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Application of optimized random forest regressors in predicting maximum principal stress of aseismic tunnel lining

Journal of Central South University 2024年第11期31卷 3900-3913页

作者： MEI Xian-cheng DING Chang-dong ZHANG Jia-min LI Chuan-qi CUI Zhen SHENG Qian CHEN Jian State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Geotechnical Mechanics and Engineering of Ministry of Water Resources Changjiang River Scientific Research InstituteWuhan 430010China SINOPEC Research Institute of Petroleum Engineering Beijing 100101China School of Resources and Safety Engineering Central South UniversityChangsha 410083China

Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with damping ***,the traditional numerical methods suffer from the complex modelling and time-consuming ***,a prediction model named the random forest regressor(RFR)is proposed based on 240 numerical simulation results of the mechanical response of tunnel *** addition,circle mapping(CM)is used to improve Archimedes optimization algorithm(AOA),reptile search algorithm(RSA),and Chernobyl disaster optimizer(CDO)to further improve the predictive performance of the RFR *** performance evaluation results show that the CMRSA-RFR is the best prediction *** damping layer thickness is the most important feature for predicting the maximum principal stress of tunnel lining containing damping *** study verifies the feasibility of combining numerical simulation with machine learning technology,and provides a new solution for predicting the mechanical response of aseismic tunnel with damping layer.

关键词： maximum principal stress aseismic tunnel lining random forest regressor machine learning

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The research on floe ice force acting on the"Xue Long"icebreaker based on synthetic ice test and virtual mass numerical method

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Journal of Hydrodynamics 2021年第2期33卷 271-281页

作者： Wei Guo Qiao-sheng Zhao Yu-kui Tian Wan-chao Zhang State Key Laboratory of Hydrodynamics China Ship Scientific Research CenterWuxi 214082China School of Naval Architecture and Ocean Engineering Jiangsu University of Science and TechnologyZhenjiang 212003China

When the ice-going ships advance in the polar region,the navigation in the floe ice region is often encountered,the hull will suffer the high-frequency contact force produced by the floe ice,which will affect the navigation performance of the ***,it is necessary to predict the water and ice force acting on the hull *** this paper,the numerical simulation and experimental verification will be conducted for the polar scientific research ship-"Xue Long".Based on the computational fluid dynamics(CFD)and DEM theory,a virtual mass method is proposed to realize the predicting of the water and ice force and the simulation of the interaction phenomenon between the ice and hull,the numerical method is built to calculate the force acting on the *** the same time,the synthetic ice is used to carry out the experimental research on the advancing straight with different speeds and the oblique navigation with different drift *** optimal virtual mass coefficient is determined through the comparison and analysis of the numerical simulation and test results under typical *** to the optimal virtual mass coefficient,the numerical simulation is carried out with different speeds and drift angles,which is compared with the experiment more *** is found that the virtual mass method based on the combination of CFD and DEM can simulate the overturning and the sliding along the hull of the floe ice vividly,the relative error of the total resistance between model test and numerical method in advancing straight is almost within 10%,while the relative error of the total longitudinal force in oblique navigation is almost within 15%.

关键词： "Xue Long"ship synthetic ice model test computational fluid dynamics(CFD) DEM virtual mass method drift angle

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Signature of room-temperature two-dimensional ferromagnetism in Ta0.67V0.33Se2

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Physical Review B 2024年第18期110卷 184427-184427页

作者： Yuhan Du Yuanji Ma Luo-Zhao Zhang Yiting Liu Xun Zhu Qi Feng Changjian Zhang Xinyi Wang Yuxiang Wang State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai 200241 China Department of Materials Science & State Key Laboratory of Molecular Engineering of Polymers Fudan University Shanghai 200438 China Shanghai Key Laboratory of Green Chemistry and Chemical Processes East China Normal University Shanghai 200241 China State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China

The discovery of ferromagnetism in van der Waals materials attracts intense research interest and holds profound implications for two-dimensional spintronic devices. However, in most cases the Curie temperature of van der Waals ferromagnets is much lower than room temperature, hindering their potential for device applications. In this study we report the discovery of room-temperature ferromagnetism in layered Ta0.67V0.33Se2. The single crystal is synthesized through the partial replacement of tantalum with vanadium. The crystal structure of Ta0.67V0.33Se2 closely resembles that of both 1T-VSe2 and 1T-TaSe2. The resultant Ta0.67V0.33Se2 exhibits a Hall sign reversal at around 60K, with the dominant carrier changing from electron type at higher temperatures to hole type at lower temperatures. The anomalous peak is observed in the longitudinal resistivity near the critical temperature, which is ascribed to the temperature-induced Lifshitz transition. Despite the fact that bulk 1T-VSe2 and 1T-TaSe2 are paramagnetic, Ta0.67V0.33Se2 displays room-temperature ferromagnetism, as evidenced by the hysteresis behavior observed in the field-dependent magnetization. Collective anomalies are observed at about 60K in both magnetization and transport measurements, indicating a strong correlation between electric and magnetic degrees of freedom. Moreover, room-temperature ferromagnetism is confirmed in few-layer Ta0.67V0.33Se2 through magneto-optic Kerr measurements. Our work provides a strategy for accessing two-dimensional high-Curie-temperature magnets, which hold promise for potential applications in spintronic devices.

关键词： Ferromagnetism Magnetic phase transitions 2-dimensional systems Single crystal materials Ultrathin films Van der Waals systems Hall bar Magnetization measurements Raman spectroscopy Resistivity measurements X-ray photoelectron spectroscopy

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Smoothing Gradient Tracking for Decentralized Optimization over the Stiefel Manifold with Non-smooth Regularizers

arXiv

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arXiv 2023年

作者： Wang, Lei Liu, Xin State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Recently, decentralized optimization over the Stiefel manifold has attacked tremendous attentions due to its wide range of applications in various fields. Existing methods rely on the gradients to update variables, which are not applicable to the objective functions with non-smooth regularizers, such as sparse PCA. In this paper, to the best of our knowledge, we propose the first decentralized algorithm for non-smooth optimization over Stiefel manifolds. Our algorithm approximates the non-smooth part of objective function by its Moreau envelope, and then existing algorithms for smooth optimization can be deployed. We establish the convergence guarantee with the iteration complexity of O(∊−4). Numerical experiments conducted under the decentralized setting demonstrate the effectiveness and efficiency of our algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Riemannian Preconditioned Algorithms for Tensor Completion via Tensor Ring Decomposition

arXiv

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arXiv 2023年

作者： Gao, Bin Peng, Renfeng Yuan, Ya-Xiang State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

We propose Riemannian preconditioned algorithms for the tensor completion problem via tensor ring decomposition. A new Riemannian metric is developed on the product space of the mode-2 unfolding matrices of the core tensors in tensor ring decomposition. The construction of this metric aims to approximate the Hessian of the cost function by its diagonal blocks, paving the way for various Riemannian optimization methods. Specifically, we propose the Riemannian gradient descent and Riemannian conjugate gradient algorithms. We prove that both algorithms globally converge to a stationary point. In the implementation, we exploit the tensor structure and adopt an economical procedure to avoid large matrix formulation and computation in gradients, which significantly reduces the computational cost. Numerical experiments on various synthetic and real-world datasets-movie ratings, hyperspectral images, and high-dimensional functions- suggest that the proposed algorithms have better or favorably comparable performance to other candidates. Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

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Smoothing Gradient Tracking for Decentralized Optimization over the Stiefel Manifold with Non-Smooth Regularizers

Smoothing Gradient Tracking for Decentralized Optimization o...

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IEEE Conference on Decision and Control

作者： Lei Wang Xin Liu State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China

Recently, decentralized optimization over the Stiefel manifold has attracted tremendous attentions due to its wide range of applications in various fields. Existing methods rely on the gradients to update variables, which are not applicable to the objective functions with non-smooth regularizers, such as sparse PCA. In this paper, to the best of our knowledge, we propose the first decentralized algorithm for non-smooth optimization over Stiefel manifolds. Our algorithm approximates the non-smooth part of objective function by its Moreau envelope, and then existing algorithms for smooth optimization can be deployed. We establish the convergence guarantee with the iteration complexity of $\mathcal{O}$ (∊ 4 ). Numerical experiments conducted under the decentralized setting demonstrate the effectiveness and efficiency of our algorithm.

关键词：

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Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval 24

Focus, Distinguish, and Prompt: Unleashing CLIP for Efficien...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zeng, Gangyan Zhang, Yuan Wei, Jin Yang, Dongbao Zhang, Peng Gao, Yiwen Qin, Xugong Zhou, Yu School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Lenovo Research Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China TMCC College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9798400706868

Scene text retrieval aims to find all images containing the query text from an image gallery. Current efforts tend to adopt an Optical Character Recognition (OCR) pipeline, which requires complicated text detection and/or recognition processes, resulting in inefficient and inflexible retrieval. Different from them, in this work we propose to explore the intrinsic potential of Contrastive Language-Image Pre-training (CLIP) for OCR-free scene text retrieval. Through empirical analysis, we observe that the main challenges of CLIP as a text retriever are: 1) limited text perceptual scale, and 2) entangled visual-semantic concepts. To this end, a novel model termed FDP (Focus, Distinguish, and Prompt) is developed. FDP first focuses on scene text via shifting the attention to the text area and probing the hidden text knowledge, and then divides the query text into content word and function word for processing, in which a semantic-aware prompting scheme and a distracted queries assistance module are utilized. Extensive experiments show that FDP significantly enhances the inference speed while achieving better or competitive retrieval accuracy compared to existing methods. Notably, on the IIIT-STR benchmark, FDP surpasses the state-of-the-art model by 4.37% with a 4 times faster speed. Furthermore, additional experiments under phrase-level and attribute-aware scene text retrieval settings validate FDP's particular advantages in handling diverse forms of query text. The source code will be available at https://***/Gyann-z/FDP. © 2024 ACM.

关键词： Semantics

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Projected gradient descent algorithm for ab initio crystal structure relaxation under a fixed unit cell volume

arXiv

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arXiv 2024年

作者： Hu, Yukuan Yin, Junlei Gao, Xingyu Liu, Xin Song, Haifeng State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Solidification Processing Northwestern Polytechnical University Shaanxi Xi’an710072 China Innovation Center NPU Chongqing Chongqing401135 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China

This paper is concerned with ab initio crystal structure relaxation under a fixed unit cell volume, which is a step in calculating the static equations of state and forms the basis of thermodynamic property calculations for materials. The task can be formulated as an energy minimization with a determinant constraint. Widely used line minimization-based methods (e.g., conjugate gradient method) lack both efficiency and convergence guarantees due to the nonconvex nature of the feasible region as well as the significant differences in the curvatures of the potential energy surface with respect to atomic and lattice components. To this end, we propose a projected gradient descent algorithm named PANBB. It is equipped with (i) search direction projections onto the tangent spaces of the nonconvex feasible region for lattice vectors, (ii) distinct curvature-aware initial trial step sizes for atomic and lattice updates, and (iii) a nonrestrictive line minimization criterion as the stopping rule for the inner loop. It can be proved that PANBB favors theoretical convergence to equilibrium states. Across a benchmark set containing 223 structures from various categories, PANBB achieves average speedup factors of approximately 1.41 and 1.45 over the conjugate gradient method and direct inversion in the iterative subspace implemented in off-the-shelf simulation software, respectively. Moreover, it normally converges on all the systems, manifesting its unparalleled robustness. As an application, we calculate the static equations of state for the high-entropy alloy AlCoCrFeNi, which remains elusive owing to 160 atoms representing both chemical and magnetic disorder and the strong local lattice distortion. The results are consistent with the previous calculations and are further validated by experimental thermodynamic data. © 2024, CC BY.

关键词： Conjugate gradient method

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Marangoni-flow-assisted assembly of single-walled carbon nanotube films for human motion sensing

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Fundamental Research 2024年第3期4卷 570-574页

作者： Yuguang Chen Yitan Li Lu Han Hao Sun Min Lyu Zeyao Zhang Shigeo Maruyama Yan Li Key Laboratory for the Physics and Chemistry of Nanodevices Beijing National Laboratory of Molecular SciencesState Key Laboratory of Rare Earth Materials Chemistry and ApplicationsCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijing 100871China Bruker(Beijing)Scientific Technology Co.Ltd. Beijing 100192China Department of Mechanical Engineering The University of TokyoTokyo 113-8656Japan Peking University Shenzhen Institute Shenzhen 518057China PKU-HKUST ShenZhen-HongKong Institution Shenzhen 518057China

Single-walled carbon nanotubes(SWCNTs)present excellent electronic and mechanical properties desired in wearable and flexible *** preparation of SWCNT films is the first step for fabricating various *** work developed a scalable and feasible method to assemble SWCNT thin films on water surfaces based on Marangoni flow induced by surface tension *** films possess a large area of 40 cm×30 cm(extensible),a tunable thickness of 15∼150 nm,a high transparency of up to 96%,and a decent *** are ready to be directly transferred to various substrates,including flexible *** strain sensors were fabricated with the films on flexible *** sensors worked with high sensitivity and *** realizing multi-functional human motion sensing,including responding to voices,monitoring artery pulses,and detecting knuckle and muscle actions,the assembled SWCNT films demonstrated the potential for application in smart devices.

关键词： Single-walled carbon nanotubes Marangoni flow Assembly Interfaces Sensors

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FPGA-Based Convolution Accelerator and Memristor IP Core for Cooperative Acceleration of the YOLO Network 4

FPGA-Based Convolution Accelerator and Memristor IP Core for...

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4th International Conference on Computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2024

作者： Tan, Aolong Yang, Yucheng Duan, Shukai Wang, Lidan College of Artificial Intelligence Southwest University Chongqing400715 China State Key Laboratory of Intelligent Vehicle Safety Technology China National Local Joint Engineering Research Center of Intelligent Transmission and Control Technology 400715 China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing400715 China Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Ministry of Education Chongqing400715 China

ISBN: (纸本)9798350378542

Object detection technology is widely used in areas such as autonomous driving, drone reconnaissance, smart surveillance and industrial automation, especially in remote or hazardous scenarios where it can improve efficiency and safety. Therefore, the development of a high-precision, low-power acceleration system integrated with low-power hardware is crucial. This paper proposes a collaborative acceleration scheme for the YOLO network based on an FPGA convolution accelerator and simulated memristors, which successfully implements and accelerates the YOLOv4-Tiny model. First, a reconfigurable single-core accelerator operator is designed for various computational operations in the model. Second, taking advantage of the digital device characteristics of memristors and the hardware resources of the FPGA, a memristor simulation IP core is built to exploit its memory characteristics for convolution operations. Through the synergistic effect of the convolution accelerator and the simulated IP core, a hybrid acceleration of the detection model is achieved. The power consumption of the convolution accelerator is only 2.75W, and in testing, the detection time for a single image is 823ms with an average accuracy of over 0.95, achieving high detection performance. This provides an opportunity to explore and research the efficient acceleration of object detection networks with integrated storage and computation. © 2024 IEEE.

关键词： Reconfigurable hardware

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