arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 847 records · Page 47Linked to original sources

arXiv2019

Optimal Power Flow in Hybrid AC and Multi-terminal HVDC Networks with Offshore Wind Farm Integration Based on Semidefinite Programming

Multi-terminal high voltage direct current (MTHVDC) technology is a promising technology for the offshore wind farm integration, which requires the new control and operation scheme. Therefore, the optimal power flow problem for this system is important to achieve the optimal economic operation. In this paper, convex relaxation model based on semidefinite programming for the MT-HVDC system considering DC/DC converters is proposed to solve the optimal power flow problem. A hybrid AC and MT-HVDC system for offshore wind farm integration is used for the test. The simulation results validate the effectiveness of the proposed model and guarantee that the global optimum solution is achieved.

eess.SY↗

arXiv2019

Large Effects of Particle Size Heterogeneity on Dynamic Saltation Threshold

Reliably predicting the geomorphology and climate of planetary bodies requires knowledge of the dynamic threshold wind shear velocity below which saltation transport ceases. Here we measure this threshold in a wind tunnel for four well-sorted and two poorly sorted sand beds by visual means and by a method that exploits a regime shift in the behavior of the surface roughness caused by momentum transfer from the wind to the saltating particles. For our poorly sorted sands, we find that these measurement methods yield different threshold values because, at the smaller visual threshold, relatively coarse particles do not participate in saltation. We further find that both methods yield threshold values that are much larger (60--250\%) for our poorly sorted sands than for our well-sorted sands with similar median particle diameter. In particular, even a rescaling of the dynamic saltation threshold based on the 90th percentile particle diameter rather than the median diameter cannot fully capture this difference, suggesting that relatively very coarse particles have a considerable control on the dynamic threshold. Similar findings were previously reported for water-driven sediment transport. Our findings have important implications for quantitative predictions of saltation transport-related geophysical processes, such as dust aerosol emission.

physics.geo-ph↗

arXiv2019

Freeform microfluidic networks encapsulated in laser printed three-dimensional macro-scale glass objects

Large-scale microfluidic microsystems with complex three-dimensional (3D) configurations are highly in demand by both fundamental research and industrial application, holding the potentials for fostering a wide range of innovative applications such as lab-on-a-chip and organ-on-a-chip as well as continuous-flow manufacturing of fine chemicals. However, freeform fabrication of such systems remains challenging for most of the current fabrication techniques in terms of fabrication resolution, flexibility, and achievable footprint size. Here, we report ultrashort pulse laser microfabrication of freeform microfluidic circuits with high aspect ratios and tunable diameters embedded in 3D printed glass objects. We achieve uniform microfluidic channel diameter by carefully distributing a string of extra access ports along the microfluidic channels for avoiding the over-etching in the thin microfluidic channels. After the chemical etching is completed, the extra access ports are sealed using carbon dioxide laser induced localized glass melting. We demonstrate a model hand of fused silica with a size of ~3 cm * 2.7 cm * 1.1 cm in which the whole blood vessel system is encapsulated.

physics.app-ph↗

arXiv2019

Non-singular string cosmology via $α^{\prime}$ corrections

In string theory, an important challenge is to show if the big-bang singularity could be resolved by the higher derivative $α'$ corrections. In this work, based on the Hohm-Zwiebach formula, we construct a series of non-singular non-perturbative cosmological solutions with the complete $α^{\prime}$ corrections, for the bosonic gravi-dilaton system. In the perturbative regime, these solutions exactly match the perturbative results given in literature. Our results show that the big-bang singularity indeed could be smoothed out by the higher derivative $α'$ corrections.

hep-th↗

arXiv2019

A compact and efficient three-dimensional microfluidic mixer

Microfluidic mixing is a fundamental functionality in most lab on a chip (LOC) systems,whereas realization of efficient mixing is challenging in microfluidic channels due to the small Reynolds numbers. Here, we design and fabricate a compact three-dimensional (3D) micromixer to enable efficient mixing at various flow rates. The performance of the fabricated micromixer was examined using blue and red inks. The extreme flexibility in fabricating microfluidic structures of arbitrary 3D geometries using femtosecond laser micromachining allows us to tackle the major disadvantageous effects for optimizing the mixing efficiency.

physics.app-ph↗

arXiv2019

Learning Depth with Convolutional Spatial Propagation Network

Depth prediction is one of the fundamental problems in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for various depth estimation tasks. Specifically, it is an efficient linear propagation model, in which the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We can append this module to any output from a state-of-the-art (SOTA) depth estimation networks to improve their performances. In practice, we further extend CSPN in two aspects: 1) take sparse depth map as additional input, which is useful for the task of depth completion; 2) similar to commonly used 3D convolution operation in CNNs, we propose 3D CSPN to handle features with one additional dimension, which is effective in the task of stereo matching using 3D cost volume. For the tasks of sparse to dense, a.k.a depth completion. We experimented the proposed CPSN conjunct algorithms over the popular NYU v2 and KITTI datasets, where we show that our proposed algorithms not only produce high quality (e.g., 30% more reduction in depth error), but also run faster (e.g., 2 to 5x faster) than previous SOTA spatial propagation network. We also evaluated our stereo matching algorithm on the Scene Flow and KITTI Stereo datasets, and rank 1st on both the KITTI Stereo 2012 and 2015 benchmarks, which demonstrates the effectiveness of the proposed module. The code of CSPN proposed in this work will be released at https://github.com/XinJCheng/CSPN.

cs.CV↗

arXiv2019

Integrating high-quality dielectrics with one-nanometer equivalent oxide thickness on two-dimensional electronic devices

Two-dimensional (2D) semiconductors are widely recognized as attractive channel materials for low-power electronics. However, an unresolved challenge is the integration of high-quality, ultrathin high-\k{appa} dielectrics that fully meet the roadmap requirements for low-power applications. With a dangling-bond free surface, the deposition of dielectrics by atomic layer deposition (ALD) on 2D materials is usually characterized with non-uniform nucleation and island formation, producing a highly porous dielectric layer with serious leakage particularly at the small equivalent oxide thickness (EOT) limit. Here, we report the robust ALD of highly uniform high-\k{appa} dielectric on 2D semiconductors by using ~0.3 nm-thick exclusively monolayer molecular crystal as seeding layer. Ultrathin dielectrics down to 1 nm EOT is realized on graphene, MoS2 and WSe2, with considerably reduced roughness, density of interface states, leakage current and improved breakdown field compared to prior methods. Taking advantage of the reduced EOT, we demonstrate graphene RF transistors operating at 60 GHz, as well as MoS2 and WSe2 complementary metal-oxide-semiconductor (CMOS) transistors with Vdd =0.8 V and ideal subthreshold swing (SS) of 60 mV/dec, 20 nm-channel-length MoS2 transistors with on/off ratio over 10^7. These studies highlight that our dielectric integration method is generally applicable for different 2D materials, and compatible with top-down fabrication process on large-area chemical vapor deposited films.

physics.app-ph↗

arXiv2019

Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration

Mobile energy storage systems (MESSs) provide promising solutions to enhance distribution system resilience in terms of mobility and flexibility. This paper proposes a rolling integrated service restoration strategy to minimize the total system cost by coordinating the scheduling of MESS fleets, resource dispatching of microgrids and network reconfiguration of distribution systems. The integrated strategy takes into account damage and repair to both the roads in transportation networks and the branches in distribution systems. The uncertainties in load consumption and the status of roads and branches are modeled as scenario trees using Monte Carlo simulation method. The operation strategy of MESSs is modeled by a stochastic multi-layer time-space network technique. A rolling optimization framework is adopted to dynamically update system damage, and the coordinated scheduling at each time interval over the prediction horizon is formulated as a two-stage stochastic mixed-integer linear program with temporal-spatial and operation constraints. The proposed model is verified on two integrated test systems, one is with Sioux Falls transportation network and four 33-bus distribution systems, and the other is the Singapore transportation network-based test system connecting six 33-bus distribution systems. The results demonstrate the effectiveness of MESS mobility to enhance distribution system resilience due to the coordination of mobile and stationary resources.

math.OC↗

arXiv2019

Non-equilibrium diffusion characteristics of a particle system and the correspondence to a social system

A Langevin equation is suggested to describe a system driven by correlated Gaussian white noise as well as with positive and negative damping demarcated by a critical velocity. The equation can be transformed into the Fokker-Planck equation by the Kramers-Moyal expansion. The solution exhibits some non-equilibrium phenomena. In the beginning the distribution curve of velocity/energy takes on a random oscillation, and then a near-equilibrium distribution described by the Boltzmann distribution is gradually established. However, a spike appears on the distribution curve and breaks this stable distribution. The spike moves in the direction of velocity/energy decreasing and is nonlinearly enlarged so as to sustain. The final distribution is a sharp peak formed by a monotonically ascending segment and a monotonically descending one. The calculating results of the statistical quantities demonstrate that the process is a sub-diffusion, and the spike originates from the correlation between noise and space. Based on a basic hypothesis that the generalized displacement is regarded as the information carried by an opinion particle, and the velocity is taken as the sensitivity of an agent to an event, we map the observations in this system to a social system to understand the propagation of public opinion or message.

physics.soc-ph↗

arXiv2019

Diffusion and memory effect in a stochastic processes and the correspondence to an information propagation in a social system

A generalized Langevin equation is suggested to describe a system with memory($u(t,t') = \frac{1}{Γ(ν)}(t - t')^ν$) as well as with positive and negative damping. The equation can be transformed into the Fokker-Planck equation by using the Kramers-Moyal expansion. The solution of Fokker-Planck equation shows that velocity obeys a Gaussian distribution. The distribution curve will flatten as the memory parameter increases, which indicates that memory can enhance the randomness of the system. There are also some other memory effects behind this distribution, which can be characterized by calculating the transport coefficients, mean square displacement and correlation between the noise and space. These discussions can be paralleled to a social system to understand the propagation of social ideology caused by memory.

physics.soc-ph↗

arXiv2019

Thermodynamics and Phase Transition of a Gauss-Bonnet Black Hole in a Cavity

Considering a canonical ensemble, in which the temperature and the charge on a wall of the cavity are fixed, we investigate the thermodynamics of a D-dimensional Gauss-Bonnet black hole in a finite spherical cavity. Moreover, it shows that the first law of thermodynamics is still satisfied. We then discuss the phase structure and transition in both five and six dimensions. Specifically, we show that there always exist two regions in the parameter space. In one region, the system possesses one single phase. However in the other region, there could coexist three phases and a van der Waals-like phase transition occurs. Finally, we find that there is a fairly close resemblance in thermodynamic properties and phase structure of a Gauss-Bonnet-Maxwell black hole, either in a cavity or in anti-de Sitter space.

gr-qc↗

arXiv2019

Efficient Automatic Meta Optimization Search for Few-Shot Learning

Previous works on meta-learning either relied on elaborately hand-designed network structures or adopted specialized learning rules to a particular domain. We propose a universal framework to optimize the meta-learning process automatically by adopting neural architecture search technique (NAS). NAS automatically generates and evaluates meta-learner's architecture for few-shot learning problems, while the meta-learner uses meta-learning algorithm to optimize its parameters based on the distribution of learning tasks. Parameter sharing and experience replay are adopted to accelerate the architectures searching process, so it takes only 1-2 GPU days to find good architectures. Extensive experiments on Mini-ImageNet and Omniglot show that our algorithm excels in few-shot learning tasks. The best architecture found on Mini-ImageNet achieves competitive results when transferred to Omniglot, which shows the high transferability of architectures among different computer vision problems.

cs.LG↗

arXiv2019

Are nonperturbative AdS vacua possible in bosonic string theory?

In this paper, following the work of Hohm and Zwiebach [arXiv:1905.06583], we show that in bosonic string theory nonperturbative anti-de Sitter (AdS) vacua could exist with all $α^{\prime}$ corrections included. We also discuss the possibility of the coexistence of nonperturbative dS and AdS vacua.

hep-th↗

arXiv2019

V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices

One of the primary challenges faced by deep learning is the degree to which current methods exploit superficial statistics and dataset bias, rather than learning to generalise over the specific representations they have experienced. This is a critical concern because generalisation enables robust reasoning over unseen data, whereas leveraging superficial statistics is fragile to even small changes in data distribution. To illuminate the issue and drive progress towards a solution, we propose a test that explicitly evaluates abstract reasoning over visual data. We introduce a large-scale benchmark of visual questions that involve operations fundamental to many high-level vision tasks, such as comparisons of counts and logical operations on complex visual properties. The benchmark directly measures a method's ability to infer high-level relationships and to generalise them over image-based concepts. It includes multiple training/test splits that require controlled levels of generalization. We evaluate a range of deep learning architectures, and find that existing models, including those popular for vision-and-language tasks, are unable to solve seemingly-simple instances. Models using relational networks fare better but leave substantial room for improvement.

cs.CV↗

arXiv2019

Strong Cosmic Censorship for a Scalar Field in a Born-Infeld-de Sitter Black Hole

It has been shown that the Strong Cosmic Censorship (SCC) can be violated by a scalar field in a near-extremal Reissner-Nordstrom-de Sitter black hole. In this paper, we investigate the Strong Cosmic Censorship in a Born-Infeld-de Sitter black hole by a scalar perturbation field with/without a charge. When the Born-Infeld parameter b becomes small, the nonlinear electrodynamics effect starts to play an important role and tends to rescue SCC. Specifically, we find that the SCC violation region decreases in size with decreasing b. Moreover, for a sufficiently small b, SCC can always be restored in a near-extremal Born-Infeld-de Sitter black hole with a fixed charge ratio.

hep-th↗

arXiv2019

Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding

Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames by watching unlabeled videos via deep convolutional network has made significant progress recently. Current state-of-the-art (SoTA) methods treat the two tasks independently. One typical assumption of the existing depth estimation methods is that the scenes contain no independent moving objects. while object moving could be easily modeled using optical flow. In this paper, we propose to address the two tasks as a whole, i.e. to jointly understand per-pixel 3D geometry and motion. This eliminates the need of static scene assumption and enforces the inherent geometrical consistency during the learning process, yielding significantly improved results for both tasks. We call our method as "Every Pixel Counts++" or "EPC++". Specifically, during training, given two consecutive frames from a video, we adopt three parallel networks to predict the camera motion (MotionNet), dense depth map (DepthNet), and per-pixel optical flow between two frames (OptFlowNet) respectively. The three types of information are fed into a holistic 3D motion parser (HMP), and per-pixel 3D motion of both rigid background and moving objects are disentangled and recovered. Comprehensive experiments were conducted on datasets with different scenes, including driving scenario (KITTI 2012 and KITTI 2015 datasets), mixed outdoor/indoor scenes (Make3D) and synthetic animation (MPI Sintel dataset). Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SoTA methods. Code will be available at: https://github.com/chenxuluo/EPC.

cs.CV↗

arXiv2019

EPNAS: Efficient Progressive Neural Architecture Search

In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search space through a novel progressive search policy with performance prediction based on REINFORCE~\cite{Williams.1992.PG}. EPNAS is designed to search target networks in parallel, which is more scalable on parallel systems such as GPU/TPU clusters. More importantly, EPNAS can be generalized to architecture search with multiple resource constraints, \eg, model size, compute complexity or intensity, which is crucial for deployment in widespread platforms such as mobile and cloud. We compare EPNAS against other state-of-the-art (SoTA) network architectures (\eg, MobileNetV2~\cite{mobilenetv2}) and efficient NAS algorithms (\eg, ENAS~\cite{pham2018efficient}, and PNAS~\cite{Liu2017b}) on image recognition tasks using CIFAR10 and ImageNet. On both datasets, EPNAS is superior \wrt architecture searching speed and recognition accuracy.

cs.LG↗

arXiv2019

A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching

Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations. To fill this gap, this paper gives a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods. A good correspondence grouping algorithm is expected to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall as well as facilitating accurate transformation estimation. Toward this rule, we deploy experiments on three benchmarks with different application contexts including shape retrieval, 3D object recognition, and point cloud registration together with various perturbations such as noise, point density variation, clutter, occlusion, partial overlap, different scales of initial correspondences, and different combinations of keypoint detectors and descriptors. The rich variety of application scenarios and nuisances result in different spatial distributions and inlier ratios of initial feature correspondences, thus enabling a thorough evaluation. Based on the outcomes, we give a summary of the traits, merits, and demerits of evaluated approaches and indicate some potential future research directions.

cs.CV↗