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Researcher profile

Rui Yang

Rui Yang contributes to research discovery and scholarly infrastructure.

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physics.flu-dynMachine LearningArtificial Intelligencequant-phRoboticsComputer VisionDatabasesmath.DSphysics.app-phphysics.comp-phastro-ph.IMComputation and Languagecond-mat.mtrl-sciDistributed, Parallel, and Cluster Computingeess.SPEmerging TechnologiesInformation Theorymath.ITmath.OCNetworking and Internet ArchitectureNeural and Evolutionary Computingphysics.acc-phphysics.atom-phphysics.geo-phphysics.med-phphysics.opticsphysics.plasm-phphysics.soc-ph

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Published work

29 published item(s)

preprint2026arXiv

EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement

Text-to-SQL enables non-expert users to query databases in natural language, yet real-world schemas often suffer from ambiguous, abbreviated, or inconsistent naming conventions that degrade model accuracy. Existing approaches treat schemas as fixed and address errors downstream. In this paper, we frame schema refinement as a constrained optimization problem: find a renaming function that maximizes downstream Text-to-SQL execution accuracy while preserving query equivalence through database views. We analyze the computational hardness of this problem, which motivates a column-wise greedy decomposition, and instantiate it as EGRefine: a four-phase pipeline that screens ambiguous columns, generates context-aware candidate names, verifies them through execution-grounded feedback, and materializes the result as non-destructive SQL views. The pipeline carries two structural properties: column-local non-degradation, ensured by the conservative selection rule in the verification phase, and database-level query equivalence, ensured by the view-based materialization phase. Together they make the resulting refinement safe by construction at the column level, with cross-column and prompt-level interactions handled empirically rather than analytically. Across controlled schema-degradation, real-world, and enterprise benchmarks, EGRefine recovers accuracy lost to schema naming noise where applicable and correctly abstains where the underlying task exceeds current Text-to-SQL capabilities, with refined schemas transferring across model families to enable refine-once, serve-many-models deployment. Code and data are publicly available at https://github.com/ai-jiaqian/EGRefine.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A two-qubit entangling gate based on a two-spin gadget

The faster speed and operational convenience of two-qubit gate with flux bias control makes it an important candidate for future large-scale quantum computers based on high coherence flux qubits. Based on a properly designed two-spin gadget which has small gaps during the evolution of energy levels, we build a CNOT-equivalent gate which can reach a fidelity larger than 99.9% within 40ns. Moreover, we also use the Schrieffer-Wolff Transformation to translate the spin model Ising coefficients schedule to circuit model flux bias schedule for realistic flux qubit circuits coupled by a tunable rf-squid. The two-qubit entangling gate scheme introduced here is suitable for realizing efficient two-qubit gates in the large scale flux qubit systems dominated by inductive couplings. Comparing with the current gate-based quantum computation systems dominated by capacitive couplings, it can resolve the conflict between the speed and a high coherence.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

All-optical graph representation learning using integrated diffractive photonic computing units

Photonic neural networks perform brain-inspired computations using photons instead of electrons that can achieve substantially improved computing performance. However, existing architectures can only handle data with regular structures, e.g., images or videos, but fail to generalize to graph-structured data beyond Euclidean space, e.g., social networks or document co-citation networks. Here, we propose an all-optical graph representation learning architecture, termed diffractive graph neural network (DGNN), based on the integrated diffractive photonic computing units (DPUs) to address this limitation. Specifically, DGNN optically encodes node attributes into strip optical waveguides, which are transformed by DPUs and aggregated by on-chip optical couplers to extract their feature representations. Each DPU comprises successive passive layers of metalines to modulate the electromagnetic optical field via diffraction, where the metaline structures are learnable parameters shared across graph nodes. DGNN captures complex dependencies among the node neighborhoods and eliminates the nonlinear transition functions during the light-speed optical message passing over graph structures. We demonstrate the use of DGNN extracted features for node and graph-level classification tasks with benchmark databases and achieve superior performance. Our work opens up a new direction for designing application-specific integrated photonic circuits for high-efficiency processing of large-scale graph data structures using deep learning.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Banyan: A Scoped Dataflow Engine for Graph Query Service

Graph query services (GQS) are widely used today to interactively answer graph traversal queries on large-scale graph data. Existing graph query engines focus largely on optimizing the latency of a single query. This ignores significant challenges posed by GQS, including fine-grained control and scheduling during query execution, as well as performance isolation and load balancing in various levels from across user to intra-query. To tackle these control and scheduling challenges, we propose a novel scoped dataflow for modeling graph traversal queries, which explicitly exposes concurrent execution and control of any subquery to the finest granularity. We implemented Banyan, an engine based on the scoped dataflow model for GQS. Banyan focuses on scaling up the performance on a single machine, and provides the ability to easily scale out. Extensive experiments on multiple benchmarks show that Banyan improves performance by up to three orders of magnitude over state-of-the-art graph query engines, while providing performance isolation and load balancing.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bowen's equations for upper metric mean dimension with potential

Firstly, we introduce a new notion called induced upper metric mean dimension with potential, which naturally generalizes the definition of upper metric mean dimension with potential given by Tsukamoto to more general cases, then we establish variational principles for it in terms of upper and lower rate distortion dimensions and show there exists a Bowen's equation between induced upper metric mean dimension with potential and upper metric mean dimension with potential. Secondly, we continue to introduce two new notions, called BS metric mean dimension and Packing BS metric mean dimension on arbitrary subsets, to establish Bowen's equations for Bowen upper metric mean dimension and Packing upper metric mean dimension with potential on subsets. Besides, we also obtain two variational principles for BS metric mean dimension and Packing BS metric mean dimension on subsets. Finally, the special interest about the Bowen upper metric mean dimension of the set of generic points of ergodic measures are also involved.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Customized quantum annealing schedules

In a typical quantum annealing protocol, the system starts with a transverse field Hamiltonian which is gradually turned off and replaced by a longitudinal Ising Hamiltonian. The ground state of the Ising Hamiltonian encodes the solution to the computational problem of interest, and the state overlap with this ground state gives the success probability of the annealing protocol. The form of the annealing schedule can have a significant impact on the ground state overlap at the end of the anneal, so precise control over these annealing schedules can be a powerful tool for increasing success probabilities of annealing protocols. Here we show how superconducting circuits, in particular capacitively shunted flux qubits (CSFQs), can be used to construct quantum annealing systems by providing tools for mapping circuit flux biases to Pauli coefficients. We use this mapping to find customized annealing schedules: appropriate circuit control biases that yield a desired annealing schedule, while accounting for the physical limitations of the circuitry. We then provide examples and proposals that utilize this capability to improve quantum annealing performance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Data-driven identification of the spatio-temporal structure of turbulent flows by streaming Dynamic Mode Decomposition

Streaming Dynamic Mode Decomposition (sDMD) (Hemati et al., Phys. Fluids 26(2014)) is a low-storage version of Dynamic Mode Decomposition (DMD) (Schmid, J. Fluid Mech. 656 (2010)), a data-driven method to extract spatio-temporal flow patterns. Streaming DMD avoids storing the entire data sequence in memory by approximating the dynamic modes through incremental updates with new available data. In this paper, we use sDMD to identify and extract dominant spatio-temporal structures of different turbulent flows, requiring the analysis of large datasets. First, the efficiency and accuracy of sDMD are compared to the classical DMD, using a publicly available test dataset that consists of velocity field snapshots obtained by direct numerical simulation of a wake flow behind a cylinder. Streaming DMD not only reliably reproduces the most important dynamical features of the flow; our calculations also highlight its advantage in terms of the required computational resources. We subsequently use sDMD to analyse three different turbulent flows that all show some degree of large-scale coherence: rapidly rotating Rayleigh--Bénard convection, horizontal convection and the asymptotic suction boundary layer. Structures of different frequencies and spatial extent can be clearly separated, and the prominent features of the dynamics are captured with just a few dynamic modes. In summary, we demonstrate that sDMD is a powerful tool for the identification of spatio-temporal structures in a wide range of turbulent flows.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Intelligent Feedback Overhead Reduction (iFOR) in Wi-Fi 7 and Beyond

The IEEE 802.11 standard based wireless local area networks (WLANs) or Wi-Fi networks are critical to provide internet access in today's world. The increasing demand for high data rate in Wi-Fi networks has led to several advancements in the 802.11 standard. Supporting MIMO transmissions with higher number of transmit antennas operating on wider bandwidths is one of the key capabilities for reaching higher throughput. However, the increase in sounding feedback overhead due to higher number of transmit antennas may significantly curb the throughput gain. In this paper, we develop an unsupervised learning-based method to reduce the sounding duration in a Wi-Fi MIMO link. Simulation results show that our method uses approximately only 8% of the number of bits required by the existing feedback mechanism and it can boost the system throughput by up to 52%.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions

Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios. However, visual distractions -- which are common in real scenes -- from high-dimensional observations can be hurtful to the learned representations in visual RL, thus degrading the performance of generalization. To tackle this problem, we propose a novel approach, namely Characteristic Reward Sequence Prediction (CRESP), to extract the task-relevant information by learning reward sequence distributions (RSDs), as the reward signals are task-relevant in RL and invariant to visual distractions. Specifically, to effectively capture the task-relevant information via RSDs, CRESP introduces an auxiliary task -- that is, predicting the characteristic functions of RSDs -- to learn task-relevant representations, because we can well approximate the high-dimensional distributions by leveraging the corresponding characteristic functions. Experiments demonstrate that CRESP significantly improves the performance of generalization on unseen environments, outperforming several state-of-the-arts on DeepMind Control tasks with different visual distractions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification

Message passing has evolved as an effective tool for designing Graph Neural Networks (GNNs). However, most existing methods for message passing simply sum or average all the neighboring features to update node representations. They are restricted by two problems, i.e., (i) lack of interpretability to identify node features significant to the prediction of GNNs, and (ii) feature over-mixing that leads to the over-smoothing issue in capturing long-range dependencies and inability to handle graphs under heterophily or low homophily. In this paper, we propose a Node-level Capsule Graph Neural Network (NCGNN) to address these problems with an improved message passing scheme. Specifically, NCGNN represents nodes as groups of node-level capsules, in which each capsule extracts distinctive features of its corresponding node. For each node-level capsule, a novel dynamic routing procedure is developed to adaptively select appropriate capsules for aggregation from a subgraph identified by the designed graph filter. NCGNN aggregates only the advantageous capsules and restrains irrelevant messages to avoid over-mixing features of interacting nodes. Therefore, it can relieve the over-smoothing issue and learn effective node representations over graphs with homophily or heterophily. Furthermore, our proposed message passing scheme is inherently interpretable and exempt from complex post-hoc explanations, as the graph filter and the dynamic routing procedure identify a subset of node features that are most significant to the model prediction from the extracted subgraph. Extensive experiments on synthetic as well as real-world graphs demonstrate that NCGNN can well address the over-smoothing issue and produce better node representations for semisupervised node classification. It outperforms the state of the arts under both homophily and heterophily.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL

Solving goal-conditioned tasks with sparse rewards using self-supervised learning is promising because of its simplicity and stability over current reinforcement learning (RL) algorithms. A recent work, called Goal-Conditioned Supervised Learning (GCSL), provides a new learning framework by iteratively relabeling and imitating self-generated experiences. In this paper, we revisit the theoretical property of GCSL -- optimizing a lower bound of the goal reaching objective, and extend GCSL as a novel offline goal-conditioned RL algorithm. The proposed method is named Weighted GCSL (WGCSL), in which we introduce an advanced compound weight consisting of three parts (1) discounted weight for goal relabeling, (2) goal-conditioned exponential advantage weight, and (3) best-advantage weight. Theoretically, WGCSL is proved to optimize an equivalent lower bound of the goal-conditioned RL objective and generates monotonically improved policies via an iterated scheme. The monotonic property holds for any behavior policies, and therefore WGCSL can be applied to both online and offline settings. To evaluate algorithms in the offline goal-conditioned RL setting, we provide a benchmark including a range of point and simulated robot domains. Experiments in the introduced benchmark demonstrate that WGCSL can consistently outperform GCSL and existing state-of-the-art offline methods in the fully offline goal-conditioned setting.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Safe Learning-Based Feedback Linearization Tracking Control for Nonlinear System with Event-Triggered Model Update

Learning-based methods are powerful in handling complex scenarios. However, it is still challenging to use learning-based methods under uncertain environments while stability, safety, and real-time performance of the system are desired to guarantee. In this paper, we propose a learning-based tracking control scheme based on a feedback linearization controller in which uncertain disturbances are approximated online using Gaussian Processes (GPs). Using the predicted distribution of disturbances given by GPs, a Control Lyapunov Function (CLF) and Control Barrier Function (CBF) based Quadratic Program is applied, with which probabilistic stability and safety are guaranteed. In addition, the trajectory is optimized first by Model Predictive Control (MPC) based on the linearized dynamics systems to further reduce the tracking error. We also design an event trigger for GPs updates to improve efficiency while stability and safety of the system are still guaranteed. The effectiveness of the proposed tracking control strategy is illustrated in numerical simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Safe Learning-based Gradient-free Model Predictive Control Based on Cross-entropy Method

In this paper, a safe and learning-based control framework for model predictive control (MPC) is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances. The control framework integrates a learning-based MPC with an auxiliary controller in a way of minimal intervention. The learning-based MPC augments the prior nominal model with incremental Gaussian Processes to learn the uncertain disturbances. The cross-entropy method (CEM) is utilized as the sampling-based optimizer for the MPC with a non-differentiable objective function. A minimal intervention controller is devised with a control Lyapunov function and a control barrier function to guide the sampling process and endow the system with high probabilistic safety. The proposed algorithm shows a safe and adaptive control performance on a simulated quadrotor in the tasks of trajectory tracking and obstacle avoidance under uncertain wind disturbances.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer

The vanilla self-attention mechanism inherently relies on pre-defined and steadfast computational dimensions. Such inflexibility restricts it from possessing context-oriented generalization that can bring more contextual cues and global representations. To mitigate this issue, we propose a Scalable Self-Attention (SSA) mechanism that leverages two scaling factors to release dimensions of query, key, and value matrices while unbinding them with the input. This scalability fetches context-oriented generalization and enhances object sensitivity, which pushes the whole network into a more effective trade-off state between accuracy and cost. Furthermore, we propose an Interactive Window-based Self-Attention (IWSA), which establishes interaction between non-overlapping regions by re-merging independent value tokens and aggregating spatial information from adjacent windows. By stacking the SSA and IWSA alternately, the Scalable Vision Transformer (ScalableViT) achieves state-of-the-art performance in general-purpose vision tasks. For example, ScalableViT-S outperforms Twins-SVT-S by 1.4% and Swin-T by 1.8% on ImageNet-1K classification.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Single-beam double-pass miniaturized atomic magnetometer for bio-magnetic imaging systems

Miniaturized atomic magnetometers, particularly spin-exchange relaxation-free atomic magnetometers, have been emerging in clinical imaging applications such magnetocardiography and magnetoencephalography. Miniaturization, portability, and low cost are primary development targets for bio-magnetic imaging technologies, as well as high sensitivity and spatial and time resolution. In this paper, we propose a low-cost solution for a bio-magnetic imaging system based on atomic magnetometers, in which one laser source is used for a multi-channel atomic magnetometer sensor array. A novel design is demonstrated for a miniaturized spin-exchange relaxation-free atomic magnetometer, consisting of a single-beam double-pass configuration based on an optical fiber circulator. The effects of temperature and laser power on the zero-field magnetic resonance line-width are characterized, and the experimental results show that the present design achieves better performance, than a traditional single-beam single-pass configuration. The noise power spectrum shows that the closed-loop miniaturized atomic magnetometer reaches a sensitivity of approximately 120 fT/Hz$^{1/2}$ at a bandwidth of 10 Hz. This design is especially suitable for atomic magnetometers operating in arrays as a basic building element for low-cost bio-magnetic imaging systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Some notes on variational principle for metric mean dimension

Firstly, we answer the problem 1 asked by Gutman and $\rm \acute{\ S}$piewak in \cite{gs20}, then we establish a double variational principle for mean dimension in terms of R$\bar{e}$nyi information dimension and show the order of $\sup$ and $\limsup$ (or $\liminf$) of the variational principle for the metric mean dimension in terms of R$\bar{e}$nyi information dimension obtained by Gutman and $\rm \acute{\ S}$piewak can be changed under the marker property. Finally, we attempt to introduce the notion of maximal metric mean dimension measure, which is an analogue of the concept called classical maximal entropy measure related to the topological entropy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Turbulent Rayleigh-Bénard convection with bubbles attached to the plate

We numerically investigate turbulent Rayleigh-Bénard convection with gas bubbles attached to the hot plate, mimicking a core feature in electrolysis, catalysis, or boiling. The existence of bubbles on the plate reduces the global heat transfer due to the much lower thermal conductivity of gases as compared to liquids and changes the structure of the boundary layers. The numerical simulations are performed in 3D at Prandtl number Pr=4.38 (water) and Rayleigh number $10^7\le Ra \le10^8$. For simplicity, we assume the bubbles to be equally-sized and having pinned contact lines. We vary the total gas-covered area fraction $0.18 \le S_0 \le 0.62$, the relative bubble height $0.02\le h/H \le0.05$ (where $H$ is the height of the Rayleigh-Bénard cell), the bubble number $40 \le n \le 144$, and their spatial distribution. In all cases, asymmetric temperature profiles are observed, which we quantitatively explain based on the heat flux conservation at each horizontal section. We further propose the idea of using an equivalent single-phase setup to mimic the system with attached bubbles. Based on this equivalence, we can calculate the heat transfer. Without introducing any free parameter, the predictions for the Nusselt number, the upper and lower thermal boundary layer thicknesses, and the mean centre temperature well agree with the numerical results. Finally, our predictions also work for the cases with much larger Pr (e.g. $400$), which indicates that our results can also be applied to predict the mass transfer in water electrolysis with bubbles attached to the electrode surface or in catalysis.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Demonstration of long-range correlations via susceptibility measurements in a one-dimensional superconducting Josephson spin chain

Spin chains have long been considered an effective medium for long-range interactions, entanglement generation, and quantum state transfer. In this work, we explore the properties of a spin chain implemented with superconducting flux circuits, designed to act as a connectivity medium between two superconducting qubits. The susceptibility of the chain is probed and shown to support long-range, cross chain correlations. In addition, interactions between the two end qubits, mediated by the coupler chain, are demonstrated. This work has direct applicability in near term quantum annealing processors as a means of generating long-range, coherent coupling between qubits.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Extended lifetime of respiratory droplets in a turbulent vapour puff and its implications on airborne disease transmission

To quantify the fate of respiratory droplets under different ambient relative humidities, direct numerical simulations of a typical respiratory event are performed. We found that, because small droplets (with initial diameter of 10um) are swept by turbulent eddies in the expelled humid puff, their lifetime gets extended by a factor of more than 30 times as compared to what is suggested by the classical picture by William F. Wells, for 50% relative humidity. With increasing ambient relative humidity the extension of the lifetimes of the small droplets further increases and goes up to around 150 times for 90% relative humidity, implying more than two meters advection range of the respiratory droplets within one second. Employing Lagrangian statistics, we demonstrate that the turbulent humid respiratory puff engulfs the small droplets, leading to many orders of magnitude increase in their lifetimes, implying that they can be transported much further during the respiratory events than the large ones. Our findings provide the starting points for larger parameter studies and may be instructive for developing strategies on optimizing ventilation and indoor humidity control. Such strategies are key in mitigating the COVID-19 pandemic in the present autumn and upcoming winter.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Heat transfer in turbulent Rayleigh-Bénard convection within two immiscible fluid layers

We numerically investigate turbulent Rayleigh-Bénard convection within two immiscible fluid layers, aiming to understand how the layer thickness and fluid properties affect the heat transfer (characterized by the Nusselt number $Nu$) in two-layer systems. Both two- and three-dimensional simulations are performed at fixed global Rayleigh number $Ra=10^8$, Prandtl number $Pr=4.38$, and Weber number $We=5$. We vary the relative thickness of the upper layer between $0.01 \le α\le 0.99$ and the thermal conductivity coefficient ratio of the two liquids between $0.1 \le λ_k \le 10$. Two flow regimes are observed: In the first regime at $0.04\leα\le0.96$, convective flows appear in both layers and $Nu$ is not sensitive to $α$. In the second regime at $α\le0.02$ or $α\ge0.98$, convective flow only exists in the thicker layer, while the thinner one is dominated by pure conduction. In this regime, $Nu$ is sensitive to $α$. To predict $Nu$ in the system in which the two layers are separated by a unique interface, we apply the Grossmann-Lohse theory for both individual layers and impose heat flux conservation at the interface. Without introducing any free parameter, the predictions for $Nu$ and for the temperature at the interface well agree with our numerical results and previous experimental data.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An Overview of Researches on Laser Ion Acceleration Using Mixed Solid Target and Single Ion Target

The essay gives an overview on researches in the field of laser ion acceleration, focusing on two types of targets. There are many types of targets while they can all be divided into targets that apply single ion or multiple ions. Mixed solid targets are proven efficient in accelerating heavy ions and generate high-quality ion beams with energy divergence lower than 5%. Traditional methods like TNSA are mainly used to accelerate protons or heavy ions and there are still many spaces for modification and improvement. Applications of laser-driven ion beams are wide in fields like detector technology, cancer therapy and so on, which is promising and necessary.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An Uplink Control Channel Design with Complementary Sequences for Unlicensed Bands

In this paper, two modulation schemes based on complementary sequences (CSs) are proposed for uplink control channels in unlicensed bands. These schemes address high peak-to-average-power ratio (PAPR) under non-contiguous resource allocation in the frequency domain and reduce the maximum PAPR to 3 dB. The first scheme allows the users to transmit a small amount of uplink control information (UCI) such as acknowledgment signals and does not introduce a trade-off between PAPR and co-channel interference (CCI). The second scheme, which enables up to 21 UCI bits for a single user or 11 UCI bits for three users in an interlace, is based on a new theorem introduced in this paper. This theorem leads distinct CSs compatible with a wide variety of resource allocations while capturing the inherent relationship between CSs and Reed-Muller (RM) codes, which makes CSs more useful for practical systems. The numerical results show that the proposed schemes maintain the low-PAPR benefits without increasing the error rate for non-contiguous resource allocations in the frequency domain.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Learning-Based Safety-Stability-Driven Control for Safety-Critical Systems under Model Uncertainties

Safety and tracking stability are crucial for safety-critical systems such as self-driving cars, autonomous mobile robots, industrial manipulators. To efficiently control safety-critical systems to ensure their safety and achieve tracking stability, accurate system dynamic models are usually required. However, accurate system models are not always available in practice. In this paper, a learning-based safety-stability-driven control (LBSC) algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model uncertainties. Gaussian Processes (GPs) are employed to learn the model error between the nominal model and the actual system dynamics, and the estimated mean and variance of the model error are used to quantify a high-confidence uncertainty bound. Using this estimated uncertainty bound, a safety barrier constraint is devised to ensure safety, and a stability constraint is developed to achieve rapid and accurate tracking. Then the proposed LBSC method is formulated as a quadratic program incorporating the safety barrier, the stability constraint, and the control constraints. The effectiveness of the LBSC method is illustrated on the safety-critical connected cruise control (CCC) system simulator under model uncertainties.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Matrix Completion Using Alternating Minimization for Distribution System State Estimation

This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method which combines the standard matrix completion method and power flow constraints has been shown to be effective in estimating voltage phasors under low-observability conditions using single-snapshot information. However, the method requires solving a semidefinite programming (SDP) problem, which becomes computationally infeasible for large systems and if multiple-snapshot (time-series) information is used. This paper proposes an efficient algorithm to solve the constrained matrix completion problem with time-series data. This algorithm is based on reformulating the matrix completion problem as a bilinear (non-convex) optimization problem, and applying the alternating minimization algorithm to solve this problem. This paper proves the summable convergence of the proposed algorithm, and demonstrates its efficacy and scalability via IEEE 123-bus system and a real utility feeder system. This paper also explores the value of adding more data from the history in terms of computation time and estimation accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Optical turbulence at Ali, China -- Results from the first year of lunar scintillometer observations

The location of an astronomical observatory is a key factor that affects its scientific productivity. The best astronomical sites are generally those found at high altitudes. Several such sites in western China and the Tibetan plateau are presently under development for astronomy. One of these is Ali, which at over 5000 m is one of the highest astronomical sites in the world. In order to further investigate the astronomical potential of Ali, we have installed a lunar scintillometer, for the primary purpose of profiling atmospheric turbulence. This paper describes the instrument and technique, and reports results from the first year of observations. We find that ground layer (GL) turbulence at Ali is remarkably weak and relatively thin. The median seeing, from turbulence in the range 11- 500 m above ground is 0.34 arcsec, with seeing better than 0.26 arcsec occurring 25 per cent of the time. Under median conditions, half of the GL turbulence lies below a height of 62 m. These initial results, and the high altitude and relatively low temperatures, suggest that Ali could prove to be an outstanding site for ground-based astronomy.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Subcritical behaviour in double diffusive convection within the diffusive regime

We conduct two- and three-dimensional simulations for double diffusive convection in the diffusive regime, where the fluid flow is driven by a destabilizing temperature gradient and stabilized by a stably stratified salinity gradient. We study how the heat flux, Reynolds number, and flow structures change with the density ratio $Λ$, which is the ratio of the buoyancy force induced by the salinity gradient to that by the temperature gradient. When $Λ$ increases from zero, the flow first behaves similarly as in pure Rayleigh-Bénard (RB) convection, both with respect to flow structure and to heat transport. The linear stability analysis of Baines & Gill (J. Fluid Mech., vol. 37, 1969, pp. 289-306) had estimated the critical density ratio $Λ_c$, above which the flow becomes stable. However, here we show that by using a large-scale circulation as initial condition (rather than the linear profiles assumed in the linear stability analysis), DDC in the diffusive regime can exhibit subcritical behaviour when $Λ> Λ_c$, i.e., coexistence of states at the same control parameters. Even though the density ratio becomes thousands times that of the critical value $Λ_c$, there is still convection with strongly enhanced heat transfer properties compared to the pure conduction case. We reveal the corresponding flow structures and find an unstably-stratified region sandwiched between two stably-stratified layers. Our results demonstrate the importance of the initial condition for DDC in the diffusive regime, especially in the situation of a large density ratio, which occurs in high-latitude ocean regions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Time-averaged transport in oscillatory squeeze flow of a viscoelastic fluid

Periodically-driven flows are known to generate non-zero, time-averaged fluxes of heat or solute species, due to the interactions of out-of-phase velocity and temperature/concentration fields, respectively. Herein, we investigate such transport (a form of the well-known Taylor--Aris dispersion) in the gap between two parallel plates, one of which oscillates vertically, generating a time-periodic squeeze flow of either a newtonian or Maxwellian fluid. Using the method of multiple time-scale homogenization, the mass/heat balance equation describing transport in this flow is reduced to a one-dimensional advection--diffusion--reaction equation. This result indicates three effective mechanisms in the mass/heat transfer in the system: an effective diffusion that spreads mass/heat along the concentration/temperature gradient, an effective advective flux, and an effective reaction that releases or absorbs mass/heat - in the time-averaged frame. Our results demonstrate that there exist resonant modes under which the velocity peaks when the dimensionless plate oscillation frequency (embodied by the Womersley number, the ratio of the transient inertia to viscous forces) approaches specific values. As a result, transport in this flow is significantly influenced by the dimensionless frequency. On the one hand, the effective, time-averaged dispersion coefficient is always larger than the molecular diffusivity, and is sharply enhanced near resonance. The interaction between fluid elasticity and the oscillatory forcing enhances the efficiency of transport in the system. On the other hand, the identified effective advection and reaction mechanisms may transport mass/heat from regions of high concentration/temperature to those of low concentration/temperature, or vice versa, depending on the value of dimensionless frequency.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Unconventional non-uniform local lattice distortion in dilute Ti-Mo solid solution

The substitutional solute atom induced local lattice distortion (LLD) in dilute metal solid solution was believed to be uniform that may even be modeled by using soap bubble raft. Contrary to this conventional picture, we report in this manuscript that the Mo induced LLD in dilute Ti-Mo solid solution is highly non-uniform as evidenced by our first principles calculations. The non-uniform LLD is ascribed to the Jahn-Teller splitting of the degenerated d states of Mo atom. We propose that the substitutional solid solutions with non-uniform LLD should satisfy two conditions. With which, the solid-solutions suffering from non-uniform LLD are predicted. The non-uniform LLD is expected to result in non-spherical stress field around the solute atom, and, therefore, challenges the application of classical solid solution hardening model to this kind of solid solutions.

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preprint2019arXiv

Fabrication of the impedance-matched Josephson parametric amplifier and the study of the gain profile

We designed and fabricated an impedance matched Josephson junction parametric amplifier (JPA) working in the flux-pump mode for the broadband amplification of microwave signals. We developed a very simple fabrication method suitable for a small lab. We studied the phase response, as well as the gain, as a function of frequency and pump power at various pump frequencies. The phase response can be explained with the behavior of the non-linear Duffing oscillator. The observed decrease of the resonance frequency as the pump power increases, as well as the emergency of an unstable bifurcation zone, are the characteristic non-linear behavior of the Duffing oscillator. The gain profile in the stable zone can be explained with a model adapted from the theoretical model for the two-dimensional gain profile of an impedance-matched current-pumped JPA. With an appropriate environmental impedance, the theoretical model captures the features and morphology of the gain profile, such as the emergence of a gain hot zone with two branches around the resonance frequency of the JPA. Based on the gain profile, we propose that the best working zone is the merging point of the two branches of the gain hot zone before the emergence of the bifurcation zone, which gives a large bandwidth and a good gain. Over 17dB gain with a bandwidth larger than 300MHz was observed. The impedance matched JPA is used in our superconducting quantum computers for improving the fast readout fidelity of the transmon qubits.

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