arXiv subjects

Rui Cheng

Publications and source records attributed to Rui Cheng.

At least 37 records · Page 2Linked to original sources

arXiv2023

Transport of intense ion beams in plasmas: collimation and energy-loss reduction

We compare the transport properties of a well-characterized hydrogen plasma for low and high current ion beams. The energy-loss of low current beams can be well understood, within the framework of current stopping power models. However, for high current proton beams, significant energy-loss reduction and collimation is observed in the experiment. We have developed a new particle-in-cell code, which includes both collective electromagnetic effects and collisional interactions. Our simulations indicate that resistive magnetic fields, induced by the transport of an intense proton beam, act to collimate the proton beam and simultaneously deplete the local plasma density along the beam path. This in turn causes the energy-loss reduction detected in the experiment.

physics.plasm-ph↗

arXiv2022

Automatic Self-Adaptive Local Voltage Control Under Limited Reactive Power

The increasing proliferation of distributed energy resources has posed new challenges to Volt/VAr control problems in distribution networks. To this end, this paper proposes an automatic self-adaptive local voltage control (ASALVC) by locally controlling VAr outputs of distributed energy resources. In this ASALVC strategy, each bus agent can locally and dynamically adjust its voltage droop function in accordance with time-varying system changes. The voltage droop function is associated with the bus-specific time-varying slope and intercept, which can be locally updated, merely based on local voltage measurements, without requiring communication. Stability, convergence, and optimality properties of this local voltage control are analytically established. In addition, the online implementation of ASALVC is further proposed to address the real-time system changes by adjusting VAr outputs of DERs online. Numerical test cases are performed to validate and demonstrate the effectiveness and superiority of ASALVC.

eess.SY↗

arXiv2022

Data-Driven Affinely Adjustable Robust Volt/VAr Control

This paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

eess.SY↗

arXiv2022

Target density effects on charge tansfer of laser-accelerated carbon ions in dense plasma

We report on charge state measurements of laser-accelerated carbon ions in the energy range of several MeV penetrating a dense partially ionized plasma. The plasma was generated by irradiation of a foam target with laser-induced hohlraum radiation in the soft X-ray regime. We used the tri-cellulose acetate (C$_{9}$H$_{16}$O$_{8}$) foam of 2 mg/cm$^{-3}$ density, and $1$-mm interaction length as target material. This kind of plasma is advantageous for high-precision measurements, due to good uniformity and long lifetime compared to the ion pulse length and the interaction duration. The plasma parameters were diagnosed to be T$_{e}$=17 eV and n$_{e}$=4 $\times$ 10$^{20}$ cm$^{-3}$. The average charge states passing through the plasma were observed to be higher than those predicted by the commonly-used semiempirical formula. Through solving the rate equations, we attribute the enhancement to the target density effects which will increase the ionization rates on one hand and reduce the electron capture rates on the other hand. In previsous measurement with partially ionized plasma from gas discharge and z-pinch to laser direct irradiation, no target density effects were ever demonstrated. For the first time, we were able to experimentally prove that target density effects start to play a significant role in plasma near the critical density of Nd-Glass laser radiation. The finding is important for heavy ion beam driven high energy density physics and fast ignitions.

physics.plasm-ph↗

arXiv2022

A Two-layer Approach for Estimating Behind-the-Meter PV Generation Using Smart Meter Data

As the cost of the residential solar system decreases, rooftop photovoltaic (PV) has been widely integrated into distribution systems. Most rooftop PV systems are installed behind-the-meter (BTM), i.e., only the net demand is metered, while the native demand and PV generation are not separately recorded. Under this condition, the PV generation and native demand are invisible to utilities, which brings challenges for optimal distribution system operation and expansion. In this paper, we have come up with a novel two-layer approach to disaggregate the unknown PV generation and native demand from the known hourly net demand data recorded by smart meters: 1) At the aggregate level, the proposed approach separates the total PV generation and native demand time series from the total net demand time series for customers with PVs. 2) At the customer level, the separated aggregate-level PV generation is allocated to individual PVs. These two layers leverage the spatial correlations of native demand and PV generation, respectively. One primary advantage of our proposed approach is that it is more independent and practical compared to previous works because it does not require PV array parameters, meteorological data and previously recorded solar power exemplars. We have verified our proposed approach using real native demand and PV generation data.

eess.SP↗

arXiv2022

Online Voltage Control for Unbalanced Distribution Networks Using Projected Newton Method

This paper proposes an online voltage control strategy of distributed energy resources (DERs), based on the projected Newton method (PNM), for unbalanced distribution networks. The optimal Volt/VAr control (VVC) problem is formulated as an optimization program, with the goal of maintaining the voltage profile across the network by coordinating the VAr outputs of DERs. To overcome the slow convergence rate of conventional gradient-based methods, a PNM-based solution algorithm is developed to solve this VVC problem. It utilizes a non-diagonal symmetric positive definite matrix, developed from the Hessian matrix of the objective, to scale the gradient, and thus a fast convergence performance can be expected in this Newton-like algorithm. Moreover, taking advantage of the instantaneous feedback of voltage measurements, the online implementation of the PNM-based VVC is further designed to deal with fast system variations. In this online PNM-based VVC scheme, each bus agent communicates the instantaneous voltage measurements to the central agent, and the central agent communicates the VAr output commands of DERs back to each bus agent. The fast convergence performance of PNM results in a stronger capability to track the system variations in real time. Finally, numerical case studies are performed to validate the effectiveness, superiority, and scalability of the proposed method.

eess.SY↗

arXiv2021

Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles

This paper develops a data-driven approach to accurately predict the restoration time of outages under different scales and factors. To achieve the goal, the proposed method consists of three stages. First, given the unprecedented amount of data collected by utilities, a sparse dictionary-based ensemble spectral clustering (SDESC) method is proposed to decompose historical outage datasets, which enjoys good computational efficiency and scalability. Specifically, each outage sample is represented by a linear combination of a small number of selected dictionary samples using a density-based method. Then, the dictionary-based representation is utilized to perform the spectral analysis to group the data samples with similar features into the same subsets. In the second stage, a knowledge-transfer-added restoration time prediction model is trained for each subset by combining weather information and outage-related features. The transfer learning technology is introduced with the aim of dealing with the underestimation problem caused by data imbalance in different subsets, thus improving the model performance. Furthermore, to connect unseen outages with the learned outage subsets, a t-distributed stochastic neighbor embedding-based strategy is applied. The proposed method fully builds on and is also tested on a large real-world outage dataset from a utility provider with a time span of six consecutive years. The numerical results validate that our method has high prediction accuracy while showing good stability against real-world data limitations.

eess.SP↗

arXiv2021

Progressive Graph Convolution Network for EEG Emotion Recognition

Studies in the area of neuroscience have revealed the relationship between emotional patterns and brain functional regions, demonstrating that dynamic relationships between different brain regions are an essential factor affecting emotion recognition determined through electroencephalography (EEG). Moreover, in EEG emotion recognition, we can observe that clearer boundaries exist between coarse-grained emotions than those between fine-grained emotions, based on the same EEG data; this indicates the concurrence of large coarse- and small fine-grained emotion variations. Thus, the progressive classification process from coarse- to fine-grained categories may be helpful for EEG emotion recognition. Consequently, in this study, we propose a progressive graph convolution network (PGCN) for capturing this inherent characteristic in EEG emotional signals and progressively learning the discriminative EEG features. To fit different EEG patterns, we constructed a dual-graph module to characterize the intrinsic relationship between different EEG channels, containing the dynamic functional connections and static spatial proximity information of brain regions from neuroscience research. Moreover, motivated by the observation of the relationship between coarse- and fine-grained emotions, we adopt a dual-head module that enables the PGCN to progressively learn more discriminative EEG features, from coarse-grained (easy) to fine-grained categories (difficult), referring to the hierarchical characteristic of emotion. To verify the performance of our model, extensive experiments were conducted on two public datasets: SEED-IV and multi-modal physiological emotion database (MPED).

cs.CV↗

arXiv2021

Analyzing Photovoltaic's Impact on Conservation Voltage Reduction in Distribution Networks

Conservation voltage reduction (CVR) has been widely implemented in distribution networks and helped utilities effectively reduce energy and peak load. However, the increasing penetration level of solar photovoltaic (PV) has affected voltage profiles and the performance of CVR. It remains an outstanding question how CVR and solar PV interact with each other. Understanding this interaction is important for utilities in implementing CVR and assessing its performance. This paper studies the impact of solar PV on CVR in a real distribution system in the Midwest U.S. using comprehensive simulations. We have considered various PV allocations and penetration levels, as well as different inverter control modes according to IEEE Std 1547-2018. Three metrics are used to quantify the impact of solar PV on CVR: voltages at the substation, voltage distribution across the network, and energy consumption reduction due to CVR. The results show that the allocations of solar PV have the most significant effect on the CVR performance, where a dispersed allocation of solar PV will help flatten voltage profile and achieve deeper voltage reductions at the substation, less energy consumption and line losses.

eess.SY↗

arXiv2021

An Online Feedback-Based Linearized Power Flow Model for Unbalanced Distribution Networks

The non-linearity and non-convexity of power flow models and the phase coupling challenge the analysis and optimization of unbalanced distribution networks. To tackle the challenges, this paper proposes an online feedback-based linearized power flow model for unbalanced distribution networks with both wye-connected and delta-connected loads. The online feedback-based linearized model is grounded on the first-order Taylor expansion of the branch flow model, and updates the model parameters via online feedback by leveraging the instantaneous measured voltages and load consumption at the previous time step. Its closed-loop nature can asymptotically mitigate the model mismatch, thus lending itself to a good performance. In addition, exploiting the connection structure of unbalanced radial distribution networks, we provide a unified matrix-vector compact form of the proposed linearized power flow model. Finally, the numerical tests on the IEEE 123-bus test system validate the effectiveness and superiority of the proposed model. A simple optimal power flow case is also provided to illustrate the application of the online feedback-based linearized model.

eess.SY↗

arXiv2021

Protein-Polymer Mixtures in the Colloid Limit: Aggregation, Sedimentation and Crystallization

While proteins have been treated as particles with a spherically symmetric interaction, of course in reality the situation is rather more complex. A simple step towards higher complexity is to treat the proteins as non--spherical particles and that is the approach we pursue here. We investigate the phase behavior of enhanced green fluorescent protein (eGFP) under the addition of a non--adsorbing polymer, polyethylene glycol (PEG). From small angle x-ray scattering we infer that the eGFP undergoes dimerization and we treat the dimers as spherocylinders with aspect ratio $L/D-1 = 1.05$. Despite the complex nature of the proteins, we find that the phase behaviour is similar to that of hard spherocylinders with ideal polymer depletant, exhibiting aggregation and, in a small region of the phase diagram, crystallization. By comparing our measurements of the onset of aggregation with predictions for hard colloids and ideal polymers [S.V. Savenko and M. Dijkstra, J. Chem. Phys 124, 234902 (2006) and F. lo Verso et al., Phys. Rev. E 73, 061407 (2006)] we find good agreement, which suggests that the eGFP proteins are consistent with hard spherocylinders and ideal polymer.

cond-mat.soft↗

arXiv2020

Anomalous stopping of laser-accelerated intense proton beam in dense ionized matter

Ultrahigh-intensity lasers (10$^{18}$-10$^{22}$W/cm$^{2}$) have opened up new perspectives in many fields of research and application [1-5]. By irradiating a thin foil, an ultrahigh accelerating field (10$^{12}$ V/m) can be formed and multi-MeV ions with unprecedentedly high intensity (10$^{10}$A/cm$^2$) in short time scale ($\sim$ps) are produced [6-14]. Such beams provide new options in radiography [15], high-yield neutron sources [16], high-energy-density-matter generation [17], and ion fast ignition [18,19]. An accurate understanding of the nonlinear behavior of beam transport in matter is crucial for all these applications. We report here the first experimental evidence of anomalous stopping of a laser-generated high-current proton beam in well-characterized dense ionized matter. The observed stopping power is one order of magnitude higher than single-particle slowing-down theory predictions. We attribute this phenomenon to collective effects where the intense beam drives an decelerating electric field approaching 1GV/m in the dense ionized matter. This finding will have considerable impact on the future path to inertial fusion energy.

physics.plasm-ph↗

arXiv2020

Multistage Model for Robust Face Alignment Using Deep Neural Networks

An ability to generalize unconstrained conditions such as severe occlusions and large pose variations remains a challenging goal to achieve in face alignment. In this paper, a multistage model based on deep neural networks is proposed which takes advantage of spatial transformer networks, hourglass networks and exemplar-based shape constraints. First, a spatial transformer - generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment. Then, stacked hourglass network is employed to obtain preliminary locations of landmarks as well as their corresponding scores. In addition, an exemplar-based shape dictionary is designed to determine landmarks with low scores based on those with high scores. By incorporating face shape constraints, misaligned landmarks caused by occlusions or cluttered backgrounds can be considerably improved. Extensive experiments based on challenging benchmark datasets are performed to demonstrate the superior performance of the proposed method over other state-of-the-art methods.

cs.CV↗

arXiv2018

Exploiting Contextual Information via Dynamic Memory Network for Event Detection

The task of event detection involves identifying and categorizing event triggers. Contextual information has been shown effective on the task. However, existing methods which utilize contextual information only process the context once. We argue that the context can be better exploited by processing the context multiple times, allowing the model to perform complex reasoning and to generate better context representation, thus improving the overall performance. Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks. In light of the multi-hop mechanism of the DMN to model the context, we propose the trigger detection dynamic memory network (TD-DMN) to tackle the event detection problem. We performed a five-fold cross-validation on the ACE-2005 dataset and experimental results show that the multi-hop mechanism does improve the performance and the proposed model achieves best $F_1$ score compared to the state-of-the-art methods.

cs.CL↗

arXiv2017

Experiments on bright field and dark field high energy electron imaging with thick target material

Using a high energy electron beam for the imaging of high density matter with both high spatial-temporal and areal density resolution under extreme states of temperature and pressure is one of the critical challenges in high energy density physics . When a charged particle beam passes through an opaque target, the beam will be scattered with a distribution that depends on the thickness of the material. By collecting the scattered beam either near or off axis, so-called bright field or dark field images can be obtained. Here we report on an electron radiography experiment using 45 MeV electrons from an S-band photo-injector, where scattered electrons, after interacting with a sample, are collected and imaged by a quadrupole imaging system. We achieved a few micrometers (about 4 micrometers) spatial resolution and about 10 micrometers thickness resolution for a silicon target of 300-600 micron thickness. With addition of dark field images that are captured by selecting electrons with large scattering angle, we show that more useful information in determining external details such as outlines, boundaries and defects can be obtained.

physics.acc-ph↗

arXiv2015

Electric field induced strong enhancement of electroluminescence in multi-Layer MoS2

The layered transition metal dichalcogenides (TMDs) have attracted considerable interest due to their unique electronic and optical properties. Here we report electric field induced strong electroluminescence in multi-layer MoS2 and WSe2. We show that GaN-Al2O3-MoS2 and GaN-Al2O3-MoS2-Al2O3-graphene vertical heterojunctions can be created with excellent rectification behaviour. Electroluminescence studies demonstrate prominent direct bandgap excitonic emission in multi-layer MoS2 over the entire vertical junction area. Importantly, the electroluminescence efficiency observed in multi-layer MoS2 is comparable to or even higher than that in monolayers, corresponding to a relative electroluminescence enhancement factor of >1000 in multi-layer MoS2 when compared to its photoluminescence. This striking enhancement of electroluminescence can be attributed to the high electric field induced carrier redistribution from low energy points (indirect bandgap) to high energy points (direct bandgap) of k-space, arising from the unique band structure of MoS2 with a much higher density of states at high energy points. The electric field induced electroluminescence is general for other TMDs including WSe2, and can provide a fundamental platform to probe the carrier injection, population and recombination in multi-layer TMDs and open up a new pathway toward TMD based optoelectronic devices.

cond-mat.mes-hall↗

arXiv2015

Beam optical design for high energy electron radiography experiment study based on THU LINAC

A special beam line for high energy electron radiography is designed, including achromat and imaging systems. The requirement of the angle and position correction on the target from imaging system can be approximately realized by fine tuning the quadrupoles used in the achromat. The imaging system is designed by fully considering the limitation from the laboratory and beam diagnostics devices space. Two kinds of imaging system are designed and both show a good performance of imaging by beam trajectory simulation. The details of the beam optical requirement and optimization design are presented here. The beam line is designed and prepared to install in Tsinghua university linear electron accelerator laboratory for further precise electron radiography experiment study.

physics.acc-ph↗

arXiv2014

Electroluminescence and photocurrent generation from atomically sharp WSe2/MoS2 heterojunction p-n diodes

The p-n diodes represent the most fundamental device building block for diverse optoelectronic functions, but are difficult to achieve in atomically thin transition metal dichalcogenides (TMDs) due to the inability to selectively dope them into p- or n-type semiconductors. Here we report the first demonstration of an atomically thin and atomically sharp heterojunction p-n diode by vertically stacking p-type monolayer tungsten diselenide (WSe2) and n-type few-layer molybdenum disulfide (MoS2). Electrical measurement demonstrates excellent diode characteristics with well-defined current rectification behaviour and an ideality factor of 1.2. Photocurrent mapping shows fast photoresponse over the entire overlapping region with a highest external quantum efficiency up to 12 %. Electroluminescence studies show prominent band edge excitonic emission and strikingly enhanced hot electron luminescence. A systematic investigation shows distinct layer-number dependent emission characteristics and reveals important insight about the origin of hot-electron luminescence and the nature of electron-orbital interaction in TMDs. We believe that these atomically thin heterojunction p-n diodes represent an interesting system for probing the fundamental electro-optical properties in TMDs, and can open up a new pathway to novel optoelectronic devices such as atomically thin photodetectors, photovoltaics, as well as spin-/valley-polarized light emitting diodes and on-chip lasers.

cond-mat.mes-hall↗