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Metasurfaces for manipulating terahertz waves

Light(Advanced Manufacturing) 2021年第2期2卷 40-64页

作者： Xiaofei Zang Bingshuang Yao Lin Chen Jingya Xie Xuguang Guo Alexei VBalakin Alexander P.Shkurinov Songlin Zhuang Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation CenterShanghai Key Lab of Modern Optical SystemUniversity of shanghai for Science and TechnologyShanghai 200093China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai200092China Department of Physics and International Laser Center Lomonosov Moscow State UniversityLeninskie Gory 1Moscow 19991 Russia ILIT RAS-Branch of the FSRC《Crystallography and Photonics》RAS Svyatoozerskaya 1140700ShaturaMoscow RegionRussia

Terahertz(THz)science and technology have attracted significant attention based on their unique applications in non-destructive imaging,communications,spectroscopic detection,and ***,traditional THz devices must be sufficiently thick to realise the desired wave-manipulating functions,which has hindered the development of THz integrated systems and ***,which are two-dimensional metamaterials consisting of predesigned meta-atoms,can accurately tailor the amplitudes,phases,and polarisations of electromagnetic waves at subwavelength resolutions,meaning they can provide a flexible platform for designing ultra-compact and high-performance THz *** review focuses on recent advancements in metasurfaces for the wavefront manipulation of THz waves,including the planar metalens,holograms,arbitrary polarisation control,special beam generation,and active metasurface *** ultra-compact devices with unique functionality make metasurface devices very attractive for applications such as imaging,encryption,information modulation,and THz *** progress report aims to highlight some novel approaches for designing ultra-compact THz devices and broaden the applications of metasurfaces in THz science.

关键词： surfaces designing attractive

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Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models

arXiv

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arXiv 2024年

作者： Lan, Zhengxing Li, Hongbo Liu, Lingshan Fan, Bo Lv, Yisheng Ren, Yilong Cui, Zhiyong The School of Transportation Science and Engineering Beihang University Beijing100191 China The State Key Lab of Intelligent Transportation System Beijing100191 China The Research Institute for Frontier Science Beihang University Beijing100191 China The Beijing Key Laboratory of Traffic Engineering College of Metropolitan Transportation Beijing University of Technology Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Zhongguancun Laboratory Beijing100194 China The Beihang Hangzhou Innovation Institute Yuhang Hangzhou310023 China

Predicting the future trajectories of dynamic traffic actors is a cornerstone task in autonomous driving. Though existing notable efforts have resulted in impressive performance improvements, a gap persists in scene cognitive and understanding of the complex traffic semantics. This paper proposes Traj-LLM, the first to investigate the potential of using Large Language Models (LLMs) without explicit prompt engineering to generate future motion from agents’ past/observed trajectories and scene semantics. Traj-LLM starts with sparse context joint coding to dissect the agent and scene features into a form that LLMs understand. On this basis, we innovatively explore LLMs’ powerful comprehension abilities to capture a spectrum of high-level scene knowledge and interactive information. Emulating the human-like lane focus cognitive function and enhancing Traj-LLM’s scene comprehension, we introduce lane-aware probabilistic learning powered by the pioneering Mamba module. Finally, a multi-modal Laplace decoder is designed to achieve scene-compliant multi-modal predictions. Extensive experiments manifest that Traj-LLM, fortified by LLMs’ strong prior knowledge and understanding prowess, together with lane-aware probability learning, outstrips state-of-the-art methods across evaluation metrics. Moreover, the few-shot analysis further substantiates Traj-LLM’s performance, wherein with just 50% of the dataset, it outperforms the majority of benchmarks relying on complete data utilization. This study explores equipping the trajectory prediction task with advanced capabilities inherent in LLMs, furnishing a more universal and adaptable solution for forecasting agent motion in a new way. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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AU Nanoparticles Decorated Gan Nanoflowers with Enhanced Nh3 Sensing Performance at Room Temperature

SSRN

引用

SSRN 2023年

作者： Han, Dan Chen, Yi Li, Donghui Shi, Jiexu Wang, Hongtao He, Xiuli Zhao, Li Wang, Weidong Sang, Shengbo Ji, Jianlong Shanxi Key Laboratory of Micro Nano Sensors and Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Bioengineering Research Center Medical Innovation Research Division Chinese PLA General Hospital Beijing China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science China

Surface modification with noble metal is considered as an effective strategy to enhance sensing performance of semiconductor gas sensors. In this work, gold nanoparticles (Au NPs) decorated Gallium Nitride nanoflowers (Au-GaN NFs) have been synthesized successfully by a feasible hydrothermal method combined with high temperature nitridation and followed by in-situ reduction process. The characterizations of the structure and morphology of as-obtained Au-GaN NFs indicated that the Au NPs were successfully loaded on the surface of GaN. The excellent gas sensing performance was observed for GaN of 2 mol% Au loaded, including the high response (86.8%) to 100 ppm NH3 with fast response/recovery time (41 s/169 s) at room temperature, low theoretical limit of detection (72 ppb), good stability and selectivity. Besides, the influence of humidity on the performance of sensors was explored, where the response can be reached 56.5% towards 100 ppm NH3 under 70% RH. The improvement of the sensing performance of Au-GaN NFs sensor can be attributed to the combined action of chemical and electronic sensitization of Au NPs. This work provides a candidate of Au-GaN NFs for the development of wireless sensing system in NH3 monitoring. © 2023, The Authors. All rights reserved.

关键词： Nanoflowers

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Variable stiffness design optimization of fiber-reinforced composite laminates with regular and irregular holes considering fiber continuity for additive manufacturing

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Chinese Journal of Aeronautics 2025年第3期 338-358页

作者： Yi LIU Zunyi DUAN Chunping ZHOU Yuan SI Chenxi GUAN Yi XIONG Bin XU Jun YAN Jihong ZHU School of Mechanics Civil Engineering & ArchitectureNorthwestern Polytechnical University Research & Development Institute of Northwestern Polytechnical University in Shenzhen Key Lab for Airborne Hi-performance Electro-magnetic Window RISAC School of System Design and Intelligent Manufacturing Southern University of Science and Technology State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsInternational Research Center for Computational MechanicsDalian University of Technology State IJR Center of Aerospace Design and Additive Manufacturing MIIT Lab of Metal Additive Manufacturing and Innovative DesignNorthwestern Polytechnical University

Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel,and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization(NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples consid

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Joint Counting, Detection and Re-Identification for Multi-Object Tracking

arXiv

引用

arXiv 2022年

作者： Ren, Weihong Wu, Denglu Cao, Hui Chen, Xi'ai Han, Zhi Liu, Honghai The School of Mechanical Engineering and Automation State Key Lab of Robotics and Systems Harbin Institute of Technology Shenzhen518055 China The Shenzhen Duorou Intelligent System Co. Ltd. Shenzhen518055 China The School of Electrical Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Beijing110169 China

The recent trend in 2D multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in crowded scenes, joint detection and tracking usually fail to find accurate object associations due to missed or false detections. In this paper, we jointly model counting, detection and re-identification in an end-to-end framework, named CountingMOT, tailored for crowded scenes. By imposing mutual object-count constraints between detection and counting, the CountingMOT tries to find a balance between object detection and crowd density map estimation, which can help it to recover missed detections or reject false detections. Our approach is an attempt to bridge the gap of object detection, counting, and re-Identification. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to failure in crowded scenes, or depend on local correlations to build a graphical relationship for matching targets. The proposed MOT tracker can perform online and real-time tracking, and achieves the state-of-the-art results on public benchmarks MOT16 (MOTA of 79.7%), MOT17 (MOTA of 81.3%) and MOT20 (MOTA of 78.9%). Source code is available at https://***/weihong9/CountingMOT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Preparation and optimization of freestanding GaN using low-temperature GaN layer

引用

Frontiers of Materials Science 2019年第3期13卷 314-322页

作者： Yuan TIAN Yongliang SHAO Xiaopeng HAO Yongzhong WU Lei ZHANG Yuanbin DAI Qin HUO Baoguo ZHANG Haixiao HU State Key Lab of Crystal Materials Shandong UniversityJinan 250100China Key Lab of Advaneed Transducers and Intelligent Control System(Ministry of Education) Taiyuan University of TechnologyTaiyuan 030024China College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuan 030024China

In this work,a method to acquire freestanding GaN by using low temperature(LT)-GaN layer was put *** obtain porous structure and increase the crystallinity,LT-GaN layers were annealed at high *** morphology of LTGaN layers with different thickness and annealing temperature before and after annealing was *** of GaN films using different LT-GaN layers was made to acquire optimal LT-GaN *** to HRXRD and Raman results,GaN grown on 800 nm LT-GaN layer which was annealed at 1090℃ has good crystal quality and small *** GaN film was successfully separated from the substrate after cooling *** self-separation mechanism of this method was ***-sectional EBSD mapping measurements were carried out to investigate the effect of LT-buffer layer on improvement of crystal quality and stress *** optical property of the obtained freestanding GaN film was also determined by PL measurement.

关键词： GaN self-separation low-temperature annealing

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A deep learning system for predicting time to progression of diabetic retinopathy

引用

NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Practical fractional-order variable-gain super-twisting control with application to wafer stages of photolithography systems

arXiv

引用

arXiv 2021年

作者： Kuang, Zhian Sun, Liting Gao, Huijun Tomizuka, Masayoshi Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Mechanical Control System Lab Mechanical Engineering Department University of California BerkeleyCA94720 United States

In this paper, a practical fractional-order variable-gain super-twisting algorithm (PFVSTA) is proposed to improve the tracking performance of wafer stages for semiconductor manufacturing. Based on the sliding mode control (SMC), the proposed PFVSTA enhances the tracking performance from three aspects: 1) alleviating the chattering phenomenon via super-twisting algorithm and a novel fractional-order sliding surface (FSS) design, 2) improving the dynamics of states on the sliding surface with fast response and small overshoots via the designed novel FSS and 3) compensating for disturbances via variable-gain control law. Based on practical conditions, this paper analyzes the stability of the controller and illustrates the theoretical principle to compensate for the uncertainties caused by accelerations. Moreover, numerical simulations prove the effectiveness of the proposed sliding surface and control scheme, and they are in agreement with the theoretical analysis. Finally, practice-based comparative experiments are conducted. The results show that the proposed PFVSTA can achieve much better tracking performance than the conventional methods from various perspectives. Copyright © 2021, The Authors. All rights reserved.

关键词： Sliding mode control

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Mechanics of Soft-Body Rolling Motion without External Torque

引用

Physical Review Letters 2025年第19期134卷 198401-198401页

作者： Xudong Liang Yimiao Ding Zihao Yuan Yuxuan Han Yutang Zhou Junqi Jiang Zongling Xie Peng Fei Yixuan Sun Pan Jia Guoying Gu Zheng Zhong Feifei Chen Guangwei Si Zhefeng Gong School of Science Harbin Institute of Technology Shenzhen 518055 China Department of Neurology of the Fourth Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou 310058 China Robotics Institute and State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Cognitive Science and Mental Health Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing 100101 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing 100049 China Huazhong University of Science and Technology Optical and Electronic Information Wuhan 430074 China Zhejiang Lab Hangzhou 311121 China Shanghai Key Laboratory of Intelligent Robotics Meta Robotics Institute Shanghai Jiao Tong University Shanghai 200240 China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine Integration State Key Laboratory of Brain-machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou 311121 China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou 310058 China

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.

关键词： Creatinine

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Enhancing Strength and Ductility of Mg-Gd-Zn Alloy by Trace Ti and Al Additions

SSRN

引用

SSRN 2022年

作者： Ding, Zhibing Zhang, Shuai Liu, Bin Hou, Hua Zhi, Xuanle Guo, Wenmin Chen, Dongrui Zhao, Yuhong College of Mechanical and Energy Engineering Shaoyang University Shaoyang422000 China Key Laboratory of Hunan Province for Efficient Power System and Intelligent Manufacturing Shaoyang University Shaoyang422000 China State Key Lab of Powder Metallurgy Central South University Changsha410083 China College of Materials Science and Engineering North University of China Taiyuan030051 China University of Science and Technology Beijing China

The effects of trace additions of Ti and Al on the microstructure and mechanical properties of Mg-Gd-Zn alloy were investigated. Trace Al addition can promote the generation of LPSO in Mg-Gd-Zn(-Ti) alloys. Trace Ti addition can not only effectively refine the microstructure of as-cast alloys, but also inhibit the growth of DRXed grains in as-rolled alloys. The maximum texture intensity of the as-rolled alloy is slightly weakened by the addition of trace Ti and Al. The aging hardening response can be improved by adding Ti, but decreased by adding Al. The fibrous 14H LPSO phases, refined DRXed grains, basal texture and fine precipitates endow the peaked-aged Mg-Gd-Zn-Al-Ti alloy with superior mechanical performance, its UTS, YTS and elongation are 486MPa, 402MPa and 8.7%, respectively. © 2022, The Authors. All rights reserved.

关键词： Gadolinium alloys

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