arXiv subjects

Ping Lu

Publications and source records attributed to Ping Lu.

At least 37 records · Page 2Linked to original sources

arXiv2023

One In-Situ Extraction Algorithm for Monitoring Bunch-by-Bunch Profile in the Storage Ring

As the brightness of synchrotron radiation (SR) light sources improves, the operation stability of light sources is weakened. To explore various beam instability related issues in light sources, one transverse beam diagnostics system for bunch-by-bunch (BbB) profile measurement has been established at Hefei Light Source-II (HLS-II). In this paper, one in-situ extraction algorithm in the data processing backend of the system is developed for BbB profiles, so as to provide important beam information of the machine operation in time.

physics.acc-ph↗

arXiv2022

Singularity as a diagnostic for secondary eyewall occurrence in tropical cyclones

Secondary eyewalls occur in 70% of major tropical cyclones (TCs), and are associated with rapid changes in storm intensity and rapid broadening of strong winds. While mechanisms of secondary eyewall formation have been investigated from various perspectives, the explicit conditions on which secondary eyewalls occur in TCs remain veiled, leaving substantial uncertainties in TC intensity forecast, especially for the most extreme events. In this study, we present a simple diagnostic, in form of a singularity, for secondary eyewall occurrence in TCs. The diagnostic is solely dependent on three basic storm characteristics (the maximum wind speed, the radius of maximum wind, and the latitude) and shown to compare well with satellite observations. It provides a valuable tool to improve the understanding, modeling and risk assessment of secondary eyewall storms.

physics.ao-ph↗

arXiv2022

Channel Estimation for Wideband MmWave MIMO OFDM System Exploiting Block Sparsity

In this letter, we investigate time-domain channel estimation for wideband millimeter wave (mmWave) MIMO OFDM system. By transmitting frequency-domain pilot symbols as well as different beamforming vectors, we observe that the time-domain mmWave MIMO channels exhibit channel delay sparsity and especially block sparsity among different spatial directions. Then we propose a time-domain channel estimation exploiting block sparsity (TDCEBS) scheme, which always aims at finding the best nonzero block achieving the largest projection of the residue at each iterations. In particular, we evaluate the system performance using the QuaDRiGa which is recommended by 5G New Radio to generate wideband mmWave MIMO channels. The effectiveness of the proposed TDCEBS scheme is verified by the simulation results, as the proposed scheme outperforms the existing schemes.

cs.IT↗

arXiv2022

Mechanical response to tension and torque of molecular chains via statistically interacting particles associated with extension, contraction, twist, and supercoiling

A methodology for the statistical mechanical analysis of polymeric chains under tension introduced previously is extended to include torque. The response of individual bonds between monomers or of entire groups of monomers to a combination of tension and torque involves, in the framework of this method of analysis, the (thermal or mechanical) activation of a specific mix of statistically interacting particles carrying quanta of extension or contraction and quanta of twist or supercoiling. The methodology, which is elucidated in applications of increasing complexity, is capable of describing the conversion between twist chirality and plectonemic chirality in quasistatic processes. The control variables are force or extension and torque or linkage (a combination of twist and writhe). The versatility of this approach is demonstrated in two applications relevant and promising for double-stranded DNA under controlled tension and torque. One application describes conformational transformations between (native) B-DNA, (underwound) S-DNA, and (overwound) P-DNA in accord with experimental data. The other application describes how the conversion between a twisted chain and a supercoiled chain accommodates variations of linkage and excess length in a buckling transition.

cond-mat.soft↗

arXiv2021

Atomic step disorder on polycrystalline surfaces leads to spatially inhomogeneous work functions

Structural disorder causes materials surface electronic properties, e.g. work function ($ϕ$) to vary spatially, yet it is challenging to prove exact causal relationships to underlying ensemble disorder, e.g. roughness or granularity. For polycrystalline Pt, nanoscale resolution photoemission threshold mapping reveals a spatially varying $ϕ= 5.70\pm 0.03$~eV over a distribution of (111) textured vicinal grain surfaces prepared by sputter deposition and annealing. With regard to field emission and related phenomena, e.g. vacuum arc initiation, a salient feature of the $ϕ$ distribution is that it is skewed with a long tail to values down to 5.4 eV, i.e. far below the mean, which is exponentially impactful to field emission via the Fowler-Nordheim relation. We show that the $ϕ$ spatial variation and distribution can be explained by ensemble variations of granular tilts and surface slopes via a Smoluchowski smoothing model wherein local $ϕ$ variations result from spatially varying densities of electric dipole moments, intrinsic to atomic steps, that locally modify $ϕ$. Atomic step-terrace structure is confirmed with scanning tunneling microscopy (STM) at several locations on our surfaces, and prior works showed STM evidence for atomic step dipoles at various metal surfaces. From our model, we find an atomic step edge dipole $μ=0.12$ D/edge atom, which is comparable to values reported in studies that utilized other methods and materials. Our results elucidate a connection between macroscopic $ϕ$ and nanostructure that may contribute to the spread of reported $ϕ$ for Pt and other surfaces, and may be useful toward more complete descriptions of polycrystalline metals in models of field emission and other related vacuum electronics phenomena, e.g. arc initiation.

cond-mat.mtrl-sci↗

arXiv2021

Hefei light source storage ring bunch by bunch 3D position measuring system is introduced and a preliminary study is shown

The 4 electrode signals of the beam position detector(BPM) of the STORAGE ring of HEFEI Light Source are directly connected to the domestic oscilloscope with 12bit resolution, 10Gsps sampling rate and 2GHz bandwidth. The acquisition program is run on the cloud host under the Zstack architecture, triggering to read a group of waveforms of 500us each time. The X (horizontal position), Y (vertical position) and Z (longitudinal phase) information of the centroid of 45 bunches 2266 cycles were extracted. The resolution of X and Y of each bunches was about 5um, and the resolution of Z was about 0.5ps. The update period of online operation was about 7 seconds. The three-dimensional tune of each bunch can be obtained by analyzing the three-dimensional position information of each bunch in normal operation. The spectrum peak of the transverse quadrupole oscillation can be observed by analyzing the button and strip electrode signals when beam is incentived.

physics.ins-det↗

arXiv2020

Low Thermal Budget High-k/Metal Surface Gate for Buried Donor-Based Devices

Atomic precision advanced manufacturing (APAM) offers creation of donor devices in an atomically thin layer doped beyond the solid solubility limit, enabling unique device physics. This presents an opportunity to use APAM as a pathfinding platform to investigate digital electronics at the atomic limit. Scaling to smaller transistors is increasingly difficult and expensive, necessitating the investigation of alternative fabrication paths that extend to the atomic scale. APAM donor devices can be created using a scanning tunneling microscope (STM). However, these devices are not currently compatible with industry standard fabrication processes. There exists a tradeoff between low thermal budget (LT) processes to limit dopant diffusion and high thermal budget (HT) processes to grow defect-free layers of epitaxial Si and gate oxide. To this end, we have developed an LT epitaxial Si cap and LT deposited Al2O3 gate oxide integrated with an atomically precise single-electron transistor (SET) that we use as an electrometer to characterize the quality of the gate stack. The surface-gated SET exhibits the expected Coulomb blockade behavior. However, the leverage of the gate over the SET is limited by defects in the layers above the SET, including interfaces between the Si and oxide, and structural and chemical defects in the Si cap. We propose a more sophisticated gate stack and process flow that is predicted to improve performance in future atomic precision devices.

cond-mat.mtrl-sci↗

arXiv2020

Applying configurational complexity to the 2D Ruddlesden-Popper crystal structure

The 2D layered Ruddlesden-Popper crystal structure can host a broad range of functionally important behaviors. Here we establish extraordinary configurational disorder in a two dimensional layered Ruddlesden-Popper (RP) structure using entropy stabilization assisted synthesis. A protype A2CuO4 RP cuprate oxide with five components (La, Pr, Nd, Sm, Eu) on the A-site sublattice is designed and fabricated into epitaxial single crystal films using pulsed laser deposition. By comparing (La0.2Pr0.2Nd0.2Sm0.2Eu0.2)2CuO4 crystals grown under identical conditions but different substrates, it is found that heteroepitaxial strain plays an important role in crystal phase formation. When grown on a near lattice matched substrate, the high entropy oxide film features a T'-type RP structure with uniform A-site cation mixing and square-planar CuO4 units, however, growing under strong compressive strain results in a single crystal non-RP cubic phase consistent with a CuX2O4 spinel structure. These observations are made with a range of combined characterizations using X-ray diffraction, atomic-resolution scanning transmission electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray absorption spectroscopy measurements. Designing configurational complexity and moving between 2D layered RP and 3D cubic crystal structures in this class of cuprate materials opens many opportunities for new design strategies related to magnetoresistance, unconventional superconductivity, ferroelectricity, catalysis, and ion transport.

cond-mat.mtrl-sci↗

arXiv2020

Molecular chains under tension: Thermal and mechanical activation of statistically interacting extension and contraction particles

This work introduces a methodology for the statistical mechanical analysis of polymeric chains under tension controlled by optical or magnetic tweezers at thermal equilibrium with an embedding fluid medium. The response of single bonds between monomers or of entire groups of monomers to tension is governed by the activation of statistically interacting particles representing quanta of extension or contraction. This method of analysis is capable of describing thermal unbending of the freely jointed or wormlike chain kind, linear or nonlinear contour elasticity, and structural transformations including effects of cooperativity. The versatility of this approach is demonstrated in an application to double-stranded DNA undergoing torsionally unconstrained stretching across three regimes of mechanical response including an overstretching transition. The three-regime force-extension characteristic, derived from a single free-energy expression, accurately matches empirical evidence.

cond-mat.soft↗

arXiv2019

Attenuating Random Noise in Seismic Data by a Deep Learning Approach

In the geophysical field, seismic noise attenuation has been considered as a critical and long-standing problem, especially for the pre-stack data processing. Here, we propose a model to leverage the deep-learning model for this task. Rather than directly applying an existing de-noising model from ordinary images to the seismic data, we have designed a particular deep-learning model, based on residual neural networks. It is named as N2N-Seismic, which has a strong ability to recover the seismic signals back to intact condition with the preservation of primary signals. The proposed model, achieving with great success in attenuating noise, has been tested on two different seismic datasets. Several metrics show that our method outperforms conventional approaches in terms of Signal-to-Noise-Ratio, Mean-Squared-Error, Phase Spectrum, etc. Moreover, robust tests in terms of effectively removing random noise from any dataset with strong and weak noises have been extensively scrutinized in making sure that the proposed model is able to maintain a good level of adaptation while dealing with large variations of noise characteristics and intensities.

cs.LG↗

arXiv2019

Uncompensated Polarization in Incommensurate Modulations of Perovskite Antiferroelectrics

Complex polar structures of incommensurate modulations (ICMs) are revealed in chemically modified PbZrO$_3$ perovskite antiferroelectrics using advanced transmission electron microscopy techniques. The Pb-cation displacements, previously assumed to arrange in a fully-compensated antiparallel fashion, are found to be either antiparallel but with different magnitudes, or in a nearly orthogonal arrangement in adjacent stripes in the ICMs. Ab initio calculations corroborate the low-energy state of these arrangements. Our discovery corrects the atomic understanding of ICMs in PbZrO$_3$-based perovskite antiferroelectrics.

cond-mat.mtrl-sci↗

arXiv2019

Enhanced Seismic Imaging with Predictive Neural Networks for Geophysics

We propose a predictive neural network architecture that can be utilized to update reference velocity models as inputs to the full waveform inversion. Deep learning models are explored to augment velocity model building workflows during processing the 3D seismic volume in salt-prone environments. Specifically, a neural network architecture, with 3D convolutional, de-convolutional layers, and 3D max-pooling, is designed to take standard amplitude 3D seismic volumes as an input. Enhanced data augmentations through generative adversarial networks and a weighted loss function enable the network to train with few sparsely annotated slices. Batch normalization is also applied for faster convergence. A 3D probability cube for salt bodies and inclusions is generated through ensembles of predictions from multiple models in order to reduce variance. Velocity models inferred from the proposed networks provide opportunities for FWI forward models to converge faster with an initial condition closer to the true model. In addition, in each iteration step, the probability cubes of salt bodies and inclusions inferred from the proposed networks can be used as a regularization term within the FWI forward modelling, which may result in an improved velocity model estimation while the output of seismic migration can be utilized as an input of the 3D neural network for subsequent iterations.

eess.IV↗

arXiv2019

Deep Learning Realm for Geophysics: Seismic Acquisition, Processing, Interpretation, and Inversion

Applying deep-learning models to geophysical applications has attracted special attentions during the past a couple of years. There are several papers published in this domain involving with different topics primarily focusing on synthetic data. Based on the presented results, reaching the satisfaction from geophysicists is largely far away the performance of the conventional algorithms. Moreover, it is extremely hard to find documents with detailed illustrations about what are the best practices that researchers should follow regarding how to design appropriate deep-learning models to effectively and precisely tackle problems relevant to the field data. This paper serves as a summarization to demonstrate successful stories and share with extensive experiences we have gained during past several years in the process of designing and deploying of deep-learning models to the geophysical projects in a large scale. Four different disciplines are discussed individually with seismic acquisition, processing, interpretation, and inversion. Finally, special attentions about designing an effective deep-neural-networks especially for geophysics are discussed.

physics.geo-ph↗

arXiv2019

Reservoir Characterizations by Deep-Learning Model: Detection of True Sand Thickness

It is an extremely challenging task to precisely identify the reservoir characteristics directly from seismic data due to its inherit nature. Here, we successfully design a deep-learning model integrated with synthetic wedge models to overcome the geophysical limitation while performing the interpretation of thickness of sand bodies from a low resolution of seismic data. Through understanding and learning the geophysical relationship between seismic responses and corresponding indicators for sand thickness, the deep-learning model could automatically detect the locations of top and base of sand bodies identified from seismic traces by precisely revealing the lithology distribution. Quantitative analysis and extensive validations from wedge models and field data prove the robustness of the proposed methodologies. The true sand thickness, identified from the deep-learning model, provides an extremely useful guidance in enhancing the interpretation of lithological and stratigraphic information from seismic data. In addition, the proposed deep-learning approach eliminates the risks of over- and under-estimation of net-to-gross with a significant improvement with respect to the accuracy.

physics.geo-ph↗

arXiv2019

Enhancement of seismic imaging: An innovative deep learning approach

Enhancing the frequency bandwidth of the seismic data is always the pursuance at the geophysical community. High resolution of seismic data provides the key resource to extract detailed stratigraphic knowledge. Here, a novel approach, based on deep learning model, is introduced by extracting reflections from well log data to broaden spectrum bandwidth of seismic data through boosting low and high frequencies. The corresponding improvement is observed from the enhancement of resolution of seismic data as well as elimination of sidelobe artifacts from seismic wavelets. During the training stage of deep learning model, geo-spatial information by taking consideration of multiple wells simultaneously is fully guaranteed, which assures that laterally and vertically geological information are constrained by and accurate away from the well controls during the inversion procedure. Extensive experiments prove that the enhanced seismic data is consistent with well log information, and honors rock property relationships defined from the wells at given locations. Uncertainty analysis could also be quantitatively assessed to determine the possibilities of a range of seismic responses by leveraging the outputs from the proposed approach.

eess.IV↗

arXiv2019

Learning-based Hamilton-Jacobi-Bellman Methods for Optimal Control

Many optimal control problems are formulated as two point boundary value problems (TPBVPs) with conditions of optimality derived from the Hamilton-Jacobi-Bellman (HJB) equations. In most cases, it is challenging to solve HJBs due to the difficulty of guessing the adjoint variables. This paper proposes two learning-based approaches to find the initial guess of adjoint variables in real-time, which can be applied to solve general TPBVPs. For cases with database of solutions and corresponding adjoint variables of a TPBVP under varying boundary conditions, a supervised learning method is applied to learn the HJB solutions off-line. After obtaining a trained neural network from supervised learning, we are able to find proper initial adjoint variables for given boundary conditions in real-time. However, when validated solutions of TPBVPs are not available, the reinforcement learning method is applied to solve HJB by constructing a neural network, defining a reward function, and setting appropriate super parameters. The reinforcement learning based HJB method can learn how to find accurate adjoint variables via an updating neural network. Finally, both learning approaches are implemented in classical optimal control problems to verify the effectiveness of the learning based HJB methods.

math.OC↗

arXiv2019

Vector and Line Quantization for Billion-scale Similarity Search on GPUs

Billion-scale high-dimensional approximate nearest neighbour (ANN) search has become an important problem for searching similar objects among the vast amount of images and videos available online. The existing ANN methods are usually characterized by their specific indexing structures, including the inverted index and the inverted multi-index structure. The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs. However, the inverted index requires high memory overhead to index the dataset effectively. The inverted multi-index structure is difficult to implement for GPUs, and also ineffective in dealing with database with different data distributions. In this paper we propose a novel hierarchical inverted index structure generated by vector and line quantization methods. Our quantization method improves both search efficiency and accuracy, while maintaining comparable memory consumption. This is achieved by reducing search space and increasing the number of indexed regions. We introduce a new ANN search system, VLQ-ADC, that is based on the proposed inverted index, and perform extensive evaluation on two public billion-scale benchmark datasets SIFT1B and DEEP1B. Our evaluation shows that VLQ-ADC significantly outperforms the state-of-the-art GPU- and CPU-based systems in terms of both accuracy and search speed. The source code of VLQ-ADC is available at https://github.com/zjuchenwei/vector-line-quantization.

cs.CV↗

arXiv2019

Performance Analysis of Directional Modulation with Finite-quantized RF Phase Shifters in Analog Beamforming Structure

The radio frequency (RF) phase shifter with finite quantization bits in analog beamforming (AB) structure forms quantization error (QE) and causes a performance loss of received signal to interference plus noise ratio (SINR) at the receiver (called Bob). By using the law of large numbers in probability theory, the closed-form expression of SINR performance loss is derived to be inversely proportional to the square of sinc (or sin(x)/x) function. Here, a phase alignment method is applied in directional modulation transmitter with AB structure. Also, the secrecy rate (SR) expression is derived with QE. From numerical simulation results, we find that the SINR performance loss gradually decreases as the number L of quantization bits increases. This loss is less than 0.3dB when L is larger than or equal to 3. As L exceeds 5, the SINR performance loss at Bob can be approximately trivial. Similarly, SR performance loss gradually reduces as L increases. In particular, the SR performance loss is about 0.1 bits/s/Hz for L=3 at signal-to-noise ratio of 15dB.

cs.IT↗