arXiv subjects

Xingchen Li

Publications and source records attributed to Xingchen Li.

53 records · Page 3Linked to original sources

arXiv2024

High precision proton beam monitor system concept design on CSNS based on SiC

A high precision beam monitor system based on silicon carbide PIN sensor is designed for China Spallation Neutron Source 1.6 GeV proton beam to monitor the proton beam fluence.The concept design of the beam monitor system is finished together with front-end electronics with silicon carbide PIN sensors, readout system and mechanical system.Several tests are performed to study the performance of each component of the system.The charge collection of the SiC PIN sensors after proton radiation is studied with 80 MeV proton beam for continuous running. Research on the performance of the front-end electronics and readout system is finished for better data acquisition.The uncertainty of proton beam fluence is below 1% in the beam monitor system.

physics.acc-ph↗

arXiv2024

Algorithm-hardware co-design for Energy-Efficient A/D conversion in ReRAM-based accelerators

Deep neural networks are widely deployed in many fields. Due to the in-situ computation (known as processing in memory) capacity of the Resistive Random Access Memory (ReRAM) crossbar, ReRAM-based accelerator shows potential in accelerating DNN with low power and high performance. However, despite power advantage, such kind of accelerators suffer from the high power consumption of peripheral circuits, especially Analog-to-Digital Converter (ADC), which account for over 60 percent of total power consumption. This problem hinders the ReRAM-based accelerator to achieve higher efficiency. Some redundant Analog-to-Digital conversion operations have no contribution to maintaining inference accuracy, and such operations can be eliminated by modifying the ADC searching logic. Based on such observations, we propose an algorithm-hardware co-design method and explore the co-design approach in both hardware design and quantization algorithms. Firstly, we focus on the distribution output along the crossbar's bit-lines and identify the fine-grained redundant ADC sampling bits. % of weight and To further compress ADC bits, we propose a hardware-friendly quantization method and coding scheme, in which different quantization strategy was applied to the partial results in different intervals. To support the two features above, we propose a lightweight architectural design based on SAR-ADC\@. It's worth mentioning that our method is not only more energy efficient but also retains the flexibility of the algorithm. Experiments demonstrate that our method can reduce about $1.6 \sim 2.3 \times$ ADC power reduction.

cs.AR↗

arXiv2023

EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration

In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. Previous calibration methods often required specific targets or manual adjustments, making them both labor-intensive and costly. Online calibration methods based on features have been proposed, but these methods encounter challenges such as imprecise feature extraction, unreliable cross-modality associations, and high scene-specific requirements. To address this, we introduce an edge-based approach for automatic online calibration of LiDAR and cameras in real-world scenarios. The edge features, which are prevalent in various environments, are aligned in both images and point clouds to determine the extrinsic parameters. Specifically, stable and robust image edge features are extracted using a SAM-based method and the edge features extracted from the point cloud are weighted through a multi-frame weighting strategy for feature filtering. Finally, accurate extrinsic parameters are optimized based on edge correspondence constraints. We conducted evaluations on both the KITTI dataset and our dataset. The results show a state-of-the-art rotation accuracy of 0.086° and a translation accuracy of 0.977 cm, outperforming existing edge-based calibration methods in both precision and robustness.

cs.CV↗

arXiv2023

Evolution of Stellar Bars in Spinning Dark Matter Halos and Stellar Bulges

We use high-resolution numerical simulations to follow the barred disk evolution in a suite of models with progressively more massive stellar bulges, with bulge-to-total (disk$+$bulge) mass ratios of $B/T\sim 0-0.25$, embedded in dark matter (DM) halos with the spin $λ\sim 0 - 0.09$. We focus on models with a sequence of initial rotational support for bulges, and analyze their spinup and spindown. We find that (1) the presence of a bulge affects the evolution of stellar bars, i.e., the timescale of bar instability, bar pattern speed and its decay, and the vertical buckling instability. The bar strength is nearly independent of $B/T$ in halos with spin $λ= 0$, and is suppressed by a factor $\sim 2$ for halos with $λ= 0.09$; (2) The main effect of the bulge is the destruction of the harmonic core which affects the buckling; (3) The bulge plays a minor role in the exchange of angular momentum between the barred disk and the DM halo, during its spinup and spindown; (4) Most interestingly, the buckling process triggers different response above/below the disk midplane, which anti-correlates with the bulge mass; (5) In spinning halos, the buckling process has a prolonged amplitude tail, extending by few Gyr, as verified by measuring distortions in the Laplace plane; (6) Furthermore, as verified by orbital spectral analysis, the bulge gains its spin from the bar mainly via the inner Lindblad resonance, while losing it via a number of resonances lying between the outer and inner Lindblad resonance.

astro-ph.GA↗

arXiv2023

Stellar Bars in Spinning Halos: Delayed Buckling and Absence of Slowdown

We use high resolution numerical simulations in order to analyze the stellar bar evolution in spinning dark matter (DM) halos. Previous works have shown that the halo spin has a substantial effect on the bar evolution and can lead to bar dissolution following the vertical buckling instability. Here, we invoke the DM spin sequence, $λ=0-0.09$, and study the effect of DM density along this $λ$-sequence by varying the compactness of DM halo. We find that (1) varying the DM density has a profound effect on the stellar bar evolution along the $λ$-sequence, namely, on its amplitude, pattern speed, buckling time, etc.; (2) For $λ\gtrsim 0.04$, the buckling instability has been delayed progressively, and does not occur when the bar has reached its maximal strength; (3) Instead, stellar bars remain near maximal strength, and their amplitude plateau stage extends over $\sim 1-7$ Gyr, terminating with the buckling instability; (4) Although stellar bars remain strong during the plateau, their pattern speed stays nearly constant. The reason for this unusual behavior of stellar bars follows from the highly reduced gravitational torques which they experience due to the DM bar being aligned with the stellar bar. The performed orbital analysis shows that the delayed buckling results from a slow evolution of stellar oscillations along the bar major and vertical axes -- thus postponing the action of the vertical 2:1 resonance which pumps the rotational energy into vertical motions; (5) Peanut/boxy shaped bulges form at the beginning of the plateau and grow with time; (6) Strong stellar bars in spinning halos can avoid fast braking, resolving the long standing discrepancy between observations and $N$-body simulations. This behavior of stellar bars along the $λ$- and DM density-sequences, reveals a wealth of stellar bar properties which require additional study.

astro-ph.GA↗

arXiv2023

USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots

In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots. In recent years, numerous public datasets have played significant roles in the advancement of autonomous cars and unmanned aerial vehicles (UAVs). However, these two platforms differ from aerial work robots: UAVs are limited in their payload capacity, while cars are restricted to two-dimensional movements. To fill this gap, we create the "Giraffe" mapping robot based on a bucket truck, which is equipped with a variety of well-calibrated and synchronized sensors: four 3D LiDARs, two stereo cameras, two monocular cameras, Inertial Measurement Units (IMUs), and a GNSS/INS system. A laser tracker is used to record the millimeter-level ground truth positions. We also make its ground twin, the "Okapi" mapping robot, to gather data for comparison. The proposed dataset extends the typical autonomous driving sensing suite to aerial scenes, demonstrating the potential of combining autonomous driving perception systems with bucket trucks to create a versatile autonomous aerial working platform. Moreover, based on the Segment Anything Model (SAM), we produce the Semantic FLICAR dataset, which provides fine-grained semantic segmentation annotations for multimodal continuous data in both temporal and spatial dimensions. The dataset is available for download at: https://ustc-flicar.github.io/.

cs.RO↗

arXiv2023

FRA-LSTM: A Vessel Trajectory Prediction Method Based on Fusion of the Forward and Reverse Sub-Network

In order to improve the vessel's capacity and ensure maritime traffic safety, vessel intelligent trajectory prediction plays an essential role in the vessel's smart navigation and intelligent collision avoidance system. However, current researchers only focus on short-term or long-term vessel trajectory prediction, which leads to insufficient accuracy of trajectory prediction and lack of in-depth mining of comprehensive historical trajectory data. This paper proposes an Automatic Identification System (AIS) data-driven long short-term memory (LSTM) method based on the fusion of the forward sub-network and the reverse sub-network (termed as FRA-LSTM) to predict the vessel trajectory. The forward sub-network in our method combines LSTM and attention mechanism to mine features of forward historical trajectory data. Simultaneously, the reverse sub-network combines bi-directional LSTM (BiLSTM) and attention mechanism to mine features of backward historical trajectory data. Finally, the final predicted trajectory is generated by fusing output features of the forward and reverse sub-network. Based on plenty of experiments, we prove that the accuracy of our proposed method in predicting short-term and mid-term trajectories has increased by 96.8% and 86.5% on average compared with the BiLSTM and Seq2seq. Furthermore, the average accuracy of our method is 90.1% higher than that of compared the BiLSTM and Seq2seq in predicting long-term trajectories.

cs.CY↗

arXiv2023

The Origin of Buckling Instability in Galactic Bars: Searching for the Scapegoat

The buckling process in stellar bars is full of unsolved issues. We analyze the origin of the buckling instability in stellar bars using high-resolution N-body simulations. Previous studies have promoted the nonresonant firehose instability to be responsible for the vertical buckling. We have analyzed the buckling process in terms of the resonant excitation of stellar orbits in the bar, which pumps energy into vertical oscillations. We find that (1) the buckling is associated with an abrupt increase in the central mass concentration and triggers velocities along the bar and along its rotation axis. The velocity field projected on one of the main axes forms circulation cells and increases vorticity, which are absent in firehose instability; (2) The bending amplitude is nonlinear when measured by isodensity contours or curvature of the Laplace plane, which has a substantial effect on the stellar motions; (3) In the linear description, the planar and vertical 2:1 resonances appear only with the buckling and quickly reach the overlapping phase, thus supporting the energy transfer; (4) Using nonlinear orbit analysis, we analyze the stellar oscillations along the bar and along the rotation axis and find that stars cross the vertical 2:1 resonance simultaneously with the buckling. The overlapping planar and vertical 2:1 resonances trapping more than 25% of the bar particles provide the 'smoking gun' pointing to a close relationship between the bending of stellar orbits and the resonant action -- these particles provide the necessary ingredient assuring the cohesive response in the growing vertical asymmetry. We conclude that resonant excitation is important in triggering the buckling instability, and the contribution from the firehose instability should be reevaluated. Finally, we discuss some observational implications of buckling.

astro-ph.GA↗

arXiv2022

Standoff Tracking Using DNN-Based MPC with Implementation on FPGA

This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height. We propose a novel Lyapunov guidance vector (LGV) field with tunable convergence rates for the UAV's trajectory planning and a deep neural network (DNN)-based model predictive control (MPC) scheme to track the reference trajectory. Then, we show how to collect samples for training the DNN offline and design an integral module (IM) to refine the tracking performance of our DNN-based MPC. Moreover, the hardware-in-the-loop (HIL) simulation with an FPGA@200MHz demonstrates that our method is a valid alternative to embedded implementations of MPC for addressing complex systems and applications which is impossible for directly solving the MPC optimization problems.

eess.SY↗

arXiv2022

Rethinking the Evaluation of Unbiased Scene Graph Generation

Current Scene Graph Generation (SGG) methods tend to predict frequent predicate categories and fail to recognize rare ones due to the severe imbalanced distribution of predicates. To improve the robustness of SGG models on different predicate categories, recent research has focused on unbiased SGG and adopted mean Recall@K (mR@K) as the main evaluation metric. However, we discovered two overlooked issues about this de facto standard metric, which makes current unbiased SGG evaluation vulnerable and unfair: 1) mR@K neglects the correlations among predicates and unintentionally breaks category independence when ranking all the triplet predictions together regardless of the predicate categories. 2) mR@K neglects the compositional diversity of different predicates and assigns excessively high weights to some oversimple category samples with limited composable relation triplet types. In addition, we investigate the under-explored correlation between objects and predicates, which can serve as a simple but strong baseline for unbiased SGG. In this paper, we refine mR@K and propose two complementary evaluation metrics for unbiased SGG: Independent Mean Recall (MR) and weighted IMR (wIMR). These two metrics are designed by considering the category independence and diversity of composable relation triplets, respectively. We compare the proposed metrics with the de facto standard metrics through extensive experiments and discuss the solutions to evaluate unbiased SGG in a more trustworthy way.

cs.CV↗

arXiv2022

Integrating Object-aware and Interaction-aware Knowledge for Weakly Supervised Scene Graph Generation

Recently, increasing efforts have been focused on Weakly Supervised Scene Graph Generation (WSSGG). The mainstream solution for WSSGG typically follows the same pipeline: they first align text entities in the weak image-level supervisions (e.g., unlocalized relation triplets or captions) with image regions, and then train SGG models in a fully-supervised manner with aligned instance-level "pseudo" labels. However, we argue that most existing WSSGG works only focus on object-consistency, which means the grounded regions should have the same object category label as text entities. While they neglect another basic requirement for an ideal alignment: interaction-consistency, which means the grounded region pairs should have the same interactions (i.e., visual relations) as text entity pairs. Hence, in this paper, we propose to enhance a simple grounding module with both object-aware and interaction-aware knowledge to acquire more reliable pseudo labels. To better leverage these two types of knowledge, we regard them as two teachers and fuse their generated targets to guide the training process of our grounding module. Specifically, we design two different strategies to adaptively assign weights to different teachers by assessing their reliability on each training sample. Extensive experiments have demonstrated that our method consistently improves WSSGG performance on various kinds of weak supervision.

cs.CV↗

arXiv2022

Data-driven Control of Unknown Linear Systems via Quantized Feedback

Control using quantized feedback is a fundamental approach to system synthesis with limited communication capacity. In this paper, we address the stabilization problem for unknown linear systems with logarithmically quantized feedback, via a direct data-driven control method. By leveraging a recently developed matrix S-lemma, we prove a sufficient and necessary condition for the existence of a common stabilizing controller for all possible dynamics consistent with data, in the form of a linear matrix inequality. Moreover, we formulate semi-definite programming to solve the coarsest quantization density. By establishing its connections to unstable eigenvalues of the state matrix, we further prove a necessary rank condition on the data for quantized feedback stabilization. Finally, we validate our theoretical results by numerical examples.

math.OC↗

arXiv2022

A deep learning method based on patchwise training for reconstructing temperature field

Physical field reconstruction is highly desirable for the measurement and control of engineering systems. The reconstruction of the temperature field from limited observation plays a crucial role in thermal management for electronic equipment. Deep learning has been employed in physical field reconstruction, whereas the accurate estimation for the regions with large gradients is still diffcult. To solve the problem, this work proposes a novel deep learning method based on patchwise training to reconstruct the temperature field of electronic equipment accurately from limited observation. Firstly, the temperature field reconstruction (TFR) problem of the electronic equipment is modeled mathematically and transformed as an image-to-image regression task. Then a patchwise training and inference framework consisting of an adaptive UNet and a shallow multilayer perceptron (MLP) is developed to establish the mapping from the observation to the temperature field. The adaptive UNet is utilized to reconstruct the whole temperature field while the MLP is designed to predict the patches with large temperature gradients. Experiments employing finite element simulation data are conducted to demonstrate the accuracy of the proposed method. Furthermore, the generalization is evaluated by investigating cases under different heat source layouts, different power intensities, and different observation point locations. The maximum absolute errors of the reconstructed temperature field are less than 1K under the patchwise training approach.

cs.LG↗

arXiv2021

Unified Style Transfer

Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer. In this paper, a novel approach, the Unified Style Transfer (UST) model, is proposed. With the introduction of a generative model for internal style representation, UST can transfer images in two approaches, i.e., Domain-based and Image-based, simultaneously. At the same time, a new philosophy based on the human sense of art and style distributions for evaluating the transfer model is presented and demonstrated, called Statistical Style Analysis. It provides a new path to validate style transfer models' feasibility by validating the general consistency between internal style representation and art facts. Besides, the translation-invariance of AdaIN features is also discussed.

cs.CV↗

arXiv2020

An Infrared Communication System based on Handstand Pendulum

This paper mainly introduces an infrared optical communication system based on stable and handstand pendulum. This system adopts the method of loading the infrared light emitting end on an handstand pendulum to realize the stability and controllability of the infrared light transmission light path. In this system, 940nm infrared light is mainly used for audio signal transmission, and an handstand pendulum based on PID is used to control the angle and stability of infrared light emission. Experimental results show that the system can effectively ensure the stability of the transmission optical path and is suitable for accurate and stable signal transmission in bumpy environments.

eess.SY↗

arXiv2020

Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities.Distinct from other scenarios (e.g., social networking or content sharing) which recommend a single item (e.g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items.Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference. However, present works focus mainly on one of the requirements and only consider either user-outfit or outfit-item relationships, thereby easily leading to suboptimal representations and limiting the performance. In this work, we unify two tasks, fashion compatibility modeling and personalized outfit recommendation. Towards this end, we develop a new framework, Hierarchical Fashion Graph Network(HFGN), to model relationships among users, items, and outfits simultaneously. In particular, we construct a hierarchical structure upon user-outfit interactions and outfit-item mappings. We then get inspirations from recent graph neural networks, and employ the embedding propagation on such hierarchical graph, so as to aggregate item information into an outfit representation, and then refine a user's representation via his/her historical outfits. Furthermore, we jointly train these two tasks to optimize these representations. To demonstrate the effectiveness of HFGN, we conduct extensive experiments on a benchmark dataset, and HFGN achieves significant improvements over the state-of-the-art compatibility matching models like NGNN and outfit recommenders like FHN.

cs.IR↗

arXiv2017

Citation personal display: A case study of personal websites by physicists in 11 well-known universities

This paper aims to investigate the extent to which researchers display citation, and wants to examine whether there are researcher differences in citation personal display at the level of university, country, and academic rank. Physicists in 11 well-known universities in USA, Britain, and China were chosen as the object of study. It was manually identified if physicists had mentioned citation counts, citation-based indices, or a link to Google Scholar Citations (GSC) on the personal websites. A chi-square test is constructed to test researcher differences in citation personal display. Results showed that the overall proportion of citation personal display is not high (14.8%), with 129 of 870 physicists displaying citation. And physicists from different well-known universities indeed had a significant difference in citation personal display. Moreover, at the national level, it was noticed that physicists in well-known Chinese universities had the highest level of citation personal display, followed by Britain and the USA. Further, this study also found that researchers who had the academic rank of professor had the highest citation personal display. In addition, the differences in h-index personal display by university, country or academic rank were analyzed, and the results showed that they were not statistically significant.

cs.DL↗