arXiv subjects

Yuan Yang

Publications and source records attributed to Yuan Yang.

At least 73 records · Page 4Linked to original sources

arXiv2019

Connectivity-Preserving Swarm Teleoperation With A Tree Network

During swarm teleoperation, the human operator may threaten the distance-dependent inter-robot communications and, with them, the connectivity of the slave swarm. To prevent the harmful component of the human command from disconnecting the swarm network, this paper develops a constructive strategy to dynamically modulate the interconnections of, and the locally injected damping at, all slave robots. By Lyapunov-based set invariance analysis, the explicit law for updating that control gains has been rigorously proven to synchronize the slave swarm while preserving all interaction links in the tree network. By properly limiting the impact of the user command rather than rejecting it entirely, the proposed control law enables the human operator to guide the motion of the slave swarm to the extent to which it does not endanger the connectivity of the swarm network. Experiment results demonstrate that the proposed strategy can maintain the connectivity of the tree network during swarm teleoperation.

math.OC↗

arXiv2019

Dynamic Interconnection and Damping Injection for Input-to-State Stable Bilateral Teleoperation

In bilateral teleoperation, the human who operates the master and the environment which interacts with the slave are part of the force feedback loop. Yet, both have time-varying and unpredictable dynamics and are challenging to model. A conventional strategy for sidestepping the demand for their models in the stability analysis is to assume passive user and environment, and to control the master-communications-slave system to be passive as well. This paper circumvents the need to model the user and environment in a novel way: it regards their forces as external excitations for a semi-autonomous force feedback loop, which it outfits with a dynamic interconnection and damping injection controller that renders bilateral teleoperation with time-varying delays exponentially input-to-state stable. The controller uses the position and velocity measurements of the local robot and the delayed position transmitted from the other robot to robustly synchronize the master and slave under the user and environment perturbations. Lyapunov-Krasovskii stability analysis shows that the proposed strategy (i) can confine the position error between the master and slave to an invariant set, and (ii) can drive it exponentially to a globally attractive set. Thus, the dynamic interconnection and damping injection approach has practical relevance for telemanipulation tasks with given precision requirements.

math.OC↗

arXiv2018

Scalable, "Dip-and-dry" Fabrication of a Wide-Angle Plasmonic Selective Absorber for High-efficiency Solar-Thermal Energy Conversion

A galvanic displacement reaction-based, room-temperature "dip-and-dry" technique is demonstrated for fabricating selectively solar-absorbing plasmonic nanostructure-coated foils (PNFs). The technique, which allows for facile tuning of the PNFs' spectral reflectance to suit different radiative and thermal environments, yields PNFs which exhibit excellent, wide-angle solar absorptance (0.96 at 15°, to 0.97 at 35°, to 0.79 at 80°) and low hemispherical thermal emittance (0.10) without the aid of antireflection coatings. The thermal emittance is on par with those of notable selective solar absorbers (SSAs) in the literature, while the wide-angle solar absorptance surpasses those of previously reported SSAs with comparable optical selectivities. In addition, the PNFs show promising mechanical and thermal stabilities at temperatures of up to 200°C. Along with the performance of the PNFs, the simplicity, inexpensiveness and environment-friendliness of the "dip-and-dry" technique makes it an appealing alternative to current methods for fabricating selective solar absorbers.

physics.app-ph↗

arXiv2018

Bounded Connectivity-Preserving Coordination of Networked Euler-Lagrange Systems

This paper derives sufficient conditions for bounded distributed connectivity-preserving coordination of Euler-Lagrange systems with only position measurements and with system uncertainties, respectively. The paper proposes two strategies that suitably scale conventional gradient-based controls to account for the actuation bounds and to reserve sufficient actuation for damping injection. For output feedback control of networked systems with only position measurements, the paper incorporates a first-order filter to estimate velocities and to inject damping for stability. For networks of uncertain systems, the paper augments conventional linear filter-based adaptive compensation with damping injection to maintain the local connectivity of the network. Analyses based on monotonically decreasing Lyapunov-like functions and Barbalat's lemma lead to sufficient conditions for bounded local connectivity-preserving coordination of Euler-Lagrange networks under the two strategies. The sufficient conditions clarify the interrelationships among the bounded actuations, initial system velocities and initial inter-system distances. Simulation results validate these conditions.

math.OC↗

arXiv2018

Connectivity-Preserving Consensus of Multi-Agent Systems with Bounded Actuation

This paper investigates the impact of bounded actuation on the connectivity-preserving consensus of two classes of multi-agent systems, with kinematic agents and with Euler- Lagrange agents. The investigation establishes that: (1) there exists a class of gradient-based controls which drive kinematic multi-agent systems to connectivity-preserving consensus even if they saturate; (2) actuator saturation restricts the initial states from which Euler-Lagrange multi-agent systems can be synchronized while preserving their local connectivity; (3) Euler-Lagrange multi-agent systems with unbounded actuation can achieve connectivity-preserving consensus without velocity measurements or exact system dynamics; and (4) a proposed indirect coupling control strategy drives Euler-Lagrange multi-agent systems with limited actuation and starting from rest to connectivitypreserving consensus without requiring velocity measurements and including in the presence of uncertain dynamics and timevarying delays.

math.OC↗

arXiv2018

Connectivity-Preserving Coordination Control of Multi-Agent Systems with Time-Varying Delays

This paper presents a distributed position synchronization strategy that also preserves the initial communication links for single-integrator multi-agent systems with time-varying delays. The strategy employs a coordinating proportional control derived from a specific type of potential energy, augmented with damping injected through a dynamic filter. The injected damping maintains all agents within the communication distances of their neighbours, and asymptotically stabilizes the multi-agent system, in the presence of time delays. Regarding the closed-loop single-integrator multi-agent system as a double-integrator system suggests an extension of the proposed strategy to connectivity-preserving coordination of Euler-Lagrange networks with time-varying delays. Lyapunov stability analysis and simulation results validate the two designs.

math.OC↗

arXiv2018

Globally Stable Output Feedback Synchronization of Teleoperation with Time-Varying Delays

This paper presents a globally stable teleoperation control strategy for systems with time-varying delays that eliminates the need for velocity measurements through novel augmented Immersion and Invariance velocity observers. The new observers simplify a recent constructive Immersion and Invariance velocity observer to achieve globally convergent velocity estimation with only $n+2$ states, where $n$ is the number of degrees of freedom of the master and slave robots. They introduce dynamic scaling factors to accelerate the speed of convergence of the velocity estimates and, thus, to limit the energy generated by the velocity estimation errors and to guarantee sufficient estimate-based damping injection to dissipate the energy generated by the time-varying delays. The paper shows that Proportional plus damping control with the simplified and augmented Immersion and Invariance-based velocity observers can synchronize the free master and slave motions in the presence of time-varying delays without using velocity measurements. Numerical results illustrate the estimation performance of the new observers and the stability of a simulated two degrees-of-freedom nonlinear teleoperation system with time-varying delays under the proposed output feedback Proportional plus damping control.

eess.SY↗

arXiv2017

Predicting Discharge Medications at Admission Time Based on Deep Learning

Predicting discharge medications right after a patient being admitted is an important clinical decision, which provides physicians with guidance on what type of medication regimen to plan for and what possible changes on initial medication may occur during an inpatient stay. It also facilitates medication reconciliation process with easy detection of medication discrepancy at discharge time to improve patient safety. However, since the information available upon admission is limited and patients' condition may evolve during an inpatient stay, these predictions could be a difficult decision for physicians to make. In this work, we investigate how to leverage deep learning technologies to assist physicians in predicting discharge medications based on information documented in the admission note. We build a convolutional neural network which takes an admission note as input and predicts the medications placed on the patient at discharge time. Our method is able to distill semantic patterns from unstructured and noisy texts, and is capable of capturing the pharmacological correlations among medications. We evaluate our method on 25K patient visits and compare with 4 strong baselines. Our methods demonstrate a 20% increase in macro-averaged F1 score than the best baseline.

cs.CL↗

arXiv2017

One Model for the Learning of Language

A major target of linguistics and cognitive science has been to understand what class of learning systems can acquire the key structures of natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire several of the key structures present natural language from positive evidence alone. The model successfully acquires regular (e.g. $(ab)^n$), context-free (e.g. $a^n b^n$, $x x^R$), and context-sensitive (e.g. $a^nb^nc^n$, $a^nb^mc^nd^m$, $xx$) formal languages. Our approach develops the concept of factorized programs in Bayesian program induction in order to help manage the complexity of representation. We show in learning, the model predicts several phenomena empirically observed in human grammar acquisition experiments.

cs.AI↗

arXiv2017

CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System

In this paper, we present LiveMedQA, a question answering system that is optimized for consumer health question. On top of the general QA system pipeline, we introduce several new features that aim to exploit domain-specific knowledge and entity structures for better performance. This includes a question type/focus analyzer based on deep text classification model, a tree-based knowledge graph for answer generation and a complementary structure-aware searcher for answer retrieval. LiveMedQA system is evaluated in the TREC 2017 LiveQA medical subtask, where it received an average score of 0.356 on a 3 point scale. Evaluation results revealed 3 substantial drawbacks in current LiveMedQA system, based on which we provide a detailed discussion and propose a few solutions that constitute the main focus of our subsequent work.

cs.CL↗

arXiv2017

Graphene Terahertz Absorption

The unique terahertz properties of graphene has been identified for novel optoelectronic applications. In a graphene sample with bias voltage added, there is an enhanced absorption in the far infrared region and a diminished absorption in the infrared region. The strength of enhancement(diminishment) increases with the gate voltage, and the enhancement compensates the diminishment. We find that it is the coherence length of electrons in graphene that allows pure electronic transitions between states differing by small momentums and makes intraband transition possible, is responsible for the far infrared enhancement. Phonon assisted processes are not necessary and would not in any case contribute to a sum rule. This naturally leads to results obeying the general sum-rule in optical absorptions. Our prediction of the strength of enhancement(diminishment) in terms of the bias agrees with experiments. This is the first direct calculation we are aware of, since the prior phonon assisted model for indirect transition should not obey a sum rule.

cond-mat.mes-hall↗

arXiv2017

Reassessing Graphene Absorption and Emission Spectroscopy

We present a new paradigm for understanding optical absorption and hot electron dynamics experiments in graphene. Our analysis pivots on assigning proper importance to phonon assisted indirect processes and bleaching of direct processes. We show indirect processes figure in the excess absorption in the UV region. Experiments which were thought to indicate ultrafast relaxation of electrons and holes, reaching a thermal distribution from an extremely non-thermal one in under 5-10 fs, instead are explained by the nascent electron and hole distributions produced by indirect transitions. These need no relaxation or ad-hoc energy removal to agree with the observed emission spectra and fast pulsed absorption spectra. The fast emission following pulsed absorption is dominated by phonon assisted processes, which vastly outnumber direct ones and are always available, connecting any electron with any hole any time. Calculations are given, including explicitly calculating the magnitude of indirect processes, supporting these views.

cond-mat.mtrl-sci↗

arXiv2017

Properties of In-Plane Graphene/MoS2 Heterojunctions

The graphene/MoS2 heterojunction formed by joining the two components laterally in a single plane promises to exhibit a low-resistance contact according to the Schottky-Mott rule. Here we provide an atomic-scale description of the structural, electronic, and magnetic properties of this type of junction. We first identify the energetically favorable structures in which the preference of forming C-S or C-Mo bonds at the boundary depends on the chemical conditions. We find that significant charge transfer between graphene and MoS2 is localized at the boundary. We show that the abundant 1D boundary states substantially pin the Fermi level in the lateral contact between graphene and MoS2, in close analogy to the effect of 2D interfacial states in the contacts between 3D materials. Furthermore, we propose specific ways in which these effects can be exploited to achieve spin-polarized currents.

cond-mat.mtrl-sci↗

arXiv2017

Efficient and Robust Recovery of Sparse Signal and Image Using Generalized Nonconvex Regularization

This work addresses the robust reconstruction problem of a sparse signal from compressed measurements. We propose a robust formulation for sparse reconstruction which employs the $\ell_1$-norm as the loss function for the residual error and utilizes a generalized nonconvex penalty for sparsity inducing. The $\ell_1$-loss is less sensitive to outliers in the measurements than the popular $\ell_2$-loss, while the nonconvex penalty has the capability of ameliorating the bias problem of the popular convex LASSO penalty and thus can yield more accurate recovery. To solve this nonconvex and nonsmooth minimization formulation efficiently, we propose a first-order algorithm based on alternating direction method of multipliers (ADMM). A smoothing strategy on the $\ell_1$-loss function has been used in deriving the new algorithm to make it convergent. Further, a sufficient condition for the convergence of the new algorithm has been provided for generalized nonconvex regularization. In comparison with several state-of-the-art algorithms, the new algorithm showed better performance in numerical experiments in recovering sparse signals and compressible images. The new algorithm scales well for large-scale problems, as often encountered in image processing.

cs.IT↗

arXiv2016

Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties

This work addresses the issue of large covariance matrix estimation in high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed. However, these algorithms cannot be directly extended to use a nonconvex penalty for sparsity inducing. Generally, a nonconvex penalty has the capability of ameliorating the bias problem of the popular convex lasso penalty, and thus is more advantageous. In this work, we propose a class of positive-definite covariance estimators using generalized nonconvex penalties. We develop a first-order algorithm based on the alternating direction method framework to solve the nonconvex optimization problem efficiently. The convergence of this algorithm has been proved. Further, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties. Moreover, extension of this algorithm to covariance estimation from sketched measurements has been considered. The performances of the new estimators have been demonstrated by both a simulation study and a gene clustering example for tumor tissues. Code for the proposed estimators is available at https://github.com/FWen/Nonconvex-PDLCE.git.

cs.IT↗

arXiv2016

Efficient $\ell_q$ Minimization Algorithms for Compressive Sensing Based on Proximity Operator

This paper considers solving the unconstrained $\ell_q$-norm ($0\leq q<1$) regularized least squares ($\ell_q$-LS) problem for recovering sparse signals in compressive sensing. We propose two highly efficient first-order algorithms via incorporating the proximity operator for nonconvex $\ell_q$-norm functions into the fast iterative shrinkage/thresholding (FISTA) and the alternative direction method of multipliers (ADMM) frameworks, respectively. Furthermore, in solving the nonconvex $\ell_q$-LS problem, a sequential minimization strategy is adopted in the new algorithms to gain better global convergence performance. Unlike most existing $\ell_q$-minimization algorithms, the new algorithms solve the $\ell_q$-minimization problem without smoothing (approximating) the $\ell_q$-norm. Meanwhile, the new algorithms scale well for large-scale problems, as often encountered in image processing. We show that the proposed algorithms are the fastest methods in solving the nonconvex $\ell_q$-minimization problem, while offering competent performance in recovering sparse signals and compressible images compared with several state-of-the-art algorithms.

cs.IT↗

arXiv2015

Theory of Graphene Raman Spectroscopy

Raman spectroscopy plays a key role in studies of graphene and related carbon systems. Graphene is perhaps the most promising material of recent times for many novel applications, including electronics. In this paper, the traditional and well established Kramers-Heisenberg-Dirac (KHD) Raman scattering theory (1925-1927) is extended to crystalline graphene for the first time. It demands different phonon production mechanisms and phonon energies than does the popular "double resonance" Raman scattering model. The latter has never been compared to KHD. Within KHD, phonons are produced instantly along with electrons and holes, in what we term an electron-hole-phonon triplet, which does not suffer Pauli blocking. A new mechanism for double phonon production we name "transition sliding" explains the brightness of the 2D mode and other overtones, as a result of linear (Dirac cone) electron dispersion. Direct evidence for sliding resides in hole doping experiments performed in 2011 \cite{chenCrommie}. Whole ranges of electronic transitions are permitted and may even constructively interfere for the same laser energy and phonon q, explaining the dispersion, bandwidth, and strength of many two phonon Raman bands. Graphene's entire Raman spectrum, including dispersive and fixed bands, missing bands not forbidden by symmetries, weak bands, overtone bands, Stokes anti-Stokes anomalies, individual bandwidths, trends with doping, and D-2D band spacing anomalies emerge naturally and directly in KHD theory.

cond-mat.mtrl-sci↗

arXiv2015

Percolation of the Site Random-Cluster Model by Monte Carlo Method

Herein, we propose a site random cluster model by introducing an additional cluster weight in the partition function of the traditional site percolation. To simulate the model on a square lattice, we combine the color-assignation and the Swendsen-Wang methods together to design a highly efficient cluster algorithm with a small critical slowing-down phenomenon. To verify whether or not it is consistent with the bond random cluster model, we measure several quantities such as the wrapping probability $R_e$, the percolation strength $P_\infty$, and the magnetic susceptibility per site $χ_p$ as well as two exponents such as the thermal exponent $y_t$ and the fractal dimension $y_h$ of the largest cluster. We find that for different exponents of cluster weight q=1.5, 2, 2.5, 3, 3.5 and 4, the numerical estimation of the exponents $y_t$ and $y_h$ are consistent with the theoretical values. The universalities of the site random cluster model and the bond random cluster model are completely identical. For larger values of $q$, we find obvious signatures of the first-order percolation transition by the histograms and the hysteresis loops of the percolation strength and the energy per site. Our results are helpful for the understanding of the percolation of traditional statistical models.

cond-mat.stat-mech↗