arXiv subjects

Hui Tian

Publications and source records attributed to Hui Tian.

At least 109 records · Page 6Linked to original sources

arXiv2022

Solar Ring Mission: Building a Panorama of the Sun and Inner-heliosphere

Solar Ring (SOR) is a proposed space science mission to monitor and study the Sun and inner heliosphere from a full 360° perspective in the ecliptic plane. It will deploy three 120°-separated spacecraft on the 1-AU orbit. The first spacecraft, S1, locates 30° upstream of the Earth, the second, S2, 90° downstream, and the third, S3, completes the configuration. This design with necessary science instruments, e.g., the Doppler-velocity and vector magnetic field imager, wide-angle coronagraph, and in-situ instruments, will allow us to establish many unprecedented capabilities: (1) provide simultaneous Doppler-velocity observations of the whole solar surface to understand the deep interior, (2) provide vector magnetograms of the whole photosphere - the inner boundary of the solar atmosphere and heliosphere, (3) provide the information of the whole lifetime evolution of solar featured structures, and (4) provide the whole view of solar transients and space weather in the inner heliosphere. With these capabilities, Solar Ring mission aims to address outstanding questions about the origin of solar cycle, the origin of solar eruptions and the origin of extreme space weather events. The successful accomplishment of the mission will construct a panorama of the Sun and inner-heliosphere, and therefore advance our understanding of the star and the space environment that holds our life.

astro-ph.SR↗

arXiv2022

Cross-loop propagation of a quasi-periodic extreme-ultraviolet wave train triggered by successive stretching of magnetic field structures during a solar eruption

Solar extreme-ultraviolet (EUV) waves generally refer to large-scale disturbances propagating outward from sites of solar eruptions in EUV imaging observations. Using the recent observations from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), we report a quasi-periodic wave train propagating outward at an average speed of $\sim$308 km s$^{-1}$. At least five wavefronts can be clearly identified with the period being $\sim$120 s. These wavefronts originate from the coronal loop expansion, which propagates with an apparent speed of $\sim$95 km s$^{-1}$, about 3 times slower than the wave train. In the absence of a strong lateral expansion, these observational results might be explained by the theoretical model of Chen et al. (2002), which predicted that EUV waves may have two components: a faster component that is a fast-mode magnetoacoustic wave or shock wave and a slower apparent front formed as a result of successive stretching of closed magnetic field lines. In this scenario, the wave train and the successive loop expansion we observed likely correspond to the fast and slow components in the model, respectively.

astro-ph.SR↗

arXiv2022

Forward Modeling of Magnetic-field Measurements at the Bases of Stellar Coronae through Extreme-Ultraviolet Spectroscopy

Measurements of the stellar coronal magnetic field are of great importance in understanding the stellar magnetic activity, yet the measurements have been extremely difficult. Recent studies proposed a new method of magnetic field measurements based on the magnetic-field-induced-transition (MIT) of the Fe~{\sc{x}} ion. Here we construct a series of stellar coronal magnetohydrodynamics (MHD) models and synthesize several Fe~{\sc{x}} emission lines at extreme-ultraviolet wavelengths, and then diagnose the magnetic field strength at the bases of the coronae using the MIT technique. Our results show that the technique can be applied to some stars with magnetic fields more than three times higher than that of the Sun at solar maximum. Furthermore, we investigate the uncertainty of the derived magnetic field strength caused by photon counting error and find that a signal-noise ratio of $\sim$50 for the Fe~{\sc{x}} 175 Å~line is required to achieve effective measurements of the stellar coronal magnetic field.

astro-ph.SR↗

arXiv2022

Research on Resource Allocation for Efficient Federated Learning

As a promising solution to achieve efficient learning among isolated data owners and solve data privacy issues, federated learning is receiving wide attention. Using the edge server as an intermediary can effectively collect sensor data, perform local model training, and upload model parameters for global aggregation. So this paper proposes a new framework for resource allocation in a hierarchical network supported by edge computing. In this framework, we minimize the weighted sum of system cost and learning cost by optimizing bandwidth, computing frequency, power allocation and subcarrier assignment. To solve this challenging mixed-integer non-linear problem, we first decouple the bandwidth optimization problem(P1) from the whole problem and obtain a closed-form solution. The remaining computational frequency, power, and subcarrier joint optimization problem(P2) can be further decomposed into two sub-problems: latency and computational frequency optimization problem(P3) and transmission power and subcarrier optimization problem(P4). P3 is a convex optimization problem that is easy to solve. In the joint optimization problem(P4), the optimal power under each subcarrier selection can be obtained first through the successive convex approximation(SCA) algorithm. Substituting the optimal power value obtained back to P4, the subproblem can be regarded as an assignment problem, so the Hungarian algorithm can be effectively used to solve it. The solution of problem P2 is accomplished by solving P3 and P4 iteratively. To verify the performance of the algorithm, we compare the proposed algorithm with five algorithms; namely Equal bandwidth allocation, Learning cost guaranteed, Greedy subcarrier allocation, System cost guaranteed and Time-biased algorithm. Numerical results show the significant performance gain and the robustness of the proposed algorithm in the face of parameter changes.

eess.SP↗

arXiv2022

Statistical Investigation of the Kinematic and Thermal Properties of Supra-arcade Downflows Observed During a Solar Flare

Supra-arcade downflows (SADs) are dark structures descending towards post-reconnection flare loops observed in extreme ultraviolet or X-ray observations and are closely related to magnetic reconnection during solar flares. Due to the lack of statistical study on SADs in a single flare, evolutions of kinematic and thermal properties of SADs during the flare process still remain obscure. In this work, we identified 81 SADs in a flare that occurred on 2013 May 22 using observations of the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). The kinematic properties of each SAD, including the appearance time, height, projective velocity, and acceleration were recorded. We found that the appearance heights of SADs become larger during the flare, which is likely due to the lift of the bottom of the plasma sheet. In the flare decay phase, the region where SADs mainly appear moves from the north part to the south side possibly related to a secondary eruption in the south side. The trajectories of most SADs can be fitted by one or two deceleration processes, while some special ones have positive accelerations during the descent. For the thermal properties, we selected 54 SADs, whose front and body could be clearly distinguished from the surrounding during the entire descent, to perform Differential Emission Measure analysis. It is revealed that the temperatures of the SAD front and body tend to increase during their downward courses, and the relationship between the density and temperature indicates that the heating is mainly caused by adiabatic compression.

astro-ph.SR↗

arXiv2022

Federated Deep Reinforcement Learning for RIS-Assisted Indoor Multi-Robot Communication Systems

Indoor multi-robot communications face two key challenges: one is the severe signal strength degradation caused by blockages (e.g., walls) and the other is the dynamic environment caused by robot mobility. To address these issues, we consider the reconfigurable intelligent surface (RIS) to overcome the signal blockage and assist the trajectory design among multiple robots. Meanwhile, the non-orthogonal multiple access (NOMA) is adopted to cope with the scarcity of spectrum and enhance the connectivity of robots. Considering the limited battery capacity of robots, we aim to maximize the energy efficiency by jointly optimizing the transmit power of the access point (AP), the phase shifts of the RIS, and the trajectory of robots. A novel federated deep reinforcement learning (F-DRL) approach is developed to solve this challenging problem with one dynamic long-term objective. Through each robot planning its path and downlink power, the AP only needs to determine the phase shifts of the RIS, which can significantly save the computation overhead due to the reduced training dimension. Simulation results reveal the following findings: I) the proposed F-DRL can reduce at least 86% convergence time compared to the centralized DRL; II) the designed algorithm can adapt to the increasing number of robots; III) compared to traditional OMA-based benchmarks, NOMA-enhanced schemes can achieve higher energy efficiency.

cs.RO↗

arXiv2022

Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning

Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel and synchronize their local models using over-the-air computation. The integrity of AirFL is vulnerable due to the obscurity of the local models aggregated over-the-air. This paper presents a novel framework to balance the accuracy and integrity of AirFL, where multi-antenna devices and base station (BS) are jointly optimized with a reconfigurable intelligent surface (RIS). The key contributions include a new and non-trivial problem jointly considering the model accuracy and integrity of AirFL, and a new framework that transforms the problem into tractable subproblems. Under perfect channel state information (CSI), the new framework minimizes the aggregated model's distortion and retains the local models' recoverability by optimizing the transmit beamformers of the devices, the receive beamformers of the BS, and the RIS configuration in an alternating manner. Under imperfect CSI, the new framework delivers a robust design of the beamformers and RIS configuration to combat non-negligible channel estimation errors. As corroborated experimentally, the novel framework can achieve comparable accuracy to the ideal FL while preserving local model recoverability under perfect CSI, and improve the accuracy when the number of receive antennas is small or moderate under imperfect CSI.

cs.LG↗

arXiv2022

Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection

RGB-thermal salient object detection (RGB-T SOD) aims to locate the common prominent objects of an aligned visible and thermal infrared image pair and accurately segment all the pixels belonging to those objects. It is promising in challenging scenes such as nighttime and complex backgrounds due to the insensitivity to lighting conditions of thermal images. Thus, the key problem of RGB-T SOD is to make the features from the two modalities complement and adjust each other flexibly, since it is inevitable that any modalities of RGB-T image pairs failure due to challenging scenes such as extreme light conditions and thermal crossover. In this paper, we propose a novel mirror complementary Transformer network (MCNet) for RGB-T SOD. Specifically, we introduce a Transformer-based feature extraction module to effective extract hierarchical features of RGB and thermal images. Then, through the attention-based feature interaction and serial multiscale dilated convolution (SDC) based feature fusion modules, the proposed model achieves the complementary interaction of low-level features and the semantic fusion of deep features. Finally, based on the mirror complementary structure, the salient regions of the two modalities can be accurately extracted even one modality is invalid. To demonstrate the robustness of the proposed model under challenging scenes in real world, we build a novel RGB-T SOD dataset VT723 based on a large public semantic segmentation RGB-T dataset used in the autonomous driving domain. Expensive experiments on benchmark and VT723 datasets show that the proposed method outperforms state-of-the-art approaches, including CNN-based and Transformer-based methods. The code and dataset will be released later at https://github.com/jxr326/SwinMCNet.

cs.CV↗

arXiv2022

STAR-RIS Integrated Non-Orthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimization

This paper integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omni-directional coverage extension. To capture the impact of non-ideal wireless channels on AirFL, a closed-form expression for the optimality gap (a.k.a. convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled non-convex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that i) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications, ii) our algorithms achieve faster convergence rate on IID and non-IID settings compared to existing baselines, and iii) both the spectrum efficiency and learning performance is significantly improved with the aid of the well-tuned STAR-RIS.

cs.IT↗

arXiv2022

A new post-hoc flat field measurement method for the Solar X-ray and Extreme Ultraviolet Imager onboard the Fengyun-3E satellite

The extreme ultraviolet (EUV) observations are widely used in solar activity research and space weather forecasting since they can observe both the solar eruptions and the source regions of the solar wind. Flat field processing is indispensable to remove the instrumental non-uniformity of a solar EUV imager in producing high-quality scientific data from original observed data. Fengyun-3E (FY-3E) is a meteorological satellite operated in Sun-synchronous orbit, and the routine EUV imaging data from the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) onboard FY-3E has the characteristics of concentric rotation. Taking advantage of the concentric rotation, we propose a post-hoc flat field measurement method for its EUV 195 channel in this paper. This method removes small-scale and time-varying component of the coronal activities by taking the median value for each pixel along the time axis of a concentric rotation data cube, and then derives large-scale and invariable component of the quiet coronal radiation, and finally generates a flat field image. Analysis shows that our method is able to measure the instrumental spot-like non-uniformity possibly caused by contamination on the detector, which mostly disappears after the in-orbit self-cleaning process. It can also measure the quasi-periodic grid-like non-uniformity, possibly from the obscuration of the support mesh on the rear filter. After flat field correction, these instrumental non-uniformities from the original data are effectively removed. X-EUVI 195 data after dark and flat field corrections are consistent with the 193 channel data from SDO/AIA, verifying the suitability of the method. Our method is not only suitable for FY-3E/X-EUVI but also a candidate method for the flat field measurement of future solar EUV telescopes.

astro-ph.SR↗

arXiv2022

Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?

With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a novel reconfigurable intelligent surface (RIS)-aided hybrid network by leveraging the RIS to flexibly adjust the signal processing order of heterogeneous data. The objective of this work is to maximize the achievable hybrid rate by jointly optimizing the transmit power, controlling the receive scalar, and designing the phase shifts. Since the concurrent transmissions of all computation and communication signals are aided by the discrete phase shifts at the RIS, the considered problem (P0) is a challenging mixed integer programming problem. To tackle this intractable issue, we decompose the original problem (P0) into a non-convex problem (P1) and a combinatorial problem (P2), which are characterized by the continuous and discrete variables, respectively. For the transceiver design problem (P1), the power allocation subproblem is first solved by invoking the difference-of-convex programming, and then the receive control subproblem is addressed by using the successive convex approximation, where the closed-form expressions of simplified cases are derived to obtain deep insights. For the reflection design problem (P2), the relaxation-then-quantization method is adopted to find a suboptimal solution for striking a trade-off between complexity and performance. Afterwards, an alternating optimization algorithm is developed to solve the non-linear and non-convex problem (P0) iteratively. Finally, simulation results reveal that 1) the proposed RIS-aided hybrid network can support the on-demand communication and computation efficiently, 2) the performance gains can be improved by properly selecting the location of the RIS, and 3) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users.

cs.IT↗

arXiv2022

Resilience in Industrial Internet of Things Systems: A Communication Perspective

Industrial Internet of Things is an ultra-large-scale system that is much more sophisticated and fragile than conventional industrial platforms. The effective management of such a system relies heavily on the resilience of the network, especially the communication part. Imperative as resilient communication is, there is not enough attention from literature and a standardized framework is still missing. In awareness of these, this paper intends to provide a systematic overview of resilience in IIoT with a communication perspective, aiming to answer the questions of why we need it, what it is, how to enhance it, and where it can be applied. Specifically, we emphasize the urgency of resilience studies via examining existing literature and analyzing malfunction data from a real satellite communication system. Resilience-related concepts and metrics, together with standardization efforts are then summarized and discussed, presenting a basic framework for analyzing the resilience of the system before, during, and after disruptive events. On the basis of the framework, key resilience concerns associated with the design, deployment, and operation of IIoT are briefly described to shed light on the methods for resilience enhancement. Promising resilient applications in different IIoT sectors are also introduced to highlight the opportunities and challenges in practical implementations.

eess.SY↗

arXiv2022

Safeguarding NOMA Networks via Reconfigurable Dual-Functional Surface under Imperfect CSI

This paper investigates the use of the reconfigurable dual-functional surface to guarantee the full-space secure transmission in non-orthogonal multiple access (NOMA) networks. In the presence of eavesdroppers, the downlink communication from the base station to the legitimate users is safeguarded by the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), where three practical operating protocols, namely energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. The joint optimization of power allocation, active and passive beamforming is investigated to maximize the secrecy energy efficiency (SEE), taking into account the imperfect channel state information (CSI) of all channels. For ES, by approximating the semi-infinite constraints with the S-procedure and general sign-definiteness, the problem is solved by an alternating optimization framework. Besides, the proposed algorithm is extended to the MS protocol by solving a mixed-integer non-convex problem. While for TS, a two-layer iterative method is proposed. Simulation results show that: 1) The proposed STAR-RIS assisted NOMA networks are able to provide up to 33.6\% higher SEE than conventional RIS counterparts; 2) TS and ES protocols are generally preferable for low and high power domain, respectively; 3) The accuracy of CSI estimation and the bit resolution power consumption are crucial to reap the SEE benefits offered by STAR-RIS.

eess.SP↗

arXiv2022

Detection of Flare-induced Plasma Flows in the Corona of EV Lac with X-ray Spectroscopy

Stellar flares are characterized by sudden enhancement of electromagnetic radiation from the atmospheres of stars. Compared to their solar counterparts, our knowledge on the coronal plasma dynamics of stellar flares and their connection to coronal mass ejections (CMEs) remains very limited. With time-resolved high-resolution spectroscopic observations from the \textit{Chandra} X-ray observatory, we detected noticeable coronal plasma flows during several stellar flares on a nearby dMe star EV Lac. In the observed spectra of O~{\sc{viii}} (3 MK), Fe~{\sc{xvii}} (6 MK), Mg~{\sc{xii}} (10 MK), and Si~{\sc{xiv}} (16 MK) lines, these flare-induced upflows/downflows appear as significant Doppler shifts of several tens to \speed{130}, and the upflow velocity generally increases with temperature. Variable line ratios of the Si~{\sc{xiii}} triplet reveal that these plasma flows in most flares are accompanied by an increase of the coronal plasma density and temperature. We interpret these results as X-ray evidences for chromospheric evaporation on EV Lac. In two successive flares, the plasma flow pattern and a sharp increase of the measured coronal density are highly suggestive of explosive evaporation. The transition from redshifts to blueshifts in such an explosive evaporation occurs at a temperature of at least 10 MK, much higher than that observed in solar flares ($\sim$1 MK). However, in one flare the cool and warm upflows appear to be accompanied by a decreasing plasma density, which might be explained by a stellar filament/prominence eruption coupled to this flare. These results provide important clues to understand the coronal plasma dynamics during flares on M dwarfs.

astro-ph.SR↗

arXiv2022

Towards Communication-Learning Trade-off for Federated Learning at the Network Edge

In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources. Although pruning is beneficial to reduce FL latency, it also deteriorates learning performance due to the information loss. Thus, a trade-off problem between communication and learning is raised. To address this challenge, we quantify the effects of network pruning and packet error on the learning performance by deriving the convergence rate of FL with a non-convex loss function. Then, closed-form solutions for pruning control and bandwidth allocation are proposed to minimize the weighted sum of FL latency and FL performance. Finally, numerical results demonstrate that 1) our proposed solution can outperform benchmarks in terms of cost reduction and accuracy guarantee, and 2) a higher pruning rate would bring less communication overhead but also worsen FL accuracy, which is consistent with our theoretical analysis.

cs.LG↗

arXiv2022

Possible detection of coronal mass ejections on late-type main-sequence stars in LAMOST medium-resolution spectra

Context. Stellar coronal mass ejections (CMEs) are the primary driver of the exoplanetary space weather and they could affect the habitability of exoplanets. However, detections of possible stellar CME signatures are extremely rare. Aims. This work aims to detect stellar CMEs from time-domain spectra observed through the LAMOST Medium-Resolution Spectroscopic Survey (LAMOST-MRS). Our sample includes 1,379,408 LAMOST-MRS spectra of 226,194 late-type main-sequence stars ($\rm T_{eff} < 6000$ K, $\rm log [g/(cm\ s^{-2})] > 4.0$). Methods. We first identified stellar CME candidates by examining the asymmetries of H$α$ line profiles, and then performed double Gaussian fitting for H$α$ contrast profiles (differences between the CME spectra and reference spectra) of the CME candidates to analyze the temporal variation of the asymmetric components. Results. Three stellar CME candidates were detected on three M dwarfs. The H$α$ and Mg I triplet lines (at 5168.94 Å, 5174.13 Å, 5185.10 Å) of candidate 1 all exhibit a blue-wing enhancement, and the corresponding Doppler shift of this enhancement shows a gradually increasing trend. The H$α$ line also shows an obvious blue-wing enhancement in candidate 2. In candidate 3, the H$α$ line shows an obvious red-wing enhancement, and the corresponding projected maximum velocity exceeds the surface escape velocity of the host star. The lower limit of the CME mass was estimated to be $\sim$$8 \times 10^{17}$ g to $4 \times 10^{18}$ g for these three candidates.

astro-ph.SR↗

arXiv2022

A hybrid privacy protection scheme for medical data

Healthcare data contains sensitive information, and it is challenging to persuade healthcare data owners to share their information for research purposes without any privacy assurance. The proposed hybrid medical data privacy protection scheme explores the possibility of providing adaptive privacy protection and data utility levels. The evaluation result demonstrates that the scheme can provide adaptive privacy and data utility levels, and the data holder can choose their preferred risk level and data utility through the scheme. The evaluation results on the heart disease and diabetes data demonstrate that the scheme can provide a wide range of adaptive privacy protection and data utility levels to meet different privacy protection and data utility requirements.

cs.CR↗

arXiv2022

A Hybrid Approach: Utilising Kmeans Clustering and Naive Bayes for IoT Anomaly Detection

The proliferation and variety of Internet of Things devices means that they have increasingly become a viable target for malicious users. This has created a need for anomaly detection algorithms that can work across multiple devices. This thesis suggests a potential alternative to the current anomaly detection algorithms to be implemented within IoT systems that can be applied across different types of devices. This algorithm is comprised of both unsupverised and supervised machine areas of machine learning combining the strongest facet of each. The algorithm involves the initial k-means clustering of attacks and assigns them to clusters. Next, the clusters are then used by the AdaBoosted Naive Bayes supervised learning algorithm in order to teach itself which piece of data should be clustered to which specific attack. This increases the accuracy of the proposed algorithm by adding clustered data before the final classification step, ensuring a more accurate algorithm. The correct indentification percentage scores for this proposed algorithm range anywhere from 90% to 100%, as well as rating the proposed algorithms accuracy, precision and recall. These high scores achieve an accurate, flexible, scalable, optimised algorithm that could potentially be in different IoT devices, ensuring strong data integrity and privacy.

cs.CR↗