arXiv subjects

Yue Hao

Publications and source records attributed to Yue Hao.

At least 55 records · Page 3Linked to original sources

arXiv2024

Time-Delayed Koopman Network-Based Model Predictive Control for the FRIB RFQ

The radio-frequency quadrupole (RFQ) at the Facility for Rare Isotope Beams (FRIB) is a critical device to accelerate heavy ion beams from 12 keV/u to 0.5 MeV/u for state-of-the-art nuclear physics experiments. Efficient control of the RFQ resonance frequency detuning still remains a challenge because the temperature-sensitive frequency is solely control by a cooling water system, exhibiting complicated transport delay and nonlinearity in the heat transfer processes. In this work, we propose a long-short term memory (LSTM)-based Koopman network model that can simultaneously learn the time-delayed and non-delayed correlations hidden in the historical operating data. It is proven that the model can effectively predict the behavior of the RFQ resonance frequency using historical data as inputs. With this model, a model predictive control (MPC) framework based on the Newton-Raphson method is proposed and tested. We demonstrate that the MPC framework utilizing deep learning model is able to provide precise and rapid control for the RFQ frequency detuning, reducing the control time by half compared to the proportional-integral-derivative (PID) controller.

physics.acc-ph↗

arXiv2024

Online regularization of Poincaré map of storage rings with Shannon entropy

Shannon entropy, as a chaos indicator, is used for online Poincaré map regularization and dynamic aperture optimization in the National Synchrotron Light Source-II (NSLS-II) ring. Although various chaos indicators are widely used in studying nonlinear dynamical systems, including modern particle accelerators, it is the first time to use a measurable one in a real-world machine for online nonlinear optimization. Poincaré maps, constructed with the turn-by-turn beam trajectory readings from beam position monitors, are commonly used to observe the nonlinearity in ring-based accelerators. However, such observations typically only provide a qualitative interpretation. We analyze their entropy to quantify the chaos in measured Poincaré maps. After some canonical transformations on the Poincaré maps, not only can the commonly used nonlinear characterizations be extracted, but more importantly, the chaos can be quantitatively calibrated with Shannon entropy, and then used as the online optimization objectives.

physics.acc-ph↗

arXiv2024

Enhanced beam-beam modeling to include longitudinal variation during weak-strong simulation

Beam-beam interactions pose substantial challenges in the design and operation of circular colliders, significantly affecting their performance. In particular, the weak-strong simulation approach is pivotal for investigating single-particle dynamics during the collider design phase. This paper evaluates the limitations of existing models in weak-strong simulations, noting that while they accurately account for energy changes due to slingshot effects, they fail to incorporate longitudinal coordinate changes ($z$-variation). To address this gap, we introduce two novel transformations that enhance Hirata's original framework by including both $z$-variation and slingshot effect-induced energy changes. Through rigorous mathematical analysis and extensive weak-strong simulation studies, we validate the efficacy of these enhancements in achieving a more precise simulation of beam-beam interactions. Our results reveal that although $z$-variation constitutes a higher-order effect and does not substantially affect the emittance growth rate within the specific design parameters of the Electron-Ion Collider (EIC), the refined model offers improved accuracy, particularly in scenarios involving the interaction between beam-beam effects and other random diffusion processes, as well as in simulations incorporating realistic lattice models.

physics.acc-ph↗

arXiv2024

Microscopic Insights into Fatigue Mechanism in Wurtzite Ferroelectric Al$_{0.65}$Sc$_{0.35}$N: Oxygen Infiltration Enabled Grain Amorphization Spanning Boundary to Bulk

For the first time, the fatigue behavior involving external oxygen in highly Sc-doped AlN ferroelectric film was observed using transmission electron microscope techniques. Despite increasing the Sc composition in AlScN film contributes to reducing the device operation voltage, the inherent affinity of Sc for oxygen introduces instability in device performance. In this study, oxygen incorporation at top electrode edges and grain boundaries accompanied with an increase in current leakage and the disappearance of ferroelectric properties, was observed in nanoscale after long-term field cycling. This observation indicates the emergence of non-ferroelectric and even amorphous states. This presented work revealed solid experimental evidence of an oxygen-involved fatigue mechanism, providing valuable insights into the physical nature of the ferroelectric properties of AlScN films.

cond-mat.mtrl-sci↗

arXiv2024

Tunable double notch filter on thin-film lithium niobate platform

Tunable optical filter at the chip scale plays a crucial role in fulfilling the need for the reconfigurability in channel routing, optical switching, and wavelength division multiplexing systems. In this letter, we propose a tunable double notch filter on thin-film lithium niobate using dual micro-ring architecture. This unique integrated filter is essential for complex photonic integrated circuits, along with multiple channels and various frequency spacing. With only one loaded voltage, the device demonstrates a wide frequency spacing tunability from 16.1 GHz to 89.9 GHz by reversely tunning the resonances of the two micro-rings while the center wavelength between the two resonances remains unaltered. Moreover, by utilizing the pronounced electro-optic properties of lithium niobate, associated with the tight light confinement nanophotonic waveguides, the device demonstrates a spacing tunability of 0.82 GHz/V and a contrast of 10~16 dB. In addition, the device has an ultracompact footprint of 0.0248 mm2.

physics.optics↗

arXiv2023

Convergence estimation and characteristic analysis of a two-level iterative algorithm for the discretized three-temperature energy linear systems

For solving the discretized three-temperature energy linear systems, Xu et al. proposed a physical-variable based coarsening two-level iterative method (PCTL algorithm) in 2009 and verified its efficiency by numerical experiments in practical applications. In this paper, we study in detail the specific convergence property of the PCTL algorithm based on the theory of algebraic multigrid method (AMG),and give a reasonable upper bound on the convergence factor, which provides a theoretical guarantee for the PCTL algorithm. Moreover, we also analyse the algebraic features that affect the convergence of the PCTL algorithm, such as diagonal dominance and coupling strength, hoping provides theoretical guidance for the applications and algorithm optimization of the PCTL algorithm.

math.NA↗

arXiv2023

Photonic Integrated Neuro-Synaptic Core for Convolutional Spiking Neural Network

Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture. Linear weighting and nonlinear spiking activation are two fundamental functions of a photonic spiking neural network (PSNN). However, they are separately implemented with different photonic materials and devices, hindering the large-scale integration of PSNN. Here, we propose, fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spiking activation based on a distributed feedback (DFB) laser with a saturable absorber (DFB-SA). A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation. Furthermore, a four-channel DFB-SA array is fabricated for realizing matrix convolution of a spiking convolutional neural network, achieving a recognition accuracy of 87% for the MNIST dataset. The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.

physics.optics↗

arXiv2022

Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning

Bayesian optimization~(BO) is often used for accelerator tuning due to its high sample efficiency. However, the computational scalability of training over large data-set can be problematic and the adoption of historical data in a computationally efficient way is not trivial. Here, we exploit a neural network model trained over historical data as a prior mean of BO for FRIB Front-End tuning.

physics.acc-ph↗

arXiv2022

Spiking SiamFC++: Deep Spiking Neural Network for Object Tracking

Spiking neural network (SNN) is a biologically-plausible model and exhibits advantages of high computational capability and low power consumption. While the training of deep SNN is still an open problem, which limits the real-world applications of deep SNN. Here we propose a deep SNN architecture named Spiking SiamFC++ for object tracking with end-to-end direct training. Specifically, the AlexNet network is extended in the time domain to extract the feature, and the surrogate gradient function is adopted to realize direct supervised training of the deep SNN. To examine the performance of the Spiking SiamFC++, several tracking benchmarks including OTB2013, OTB2015, VOT2015, VOT2016, and UAV123 are considered. It is found that, the precision loss is small compared with the original SiamFC++. Compared with the existing SNN-based target tracker, e.g., the SiamSNN, the precision (succession) of the proposed Spiking SiamFC++ reaches 85.24% (64.37%), which is much higher than that of 52.78% (44.32%) achieved by the SiamSNN. To our best knowledge, the performance of the Spiking SiamFC++ outperforms the existing state-of-the-art approaches in SNN-based object tracking, which provides a novel path for SNN application in the field of target tracking. This work may further promote the development of SNN algorithms and neuromorphic chips.

cs.CV↗

arXiv2022

Predicting Beam Transmission Using 2-Dimensional Phase Space Projections Of Hadron Accelerators

We present a method to compressed the 2D transverse phase space projections from a hadron accelerator and use that information to predict the beam transmission. This method assumes that it is possible to obtain at least three projections of the 4D transverse phase space and that an accurate simulation model is available for the beamline. Using a simulated model we show that, a procedure using a convolutional autoencoder can be trained to reduce phase-space information which can later be used to predict the beam transmission. Finally, we argue that although using projections from a realistic non-linear distribution produces less accurate results, the method still generalizes well.

physics.acc-ph↗

arXiv2022

Origami-based Zygote structure enables pluripotent shape-transforming deployable structure

We propose an algorithmic framework of a pluripotent structure evolving from a simple compact structure into diverse complex 3-D structures for designing the shape transformable, reconfigurable, and deployable structures and robots. Our algorithmic approach suggests a way of transforming a compact structure consisting of uniform building blocks into a large, desired 3-D shape. Analogous to the pluripotent stem cells that can grow into a preprogrammed shape according to coded information, which we call DNA, compactly stacked panels named the zygote structure can evolve into arbitrary 3-D structures by programming their connection path. Our stacking algorithm obtains this coded sequence by inversely stacking the voxelized surface of the desired structure into a tree. Applying the connection path obtained by the stacking algorithm, the compactly stacked panels named the zygote structure can be deployed into diverse large 3-D structures. We conceptually demonstrated our pluripotent evolving structure by energy releasing commercial spring hinges and thermally actuated shape memory alloy (SMA) hinges, respectively. We also show that the proposed concept enables the fabrication of large structures in a significantly smaller workspace.

cs.CE↗

arXiv2022

Electrically tunable second harmonic generation in atomically thin ReS2

Electrical tuning of second-order nonlinearity in optical materials is attractive to strengthen and expand the functionalities of nonlinear optical technologies, though its implementation remains elusive. Here, we report the electrically tunable second-order nonlinearity in atomically thin ReS2 flakes benefiting from their distorted 1T crystal structure and interlayer charge transfer. Enabled by the efficient electrostatic control of the few-atomic-layer ReS2, we show that second harmonic generation (SHG) can be induced in odd-number-layered ReS2 flakes which are centrosymmetric and thus without intrinsic SHG. Moreover, the SHG can be precisely modulated by the electric field, reversibly switching from almost zero to an amplitude more than one order of magnitude stronger than that of the monolayer MoS2. For the even-number-layered ReS2 flakes with the intrinsic SHG, the external electric field could be leveraged to enhance the SHG. We further perform the first-principles calculations which suggest that the modification of in-plane second-order hyperpolarizability by the redistributed interlayer-transferring charges in the distorted 1T crystal structure underlies the electrically tunable SHG in ReS2. With its active SHG tunability while using the facile electrostatic control, our work may further expand the nonlinear optoelectronic functions of two-dimensional materials for developing electrically controllable nonlinear optoelectronic devices.

physics.optics↗

arXiv2022

Synchro-betatron Resonance of Crab Crossing Scheme with Large Crossing Angle and Finite Bunch Length

Crab crossing scheme is an essential collision scheme to achieve high luminosity for the future colliders with large crossing angles. However, when bunch length of one or both colliding beams is comparable with the wavelength of the crab cavity voltage, the nonlinear dependence of the crabbing kick may present a challenge to the beam dynamics of the colliding beams and impact the beam quality as well as the luminosity lifetime. In this paper, the results of nonlinear dynamics in the crab crossing scheme are presented, using both analytical and numerical studies. The result indicates that higher-order synchro-betatron resonances may be excited in the crab crossing scheme with large crossing angle, which causes the beam quality deterioration and luminosity degradation. The studies also reveal possible countermeasures to suppress the synchro-beta resonance, hence mitigate the degradation of beam quality and luminosity.

physics.acc-ph↗

arXiv2022

Combined effects of Crab Dispersion and Momentum Dispersion in Colliders with Local Crab Crossing Scheme

In this paper, we present the effects of linear transverse-longitudinal coupling on beam size at Interaction Point (IP) of a collider with local crab crossing scheme, when time dependent transverse deflection (crab kicks) and dispersive orbit intertwine near IP. The analytic propagation formula and the closed orbit form of the crab dispersion and momentum dispersion are derived. The non-zero momentum dispersion at crab cavities and the non-ideal phase from crab cavities to IP are detailed with the derived propagation formula to predict the beam size distortion at IP with or without the beam-beam interaction. The linear results are compared with nonlinear simulation using the weak-strong beam-beam code.

physics.acc-ph↗

arXiv2022

Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-Pérot laser with saturable absorber

Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-Pérot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.

cs.ET↗

arXiv2021

Metal Blossom: Laser Forming Complex and Freeform Metal Structures Imitating Flower Blooming

For centuries, human civilizations devised metal forming techniques to make tools and items; yet, customized metal forming remains costly and intricate. Laser-forming origami} (lasergami) is a metal forming process where a laser beam cuts and folds a planar metal sheet to form a three-dimensional (3D) shape. Designing foldable structures formable by lasers, however, has long been a trial-and-error practice that requires significant mental effort and hinders the possibility of creating practical structures. This work demonstrates for the first time that lasergami can form a freeform set of metallic structures previously believed to have been impossible to be laser-formed. This technological breakthrough is enabled by new computational origami methods that imitate flower blooming and optimize laser folding instructions. Combined with new ideas that address laser line of sight and minimize fabrication energy, we report a low-cost manufacturing framework that can be readily adopted by hobbyists and professionals alike.

cs.RO↗

arXiv2020

Planning Folding Motion with Simulation in the Loop Using Laser Forming Origami and Thermal Behaviors as an Example

Designing a robot or structure that can fold itself into a target shape is a process that involves challenges originated from multiple sources. For example, the designer of rigid self-folding robots must consider foldability from geometric and kinematic aspects to avoid self-intersection and undesired deformations. Recent works have shown success in estimating foldability of a design using robot motion planners. However, many foldable structures are actuated using physically coupled reactions (i.e., folding originated from thermal, chemical, or electromagnetic loads). Therefore, a reliable foldability analysis must consider additional constraints that resulted from these critical phenomena. This work investigates the idea of efficiently incorporating computationally expensive physics simulation within the folding motion planner to provide a better estimation of the foldability. In this paper, we will use laser forming origami as an example to demonstrate the benefits of considering the properties beyond geometry. We show that the design produced by the proposed method can be folded more efficiently.

cs.RO↗

arXiv2020

Leidenfrost drop impact on inclined superheated substrates

In real applications, drops always impact on solid walls with various inclinations. For the oblique impact of a Leidenfrost drop, which has a vapor layer under its bottom surface to prevent its direct contact with the superheated substrate, the drop can nearly frictionlessly slide along the substrate accompanied by the spreading and the retracting. To individually study these processes, we experimentally observe ethanol drops impact on superheated inclined substrates using high-speed imaging from two different views synchronously. We first study the dynamic Leidenfrost temperature, which mainly depends on the normal Weber number ${We}_\perp $. Then the substrate temperature is set to be high enough to study the Leidenfrost drop behaviors. During the spreading process, drops always keep uniform. And the maximum spreading factor $D_m/D_0$ follows a power-law dependence on the large normal Weber number ${We}_\perp $ as $D_m/D_0 = \sqrt{We_\perp /12+2}$ for $We_\perp \geq 30$. During the retracting process, drops with low impact velocities become non-uniform due to the gravity effect. For the sliding process, the residence time of all studied drops is nearly a constant, which is not affected by the inclination and $We$ number. The frictionless vapor layer results in the dimensionless sliding distance $L/D_0$ follows a power-law dependence on the parallel Weber number $We_\parallel$ as $L/D_0 \propto We_\parallel^{1/2}$. Without direct contact with the substrate, the behaviors of drops can be separately determined by ${We}_\perp $ and $We_\parallel$. When the impact velocity is too high, the drop fragments into many tiny droplets, which is called the splashing phenomenon. The critical splashing criterion is found to be $We_\perp ^*\simeq$ 120 or $K_\perp = We_\perp Re_\perp^{1/2} \simeq$ 5300 in the current parameter regime.

physics.flu-dyn↗