arXiv subjects

Yuming Zhu

Publications and source records attributed to Yuming Zhu.

6 recordsLinked to original sources

arXiv2026

A Monolithic Hand with Asymmetric Origami Bending and Dual-chamber Actuators

The passive adaptability inherent in soft robotic hands affords them advantages in applications that require safe and compliant interaction. However, existing soft robotic hands often struggle to simultaneously achieve adequate output performance and easy manufacturing due to their complicated structures. In this paper, we introduce the asymmetric origami bending (AOB) pattern for generating bending motion and the asymmetric dual-chamber (ADC) design for obtaining multifunction capability. The AOB single (AOB-S) chamber and AOB dual-chamber (AOB-D) units are designed and constitute the finger and palm actuators of the proposed Origami-inspired SOft Robotic (OSOR) hand. The OSOR hand achieves bio-inspired fingers-palm motions and adequate output performance within a monolithic structure that significantly simplifies the manufacturing process. By defining the asymmetric ratio to characterize the geometric asymmetry of the unit, the analytical models of the AOB and ADC structures are proposed. The Finite Element Analysis tool for the design of AOB actuators is obtained by geometric analysis. The asymmetric origami design grants the integrated manufacturing of the OSOR hand through a Selective Laser Sintering printing process with a single thermoplastic polyurethane material. The model and simulations are validated by experimental results. Experiments show the finger and palm maximum bending motion range of 203{\deg} and 40{\deg}, respectively, with output forces of 6.3 N and 16 N. The OSOR hand is capable of pinching a piece of tissue, stably grasping water bottles with two fingers, palm-only grasping, and completing the power grasps in the taxonomy of manufacturing grasps. The compactness, performance, and easy manufacturing of the proposed hand benefit the development of the soft robotic hand with new possibilities.

cs.RO↗

arXiv2026

Toward Site-Aware MR Art Exhibitions: A SLAM-Based Deployment Pipeline for Spatial Coherence and Exhibition Experience

Mixed Reality (MR) is increasingly being used in exhibition settings to bring digital artworks into relation with the physical environment. However, existing MR exhibition systems are often confined to prototypes or case-specific deployments, offering limited guidance for large-scale practical implementation. To address this gap, this paper presents a practical pipeline for designing and deploying large-scale MR art exhibitions, treating spatial alignment not only as a technical mechanism but also as an experiential design decision. We first conducted a pilot study comparing marker-based and Simultaneous Localization and Mapping (SLAM)-based alignment methods in an MR exhibition setting. Based on the results, we developed a SLAM-based pipeline for MR exhibitions that integrates technical deployment with exhibition curation. We then evaluated the pipeline through both system overhead measures and users' experiential feedback. The results show that spatial alignment influences not only technical stability, but also overall exhibition coherence, visitors' sense of continuity and immersion, and artwork interpretation. These findings provide an empirically grounded reference for future large-scale MR art exhibition deployment.

cs.MM↗

arXiv2026

DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos

Satellite videos provide continuous observations of surface dynamics but pose significant challenges for multi-object tracking (MOT), especially under unstabilized conditions where platform jitter and the weak appearance of tiny objects jointly degrade tracking performance. To address this problem, we propose DeTracker, a joint-detection-and-tracking framework tailored for unstabilized satellite videos. DeTracker introduces a task-driven Global-Local Motion Decoupling (GLMD) module to address the motion imbalance between dominant platform motion and weak target motion. It suppresses background-dominated motion via global semantic alignment at the feature level and captures target-specific motion through local refinement, improving trajectory stability and identity consistency. In addition, a Temporal Dependency Feature Pyramid (TDFP) module is developed to perform cross-frame temporal feature fusion, enhancing the continuity and discriminability of tiny-object representations. We further construct a new benchmark dataset, SDM-Car-SU, which simulates multi-directional and multi-speed platform motions to enable systematic evaluation of tracking robustness under varying motion perturbations. Extensive experiments on both simulated and real unstabilized satellite videos demonstrate that DeTracker significantly outperforms existing methods, achieving 61.1% MOTA on SDM-Car-SU and 45.3% MOTA on real satellite video data. The code and dataset will be publicly available at https://github.com/alex-chenjiajun/DeTracker.

cs.CV↗

arXiv2025

Least Absolute Deviation Utility for Trapezoidal Fuzzy Preference Relations

Preference relations (PRs) are widely used to model expert judgments because they allow for eliciting the decision-makers' opinions from pairwise comparisons. Traditionally, PRs have been elicited using real numbers. However, in real-world decision-makers usually feel more comfortable using linguistic expressions closer to natural language. In this context, our purpose is to extend the classical idea of PR into the environment of Trapezoidal Fuzzy Numbers (TrFNs) by addressing several drawbacks in current research. Existing fuzzy extensions for Fuzzy Preference Relations (FPRs) and Multiplicative Preference Relations (MPRs) assume that the notion of neutrality must be modeled by a crisp real number, which fails to capture the subjective and diverse ways in which decision-makers may perceive indifference. Moreover, current research lacks a theoretical framework that unifies both FPRs and MPRs for fuzzy numbers and simultaneously generalizes the involved classical notions (e.g., reciprocity, consistency) from the perspective of fuzzy arithmetic. Therefore, we introduce the notion of neutral TrFN, a fuzzy number that can model indifference on a certain fuzzy scale. This provides a flexible representation of neutrality, leading to a more realistic extension of PRs into the fuzzy environment. Building upon this, we propose the Trapezoidal FPR (TrFPR), analyze its properties and how reciprocity and consistency are extended using fuzzy arithmetic. Furthermore, we introduce the Least Absolute Deviation (LAD) utility vector associated with a consistent TrFPR and develop an optimization model to derive it from an inconsistent TrFPR to compute priority values. These ideas are also extended to MPRs in the fuzzy domain. Finally, the applicability of the proposed LAD utility method is demonstrated in evaluating land development projects, where comparison with fuzzy AHP illustrates its superiority.

math.GM↗

arXiv2024

Topological Dynamics of Synthetic Molecules

We study the dynamics of synthetic molecules whose architectures are generated by space transformations from a point group acting on seed resonators. We show that the dynamical matrix of any such molecule can be reproduced as the left regular representation of a self-adjoint element from the stabilized group's algebra. Furthermore, we use elements of representation theory and K-theory to rationalize the dynamical features supported by such synthetic molecules up to topological equivalences. These tools enable us to identify a set of fundamental models which generate by superposition all possible dynamical matrices up to homotopy equivalences. Interpolations between these fundamental models give rise to topological spectral flows.

math-ph↗

arXiv2023

Towards Intelligent Network Management: Leveraging AI for Network Service Detection

As the complexity and scale of modern computer networks continue to increase, there has emerged an urgent need for precise traffic analysis, which plays a pivotal role in cutting-edge wireless connectivity technologies. This study focuses on leveraging Machine Learning methodologies to create an advanced network traffic classification system. We introduce a novel data-driven approach that excels in identifying various network service types in real-time, by analyzing patterns within the network traffic. Our method organizes similar kinds of network traffic into distinct categories, referred to as network services, based on latency requirement. Furthermore, it decomposes the network traffic stream into multiple, smaller traffic flows, with each flow uniquely carrying a specific service. Our ML models are trained on a dataset comprised of labeled examples representing different network service types collected on various Wi-Fi network conditions. Upon evaluation, our system demonstrates a remarkable accuracy in distinguishing the network services. These results emphasize the substantial promise of integrating Artificial Intelligence in wireless technologies. Such an approach encourages more efficient energy consumption, enhances Quality of Service assurance, and optimizes the allocation of network resources, thus laying a solid groundwork for the development of advanced intelligent networks.

cs.NI↗