arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 1,657 records · Page 92Linked to original sources

arXiv2014

Directly grown monolayer MoS2 on Au foils as efficient hydrogen evolution catalysts

Synthesis of monolayer MoS2 is essential for fulfilling the potential of MoS2 in catalysis, optoelectronics and valleytronics, etc. Herein, we report for the first time the scalable growth of high quality, domain size tunable (edge length from ~ 200 nm to 50 μm), strictly monolayer MoS2 on commercially available Au foils, via a low pressure chemical vapor deposition method. The nanosized triangular MoS2 flakes on Au foils was proved to be an excellent electrocatalyst for hydrogen evolution reaction (HER), featured by a rather low Tafel slope (61 mV/decade) and a supreme exchange current density (38.1 μA/cm2). The abundant active edge sites and the excellent electron coupling between MoS2 and Au foils account for the extraordinary HER activity. Our work presents a sound proof that strictly monolayer MoS2 assembled on a well selected electrode can manifest comparable or even superior HER property than that of nanoparticles or few-layer MoS2 electrocatalyst.

cond-mat.mtrl-sci↗

arXiv2014

A Formal Analysis of Required Cooperation in Multi-agent Planning

Research on multi-agent planning has been popular in recent years. While previous research has been motivated by the understanding that, through cooperation, multi-agent systems can achieve tasks that are unachievable by single-agent systems, there are no formal characterizations of situations where cooperation is required to achieve a goal, thus warranting the application of multi-agent systems. In this paper, we provide such a formal discussion from the planning aspect. We first show that determining whether there is required cooperation (RC) is intractable is general. Then, by dividing the problems that require cooperation (referred to as RC problems) into two classes -- problems with heterogeneous and homogeneous agents, we aim to identify all the conditions that can cause RC in these two classes. We establish that when none of these identified conditions hold, the problem is single-agent solvable. Furthermore, with a few assumptions, we provide an upper bound on the minimum number of agents required for RC problems with homogeneous agents. This study not only provides new insights into multi-agent planning, but also has many applications. For example, in human-robot teaming, when a robot cannot achieve a task, it may be due to RC. In such cases, the human teammate should be informed and, consequently, coordinate with other available robots for a solution.

cs.AI↗

arXiv2014

Gravitational-wave radiation from double compact objects with eLISA in the Galaxy

The phase of in-spiral of double compact objects (DCOs: NS+WD, NS+NS, BH+NS, and BH+BH binaries) in the disk field population of the Galaxy provides a potential source in the frequency range from $10^{-4}$ to 0.1 Hz, which can be detected by the European New Gravitational Observatory (NGO: eLISA is derived from the previous LISA proposal) project. In this frequency range, much stronger gravitational wave (GW) radiation can be obtained from DCO sources because they possess more mass than other compact binaries (e.g., close double white dwarfs). In this study, we aim to calculate the gravitational wave signals from the resolvable DCO sources in the Galaxy using a binary population synthesis approach, and to carry out physical properties of these binaries using Monte Carlo simulations. Combining the sensitivity curve of the eLISA detector and a confusion-limited noise floor of close double white dwarfs, we find that only a handful of DCO sources can be detected by the eLISA detector. The detectable number of DCO sources reaches 160, in the context of low-frequency eLISA observations we find that the number of NS+WD, NS+NS, BH+NS, and BH+BH are 132, 16, 3, and 6, respectively.

astro-ph.SR↗

arXiv2014

Electricity Market Forecasting via Low-Rank Multi-Kernel Learning

The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure. Aligned to this end, modern statistical learning tools are leveraged here for electricity market inference. Day-ahead price forecasting is cast as a low-rank kernel learning problem. Uniquely exploiting the market clearing process, congestion patterns are modeled as rank-one components in the matrix of spatio-temporally varying prices. Through a novel nuclear norm-based regularization, kernels across pricing nodes and hours can be systematically selected. Even though market-wide forecasting is beneficial from a learning perspective, it involves processing high-dimensional market data. The latter becomes possible after devising a block-coordinate descent algorithm for solving the non-convex optimization problem involved. The algorithm utilizes results from block-sparse vector recovery and is guaranteed to converge to a stationary point. Numerical tests on real data from the Midwest ISO (MISO) market corroborate the prediction accuracy, computational efficiency, and the interpretative merits of the developed approach over existing alternatives.

stat.ML↗

arXiv2014

Efficient Decentralized Economic Dispatch for Microgrids with Wind Power Integration

Decentralized energy management is of paramount importance in smart microgrids with renewables for various reasons including environmental friendliness, reduced communication overhead, and resilience to failures. In this context, the present work deals with distributed economic dispatch and demand response initiatives for grid-connected microgrids with high-penetration of wind power. To cope with the challenge of the wind's intrinsically stochastic availability, a novel energy planning approach involving the actual wind energy as well as the energy traded with the main grid, is introduced. A stochastic optimization problem is formulated to minimize the microgrid net cost, which includes conventional generation cost as well as the expected transaction cost incurred by wind uncertainty. To bypass the prohibitively high-dimensional integration involved, an efficient sample average approximation method is utilized to obtain a solver with guaranteed convergence. Leveraging the special infrastructure of the microgrid, a decentralized algorithm is further developed via the alternating direction method of multipliers. Case studies are tested to corroborate the merits of the novel approaches.

math.OC↗

arXiv2014

An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data

Crowdsourcing platforms offer a practical solution to the problem of affordably annotating large datasets for training supervised classifiers. Unfortunately, poor worker performance frequently threatens to compromise annotation reliability, and requesting multiple labels for every instance can lead to large cost increases without guaranteeing good results. Minimizing the required training samples using an active learning selection procedure reduces the labeling requirement but can jeopardize classifier training by focusing on erroneous annotations. This paper presents an active learning approach in which worker performance, task difficulty, and annotation reliability are jointly estimated and used to compute the risk function guiding the sample selection procedure. We demonstrate that the proposed approach, which employs active learning with Bayesian networks, significantly improves training accuracy and correctly ranks the expertise of unknown labelers in the presence of annotation noise.

cs.LG↗

arXiv2014

Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real EEG data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from ten healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.

stat.ML↗

arXiv2013

Robust Optimal Power Flow with Wind Integration Using Conditional Value-at-Risk

Integrating renewable energy into the power grid requires intelligent risk-aware dispatch accounting for the stochastic availability of renewables. Toward achieving this goal, a robust DC optimal flow problem is developed in the present paper for power systems with a high penetration of wind energy. The optimal dispatch is obtained as the solution to a convex program with a suitable regularizer, which is able to mitigate the potentially high risk of inadequate wind power. The regularizer is constructed based on the energy transaction cost using conditional value-at-risk (CVaR). Bypassing the prohibitive high-dimensional integral, the distribution-free sample average approximation method is efficiently utilized for solving the resulting optimization problem. Case studies are reported to corroborate the efficacy of the novel model and approach tested on the IEEE 30-bus benchmark system with real operation data from seven wind farms.

math.OC↗

arXiv2013

Large deviations in the reinforced random walk model on trees

In this paper, we consider the linearly reinforced and the once-reinforced random walk models in the transient phase on trees. We show the large deviations for the upper tails for both models. We also show the exponential decay for the lower tail in the once-reinforced random walk model. On the other hand, the lower tail is in polynomial decay for the linearly reinforced random walk model.

math.PR↗

arXiv2013

Disaggregated Bundle Methods for Distributed Market Clearing in Power Networks

A fast distributed approach is developed for the market clearing with large-scale demand response in electric power networks. In addition to conventional supply bids, demand offers from aggregators serving large numbers of residential smart appliances with different energy constraints are incorporated. Leveraging the Lagrangian relaxation based dual decomposition, the resulting optimization problem is decomposed into separate subproblems, and then solved in a distributed fashion by the market operator and each aggregator aided by the end-user smart meters. A disaggregated bundle method is adapted for solving the dual problem with a separable structure. Compared with the conventional dual update algorithms, the proposed approach exhibits faster convergence speed, which results in reduced communication overhead. Numerical results corroborate the effectiveness of the novel approach.

math.OC↗

arXiv2013

Precisely aligned graphene grown on hexagonal boron nitride by catalyst free chemical vapor deposition

To grow precisely aligned graphene on h-BN without metal catalyst is extremely important, which allows for intriguing physical properties and devices of graphene/h-BN hetero-structure to be studied in a controllable manner. In this report, such hetero-structures were fabricated and investigated by atomic resolution scanning probe microscopy. Moirre patterns are observed and the sensitivity of moirre interferometry proves that the graphene grains can align precisely with the underlying h-BN lattice within an error of less than 0.05 degree. The occurrence of moirre pattern clearly indicates that the graphene locks into h-BN via van der Waals epitaxy with its interfacial stress greatly released. It is worthy to note that the edges of the graphene grains are primarily oriented along the armchair direction. The field effect mobility in such graphene flakes exceeds 20,000 cm2/V.s at ambient condition. This work opens the door of atomic engineering of graphene on h-BN, and sheds light on fundamental research as well as electronic applications based on graphene/h-BN hetero-structure.

cond-mat.mtrl-sci↗

arXiv2013

Ensemble of Distributed Learners for Online Classification of Dynamic Data Streams

We present an efficient distributed online learning scheme to classify data captured from distributed, heterogeneous, and dynamic data sources. Our scheme consists of multiple distributed local learners, that analyze different streams of data that are correlated to a common event that needs to be classified. Each learner uses a local classifier to make a local prediction. The local predictions are then collected by each learner and combined using a weighted majority rule to output the final prediction. We propose a novel online ensemble learning algorithm to update the aggregation rule in order to adapt to the underlying data dynamics. We rigorously determine a bound for the worst case misclassification probability of our algorithm which depends on the misclassification probabilities of the best static aggregation rule, and of the best local classifier. Importantly, the worst case misclassification probability of our algorithm tends asymptotically to 0 if the misclassification probability of the best static aggregation rule or the misclassification probability of the best local classifier tend to 0. Then we extend our algorithm to address challenges specific to the distributed implementation and we prove new bounds that apply to these settings. Finally, we test our scheme by performing an evaluation study on several data sets. When applied to data sets widely used by the literature dealing with dynamic data streams and concept drift, our scheme exhibits performance gains ranging from 34% to 71% with respect to state of the art solutions.

cs.LG↗

arXiv2013

Incipient infinite cluster in 2D Ising percolation

We consider the percolation problem in the high-temperature Ising model on the two-dimensional square lattice at or near critical external fields. The incipient infinite cluster (IIC) measure in the sense of Kesten is constructed. As a consequence, we can obtain some geometric properties of IIC. The result holds also for the triangular lattice.

math.PR↗

arXiv2013

Risk-Aware Management of Distributed Energy Resources

High wind energy penetration critically challenges the economic dispatch of current and future power systems. Supply and demand must be balanced at every bus of the grid, while respecting transmission line ratings and accounting for the stochastic nature of renewable energy sources. Aligned to that goal, a network-constrained economic dispatch is developed in this paper. To account for the uncertainty of renewable energy forecasts, wind farm schedules are determined so that they can be delivered over the transmission network with a prescribed probability. Given that the distribution of wind power forecasts is rarely known, and/or uncertainties may yield non-convex feasible sets for the power schedules, a scenario approximation technique using Monte Carlo sampling is pursued. Upon utilizing the structure of the DC optimal power flow (OPF), a distribution-free convex problem formulation is derived whose complexity scales well with the wind forecast sample size. The efficacy of this novel approach is evaluated over the IEEE 30-bus power grid benchmark after including real operation data from seven wind farms.

math.OC↗

arXiv2013

Robust Energy Management for Microgrids With High-Penetration Renewables

Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supply-demand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.

math.OC↗

arXiv2013

Nonlinear light scattering in molecules triggered by an impulsive X-ray Raman process

The time-and-frequency resolved nonlinear light scattering (NLS) signals from a time evolving charge distribution of valence electrons prepared by impulsive X-ray pulses are calculated using a superoperator Green's function formalism. The signal consists of a coherent $\sim N^2$-scaling difference frequency generation and an incoherent fluorescence $\sim N$-scaling component where $N$ is the number of active molecules. The former is given by the classical Larmor formula based on the time-dependent charge density. The latter requires additional information about the electronic structure and may be recast in terms of transition amplitudes representing quantum matter pathways.

quant-ph↗

arXiv2013

The gravitational wave signal from isolated objects

According to the theoretical study, a deformation object (e.g., a spinning non-axisymmetric pulsar star) will radiate a gravitational wave (GW) signal during an accelaration motion process by LIGO science project. These types of disturbance sources with a large bump or dimple on the equator would survive and be identifiable as GW sources. In this work, we aim to provide a method for exploring GW radiation from isolated neutron stars (NSs) with deformation state using some observational results, which can be confirmed by the next LIGO project. Combination with the properties in observation results (e.g., PSR J1748-2446, PSR 1828-11 and Cygnus X-1), based on a binary population synthesis (BPS) approach we give a numerical GW radiation under the assumption that NS should have non-axisymmetric and give the results of energy spectrum. We find that the GW luminosity of $L_{GW}$ can be changed from about $10^{40}\rm erg/s$-- $10^{55}\rm erg/s$.

astro-ph.SR↗

arXiv2013

Time Dependent Quantum Transport Through Graphene Nanoribbons

Time-dependent quantum transport for graphene nanoribbons (GNR) are calculated by the hierarchical equation of motion (HEOM) method based on the nonequilibrium Green's function (NEGF) theory (Xie et.al, J. Chem. Phys. 137, 044113, 2012). In this paper, a new steady state calculation technique is introduced and accelerated by the contour integration, which is suitable for large systems. Three Lorentzian fitting schemes for the self-energy matrices are developed based on the nonlinear least square method. Within these schemes, the number of Lorentzians is effectively reduced and the fitting results are good and convergent. With these two developments in HEOM, we have calculated the transient currents in GNR. We find a new type of edge state with delta-function-like density of states in many semi-infinite armchair-type GNR.

cond-mat.mes-hall↗