arXiv subjects

Zhang Rui

Publications and source records attributed to Zhang Rui.

8 recordsLinked to original sources

arXiv2025

Observation and research on cosmic ray muons and solar modulation effect based on plastic scintillator detector

Cosmic rays, originating from stars, supernovae, and other astrophysical sources, are composed of high-energy particles that enter Earths atmosphere. Upon interaction with atmospheric nuclei, these primary cosmic rays generate secondary particles, including neutrons, electrons, and muons, with muons constituting a dominant component at ground level. Muons, due to their relative abundance, stability, and well-characterized energy loss mechanisms, serve as critical probes for investigating the fundamental properties of cosmic rays. Studies of muon energy distribution, diurnal anisotropy, and their modulation by solar activity provide critical insights into the mechanism of particle acceleration in cosmic ray sources and the effects of solar and atmospheric.This study aims to characterize the counting spectra and anisotropic properties of cosmic ray muons by using a plastic scintillator detector system. The experiment was conducted over a three-month period, from December 2023 to February 2024, leveraging long-bar plastic scintillator detectors equipped with dual-end photomultiplier tubes (PMTs) and a high-resolution digital data acquisition system. A dual-end coincidence measurement technique was used to enhance the signal-to-noise ratio by suppressing thermal noise and other background interferences. Diurnal variations in muon count rates exhibit a pronounced pattern, with a systematic reduction occurring between 8:00 AM and 1:00 PM. This phenomenon is attributed to the solar shielding effects, where enhanced solar activity during daytime hours modulates the flux of galactic cosmic rays reaching Earths surface. The study further corroborates these findings through cross-comparisons with data from the Yangbajing Cosmic Ray Observatory. These observations underscore the robustness of the plastic scintillator detector system for capturing detailed muon spectra and anisotropic patterns.

astro-ph.HE↗

arXiv2025

A Novel Brain-Computer Interface Architecture: The Brain-Muscle-Hand Interface for replicating the motor pathway

Myoelectric interfaces enable intuitive and natural control by decoding residual muscle activity, providing an effective pathway for motor restoration in individuals with preserved musculature. However, in patients with severe muscular atrophy or high-level spinal cord injury, the absence of reliable muscle activity renders myoelectric control infeasible. In such cases, motor brain-computer interfaces (BCIs) offer an alternative route. However, conventional brain-computer interface systems rely mainly on noisy cortical signals and classification-based decoding algorithms, which often result in low signal fidelity, limited controllability, and unstable real-time performance. Inspired by the motor pathway--an evolutionarily optimized system that filters, integrates, and transmits motor commands from the brain to the muscles--this study proposes the Brain-Muscle-Hand Interface (BMHI). BMHI decodes cortical EEG signals to reconstruct muscle-level EMG activity, functionally substituting for the muscles and enabling regression-based, continuous, and natural control via a myoelectric interface. To validate this architecture, we performed offline verification, comparative analysis, and online control experiments. Results demonstrate that: (1) the BMHI achieves a prediction accuracy of 0.79; (2) compared with conventional end-to-end brain-hand interfaces, it reduces training time by approximately eighteenfold while improving decoding accuracy; and (3) in online operation, the BMHI enables stable and efficient manipulation of both a virtual hand and a robotic arm. Compared with conventional BCIs, the BMHI, by replicating the motor pathway, enables continuous, stable, and naturally intuitive control.

q-bio.NC↗

arXiv2025

Super-Resolution Coherent Diffractive Imaging via Titled-Incidence Multi-Rotation-Angle Fusion Ptychography

Coherent diffractive imaging (CDI) enables lensless imaging with experimental simplicity and a flexible field of view, yet its resolution is fundamentally constrained by the Abbe diffraction limit. To overcome this limitation, we introduce a novel Tilted-Incidence Multi-Rotation-Angle Fusion Ptychography technique. This approach leverages a tilted-incidence geometry to extend the collection angle beyond the Abbe limit, achieving up to a -fold resolution enhancement. By acquiring diffraction patterns at multiple sample rotation angles, we capture complementary spatial frequency information. A tilted-incidence multi-rotation-angle fusion ptychographic iterative engine (tmf-PIE) algorithm is then employed to integrate these datasets, enabling super-resolution image reconstruction. Additionally, this method mitigates the anisotropic resolution artifacts inherent to tilted CDI geometries. Our technique represents a novel advancement in super-resolution imaging, providing a novel alternative alongside established methods such as STED, SIM, and SMLM.

physics.optics↗

arXiv2021

Video Joint Modelling Based on Hierarchical Transformer for Co-summarization

Video summarization aims to automatically generate a summary (storyboard or video skim) of a video, which can facilitate large-scale video retrieval and browsing. Most of the existing methods perform video summarization on individual videos, which neglects the correlations among similar videos. Such correlations, however, are also informative for video understanding and video summarization. To address this limitation, we propose Video Joint Modelling based on Hierarchical Transformer (VJMHT) for co-summarization, which takes into consideration the semantic dependencies across videos. Specifically, VJMHT consists of two layers of Transformer: the first layer extracts semantic representation from individual shots of similar videos, while the second layer performs shot-level video joint modelling to aggregate cross-video semantic information. By this means, complete cross-video high-level patterns are explicitly modelled and learned for the summarization of individual videos. Moreover, Transformer-based video representation reconstruction is introduced to maximize the high-level similarity between the summary and the original video. Extensive experiments are conducted to verify the effectiveness of the proposed modules and the superiority of VJMHT in terms of F-measure and rank-based evaluation.

cs.CV↗

arXiv2021

Precondition and Effect Reasoning for Action Recognition

Human action recognition has drawn a lot of attention in the recent years due to the research and application significance. Most existing works on action recognition focus on learning effective spatial-temporal features from videos, but neglect the strong causal relationship among the precondition, action and effect. Such relationships are also crucial to the accuracy of action recognition. In this paper, we propose to model the causal relationships based on the precondition and effect to improve the performance of action recognition. Specifically, a Cycle-Reasoning model is proposed to capture the causal relationships for action recognition. To this end, we annotate precondition and effect for a large-scale action dataset. Experimental results show that the proposed Cycle-Reasoning model can effectively reason about the precondition and effect and can enhance action recognition performance.

cs.CV↗

arXiv2019

Network2Vec Learning Node Representation Based on Space Mapping in Networks

Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixed-length vector for each node in an embedding space, where the node properties in the original graph are preserved. Existing methods mainly focus on learning embedding vectors to preserve nodes proximity, i.e., nodes next to each other in the graph space should also be closed in the embedding space, but do not enforce algebraic statistical properties to be shared between the embedding space and graph space. In this work, we propose a lightweight model, entitled Network2Vec, to learn network embedding on the base of semantic distance mapping between the graph space and embedding space. The model builds a bridge between the two spaces leveraging the property of group homomorphism. Experiments on different learning tasks, including node classification, link prediction, and community visualization, demonstrate the effectiveness and efficiency of the new embedding method, which improves the state-of-the-art model by 19% in node classification and 7% in link prediction tasks at most. In addition, our method is significantly faster, consuming only a fraction of the time used by some famous methods.

cs.SI↗

arXiv2015

Downlink SINR Balancing in C-RAN under Limited Fronthaul Capacity

Cloud radio access network (C-RAN) with centralized baseband processing is envisioned as a promising candidate for the next-generation wireless communication network. However, the joint processing gain of C-RAN is fundamentally constrained by the finite-capacity fronthaul links between the central unit (CU) where joint processing is implemented and distributed access points known as remote radio heads (RRHs). In this paper, we consider the downlink communication in a C-RAN with multi-antenna RRHs and single-antenna users, and investigate the joint RRH beamforming and user-RRH association problem to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users subject to each RRH's individual fronthaul capacity constraint. The formulated problem is in general NP-hard due to the fronthaul capacity constraints and thus is difficult to be solved optimally. In this paper, we propose a new iterative method for this problem which decouples the design of beamforming and user association, where the number of users served by each RRH is iteratively reduced until the obtained beamforming and user association solution satisfies the fronthaul capacity constraints of all RRHs. A monotonic convergence is proved for the proposed algorithm, and it is shown by simulation that the algorithm achieves significant performance improvement over other heuristic solutions.

cs.IT↗

arXiv2013

Improvement of Main Drift Chamber Monte-Carlo tuning model at BESIII

Based on real data, a new parameterized model of the Main Drift Chamber response is proposed. In this model, we tune the ratio of good hits and the residual distribution separately. The difference between simulation and data in track reconstruction efficiency reduces from 1% to 0.5%, and the momentum resolution agreement improves significantly.

physics.ins-det↗