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Yue Zhu

Publications and source records attributed to Yue Zhu.

At least 55 records · Page 3Linked to original sources

The Case for Universal Basic Computing Power

The Universal Basic Computing Power (UBCP) initiative ensures global, free access to a set amount of computing power specifically for AI research and development (R&D). This initiative comprises three key elements. First, UBCP must be cost free, with its usage limited to AI R&D and minimal additional conditions. Second, UBCP should continually incorporate the state of the art AI advancements, including efficiently distilled, compressed, and deployed training data, foundational models, benchmarks, and governance tools. Lastly, it's essential for UBCP to be universally accessible, ensuring convenience for all users. We urge major stakeholders in AI development large platforms, open source contributors, and policymakers to prioritize the UBCP initiative.

cs.AI↗

H3WB: Human3.6M 3D WholeBody Dataset and Benchmark

We present a benchmark for 3D human whole-body pose estimation, which involves identifying accurate 3D keypoints on the entire human body, including face, hands, body, and feet. Currently, the lack of a fully annotated and accurate 3D whole-body dataset results in deep networks being trained separately on specific body parts, which are combined during inference. Or they rely on pseudo-groundtruth provided by parametric body models which are not as accurate as detection based methods. To overcome these issues, we introduce the Human3.6M 3D WholeBody (H3WB) dataset, which provides whole-body annotations for the Human3.6M dataset using the COCO Wholebody layout. H3WB comprises 133 whole-body keypoint annotations on 100K images, made possible by our new multi-view pipeline. We also propose three tasks: i) 3D whole-body pose lifting from 2D complete whole-body pose, ii) 3D whole-body pose lifting from 2D incomplete whole-body pose, and iii) 3D whole-body pose estimation from a single RGB image. Additionally, we report several baselines from popular methods for these tasks. Furthermore, we also provide automated 3D whole-body annotations of TotalCapture and experimentally show that when used with H3WB it helps to improve the performance. Code and dataset is available at https://github.com/wholebody3d/wholebody3d

cs.CV↗

How to adequately describe full range intercalation -- a two-sided approach

Here we show the description of the full incorporation thermodynamics in nanocrystalline LixFePO4 where the full range from x=0 to 1 is experimentally accessible. The following points are most important: the treatment in terms of the neutral lattice-gas model is incorrect rather ions and electrons need to be considered separately. One needs to invoke point defect chemistry which gives a satisfactory agreement over the full storage range if we treat the problem from the two-sides: from the FePO4 side where Li+ is incorporated interstitially and from the LiFePO4 side where Li+ is filling the vacancies. Furthermore, we clarify how non-idealities such as ion-electron and electron-electron interactions can be introduced to obtain a mechanistic understanding.

cond-mat.mtrl-sci↗

Experimental demonstration of diffusion limitations on resolution and SNR in MR microscopy

Magnetic resonance microscopy images at cellular resolution (< 10 microns) are limited by diffusion. SNR and spatial resolution suffer from the dephasing of transverse magnetization caused by diffusion of spins in strong gradients. Such effects may be reduced by using phase encoding instead of frequency encoding readout gradients. Demonstration of the benefits of phase encoding are lacking, and the conditions in which it is preferred are not clearly established. We quantify when phase encoding outperforms a readout gradient with emphasis on the detrimental effects of diffusion on SNR and resolution. A 15.2T MRI scanner, with 1 T/m gradients, and micro solenoid RF coils < 1 mm in diameter, were used to quantify diffusion effects on resolution and SNR of frequency and phase encoded acquisitions. Frequency and phase encoding resolution and SNR per square root time were calculated and measured for images at the diffusion limited resolution. The point-spread-function was measured for phase and frequency encoding using additional constant time gradients with voxels 3-15 microns. The effect of diffusion during the readout gradient on SNR was experimentally demonstrated. The achieved resolutions of frequency and phase encoded acquisitions were measured via the point-spread-function. SNR per square root time and actual resolution were calculated for a wide range of gradient amplitudes, diffusion coefficients, and relaxation properties. The results provide a practical guide on how to choose between phase and frequency encoding. Images of excised rat spinal cord at 10 x 10 microns in-plane demonstrate benefits of phase encoding in the form of higher measured resolution and SNR vs the same image acquired with a conventional readout. We demonstrate the extent to which phase encoding outperforms readout gradients in SNR and resolution over a wide range of voxel sizes, sample, and hardware properties.

physics.med-ph↗

Decanus to Legatus: Synthetic training for 2D-3D human pose lifting

3D human pose estimation is a challenging task because of the difficulty to acquire ground-truth data outside of controlled environments. A number of further issues have been hindering progress in building a universal and robust model for this task, including domain gaps between different datasets, unseen actions between train and test datasets, various hardware settings and high cost of annotation, etc. In this paper, we propose an algorithm to generate infinite 3D synthetic human poses (Legatus) from a 3D pose distribution based on 10 initial handcrafted 3D poses (Decanus) during the training of a 2D to 3D human pose lifter neural network. Our results show that we can achieve 3D pose estimation performance comparable to methods using real data from specialized datasets but in a zero-shot setup, showing the generalization potential of our framework.

cs.CV↗

Impedance-based Root-cause Analysis: Comparative Study of Impedance Models and Calculation of Eigenvalue Sensitivity

Impedance models of power systems are useful when state-space models of apparatus such as inverter-based resources (IBRs) have not been made available and instead only black-box impedance models are available. For tracing the root causes of poor damping and tuning modes of the system, the sensitivity of the modes to components and parameters are needed. The so-called critical admittance-eigenvalue sensitivity based on nodal admittance model has provided a partial solution but omits meaningful directional information. The alternative whole-system impedance model yields participation factors of shunt-connected apparatus with directional information that allows separate tuning for damping and frequency, yet do not cover series-connected components. This paper formalises the relationships between the two forms of impedance models and between the two forms of root-cause analysis. The calculation of system eigenvalue sensitivity in impedance models is further developed, which fills the gaps of previous research and establishes a complete theory of impedance-based root-cause analysis. The theoretical relationships and the tuning of parameters have been illustrated with a three-node passive network, a modified IEEE 14-bus network and a modified NETS-NYPS 68-bus network, showing that tools can be developed for tuning of IBR-rich power systems where only black-box impedance models are available.

eess.SY↗

Intelligent Traffic Light via Policy-based Deep Reinforcement Learning

Intelligent traffic lights in smart cities can optimally reduce traffic congestion. In this study, we employ reinforcement learning to train the control agent of a traffic light on a simulator of urban mobility. As a difference from existing works, a policy-based deep reinforcement learning method, Proximal Policy Optimization (PPO), is utilized other than value-based methods such as Deep Q Network (DQN) and Double DQN (DDQN). At first, the obtained optimal policy from PPO is compared to those from DQN and DDQN. It is found that the policy from PPO performs better than the others. Next, instead of the fixed-interval traffic light phases, we adopt the light phases with variable time intervals, which result in a better policy to pass the traffic flow. Then, the effects of environment and action disturbances are studied to demonstrate the learning-based controller is robust. At last, we consider unbalanced traffic flows and find that an intelligent traffic light can perform moderately well for the unbalanced traffic scenarios, although it learns the optimal policy from the balanced traffic scenarios only.

cs.LG↗

Energy Response of GECAM Gamma-Ray Detector Based on LaBr3:Ce and SiPM Array

The Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) , composed of two small satellites, is a new mission to monitor the Gamma-Ray Bursts (GRBs) coincident with Gravitational Wave (GW) events with a FOV of 100% all-sky.Each GECAM satellite detects and localizes GRBs using 25 compact and novel Gamma-Ray Detectors (GRDs) in 6 keV-5 MeV. Each GRD module is comprised of LaBr3:Ce scintillator, SiPM array and preamplifier. A large dynamic range of GRD is achieved by the high gain and low gain channels of the preamplifier. The energy response of GRD prototype was evaluated using radioactive sources in the range of 5.9-1332.5 keV. A energy resolution of 5.3% at 662 keV was determined from the 137Cs pulse height spectra, which meets the GECAM requirement (< 8% at 662 keV). Energy to channel conversion was evaluated and a nonlinearity correction was performed to reduce the residuals (< 1.5%). Also, a Geant4-based simulated in-flight background and a measured GRD LaBr3:Ce intrinsic activity were used to evaluate the capability of in-flight calibration. These results demonstrate the design of GRD.

physics.ins-det↗

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM, which is an all-sky gamma-ray monitor launched in Dec 10, 2020. We developed a multifunctional trigger and localization software operating on the CPU of the GECAM electronic box (EBOX). This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for short and long bursts respetively, short time latency of the trigger information which is downlinked throught the BeiDou satellite navigation System (BDS). This paper presents the detailed design and deveopment of this trigger and localization software system of GECAM, including the main functions, general design, workflow and algorithms, as well as the verification and demonstration of this software, including the on-ground trigger tests with simulated gamma-ray bursts made by a dedicated X-ray tube and the in-flight performance to real gamma-ray bursts and magnetar bursts.

astro-ph.IM↗

Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods

Convolutional networks are successful due to their equivariance/invariance under translations. However, rotatable data such as images, volumes, shapes, or point clouds require processing with equivariance/invariance under rotations in cases where the rotational orientation of the coordinate system does not affect the meaning of the data (e.g. object classification). On the other hand, estimation/processing of rotations is necessary in cases where rotations are important (e.g. motion estimation). There has been recent progress in methods and theory in all these regards. Here we provide an overview of existing methods, both for 2D and 3D rotations (and translations), and identify commonalities and links between them.

cs.LG↗

Participation Analysis in Impedance Models: The Grey-Box Approach for Power System Stability

This paper develops a grey-box approach to small-signal stability analysis of complex power systems that facilitates root-cause tracing without requiring disclosure of the full details of the internal control structure of apparatus connected to the system. The grey-box enables participation analysis in impedance models, which is popular in power electronics and increasingly accepted in power systems for stability analysis. The Impedance participation factor is proposed and defined in terms of the residue of the whole-system admittance matrix. It is proved that, the so defined impedance participation factor equals the sensitivity of the whole-system eigenvalue with respect to apparatus impedance. The classic state participation factor is related to the impedance participation factor via a chain-rule. Based on the chain-rule, a three-layer grey-box approach, with three degrees of transparency, is proposed for root-cause tracing to different depths, i.e. apparatus, states, and parameters, according to the available information. The association of impedance participation factor with eigenvalue sensitivity points to the re-tuning that would stabilize the system. The impedance participation factor can be measured in the field or calculated from the black-box impedance spectra with little prior knowledge required.

eess.SY↗

An Energy Efficient Authentication Scheme using Chebyshev Chaotic Map for Smart Grid Environment

As one of the important applications of Smart grid, charging between electric vehicles has attracted much attention. However, authentication between vehicle users and an aggregator may be vulnerable to various attacks due to the usage of wireless communications. In order to reduce the computational costs yet preserve required security, the Chebyshev chaotic map based authentication schemes are proposed. However, the security requirements of Chebyshev polynomials bring a new challenge to the design of authentication schemes based on Chebyshev chaotic maps. To solve this issue, we propose a practical Chebyshev polynomial algorithm by using a binary exponentiation algorithm based on square matrix to achieve secure and efficient Chebyshev polynomial computation. We further apply the proposed algorithm to construct an energy-efficient authentication and key agreement scheme for smart grid environments. Compared with state-of-the-art schemes, the proposed authentication scheme effectively reduces the computational costs and communication costs by adopting the proposed Chebyshev polynomial algorithm. Furthermore, the ProVerif tool is employed to analyze the security of the proposed authentication scheme. Our experimental results justified that our proposed authentication scheme can outperform state-of-the-art schemes in terms of the computational overhead while achieving privacy protection.

cs.CR↗

Transferring Inter-Class Correlation

The Teacher-Student (T-S) framework is widely utilized in the classification tasks, through which the performance of one neural network (the student) can be improved by transferring knowledge from another trained neural network (the teacher). Since the transferring knowledge is related to the network capacities and structures between the teacher and the student, how to define efficient knowledge remains an open question. To address this issue, we design a novel transferring knowledge, the Self-Attention based Inter-Class Correlation (ICC) map in the output layer, and propose our T-S framework, Inter-Class Correlation Transfer (ICCT).

cs.CV↗

Resilient Secondary Voltage Control of Islanded Microgrids: An ESKBF-Based Distributed Fast Terminal Sliding Mode Control Approach

This paper proposes a distributed secondary voltage control method based on extended state Kalman-Bucy filter (ESKBF) and fast terminal sliding mode (FTSM) control for the resilient operation of an islanded microgrid (MG) with inverter-based distributed generations (DGs). To tackle the co-existence of multiple uncertainties, a unified modelling framework is proposed to represent the set of different types of disturbances, including parameter perturbation, measurement noise, and immeasurably external variables, by an extended state method. Kalman-Bucy filter is then applied to accurately estimate the state information of the extended DG model. In addition, based on the accurate estimation, a fast terminal sliding mode (FTSM) surface with terminal attractors is designed to maintain the system stability and accelerate the convergence of consensus tracking, which significantly improves the performance of secondary voltage control under both normal and plug-and-play operation. Finally, case studies are conducted in both MATLAB/Simulink and an experimental testbed to demonstrate the effectiveness of the proposed method.

eess.SY↗

Impedance-Based Whole-System Modeling for a Composite Grid via Frame-Dynamics Embedding

The paper establishes a methodology to overcome the difficulty of dynamic frame alignment and system separation in impedance modeling of ac grids, and thereby enables impedance-based whole-system modeling of generator-converter composite power systems. The methodology is based on a frame-dynamics-embedding transformation via an intermediary steady frame between local and global frames, which yields a locally defined impedance model for each generator or converter that does not rely on a global frame but retains all frame dynamics. The individual impedance model can then be readily combined into a whole-system model even for meshed networks via the proposed closed-loop formulation without network separation. Compared to start-of-the-art impedance-based models, the proposed method retains both frame dynamics and scalability, and is generally applicable to various network topologies (meshed, radial, etc) and combinations of machines (generators, motors, converters, etc). The methodology is used to analyze the dynamic interaction between generators and converters in a composite grid, which yields important findings and potential solutions for unstable oscillation caused by PLL-swing coupling in low-inertia grids.

eess.SY↗

Calibration of the Instrumental Response of Insight-HXMT/HE CsI Detectors for Gamma-Ray Monitoring

The CsI detectors of the High Energy X-ray Telescope of the Hard X-ray Modulation Telescope (HXMT/CsI) can be used for gamma-ray all sky monitoring and searching for the electromagnetic counterpart of gravitational wave source. The instrumental responses are mainly obtained by Monte Carlo simulation with the Geant4 tool and the mass model of both the satellite and all the payloads, which is updated and tested with the Crab pulse emission in various incident directions. Both the Energy-Channel relationship and the energy resolution are calibrated in two working modes (Normal-Gain mode & Low-Gain Mode) with the different detection energy ranges. The simulative spectral analyses show that HXMT/CsI can constrain the spectral parameters much better in the high energy band than that in the low energy band. The joint spectral analyses are performed to ten bright GRBs observed simultaneously with HXMT/CsI and other instruments (Fermi/GBM, Swift/BAT, Konus-Wind), and the results show that the GRB flux given by HXMT/CsI is systematically higher by $7.0\pm8.8\%$ than those given by the other instruments. The HXMT/CsI-Fermi/GBM joint fittings also show that the high energy spectral parameter can be constrained much better as the HXMT/CsI data are used in the joint fittings.

astro-ph.IM↗

The Low Energy X-ray telescope (LE) onboard the Insight-HXMT astronomy satellite

The low energy (LE) X-ray telescope is one of the three main instruments of the Insight-Hard X-ray Modulation Telescope (Insight-HXMT). It is equipped with Swept Charge Device (SCD) sensor arrays with a total geometrical area of 384 cm2 and an energy band from 0.7 keV to 13 keV. In order to evaluate the particle induced X-ray background and the cosmic X-ray background simultaneously, LE adopts collimators to define four types of Field Of Views (FOVs). LE is constituted of three detector boxes (LEDs) and an electric control box (LEB) and achieves a good energy resolution of 140 eV at 5.9 keV, an excellent time resolution of 0.98 ms, as well as an extremely low pileup (<1% at 18000 cts/s). Detailed performance tests and calibration on the ground have been performed, including energy-channel relation, energy response, detection efficiency and time response.

astro-ph.IM↗

The $\rm{Y_{SZ,Planck} - Y_{SZ,XMM}}$ scaling relation and its difference between cool-core and non-cool-core clusters

We construct a sample of 70 clusters using data from XMM-Newton and Planck to investigate the $Y_{\rm SZ,Planck}-Y_{\rm SZ, XMM}$ scaling relation and the cool-core influences on the relation. $Y_{\rm SZ,XMM}$ is calculated by accurate de-projected temperature and electron number density profiles derived from XMM-Newton. $Y_{\rm SZ,Planck}$ is the latest Planck data restricted to our precise X-ray size $θ_{\rm 500}$. To study the cool-core influences on$Y_{\rm SZ,Planck}-Y_{\rm SZ, XMM}$ scaling relation, we apply two criteria, limits of central cooling time and classic mass deposition rate, to distinguish cool-core clusters (CCCs) from non-cool-core clusters (NCCCs). We also use $Y_{\rm SZ,Planck}$ from other papers, which are derived from different methods, to confirm our results. The intercept and slope of the$Y_{\rm SZ,Planck}-Y_{\rm SZ, XMM}$ scaling relation are $A=-0.86 \pm 0.30$, $B=0.83 \pm 0.06$. The intrinsic scatter is $σ_{\rm ins}= 0.14 \pm 0.03$. The ratio of \mbox{$Y_{\rm SZ,Planck}/Y_{\rm SZ, XMM}$} is $1.03 \pm 0.05$, which is perfectly agreed with unity. Discrepancies of $Y_{\rm SZ,Planck}-Y_{\rm SZ, XMM}$ scaling relation between CCCs and NCCCs are found in observation. They are independent of cool-core classification criteria and $Y_{\rm SZ,Planck}$ calculation methods, although discrepancies are more significant under the classification criteria of classic mass deposition rate. The intrinsic scatter of CCCs (0.04) is quite small compared to that of NCCCs (0.27). The ratio of $Y_{\rm SZ,Planck}/Y_{\rm SZ, XMM}$ for CCCs is $0.89 \pm 0.05$, suggesting that CCCs' $Y_{\rm SZ,XMM}$ may overestimate SZ signal. By contrast, the ratio of $Y_{\rm SZ,Planck}/Y_{\rm SZ, XMM}$ for NCCCs is $1.14 \pm 0.12$, which indicates that NCCCs' $Y_{\rm SZ,XMM}$ may underestimate SZ signal.

astro-ph.CO↗