arXiv subjects

Yanyan Zhang

Publications and source records attributed to Yanyan Zhang.

36 records · Page 2Linked to original sources

arXiv2024

Microcavity induced by few-layer GaSe crystal on silicon photonic crystal waveguide for efficient optical frequency conversion

We demonstrate the post-induction of high-quality microcavity on silicon photonic crystal (PC) waveguide by integrating few-layer GaSe crystal, which promises highly efficient on-chip optical frequency conversions. The integration of GaSe shifts the dispersion bands of the PC waveguide mode into the bandgap, resulting in localized modes confined by the bare PC waveguides. Thanks to the small contrast of refractive index at the boundaries of microcavity, it is reliably to obtain quality (Q) factors exceeding 10^4. With the enhanced light-GaSe interaction by the microcavity modes and high second-order nonlinearity of GaSe, remarkable second-harmonic generation (SHG) and sum-frequency generation (SFG) are achieved. A record-high on-chip SHG conversion efficiency of 131100% W^-1 is obtained, enabling the clear SHG imaging of the resonant modes with the pump of sub-milliwatts continuous-wave (CW) laser. Driven by a pump of on-resonance CW laser, strong SFGs are successfully carried out with the other pump of a CW laser spanning over the broad telecom-band. Broadband frequency conversion of an incoherent superluminescent light-emitting diode with low spectral power density is also realized in the integrated GaSe-PC waveguide. Our results are expected to provide new strategies for high-efficiency light-matter interactions, nonlinear photonics and light source generation in silicon photonic integrated circuits.

physics.optics↗

arXiv2024

Nonlinear photodetector based on InSe p-n homojunction for improving spatial imaging resolution

We demonstrate an efficient nonlinear photodetector (NLPD) with quadratic response based on a few-layer InSe p-n homojunction, which is beneficial from the strong second harmonic generation (SHG) process in InSe and effective harvest of photocarriers actuated by the high-quality homojunction. The NLPD can sense light with photon energy smaller than InSe electronic bandgap because the SHG process in InSe doubles the frequency of incident light, extending InSe photodetection wavelength range to 1750 nm. The InSe p-n homojunction, which is electrostatically doped by two split back gates, presents a rectification ratio exceeding 106 with a dark current down to 2 pA and a high normalized responsivity of 0.534 A/W2 for the telecom-band pulsed light at 1550 nm. The photocurrents of the SHG-assisted photodetection have a quadratic dependence on the optical powers, making the NLPD highly sensitive to light intensity variation with improved spatial resolution. As examples, the NLPD is employed to precisely determine the localization point of a focused laser beam waist and implement spatial imaging with an improved resolution compared with the linear photodetector. These features highlight the potential of the proposed NLPD in developing advanced optical sensing and imaging systems.

physics.optics↗

arXiv2024

Compact on-chip power splitter based on topological photonic crystal

We propose and demonstrate an on-chip 1*N power splitter based on topological photonic crystal (TPC) on a monolithic silicon photonic platform. Benefiting from the valley-locked propagation mode at the interface of TPCs with different topological phases, the proposed power splitter has negligible backscattering around the sharp bendings and good robustness to fabrication defects, which therefore enable lower insertion loss, better uniformity, and more compact footprint than the conventional designs. For the fabricated 1*2 (8) power splitter, the uniformity among the output ports is below 0.35 (0.65) dB and the maximum insertion loss is 0.38 (0.58) dB with compact footprint of 5*5 um2 (10*12 um2) within a bandwidth of 70 nm. In addition, the topological power splitter only requires simple configurations of TPCs with different topological phases, which is more reliable in design and fabrication compared with the conventional designs.

physics.optics↗

arXiv2023

Study on the behaviors of rupture solutions for a class of elliptic MEMS equations in $\R^2$

This study examines nonnegative solutions to the problem \begin{equation*}\left\{\arraycolsep=1.5pt \begin {array}{lll} Δu=\displaystyle\frac{λ|x|^α}{u^p} \ \ &\hbox{ in} \,\ \R ^2\setminus \{0\},\\[2mm] u(0)=0 \ \text{and}\ u> 0 \ \ &\hbox{ in} \,\ \R ^2\setminus \{0\},\\ \end{array}\right. \label{eqn} \end{equation*} where $\lam >0,$ $\alp>-2$, and $p>0$ are constants. The possible asymptotic behaviors of $u(x)$ at $|x|=0$ and $|x|=\infty$ are classified according to $(α,p)$. In particular, the results show that for some $(α,p)$, $u(x)$ exhibits only ``isotropic" behavior at $|x|=0$ and $|x|=\infty$. However, in other cases, $u(x)$ may exhibit the "anisotropic" behavior at $|x|=0$ or $|x|=\infty$. Furthermore, the relation between the limit at $|x|=0$ and the limit at $|x|=\infty$ for a global solution is investigated.

math.AP↗

arXiv2023

A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast

The increasing installation rate of wind power poses great challenges to the global power system. In order to ensure the reliable operation of the power system, it is necessary to accurately forecast the wind speed and power of the wind turbines. At present, deep learning is progressively applied to the wind speed prediction. Nevertheless, the recent deep learning methods still reflect the embarrassment for practical applications due to model interpretability and hardware limitation. To this end, a novel deep knowledge-based learning method is proposed in this paper. The proposed method hybridizes pre-training method and auto-encoder structure to improve data representation and modeling of the deep knowledge-based learning framework. In order to form knowledge and corresponding absorbers, the original data is preprocessed by an optimization model based on correlation to construct multi-layer networks (knowledge) which are absorbed by sequence to sequence (Seq2Seq) models. Specifically, new cognition and memory units (CMU) are designed to reinforce traditional deep learning framework. Finally, the effectiveness of the proposed method is verified by three wind prediction cases from a wind farm in Liaoning, China. Experimental results show that the proposed method increases the stability and training efficiency compared to the traditional LSTM method and LSTM/GRU-based Seq2Seq method for applications of wind speed forecasting.

cs.LG↗

arXiv2021

Global existence, uniform boundedness, and stabilization in a chemotaxis system with density-suppressed motility and nutrient consumption

Well-posedness and uniform-in-time boundedness of classical solutions are investigated for a three-component parabolic system which describes the dynamics of a population of cells interacting with a chemoattractant and a nutrient. The former induces a chemotactic bias in the diffusive motion of the cells and is accounted for by a density-suppressed motility. Well-posedness is first established for generic positive and non-increasing motility functions vanishing at infinity. Growth conditions on the motility function guaranteeing the uniform-in-time boundedness of solutions are next identified. Finally, for sublinearly decaying motility functions, convergence to a spatially homogeneous steady state is shown, with an exponential rate for consumption rates behaving linearly near zero.

math.AP↗

arXiv2021

Asymptotic and Quenching Behavior for a family of Parabolic System with General Singular Nonlinearities

This study is concerned with a family of parabolic system with general singular nonlinearities, which is a generalization of MEMS system. To some extent, the classification of global existence and quenching according to parameters and initial data is given. Moreover, the convergence rate is also obtained. We point out that compared to single MEMS equation, new ideas and techniques are introduced in obtaining the convergence rate for system in our study. In fact, due to the lack of variational characterization for the first eigenvalue of the linearized elliptic system, the methods in obtaining convergence rate for single equation cannot work completely here.

math.AP↗

arXiv2020

Singular Behavior of an Electrostatic--Elastic Membrane System with an External Pressure

We analyze nonnegative solutions of the nonlinear elliptic problem $Δu=\frac{λf(x)}{u^2}+P$, where $λ>0$ and $P\geq0$, on a bounded domain $Ω$ of $\mathbb{R}^N$ ($N\geq 1$) with a Dirichlet boundary condition. This equation models an electrostatic--elastic membrane system with an external pressure $P\geq 0$, where $λ>0$ denotes the applied voltage. First, we completely address the existence and nonexistence of positive solutions. The classification of all possible singularities at $|x|=0$ for nonnegative solutions $u(x)$ satisfying $u(0)=0$ is then analyzed for the special case where $Ω=B_1(0)\subset \mathbb{R}^2$ and $f(x)=|x|^α$ with $α\geq0$. In particular, we show that for some $α,$ $u(x)$ admits only the "isotropic" singularity at $|x|=0$, and otherwise $u(x)$ may admit the "anisotropic" singularity at $|x|=0$. When $u(x)$ admits the "isotropic" singularity at $|x|=0$, the refined singularity of $u(x)$ at $|x|=0$ is further investigated, depending on whether $P>0$, by applying Fourier analysis.

math.AP↗

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

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

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

Finding Theme Communities from Database Networks

Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities. Here, a theme community is a cohesive subgraph such that a common pattern is frequent in all transaction databases associated with the vertices in the subgraph. Finding all theme communities from a database network enjoys many novel applications. However, it is challenging since even counting the number of all theme communities in a database network is #P-hard. Inspired by the observation that a theme community shrinks when the length of the pattern increases, we investigate several properties of theme communities and develop TCFI, a scalable algorithm that uses these properties to effectively prune the patterns that cannot form any theme community. We also design TC-Tree, a scalable algorithm that decomposes and indexes theme communities efficiently. Retrieving a ranked list of theme communities from a TC-Tree of hundreds of millions of theme communities takes less than 1 second. Extensive experiments and a case study demonstrate the effectiveness and scalability of TCFI and TC-Tree in discovering and querying meaningful theme communities from large database networks.

cs.DB↗

arXiv2019

Flux-approximation limits of solutions to the Brio system with two independent parameters

By the flux-approximation method, we study limits of Riemann solutions to the Brio system with two independent parameters. The Riemann problem of the perturbed system is solved analytically, and four kinds of solutions are obtained constructively. It is shown that, as the two-parameter flux perturbation vanishes, any two-shock-wave and two-rarefaction-wave solutions of the perturbed Brio system converge to the delta-shock and vacuum solutions of the transport equations, respectively. In addition, we specially pay attention to the Riemann problem of a simplified system of conservation laws derived from the perturbed Brio system by neglecting some quadratic term. As one of the purebred parameters of the Brio system goes to zero, the solution of which consisting of two shock waves tends to a delta-shock solution to this simplified system. By contrast, the solution containing two rarefaction waves converges to a contact discontinuity and a rarefaction wave of the simplified system. What is more, the formation mechanisms of delta shock waves under flux approximation with both two parameters and only one parameter are clarified.

math.AP↗

arXiv2018

Shear driven formation of nano-diamonds at sub-gigapascals and 300 K

The transformation pathways of carbon at high pressures are of broad interest for synthesis of novel materials and for revealing the Earth's geological history. We have applied large plastic shear on graphite in rotational anvils to form hexagonal and nanocrystalline cubic diamond at extremely low pressures of 0.4 and 0.7 GPa, which are 50 and 100 times lower than the transformation pressures under hydrostatic compression and well below the phase equilibrium. Large shearing accompanied with pressure elevation to 3 GPa also leads to formation of a new orthorhombic diamond phase. Our results demonstrate new mechanisms and new means for plastic shear-controlled material synthesis at drastically reduced pressures, enabling new technologies for material synthesis. The results indicate that the micro-diamonds found in the low pressure-temperature crust could have formed during a large shear producing event, such as tectonic rifting and continued plate collision, without the need to postulate subduction to the mantle.

cond-mat.mtrl-sci↗

arXiv2018

Mining Density Contrast Subgraphs

Dense subgraph discovery is a key primitive in many graph mining applications, such as detecting communities in social networks and mining gene correlation from biological data. Most studies on dense subgraph mining only deal with one graph. However, in many applications, we have more than one graph describing relations among a same group of entities. In this paper, given two graphs sharing the same set of vertices, we investigate the problem of detecting subgraphs that contrast the most with respect to density. We call such subgraphs Density Contrast Subgraphs, or DCS in short. Two widely used graph density measures, average degree and graph affinity, are considered. For both density measures, mining DCS is equivalent to mining the densest subgraph from a "difference" graph, which may have both positive and negative edge weights. Due to the existence of negative edge weights, existing dense subgraph detection algorithms cannot identify the subgraph we need. We prove the computational hardness of mining DCS under the two graph density measures and develop efficient algorithms to find DCS. We also conduct extensive experiments on several real-world datasets to evaluate our algorithms. The experimental results show that our algorithms are both effective and efficient.

cs.SI↗

arXiv2018

Multi-cavity ultra-stable laser towards 10-18

In this letter, we demonstrate a technique of making an ultrastable laser referenced to a multi-cavity, corresponding to a lower thermal noise limit due to the larger equivalent beam size. The multi-cavity consists of several pairs of mirrors and a common spacer. We can stabilize the laser frequencies on these cavities, and average the laser frequencies with synthesizing technique. In comparison with a single cavity system, relative frequency instability of the synthesized laser can be improved by a factor of the squire root of the cavity number (n). In addition, we perform an experiment to simulate a two-cavity system. Experimental results show that frequency instability of the synthesized laser is improved by a factor of 1.4, and discrimination of the laser frequency instability, introduced by the process of lasers synthesizing, is negligible, and can reach a floor at low level 10-18 limited by noise of currently used signal generators. This technique is comparable with other techniques; thus, it can gain a factor of the squre root of n on the frequency instability of an ultrastable laser to an unprecedented level.

physics.ins-det↗

arXiv2015

Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem.

cs.IT↗