices, however, the distinct negative increase of conductance versus pressure indicates that armchair MBP devices can work as "negative" pressure sensors. When pressure is relatively large, both armchair MBP devices and zigzag MBP devices show favorable properties of "positive" pressure sensors, whose conductivities rise promptly versus pressure. The longer the device, the more the pressure sensitivity. Band alignment analysis and empirical Wentzel$-$Kramers$-$Brillouin (WKB) approximations are also performed to testify the tunneling process of pure MBP devices from first principles calculation.

cond-mat.mtrl-sci↗

arXiv2018

Electric Vehicle Driver Clustering using Statistical Model and Machine Learning

Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances. The randomness of the EV driver behaviors make the optimal charging or discharging scheduling even more difficult due to the uncertain charging session parameters. To minimize the impact of behavioral uncertainties, it is critical to develop effective methods to predict EV load for smart EV energy management. Using the EV smart charging infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of EV charging behaviors, based on which we proposed an EV user modeling technique which combines statistical analysis and machine learning approaches. Specifically, unsupervised clustering algorithm, and multilayer perceptron are applied to historical charging record to make the day-ahead EV parking and load prediction. Experimental results with cross-validation show that our model can achieve good performance for charging control scheduling and online EV load forecasting.

cs.LG↗

arXiv2018

Relieving the Tension between Weak Lensing and Cosmic Microwave Background with Interacting Dark Matter and Dark Energy Models

We constrain interacting dark matter and dark energy (IDMDE) models using a 450-degree-square cosmic shear data from the Kilo Degree Survey (KiDS) and the angular power spectra from Planck's latest cosmic microwave background measurements. We revisit the discordance problem in the standard Lambda cold dark matter ($Λ$CDM) model between weak lensing and Planck datasets and extend the discussion by introducing interacting dark sectors. The IDMDE models are found to be able to alleviate the discordance between KiDS and Planck as previously inferred from the $Λ$CDM model, and moderately favored by a combination of the two datasets.

astro-ph.CO↗

arXiv2018

Predictive Management of Electric Vehicles in a Community Microgrid

The charging load from Electric vehicles (EVs) is modeled as deferrable load, meaning that the power consumption can be shifted to different time windows to achieve various grid objectives. In local community scenarios, EVs are considered as controllable storage devices in a global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system, etc. However, the uncertainties in the driver behaviors have tremendous impact on the cost effectiveness of microgrid operations, which has not been fully explored in previous literature. In this paper, we propose a predictive EV management strategy in a community microgrid, and evaluate it using real-world datasets of system baseload, solar generation and EV charging behaviors. A two-stage operation model is established for cost-effective EV management, i.e. wholesale market participation in the first stage and load profile following in the second stage. Predictive control strategies, including receding horizon control, are adapted to solve the energy allocation problem in a decentralized fashion. The experimental results indicate the proposed approach can considerably reduce the total energy cost and decrease the ramping index of total system load up to 56.3%.

eess.SP↗

arXiv2018

Arbitrary-order functionally fitted energy-diminishing methods for gradient systems

It is well known that for gradient systems in Euclidean space or on a Riemannian manifold, the energy decreases monotonically along solutions. In this letter we derive and analyse functionally fitted energy-diminishing methods to preserve this key property of gradient systems. It is proved that the novel methods are unconditionally energy-diminishing and can achieve damping for very stiff gradient systems. We also show that the methods can be of arbitrarily high order and discuss their implementations. A numerical test is reported to illustrate the efficiency of the new methods in comparison with three existing numerical methods in the literature.

math.NA↗

arXiv2018

Looking for a new test of general relativity in the solar system

This paper discusses three matter-of-principle methods for measuring the general relativity correction to the Newtonian values of the position of collinear Lagrangian points L1 and L2 of the Sun-Earth-satellite system. All approaches are based on time measurements. The first approach exploits a pulsar emitting signals and two receiving antennas located at L1 and L2, respectively. The second method is based on a relativistic positioning system based on the Lagrangian points themselves. These first two methods depend crucially on the synchronization of clocks at L1 and L2. The third method combines a pulsar and an artificial emitter at the stable points L4 or L5 forming a basis for the positioning of the collinear points L1 and L2. Further possibilities are mentioned and the feasibility of the measurements is considered.

gr-qc↗

arXiv2018

Error analysis of one-stage explicit extended RKN integrators for semilinear wave equations

In this paper, we present an error analysis of one-stage explicit extended Runge--Kutta--Nyström integrators for semilinear wave equations. These equations are analysed by using spatial semidiscretizations with periodic boundary conditions in one space dimension. Optimal second-order convergence is proved without requiring Lipschitz continuous and higher regularity of the exact solution. Moreover, the error analysis is not restricted to the spectral semidiscretization in space.

math.NA↗

arXiv2017

Leveraging Text and Knowledge Bases for Triple Scoring: An Ensemble Approach - The BOKCHOY Triple Scorer at WSDM Cup 2017

We present our winning solution for the WSDM Cup 2017 triple scoring task. We devise an ensemble of four base scorers, so as to leverage the power of both text and knowledge bases for that task. Then we further refine the outputs of the ensemble by trigger word detection, achieving even better predictive accuracy. The code is available at https://github.com/wsdm-cup-2017/bokchoy.

cs.IR↗

arXiv2017

Functionally-fitted energy-preserving integrators for Poisson systems

In this paper, a new class of energy-preserving integrators is proposed and analysed for Poisson systems by using functionally-fitted technology. The integrators exactly preserve energy and have arbitrarily high order. It is shown that the proposed approach allows us to obtain the energy-preserving methods derived in BIT 51 (2011) by Cohen and Hairer and in J. Comput. Appl. Math. 236 (2012) by Brugnano et al. for Poisson systems. Furthermore, we study the sufficient conditions that ensure the existence of a unique solution and discuss the order of the new energy-preserving integrators.

math.NA↗

arXiv2017

Diagonal implicit symplectic ERKN methods for solving oscillatory Hamiltonian systems

This paper studies diagonal implicit symplectic extended Runge--Kutta--Nyström (ERKN) methods for solving the oscillatory Hamiltonian system $H(q,p)=\dfrac{1}{2}p^{T}p+\dfrac{1}{2}q^{T}Mq+U(q)$. Based on symplectic conditions and order conditions, we construct some diagonal implicit symplectic ERKN methods. The stability of the obtained methods is discussed. Three numerical experiments are carried out and the numerical results demonstrate the remarkable numerical behavior of the new diagonal implicit symplectic methods when applied to the oscillatory Hamiltonian system.

math.NA↗