Expertini Research Research

Browse Research Papers

1,623+ open-access research outputs.

โœ• Clear
๐Ÿ” yangming wu ๐Ÿ“‚ Engineering
Showing 1623 results for "yangming wu" in Engineering
Engineering Preprint PDF DOI

RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects

Tim Missal, Lucas Domingues, Berk Guler, Simon Manschitz, Jan Peters, Paula Dornhofer Paro Costa ยท 2026

The robotic manipulation of Deformable Linear Objects (DLOs) is a fundamental challenge due to the high-dimensional, non-linear dynamics of flexible structures and the complexity of maintaining topoloโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Bridging the Indoor-Outdoor Gap: Cross-Technology Ranging for Seamless Robot Navigation

Paul Schwarzbach ยท 2026

Mobile robots that move between outdoor and indoor environments still struggle with consistent positioning. Satellite-based and terrestrial ranging each work well in their home domains, but combining โ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing

Sakiko Mishima, Yoshiyuki Yajima, Noriyuki Tonami, Tomoyuki Hino, Shugo Aibe, Junichiro Saikawa, Koji Mizuguchi ยท 2026

This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic Sensing (DAS), which is one of the distributed fibโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Predicting food taste with bound-driven optimization

Pagkratis Tagkopoulos, Dimitris Sfondilis, Ilias Tagkopoulos, Tarek Zohdi ยท 2026

The prediction of sensory attributes from ingredient-level formulations is an emerging challenge at the intersection of food science and artificial intelligence. We address the fundamental question ofโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Gated Memory Policy

Yihuai Gao, Jinyun Liu, Shuang Li, Shuran Song ยท 2026

Robotic manipulation tasks exhibit varying memory requirements, ranging from Markovian tasks that require no memory to non-Markovian tasks that depend on historical information spanning single or multโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Enabling Safety-Critical Wireless Communications via Safe Reinforcement Learning

Haoran Peng, Tong Wu, Hang Liu, Weijia Zheng, Ying-Jun Angela Zhang, Anna Scaglione ยท 2026

Ensuring strict safety guarantees is the paramount challenge for emerging 5G/6G wireless systems, particularly as they increasingly govern mission-critical applications ranging from autonomous UAV swaโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Experimental Characterization Data for Battery Modules with Parallel-Connected Cells across Diverse Module-Level State of Health and Cell-to-Cell Variations

Qinan Zhou, Daniel Stephens, Jing Sun ยท 2026

This experimental dataset presents both module-level and cell-level characterization data for lithium-ion battery modules composed of three parallel-connected inhomogeneous cells across a wide range oโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

CT-VIR: Continuous-Time Visual-Inertial-Ranging Fusion for Indoor Localization with Sparse Anchors

Yu-An Liu, Li Zhang ยท 2026

Visual-inertial odometry (VIO) is widely used for mobile robot localization, but its long-term accuracy degrades without global constraints. Incorporating ranging sensors such as ultra-wideband (UWB) โ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

From Brain Models to Executable Digital Twins: Execution Semantics and Neuro-Neuromorphic Systems

Alexandre Muzy (ILLS) ยท 2026

Brain digital twins aim to provide faithful, individualized computational representations of brains as dynamical systems, enabling mechanistic understanding and supporting prediction of clinical interโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Disentangled Point Diffusion for Precise Object Placement

Lyuxing He, Eric Cai, Shobhit Aggarwal, Jianjun Wang, David Held ยท 2026

Recent advances in robotic manipulation have highlighted the effectiveness of learning from demonstration. However, while end-to-end policies excel in expressivity and flexibility, they struggle both โ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation

Mingyang Li, Haofan Xu, Haowen Sun, Xinzhe Chen, Sihua Ren, Liqi Huang, Xinyang Sui, Chenyang Miao, Qiongjie Cui, Zeyang Liu, Xingyu Chen, Xuguang Lan ยท 2026

Simulation-based data generation has become a dominant paradigm for training robotic manipulation policies, yet existing platforms do not incorporate object affordance information into trajectory geneโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Trajectory-based actuator identification via differentiable simulation

Vyacheslav Kovalev, Ekaterina Chaikovskaia, Egor Davydenko, Roman Gorbachev ยท 2026

Accurate actuation models are critical for bridging the gap between simulation and real robot behavior, yet obtaining high-fidelity actuator dynamics typically requires dedicated test stands and torquโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Compact single-shot ranging and near-far imaging using metasurfaces

Junjie Luo, Yuxuan Liu, Wei Ting Chen, Qing Wang, Qi Guo ยท 2026

We present a metasurface imaging system capable of simultaneously capturing two images at close range (1-2~cm) and an additional image at long range (about 40~cm) on a shared photosensor. The close-raโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Training-free, Perceptually Consistent Low-Resolution Previews with High-Resolution Image for Efficient Workflows of Diffusion Models

Wongi Jeong, Hoigi Seo, Se Young Chun ยท 2026

Image generative models have become indispensable tools to yield exquisite high-resolution (HR) images for everyone, ranging from general users to professional designers. However, a desired outcome ofโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices

Alvin Kimbowa, Arjun Parmar, Ibrahim Mujtaba, Will Wei, Maziar Badii, Matthew Harkey, David Liu, Ilker Hacihaliloglu ยท 2026

Objective: To develop a robust and compact deep learning model for automated knee cartilage segmentation on point-of-care ultrasound (POCUS) devices. Methods: We propose MonoUNet, an ultra-compact Uโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes

Mu Lin, Yi-Lin Wei, Jiaxuan Chen, Yuhao Lin, Shuoyu Chen, Jiangran Lyu, Jiayi Chen, Yansong Tang, He Wang, Wei-Shi Zheng ยท 2026

Bimanual dexterous grasping is a fundamental and promising area in robotics, yet its progress is constrained by the lack of comprehensive datasets and powerful generation models. In this work, we propโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Hyperfastrl: Hypernetwork-based reinforcement learning for unified control of parametric chaotic PDEs

Anil Sapkota, Omer San ยท 2026

Spatiotemporal chaos in fluid systems exhibits severe parametric sensitivity, rendering classical adjoint-based optimal control intractable because each operating regime requires recomputing the contrโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Enhancing 6G Wireless Intelligence: Do LLMs Work for CSI Prediction?

Mohsen Kazemian, Jurgen Jasperneite ยท 2026

In high-mobility 6G scenarios, rapidly time-varying channels lead to very short coherence times, which makes conventional pilot-based channel state information (CSI) estimation approaches prone to outโ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Amalgamation of Physics-Informed Neural Network and LBM for the Prediction of Unsteady Fluid Flows in Fractal-Rough Microchannels

Ganesh Sahadeo Meshram, Partha Pratim Chakrabarti, Suman Chakraborty ยท 2026

One of the biggest challenges in the optimization of micro-scale fluid transport phenomena is the prediction of unsteady fluid flow in the presence of rough channel walls. Even though the accuracy of โ€ฆ

Read Paper โ†’
Engineering Preprint PDF DOI

Lattice-Boltzmann-Driven Physics-Informed Neural Networks for Droplet Wettability on Rough Surfaces

Ganesh Sahadeo Meshram, Partha Pratim Chakrabarti, Suman Chakraborty ยท 2026

We introduce a Lattice-Boltzmann-driven kinetic physics-informed neural network (K-PINN) for predictive modeling of droplet dynamics on structured surfaces, in which the discrete Boltzmann-BGK equatioโ€ฆ

Read Paper โ†’
Page 1 of 82 Next โ†’