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Liping Wang

Publications and source records attributed to Liping Wang.

At least 55 records · Page 3Linked to original sources
arXiv2022

No-go rules for multitime Landau-Zener models

Multitime Landau-Zener (MTLZ) model is a class of exactly solvable quantum many-body models which is multitstate and multitime generalization of the two-state Landau-Zener model. Currently discovered MTLZ models include the "hypercubes", the "fans" and their direct product models. In this work, we prove two no-go rules, named the "no $K_{3,3}$" rule and the "no $1221$" rule, which forbid the existence of exact solutions for models with certain structures of interactions. We further apply these rules to show that for models with no more than $9$ states, besides the models mentioned above there are no other MTLZ models. We also propose a scheme to systematically classify cases that could possibly host MTLZ models. Our work could serve as a guideline to search for new exactly solvable models within the MTLZ class.

quant-ph↗
arXiv2022

A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks

Modern indoor localization techniques are essential to overcome the weak GPS coverage in indoor environments. Recently, considerable progress has been made in Channel State Information (CSI) based indoor localization with signal fingerprints. However, CSI signal patterns can be complicated in the large and highly dynamic indoor spaces with complex interiors, thus a solution for solving this issue is urgently needed to expand the applications of CSI to a broader indoor space. In this paper, we propose an end-to-end solution including data collection, pattern clustering, denoising, calibration and a lightweight one-dimensional convolutional neural network (1D CNN) model with CSI fingerprinting to tackle this problem. We have also created and plan to open source a CSI dataset with a large amount of data collected across complex indoor environments at Colorado State University. Experiments indicate that our approach achieves up to 68.5% improved performance (mean distance error) with minimal number of parameters, compared to the best-known deep machine learning and CSI-based indoor localization works.

eess.SP↗
arXiv2021

Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning

One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative.The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework (PMI), to carry out an explicit inverse design. PMI calculates the design explicitly without excessive costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work, the framework will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades.

eess.SP↗
arXiv2021

Label-informed Graph Structure Learning for Node Classification

Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this problem, some studies exploit different graph structure learning strategies to refine the original graph structure. However, these methods only consider feature information while ignoring available label information. In this paper, we propose a novel label-informed graph structure learning framework which incorporates label information explicitly through a class transition matrix. We conduct extensive experiments on seven node classification benchmark datasets and the results show that our method outperforms or matches the state-of-the-art baselines.

cs.LG↗
arXiv2021

Fully Hyperbolic Graph Convolution Network for Recommendation

Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs. However, real-world datasets usually exhibit tree-like hierarchical structures, which make Euclidean space less effective in capturing user-item relationship. In contrast, hyperbolic space, as a continuous analogue of a tree-graph, provides a promising alternative. In this paper, we propose a fully hyperbolic GCN model for recommendation, where all operations are performed in hyperbolic space. Utilizing the advantage of hyperbolic space, our method is able to embed users/items with less distortion and capture user-item interaction relationship more accurately. Extensive experiments on public benchmark datasets show that our method outperforms both Euclidean and hyperbolic counterparts and requires far lower embedding dimensionality to achieve comparable performance.

cs.IR↗
arXiv2021

CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning

GPS technology has revolutionized the way we localize and navigate outdoors. However, the poor reception of GPS signals in buildings makes it unsuitable for indoor localization. WiFi fingerprinting-based indoor localization is one of the most promising ways to meet this demand. Unfortunately, most work in the domain fails to resolve challenges associated with deployability on resource-limited embedded devices. In this work, we propose a compression-aware and high-accuracy deep learning framework called CHISEL that outperforms the best-known works in the area while maintaining localization robustness on embedded devices.

cs.LG↗
arXiv2021

High speed and reconfigurable optronic neural network with digital nonlinear activation

With its unique parallel processing capability, optical neural network has shown low-power consumption in image recognition and speech processing. At present, the manufacturing technology of programmable photonic chip is not mature, and the realization of optical neural network in free-space is still a hot spot of intelligent optical computing. In this article, based on MNIST datasets and 4f system, three-layer optical neural networks are constructed, whose recognition accuracy can reach 93.66%. Our network is programmable, high speed, reconfigurable and is better than the existing free-space optical neural network in terms of spatial complexity.

cs.ET↗
arXiv2021

Semiconductor-based selective emitter with sharp cutoff for thermophotovoltaic energy conversion

Semiconductor emitter can possibly achieve sharp cutoff wavelength due to its intrinsic bandgap absorption and almost zero sub-bandgap emission without doping. A germanium wafer based selective emitter with front-side antireflection and backside metal coating is studied here for thermophotovoltaic (TPV) energy conversion. Optical simulation predicts the spectral emittance above 0.9 in the wavelengths from 1 to 1.85 um and below 0.2 in the sub-bandgap range with sharp cutoff around the bandgap, indicating superior spectral selectivity behavior. This is confirmed by excellent agreement with indirectly measured spectral emittance of the fabricated Ge-based selective emitter sample. Furthermore, the TPV efficiency by paring the Ge-based selective emitter with a GaSb cell is theoretically analyzed at different temperatures. This work will facilitate the development of the semiconductor-based selective emitters for enhancing TPV performance.

physics.optics↗
arXiv2021

Seasonal association between viral causes of hospitalised acute lower respiratory infections and meteorological factors in China: a retrospective study

Acute lower respiratory infections caused by respiratory viruses are common and persistent infectious diseases worldwide and in China, which have pronounced seasonal patterns. Meteorological factors have important roles in the seasonality of some major viruses. Our aim was to identify the dominant meteorological factors and to model their effects on common respiratory viruses in different regions of China. We analysed monthly virus data on patients from 81 sentinel hospitals in 22 provinces in mainland China from 2009 to 2013. The geographical detector method was used to quantify the explanatory power of each meteorological factor, individually and interacting in pairs. 28369 hospitalised patients with ALRI were tested, 10387 were positive for at least one virus, including RSV, influenza virus, PIV, ADV, hBoV, hCoV and hMPV. RSV and influenza virus had annual peaks in the north and biannual peaks in the south. PIV and hBoV had higher positive rates in the spring summer months. hMPV had an annual peak in winter spring, especially in the north. ADV and hCoV exhibited no clear annual seasonality. Temperature, atmospheric pressure, vapour pressure, and rainfall had most explanatory power on most respiratory viruses in each region. Relative humidity was only dominant in the north, but had no significant explanatory power for most viruses in the south. Hours of sunlight had significant explanatory power for RSV and influenza virus in the north, and for most viruses in the south. Wind speed was the only factor with significant explanatory power for human coronavirus in the south. For all viruses, interactions between any two of the paired factors resulted in enhanced explanatory power, either bivariately or non-linearly.

stat.AP↗
arXiv2021

Graph Classification by Mixture of Diverse Experts

Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imbalanced. Recently, Graph Neural Network (GNN) models have achieved superior performance on various real-world datasets. Despite their success, most of current GNN models largely overlook the important setting of imbalanced class distribution, which typically results in prediction bias towards majority classes. To alleviate the prediction bias, we propose to leverage semantic structure of dataset based on the distribution of node embedding. Specifically, we present GraphDIVE, a general framework leveraging mixture of diverse experts (i.e., graph classifiers) for imbalanced graph classification. With a divide-and-conquer principle, GraphDIVE employs a gating network to partition an imbalanced graph dataset into several subsets. Then each expert network is trained based on its corresponding subset. Experiments on real-world imbalanced graph datasets demonstrate the effectiveness of GraphDIVE.

cs.LG↗
arXiv2021

Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models

There has been a surge in the interest of using machine learning techniques to assist in the scientific process of formulating knowledge to explain observational data. We demonstrate the use of Bayesian Hidden Physics Models to first uncover the physics governing the propagation of acoustic impulses in metallic specimens using data obtained from a pristine sample. We then use the learned physics to characterize the microstructure of a separate specimen with a surface-breaking crack flaw. Remarkably, we find that the physics learned from the first specimen allows us to understand the backscattering observed in the latter sample, a qualitative feature that is wholly absent from the specimen from which the physics were inferred. The backscattering is explained through inhomogeneities of a latent spatial field that can be recognized as the speed of sound in the media.

cs.LG↗
arXiv2020

Data-based Discovery of Governing Equations

Most common mechanistic models are traditionally presented in mathematical forms to explain a given physical phenomenon. Machine learning algorithms, on the other hand, provide a mechanism to map the input data to output without explicitly describing the underlying physical process that generated the data. We propose a Data-based Physics Discovery (DPD) framework for automatic discovery of governing equations from observed data. Without a prior definition of the model structure, first a free-form of the equation is discovered, and then calibrated and validated against the available data. In addition to the observed data, the DPD framework can utilize available prior physical models, and domain expert feedback. When prior models are available, the DPD framework can discover an additive or multiplicative correction term represented symbolically. The correction term can be a function of the existing input variable to the prior model, or a newly introduced variable. In case a prior model is not available, the DPD framework discovers a new data-based standalone model governing the observations. We demonstrate the performance of the proposed framework on a real-world application in the aerospace industry.

cs.LG↗
arXiv2020

Optoelectronic Analysis of Spectrally Selective Nanophotonic Metafilm Cell for Thermophotovoltaic Energy Conversion

This work theoretically explores a spectrally selective TPV cell based on an asymmetric Fabry-Perot resonance cavity structure with sub-100-nm GaSb layer. The simulated spectral property of the ultrathin nanophotonic cell structure exhibits a high absorption peak above the bandgap due to the interference effect with electromagnetic field enhanced inside the GaSb layer between top and bottom silver electrodes, while the sub-bandgap absorption is as low as a few percent because of high reflectivity of the metal. An absorption enhancement nearly 20 times at particular frequency above bandgap is achieved within the sub-100-nm GaSb layer with the nanophotonic cell structure compared to the free-standing one. Besides, a thin layer of MoOx is incorporated into the metafilm cell structure as a hole transport layer to consider the charge collection in practice. With rigorous optoelectronic analysis by considering both radiative and nonradiative recombinations (Shockley-Reed-Hall and Auger), the nanophotonic cell is predicted to achieve a TPV efficiency of 22.8% and output power of 0.62 W/cm2 with a black emitter at 1500 K due to spectrally enhanced in-band absorption and low sub-bandgap absorption. With an ideal selective emitter which is hard to achieve in practice along with thermal stability concerns, the efficiency can be further improved to 28% by eliminating sub-bandgap photons. The proposed spectrally selective nanophotonic metafilm cell could be a viable route to achieve high-efficiency and low-cost TPV energy conversion.

physics.optics↗
arXiv2020

Enhancing Solar Thermal Energy Conversion with Silicon-cored Tungsten Nanowire Selective Metamaterial Absorbers

This work experimentally studies silicon-cored tungsten nanowire selective metamaterial absorber to enhance solar-thermal energy harvesting, simply fabricated by conformally coating a thin tungsten layer onto a commercial silicon nanowire stamp. Scanning electron microscopy is used to characterize the morphology change before and after the tungsten deposition, while optical spectroscopy is carried out to measure the spectral absorptance (or emittance) in the broad wavelength range from solar spectrum to infrared. It is shown that the tungsten nanowire absorber exhibits almost the same total solar absorptance around 0.85 as the silicon nanowire but with greatly reduced total emittance down to 0.18, which could significantly suppress the infrared emission heat loss. A lab-scale solar-thermal test apparatus is used to measure the solar-thermal efficiency of the tungsten nanowire absorbers from 1 up to 20 suns, experimentally demonstrating the improved performance due to excellent spectral selectivity over the silicon nanowire and black absorbers. With detailed heat transfer analysis, it is found that the tungsten nanowire absorber achieves an experimental efficiency of 41% at temperature 203degC during the solar-thermal test with stagnation temperature 273degC under 6.3 suns. It is projected to reach 74% efficiency at same temperature 203degC with stagnation temperature of 430degC for practical application without parasitic radiative losses from side and bottom surfaces, greatly outperforming the silicon nanowire and black absorbers. The results would facilitate the development of novel metamaterial selective absorbers at low cost for highly-efficient solar thermal energy systems.

physics.app-ph↗
arXiv2020

Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

Industrial dynamical systems often exhibit multi-scale response due to material heterogeneities, operation conditions and complex environmental loadings. In such problems, it is the case that the smallest length-scale of the systems dynamics controls the numerical resolution required to effectively resolve the embedded physics. In practice however, high numerical resolutions is only required in a confined region of the system where fast dynamics or localized material variability are exhibited, whereas a coarser discretization can be sufficient in the rest majority of the system. To this end, a unified computational scheme with uniform spatio-temporal resolutions for uncertainty quantification can be very computationally demanding. Partitioning the complex dynamical system into smaller easier-to-solve problems based of the localized dynamics and material variability can reduce the overall computational cost. However, identifying the region of interest for high-resolution and intensive uncertainty quantification can be a problem dependent. The region of interest can be specified based on the localization features of the solution, user interest, and correlation length of the random material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update our prior knowledge on the localized region of interest using measurements and system response. To address the computational cost of the Bayesian inference, we construct a Gaussian process surrogate for the forward model. Once, the localized region of interest is identified, we use polynomial chaos expansion to propagate the localization uncertainty. We demonstrate our framework through numerical experiments on a three-dimensional elastodynamic problem.

physics.comp-ph↗
arXiv2020

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables. The "projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging three-dimensional aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional airfoil shape parameterization on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction.

stat.ML↗
arXiv2020

Near-Field Thermophotovoltaic Energy Conversion with Thin-film Tandem Cells

The performance of a near-field thermophotovoltaic system with a tandem-cell structure composed of thin-film p-doped GaSb and n-doped InAs sub-cells on a gold backside reflector is theoretically investigated. The temperatures of the Ga-doped ZnO emitter and the tandem cells are set as 1800 K and 300 K, while the thicknesses of GaSb and InAs sub-cells are considered as 1.5um and 0.5um, respectively. Fluctuational electrodynamics along with the multilayer dyadic Green's function is used to study near-field radiative heat transfer with the consideration of photon chemical potential, whereas radiative recombination and nonradiative Auger recombination are taken into account for evaluating the electrical performance of the tandem cells. At a vacuum gap of 50 nm, it is found that the tandem cells with independent charge collections achieve electrical power output 468.8 kW/m2 at a conversion efficiency of 41%, generating relatively 87% (or 21%) more power with about absolute 5% (or 10%) higher efficiency than the single GaSb (or InAs) cell of the same 2-um thickness. The physical mechanism of near-field spectral heat transfer is elucidated with energy transmission coefficient, while the current-voltage characteristics of sub-cells are discussed in detail. This work will pave the way to enhance near-field thermophotovoltaic energy conversion performance with tandem or multi-junction cells.

physics.app-ph↗