arXiv subjects

Ping Wu

Publications and source records attributed to Ping Wu.

At least 37 records · Page 2Linked to original sources

arXiv2023

Unidirectional electron-phonon coupling as a "fingerprint'' of the nematic state in a kagome superconductor

Electronic nematicity has been commonly observed in juxtaposition with unconventional superconductivity. Understanding the nature of the nematic state, as well as its consequence on the electronic band structure and superconductivity, has become a pivotal focus in condensed matter physics. Here we use spectroscopic imaging-scanning tunneling microscopy to visualize how the interacting quasiparticles organize themselves in the nematic state of kagome superconductor CsV$_{3-x}$Ti$_x$Sb$_5$, in which twofold symmetric (C$_2$) quasiparticle scattering interference of the vanadium kagome bands emerges below the bulk nematic transition temperature (T$_{nem}$). Surprisingly, we find that the coupling to collective modes, i.e., the phonon, dramatically alters the electrons self-energy and renormalizes the Fermi velocity of the in-plane vanadium d$_{xy/x^2-y^2}$ bands only along the C$_2$ direction, making the low-energy dispersion and electron dynamics highly nonequivalent along the three lattice directions. The anti-correlation between T$_{nem}$ and the superconducting transition temperature upon Ti substitution further suggests a possible competition between superconductivity and electron nematicity in this series, with a principal superconducting gap opening on the same V bands once the nematic state is totally suppressed. The organizing principle of these quasiparticles provides essential information for understanding the interplay between charge density wave and superconductivity in these kagome superconductors, and also reveals a previously unexplored form that expands the landscape for modelling electronic nematicity in systems where electron correlations and lattice degree of freedom act in concert.

cond-mat.supr-con↗

arXiv2021

Three-dimensional charge density wave and robust zero-bias conductance peak inside the superconducting vortex core of a kagome superconductor CsV$_3$Sb$_5$

The transition-metal-based kagome metals provide a versatile platform for correlated topological phases hosting various electronic instabilities. While superconductivity is rare in layered kagome compounds, its interplay with nontrivial topology could offer an engaging space to realize exotic excitations of quasiparticles. Here, we use scanning tunneling microscopy (STM) to study a newly discovered Z$_2$ topological kagome metal CsV$_3$Sb$_5$ with a superconducting ground state. We observe charge modulation associated with the opening of an energy gap near the Fermi level. When across single-unit-cell surface step edges, the intensity of this charge modulation exhibits a π-phase shift, suggesting a three-dimensional 2$\times$2$\times$2 charge density wave ordering. Interestingly, a robust zero-bias conductance peak is observed inside the superconducting vortex core on the Cs 2$\times$2 surfaces that does not split in a large distance when moving away from the vortex center, resembling the Majorana bound states arising from the superconducting Dirac surface states in Bi$_2$Te$_3$/NbSe$_2$ heterostructures. Our findings establish CsV$_3$Sb$_5$ as a promising candidate for realizing exotic excitations at the confluence of nontrivial lattice geometry, topology and multiple electronic orders.

cond-mat.supr-con↗

arXiv2020

Fault-Tolerant Individual Pitch Control of Floating Offshore Wind Turbines via Subspace Predictive Repetitive Control

Individual Pitch Control (IPC) is an effective and widely-used strategy to mitigate blade loads in wind turbines. However, conventional IPC fails to cope with blade and actuator faults, and this situation may lead to an emergency shutdown and increased maintenance costs. In this paper, a Fault-Tolerant Individual Pitch Control (FTIPC) scheme is developed to accommodate these faults in Floating Offshore Wind Turbines (FOWTs), based on a Subspace Predictive Repetitive Control (SPRC) approach. To fulfill this goal, an online subspace identification paradigm is implemented to derive a linear approximation of the FOWT system dynamics. Then, a repetitive control law is formulated to attain load mitigation under operational conditions, both in healthy and faulty conditions. Since the excitation noise used for the online subspace identification may interfere with the nominal power generation of the wind turbine, a novel excitation technique is developed to restrict excitation at specific frequencies. Results show that significant load reductions are achieved by FTIPC, while effectively accommodating blade and actuator faults and while restricting the energy of the persistently exciting control action.

eess.SY↗

arXiv2020

Fast Adaptive Fault Accommodation in Floating Offshore Wind Turbines via Model-Based Fault Diagnosis and Subspace Predictive Repetitive Control

As Floating Offshore Wind Turbines (FOWTs) operate in deep waters and are subjected to stressful wind and wave induced loads, they are more prone than onshore counterparts to experience faults and failure. In particular, the pitch system may experience Pitch Actuator Stuck (PAS) type of faults, which will result in a complete loss of control authority. In this paper, a novel fast and adaptive solution is developed by integrating a model-based Fault Diagnosis (FD) scheme and the Subspace Predictive Repetitive Control (SPRC). The FD role is to quickly detect and isolate the failed pitch actuator. Based on the fault isolation results, a pre-tuned adaptive SPRC is switched online in place of the existing one, whose initial values of the parameters has been tuned offline to match the specific faulty case. After that, SPRC employs subspace identification to continuously identify a linear model of the wind turbine over a moving time window, and thereby formulate an adaptive control law to alleviate the PAS-induced loads. Results show that the developed architecture allows to achieve a considerable reduction of the PAS-induced blade loads. More importantly, the time needed to reduce the PAS-induced loads are significantly shortened, thus avoiding further damage to other components during the adaption time and allowing continued power generation.

eess.SY↗

arXiv2020

Fault Diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: a Mixed Model and Signal-based Approach

Floating Offshore Wind Turbines (FOWTs) operate in the harsh marine environment with limited accessibility and maintainability. Not only failures are more likely to occur than in land-based turbines, but also corrective maintenance is more expensive. In the present study, a mixed model and signal-based Fault Diagnosis (FD) architecture is developed to detect and isolate critical faults in FOWTs. More specifically, a model-based scheme is developed to detect and isolate the faults associated with the turbine system. It is based on a fault detection and approximation estimator and fault isolation estimators, with time-varying adaptive thresholds to guarantee against false-alarms. In addition, a signal-based scheme is established, within the proposed architecture, for detecting and isolating two representative mooring lines faults. For the purpose of verification, a 10MW FOWT benchmark is developed and its operating conditions, which contains predefined faults, are simulated by extending the high-fidelity simulator. Based on it, the effectiveness of the proposed architecture is illustrated. In addition, the advantages and limitations are discussed by comparing its fault detection to the results delivered by other approaches. Results show that the proposed architecture has the best performance in detecting and isolating the critical faults in FOWTs under diverse operating conditions.

eess.SY↗

arXiv2020

Fault Detection of the Mooring system in Floating Offshore Wind Turbines based on the Wave-excited Linear Model

Floating Offshore Wind Turbines (FOWTs) are more prone to suffer from faults and failures than bottom-fixed counterparts due to the severe wind and wave loads typical of deep water sites. In particular, mooring line faults may lead to unacceptably high operation and maintenance costs due to the limited accessibility of FOWTs. Detecting the mooring line faults is therefore critical, but the application of Fault Detection (FD) techniques has not been investigated yet. In this paper, an FD scheme based on a wave-excited linear model is developed to detect in a reliable way critical mooring line faults occurring at the fairlead and anchor ends. To reach the goal, a linear model of the FOWT is obtained by approximating the wave radiation and incident wave forces. Based on this model, an observer is built to predict the rigid rotor and platform dynamics. The FD scheme is thus implemented by comparing the Mahalanobis Distance of the observer prediction error against a probabilistic detection threshold. Numerical simulations in some selected fault scenarios show that the wave-excited linear model can predict the FOWT dynamics with good accuracy. Based on this, the FD scheme capabilities are demonstrated, showing that it is able to effectively detect two critical mooring line faults.

eess.SY↗

arXiv2019

A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images

Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the diagnostic criteria of PD are relatively well defined, the current medical imaging diagnostic procedures are expertise-demanding, and thus call for a higher-integrated AI-based diagnostic algorithm. Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD. Multiple modalities, including T1- weighted MRI and 11C-CFT PET, were used in the proposed framework. The reliability of this framework was then validated on a dataset from the PET center of Huashan Hospital, as the dataset contains paired T1-MRI and CFT-PET images of 18 Normal (NL) subjects and 49 PD subjects. Results: We obtained an accuracy of 100% for the PD/NL classification task, besides, we conducted several comparative experiments to validate the diagnosis ability of our framework. Conclusion: Through experiment we illustrate that (1) automatic segmentation has the same classification effect as the manual segmentation, (2) the multi-modality images generates a better prediction than single modality images, and (3) volume feature is shown to be irrelevant to PD diagnosis.

q-bio.QM↗

arXiv2018

Learning Optimal Deep Projection of $^{18}$F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.

cs.CV↗

arXiv2018

Suppression of Surfaces States at Cubic Perovskite (001) Surfaces by CO${_2}$ Adsorption

By using first-principles approach, the interaction of CO${_2}$ with (001) surfaces of six cubic ABO${_3}$ perovskites (A = Ba, Sr and B = Ti, Zr, Hf) is studied in detail. We show that CO${_2}$ adsorption results in the formation of highly stable CO${_3}$-like complexes with similar geometries for all investigated compounds. This reaction leads to the suppression of the surfaces states, opening the band gaps of the slab systems up to the corresponding bulk energy limits. For most AO-terminated ABO${_3}$(001) perovskite surfaces, a CO${_2}$ coverage of 0.25 was found to be sufficient to fully suppress the surface states, whereas the same effect can only be achieved at 0.50 CO${_2}$ coverage for the BO${_2}$ terminations. The largest band gap modulation among the AO-terminated surfaces was found for SrHfO${_3}$(001) and BaHfO${_3}$(001), whereas the most profound effect among the BO${_2}$ terminations was identified for SrTiO${_3}$(001) and BaTiO${_3}$(001). Based on these results and considering practical difficulties associated with measuring conductivity of highly resistive materials, TiO${_2}$-terminated SrTiO${_3}$(001) and BaTiO${_3}$(001) were identified as the most prospective candidates for chemiresistive CO${_2}$ sensing applications.

cond-mat.mtrl-sci↗

arXiv2018

A New Two-Dimensional Functional Material with Desirable Bandgap and Ultrahigh Carrier Mobility

Two-dimensional (2D) semiconductors with direct and modest bandgap and ultrahigh carrier mobility are highly desired functional materials for nanoelectronic applications. Herein, we predict that monolayer CaP3 is a new 2D functional material that possesses not only a direct bandgap of 1.15 eV (based on HSE06 computation), and also a very high electron mobility up to 19930 cm2 V-1 s-1, comparable to that of monolayer phosphorene. More remarkably, contrary to the bilayer phosphorene which possesses dramatically reduced carrier mobility compared to its monolayer counterpart, CaP3 bilayer possesses even higher electron mobility (22380 cm2 V-1 s-1) than its monolayer counterpart. The bandgap of 2D CaP3 can be tuned over a wide range from 1.15 to 0.37 eV (HSE06 values) through controlling the number of stacked CaP3 layers. Besides novel electronic properties, 2D CaP3 also exhibits optical absorption over the entire visible-light range. The combined novel electronic, charge mobility, and optical properties render 2D CaP3 an exciting functional material for future nanoelectronic and optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2017

Tailoring electronic properties of multilayer phosphorene by siliconization

Controlling a thickness dependence of electronic properties for two-dimensional (2d) materials is among primary goals for their large-scale applications. Herein, employing a first-principles computational approach, we predict that Si interaction with multilayer phosphorene (2d-P) can result in the formation of highly stable 2d-SiP and 2d-SiP$_2$ compounds with a weak interlayer interaction. Our analysis demonstrates that these systems are semiconductors with band gap energies that can be governed by varying the thickness and stacking order. Specifically, siliconization of phosphorene allows to design 2d-SiP$_x$ materials with significantly weaker thickness dependence of electronic properties than that in 2d-P and to develop ways for their tailoring. We also reveal the spatial dependence of electronic properties for 2d-SiP$_x$ highlighting difference in effective band gaps for different layers. Particularly, our results show that central layers in the multilayer 2d systems determine overall electronic properties, while the role of the outermost layers is noticeably smaller.

cond-mat.mtrl-sci↗

arXiv2017

Band Gap Modulation of SrTiO3 upon CO2 Adsorption

CO2 chemisorption on SrTiO3(001) surfaces is studied using ab initio calculations in order to establish new chemical sensing mechanisms. We find that CO2 adsorption opens the material band gap, however, while the adsorption on the TiO2-terminated surface neutralizes surface states at the valence band (VB) maximum, CO2 on the SrO-terminated surfaces suppresses the conduction band (CB) minimum. For the TiO2-terminated surface, the effect is explained by the passivation of dangling bonds, whereas for the SrO-terminated surface, the suppression is caused by the surface relaxation. Modulation of the VB states implies a more direct change in charge distribution, and thus the induced change in band gap is more prominent at the TiO2 termination. Further, we show that both CO2 adsorption energy and surface band gap are strongly dependent on CO2 coverage, suggesting that the observed effect can be utilized for sensing application in a wide range of CO2 concentrations.

cond-mat.mtrl-sci↗

arXiv2015

Evaluation of Phosphorene as Anode Material for Na-ion Batteries from First Principles

We systematically evaluate the prospects of a novel 2D nanomaterial, phosphorene, as anode for Na-ion batteries. Using first-principles calculations, we determine the Na adsorption energy, specific capacity and Na diffusion barriers on monolayer phosphorene. We examine the main trends in electronic structure and mechanical properties as a function of Na concentration. We find favorable Na-phosphorene interaction with theoretical capacity, exceeding those of alternative monolayer anodes for Na-ion batteries. We find that Na-phosphorene undergoes semiconductor-metal transition at high Na concentration. Our results show that Na diffusion on phosphorene is fast and anisotropic with the energy barrier of only 0.04 eV. Owing to the high capacity, good stability, excellent electrical conductivity and high Na mobility, monolayer phosphorene is a very promising anode material for Na-ion batteries. The calculated performance in terms of specific capacity and diffusion barrier is compared to other layered 2D electrode materials, such as graphene, MoS2, polysilane, etc.

cond-mat.mtrl-sci↗

arXiv2014

Field and long-term demonstration of a wide area quantum key distribution network

A wide area quantum key distribution (QKD) network deployed on communication infrastructures provided by China Mobile Ltd. is demonstrated. Three cities and two metropolitan area QKD networks were linked up to form the Hefei-Chaohu-Wuhu wide area QKD network with over 150 kilometers coverage area, in which Hefei metropolitan area QKD network was a typical full-mesh core network to offer all-to-all interconnections, and Wuhu metropolitan area QKD network was a representative quantum access network with point-to-multipoint configuration. The whole wide area QKD network ran for more than 5000 hours, from 21 December 2011 to 19 July 2012, and part of the network stopped until last December. To adapt to the complex and volatile field environment, the Faraday-Michelson QKD system with several stability measures was adopted when we designed QKD devices. Through standardized design of QKD devices, resolution of symmetry problem of QKD devices, and seamless switching in dynamic QKD network, we realized the effective integration between point-to-point QKD techniques and networking schemes.

quant-ph↗

arXiv2014

Bilayer Graphene Growth via a Penetration Mechanism

From both fundamental and technical points of view, a precise control of the layer number of graphene samples is very important. To reach this goal, atomic scale mechanisms of multilayer graphene growth on metal surfaces should be understood. Although it is a geometrically favorable pathway to transport carbon species to interface and then form a new graphene layer there, penetration of a graphene overlayer is not a chemically straightforward process. In this study, the possibility of different active species to penetrate a graphene overlayer on Cu(111) surface is investigated based on first principles calculations. It is found that carbon atom penetration can be realized via an atom exchange process, which leads to a new graphene growth mechanism. Based on this result, a bilayer graphene growth protocol is proposed to obtain high quality samples. Such a penetration possibility also provides a great flexibility for designed growth of graphene nanostructures.

cond-mat.mtrl-sci↗

arXiv2013

Orientation Sensitive Nonlinear Growth of Graphene: A Geometry-determined Epitaxial Growth Mechanism

Although the corresponding carbon-metal interactions can be very different, a similar nonlinear growth behavior of graphene has been observed for different metal substrates. To understand this interesting experimental observation, a multiscale $\lq\lq$standing-on-the-front" kinetic Monte Carlo study is performed. An extraordinary robust geometry effect is identified, which solely determines the growth kinetics and makes the details of carbon-metal interaction not relevant at all. Based on such a geometry-determined mechanism, epitaxial growth behavior of graphene can be easily predicted in many cases. As an example, an orientation-sensitive growth kinetics of graphene on Ir(111) surface has been studied. Our results demonstrate that lattice mismatch pattern at the atomic level plays an important role for macroscopic epitaxial growth.

cond-mat.mtrl-sci↗

arXiv2012

Efficient estimation of moments in linear mixed models

In the linear random effects model, when distributional assumptions such as normality of the error variables cannot be justified, moments may serve as alternatives to describe relevant distributions in neighborhoods of their means. Generally, estimators may be obtained as solutions of estimating equations. It turns out that there may be several equations, each of them leading to consistent estimators, in which case finding the efficient estimator becomes a crucial problem. In this paper, we systematically study estimation of moments of the errors and random effects in linear mixed models.

math.ST↗

arXiv2011

Atomistic Mechanisms of Nonlinear Graphene Growth on Ir Surface

As a two-dimensional material, graphene can be naturally obtained via epitaxial growth on a suitable substrate. Growth condition optimization usually requires an atomistic level understanding of the growth mechanism. In this article, we perform a mechanistic study about graphene growth on Ir(111) surface by combining first principles calculations and kinetic Monte Carlo (kMC) simulations. Small carbon clusters on the Ir surface are checked first. On terraces, arching chain configurations are favorable in energy and they are also of relatively high mobilities. At steps, some magic two-dimensional compact structures are identified, which show clear relevance to the nucleation process. Attachment of carbon species to a graphene edge is then studied. Due to the effect of substrate, at some edge sites, atomic carbon attachment becomes thermodynamically unfavorable. Graphene growth at these difficult sites has to proceed via cluster attachment, which is the growth rate determining step. Based on such an inhomogeneous growth picture, kMC simulations are made possible by successfully separating different timescales, and they well reproduce the experimentally observed nonlinear kinetics. Different growth rates and nonlinear behaviors are predicted for different graphene orientations, which is consistent with available experimental results. Importantly, as a phenomenon originated from lattice mismatch, inhomogeneity revealed in this case is expected to be quite universal and it should also make important roles in many other hetero-epitaxial systems.

cond-mat.mtrl-sci↗