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666 results on '"YANG Ruiqi"'

1. Low pilot overhead parametric channel estimation scheme for RIS-assisted mmWave MIMO systems

Author

LI Shuangzhi, YANG Ruiqi, GUO Xin, and HUANG Sai

Subjects
RIS, mmWave, MIMO, channel estimation, tensor, Telecommunication, TK5101-6720
Abstract

To address the timely acquisition of channel state information in reconfigurable intelligent surface (RIS)-assisted millimetre wave (mmWave) multiple-input multiple-output (MIMO) systems, a channel estimation scheme based on tensor decomposition was proposed. Firstly, a channel training mechanism with low pilot overhead was designed using a few passive reflection units and constructing a phase shift matrix. Then, a non-iterative channel estimation algorithm was derived using tensor canonical polyadic decomposition with Vandermonde structure constraints. Theoretical analysis indicated that the minimum pilot overhead of the proposed scheme only depended on the product of the subchannel path numbers of the reflection links and exhibited low computational complexity. Simulation results further verify the superiority of the proposed scheme compared to other methods.

Published
2024
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2. Bending failure behavior of aluminum foam sandwich based on transient liquid phase bonding method

Author

ZHANG Junshan, MA Haoyuan, AN Yukun, CAO Mengzhen, and YANG Ruiqi

Subjects
aluminum foam sandwich structure, ta2 panel, bending performance, failure mode, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

Aluminum foam sandwich structure （aluminum foam sandwich, AFS） not only has the excellent characteristics of aluminum foam lightweight, damping and shock absorption, energy absorption and protection, but also can solve the problems of low strength and easy damage of single aluminum foam, so it has a broad application prospect in aerospace, automobile manufacturing, rail transportation, precision machine tools and other industrial fields. In this paper, based on the melt foaming method, an aluminum foam sandwich structure with the size of 80 mm×80 mm×18 mm was successfully prepared using pure TA2 as the panel and Al-2Ca alloy as the foaming base material, and a large number of uniform pores were observed in the foam core, of which the polyhedral pores occupied a large area. A binding interface with an average thickness of 7.5 μm was observed between the panel and the core layer，where the elements diffused at the binding layer and existed in the form of intermetallic compounds. The bending test results show that the load curves of sandwich structures with different densities show three distinct areas: linear elastic zone, rapid load drop zone and flatform zone. The maximum peak load and yield strength of the sandwich structure are 1120.5 N and 15.64 MPa respectively. The bending strength of AFS materials increases with the increase of density and the decrease of core porosity. When the bending degree is 15 mm and the AFS density increases by 15.9%, the bending energy absorption WEA and WSEA increase by 3.59 and 3.22 times respectively. In the process of bending test, the failure modes are composed of compressive compaction deformation of aluminum foam core layer, shear and cracking of core material, bending deformation and peeling failure of TA2 panel, and various failure modes of AFS material are formed under the joint action of different stresses at different positions of the sample.

Published
2023
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3. PBW bases for $\imath$quantum groups

Author

Lu, Ming, Yang, Ruiqi, and Zhang, Weinan

Subjects
Mathematics - Representation Theory, Mathematics - Quantum Algebra, 17B37
Abstract

We establish PBW type bases for $\imath$quantum groups of arbitrary finite type, using the relative braid group symmetries. Explicit formulas for root vectors are provided for $\imath$quantum groups of each rank 1 type. We show that our PBW type bases give rise to integral bases for the modified $\imath$quantum groups. The leading terms of our bases can be identified with the usual PBW bases in the theory of quantum groups., Comment: 33 pages; comments are welcome

Published
2024

5. Approximating Continuous Multi-agent Contracts with Lyapunov Function Methods

Author

Gong, Qinqin, Du, Donglei, Gai, Ling, Xu, Dachuan, Yang, Ruiqi, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chen, Yong, editor, Gao, Xiaofeng, editor, Sun, Xiaoming, editor, and Zhang, An, editor

Published
2025
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6. Approximating Principal-Agent Problem Under Bayesian

Author

Gong, Qinqin, Gai, Ling, Wang, Yijing, Xu, Dachuan, Yang, Ruiqi, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Bo, editor, Li, Minming, editor, and Sun, Xiaoming, editor

Published
2025
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7. Isolation and identification of a high-efficiency phenol-degrading bacteria and optimization of its degradation conditions

Author

Sun Haili, Gao Tianpeng, Chang Guohua, Tai Xisheng, Yang Ruiqi, Zhang Qing, and Yue Bin

Subjects
Environmental sciences, GE1-350
Abstract

Phenol is widely used in China, it not only pollutes the environment, but also accumulates toxic substances in the human body through the food chain, further harming humans. In this experiment, a strain of high-efficiency low-temperature degradation phenol bacteria B5 was selected from the soil contaminated by organic matter of Lanzhou. Through research methods such as Gram staining observation, DNA extraction, PCR amplification, sequencing and comparison, it was found that this strain was Pantoea agglomerans. Through the subsequent optimization of degradation conditions, it was found that the B5 strain can degrade 500mg/L of phenol to 24.8mg/L in 36h. The ability to degrade phenol is stronger between pH5.5-pH6.0, and the ability to degrade phenol is higher in a medium containing 4-8g/L sodium chloride. This research can provide certain theoretical guidance for phenol degradation.

Published
2021
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10. ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data

Author

Yang, Ruiqi and Modesitt, Eric

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Signal Processing
Abstract

In this study, we demonstrate the application of a hybrid Vision Transformer (ViT) model, pretrained on ImageNet, on an electroencephalogram (EEG) regression task. Despite being originally trained for image classification tasks, when fine-tuned on EEG data, this model shows a notable increase in performance compared to other models, including an identical architecture ViT trained without the ImageNet weights. This discovery challenges the traditional understanding of model generalization, suggesting that Transformer models pretrained on seemingly unrelated image data can provide valuable priors for EEG regression tasks with an appropriate fine-tuning pipeline. The success of this approach suggests that the features extracted by ViT models in the context of visual tasks can be readily transformed for the purpose of EEG predictive modeling. We recommend utilizing this methodology not only in neuroscience and related fields, but generally for any task where data collection is limited by practical, financial, or ethical constraints. Our results illuminate the potential of pretrained models on tasks that are clearly distinct from their original purpose., Comment: 8 pages, 6 for article, 1 for citation, 1 for appendix. Accepted to KDD-UC 2023

Published
2023

16. Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data

Author

Modesitt, Eric, Yang, Ruiqi, and Liu, Qi

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Classifying EEG data is integral to the performance of Brain Computer Interfaces (BCI) and their applications. However, external noise often obstructs EEG data due to its biological nature and complex data collection process. Especially when dealing with classification tasks, standard EEG preprocessing approaches extract relevant events and features from the entire dataset. However, these approaches treat all relevant cognitive events equally and overlook the dynamic nature of the brain over time. In contrast, we are inspired by neuroscience studies to use a novel approach that integrates feature selection and time segmentation of EEG data. When tested on the EEGEyeNet dataset, our proposed method significantly increases the performance of Machine Learning classifiers while reducing their respective computational complexity., Comment: 6 pages, 3 figures, HCI International 2023 Poster

Published
2023

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