Haotian Chen

University of Chinese Academy of Sciences

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Zongjun Tian

Nanjing University of Aeronautics and Astronautics

Boyuan Xu

Institute of Information Engineering

Guoran Hua

Nantong University

Liangchen Ge

Nanjing University of Aeronautics and Astronautics

Liangchen Ge

Nanjing University of Aeronautics and Astronautics

Xiaoming Liu

Beijing Institute of Technology

Jizhi Zhao

Peking Union Medical College Hospital

Xingyu Lin

Zhejiang University

Dechao Zhang

China Mobile (China)

Chong Pan

Beihang University

Cooperative Institutions

Chinese Academy of Sciences

Computer Network Information Center

University of Toronto

Nanjing University of Aeronautics and Astronautics

China Mobile (China)

University of Chinese Academy of Sciences

Beijing Institute of Technology

Huazhong University of Science and Technology

Beijing University of Posts and Telecommunications

Zhejiang University

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Stitching Interferometry of Cylindrical Inner Surface Profile Measurement

Haotian Chen Xingyu Lin Yingjie Yu

Image stitching

10.2139/ssrn.4999402

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Transient apical peak pressure measurement of Er:YAG laser–activated irrigation in different in vitro tooth models using a high-frequency sensor system

Lasers in Medical Science (2023)

Xinyu He Yizhou Li Mingyuan Nie Yue Yu Haotian Chen

Er:YAG laser

10.1007/s10103-023-03779-1

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Soft Plumbing: Direct-Writing and Controllable Perfusion of Tubular Soft Materials

Bulletin of the American Physical Society (2016)

Axel Güenther Patricia Omoruwa Haotian Chen Arianna McAllister Mark D. Jeronimo

Source

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Relationship between surface residual stress and dislocation configuration after laser shock processing of TC4 titanium alloy

Optics & Laser Technology (2022)

Liangchen Ge Haotian Chen Zongjun Tian Boyuan Xu

Titanium alloy

Surface stress

Surface layer

10.1016/j.optlastec.2022.108702

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Citations (9)

Strengthening effect of laser shock: concave model with and without reflection

Surface Topography Metrology and Properties (2021)

Liangchen Ge Haotian Chen Zongjun Tian Boyuan Xu Guoran Hua

Abstract When a laser shock wav e is loaded onto the surface of a concave model, many factors affect the laser shock effect, including loading pressure, curvature, surface wave and reflection of longitudinal wave. It is difficult to clarify the mixed effects of these factors, as such. The reflection of longitudinal wave is the most important factor, and the non-reflective concave model can effectively distinguish the influence of longitudinal wave reflection. In this paper, the concave models with and without reflection are established using the ABAQUS software. The effect of loading pressure, curvature, surface wave, and reflection of longitudinal wave on laser shock processing are analyzed. Curvature changes the incident angle of the laser during processing, so the actual pressure on the material surface changes. The results show that the optimal loading pressure is 2000 MPa/4 J when the curvature is 1/5 and the spot radius is 4 mm. The experimental results are consistent with the simulation results, so the simulation results are reliable.

Reflection

Radius of curvature

10.1088/2051-672x/abf9f6

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Elucidating the mechanism of defect formation in the 7050-T7451 aluminum alloy by laser shock peening

Waves in Random and Complex Media (2022)

Liangchen Ge Haotian Chen Guoran Hua Boyuan Xu Zongjun Tian

The laser energy directly affects the strengthening of materials during laser shock peening (LSP). In this paper, the effect of propagation and attenuation shock wave on LSP was studied. Combined with the strengthening effect and defect degree, the best impact parameters and the effects of different impact pressures were obtained. The simulation and experimental results demonstrated when the unloading wave chased the shock wave and caught up at 100-200µm in the depth direction, the possibility of strengthening defects increased and the occurrence position deepened with the increase of laser energy. There was no strengthening defect on the surface of alloy when the impact pressure 4σHEL > (P) > 3.5σHEL. Strengthening defects appeared in the form of cracks 184.61µm from the surface of the alloy. When P > 4σHEL, the strengthening defects appeared on both the surface and subsurface, where the surface defects in the form of holes. The subsurface defects in the form of cracks that appeared 200µm away from the surface resulted from the unloading wave meeting the shock wave. Based on the research and the Hugoniot elastic limit, the optimal impact pressure range to demonstrate the best strengthening effects and to mitigate the formation of strengthening defects is 2σHEL < P < 3.5σHEL.

10.1080/17455030.2022.2048124

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Field trial of concurrent co-cable and co-trench optical fiber online identification based on ensemble learning

Optics Express (2023)

Yunbo Li Dechao Zhang Zhiwei Wang Hui Yang Tiankuo Yu

The co-route optical fibers, comprising both co-cable and co-trench fibers, pose a significant potential risk to network service quality assurance by operators. They are incapable of achieving high-precision recognition and visual state management. In this study, we gathered both static and dynamic optical fiber data using a linewidth tunable light source (LTLS) and introduced a multimodal detection architecture that applies ensemble learning to the collected data. This constitutes what we believe to be the first field trial of concurrent recognition of optical fibers found both in co-cables and co-trenches. To identify co-cable fibers, we employed a double-layer cascaded Random Forest (DLC-RF) model based on the static features of fibers. For co-trench fiber, the dynamic characteristics of fiber vibrations are utilized in combination with multiple independent curve similarity contrast learners for classifying tasks. The proposed architecture is capable of automatically detecting the condition of the optical fiber and actively identifying the same routing segment within the network, eliminating the need for human intervention and enabling the visualization of passive optical fiber resources. Finally, after rigorous testing and validation across 11 sites in a typical urban area, including aggregation and backbone scenarios within the operator's live network environments, we have confirmed that the solution's ability to identify co-routes is accurate, exceeding 95%. This provides strong empirical evidence of its effectiveness.

Optical fiber cable

Field trial

10.1364/oe.506212

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Strengthening effect of laser shock: convex model with and without reflection

Surface Topography Metrology and Properties (2021)

Haotian Chen Liangchen Ge Zongjun Tian Xiaoming Liu Boyuan Xu

Reflection

10.1088/2051-672x/ac1f7f

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Effect of curvature of concave model on strengthening effect of 7050-T7451 aluminum alloy by laser shock

Applied Physics A (2022)

Haotian Chen Liangchen Ge Xiaoming Liu Zongjun Tian Boyuan Xu

10.1007/s00339-022-05771-y

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Deep Learning for Refined Lithology Identification of Sandstone Microscopic Images

Minerals (2024)

Chengrui Wang Pengjiang Li Qingqing Long Haotian Chen Pengfei Wang

Refined lithology identification is an essential task, often constrained by the subjectivity and low efficiency of classical methods. Computer-aided automatic identification, while useful, has seldom been specifically geared toward refined lithology identification. In this study, we introduce Rock-ViT, an innovative machine learning approach. Its architecture, enhanced with supervised contrastive loss and rooted in visual Transformer principles, markedly improves accuracy in identifying complex lithological patterns. To this end, we have collected public datasets and implemented data augmentation, aiming to validate our method using sandstone as a focal point. The results demonstrate that Rock-ViT achieves superior accuracy and effectiveness in the refined lithology identification of sandstone. Rock-ViT presents a new perspective and a feasible approach for detailed lithological analysis, offering fresh insights and innovative solutions in geological analysis.

Lithology

Identification

10.3390/min14030275

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Citations (2)