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Research on the technology of vibration reduction by air curtain to control fluctuating pressure of propeller 28

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Research on the technology of vibration reduction by air cur...

28th International Ocean and Polar Engineering Conference, ISOPE 2018

作者： Gu, Xin Wu, Weiguo Lin, Yongshui Yang, Tao Wei, Jiezheng Key Laboratory of High Performance Ship Technology of Ministry of Education Department of Structural Engineering School of Transportation Wuhan University of Technology Wuhan Hubei China Department of Mechanics and Engineering Structure Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of Science Wuhan University of Technology Wuhan Hubei China

ISBN: (纸本)9781880653876

There are various vibration sources on the ship, including propulsion system, main engine and other auxiliary equipment. As one of the major vibration sources of ship, the propeller has a great influence on the vibration of stern. The fluctuating pressure is the most important inducing factors. In this paper, the propeller fluctuating pressure of a passenger ship and the technology of vibration reduction by air curtain are predicted based on CFD method to analyze the distribution of propeller fluctuating pressure at the stern bottom surface. The control of fluctuating pressure is realized by setting an air nozzle on the ship bottom plate in front of the propeller to produce an air curtain on the surface of ship to reduce the influence of fluctuating pressure working on the ship bottom. The influence on fluctuating pressure of different jet flow is studied by simulation. The result shows that the peak value of the fluctuating pressure decrease gradually with the jet flow increased, but the decreasing trend is slowing down. Copyright © 2018 by the International Society of Offshore and Polar Engineers (ISOPE)

关键词： Computational fluid dynamics

Style Transformer for Image Inversion and Editing

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arXiv 2022年

作者： Hu, Xueqi Huang, Qiusheng Shi, Zhengyi Li, Siyuan Gao, Changxin Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China Huazhong University of Science and Technology Wuhan China

Existing GAN inversion methods fail to provide latent codes for reliable reconstruction and flexible editing simultaneously. This paper presents a transformer-based image inversion and editing model for pretrained StyleGAN which is not only with less distortions, but also of high quality and flexibility for editing. The proposed model employs a CNN encoder to provide multi-scale image features as keys and values. Meanwhile it regards the style code to be determined for different layers of the generator as queries. It first initializes query tokens as learnable parameters and maps them into W+ space. Then the multi-stage alternate selfand cross-attention are utilized, updating queries with the purpose of inverting the input by the generator. Moreover, based on the inverted code, we investigate the referenceand label-based attribute editing through a pretrained latent classifier, and achieve flexible image-to-image translation with high quality results. Extensive experiments are carried out, showing better performances on both inversion and editing tasks within StyleGAN. Codes are available at https://***/sapphire497/style-transformer. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

Search for lepton portal dark matter in the PandaX-4T experiment

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2025年 864卷

作者： Ning, Xuyang Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Guofang Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Jiayi Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen Bai, Yang Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 201210 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Dark Matter Physics Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology New Cornerstone Science Laboratory Shanghai Jiao Tong University Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Liangshan 615000 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Department of Physics Yantai University Yantai 264005 China Key Laboratory of Nuclear Physics Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Beihang University Beijing 102206 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai 200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park 20742 MD United States School of Physics Nankai University Tianjin 300071 China School of Physics Sun Yat-Sen University Guangzhou 510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Mod

We report a search for a lepton-portal dark matter model, where dark matter couples to a charged lepton in the standard model. This simplified model naturally leads to photon-mediated dark matter interactions with nuclei, making it suitable for direct dark matter detection experiments. Matching to the framework of non-relativistic effective field theory for dark matter, we report the first sensitive search for this model using data from the PandaX-4T commissioning run. Our results yield strong constraints on Dirac fermion dark matter but relatively weaker constraints on Majorana dark matter due to the suppression of effective photon interactions. These constraints complement those obtained from the collider and indirect detection experiments. © 2025 The Author(s)

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Causal Mediation Analysis with a Three-Dimensional Image Mediator

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arXiv 2023年

作者： Chen, Minghao Zhou, Yingchun Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE Institute of Brain and Education Innovation School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai200062 China

Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies, etc. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image being a mediator emerged. In this study, a novel causal mediation analysis method with a three-dimensional image mediator is proposed. We define the average casual effects under the potential outcome framework, explore several sufficient conditions for the valid identification, and develop techniques for estimation and inference. To verify the effectiveness of the proposed method, a series of simulations under various scenarios is performed. Finally, the proposed method is applied to a study on the causal effect of mother's delivery mode on child's IQ development. It is found that the white matter in certain regions of the frontal-parietal and brainstem-cerebellum areas has mediating effects. © 2023, CC BY.

关键词： Magnetic resonance

OD-DETR: Online Distillation for Stabilizing Training of Detection Transformer

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arXiv 2024年

作者： Wu, Shengjian Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China FinVolution Group Iran Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University China

DEtection TRansformer (DETR) becomes a dominant paradigm, mainly due to its common architecture with high accuracy and no post-processing. However, DETR suffers from unstable training dynamics. It consumes more data and epochs to converge compared with CNN-based detectors. This paper aims to stabilize DETR training through the online distillation. It utilizes a teacher model, accumulated by Exponential Moving Average (EMA), and distills its knowledge into the online model in following three aspects. First, the matching relation between object queries and ground truth (GT) boxes in the teacher is employed to guide the student, so queries within the student are not only assigned labels based on their own predictions, but also refer to the matching results from the teacher. Second, the teacher’s initial query is given to the online student, and its prediction is directly constrained by the corresponding output from the teacher. Finally, the object queries from teacher’s different decoder stages are used to build the auxiliary group to accelerate the convergence. For each GT, two queries with the least matching costs are selected into this extra group, and they predict the GT box and participate the optimization. Extensive experiments show that the proposed OD-DETR successfully stabilizes the training, and significantly increases the performance without bringing in more parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

Distributed algorithms for u-statistics-based empirical risk minimization

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2023年第1期24卷 12301-12343页

作者： Lanjue Chen Alan T.K. Wan Shuyi Zhang Yong Zhou Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education Academy of Statistics and Interdisciplinary Sciences and School of Statistics East China Normal University Shanghai China Department of Management Sciences School of Data Science and Department of Biostatistics City University of Hong Kong Kowloon Hong Kong

Empirical risk minimization, where the underlying loss function depends on a pair of data points, covers a wide range of application areas in statistics including pairwise ranking and survival analysis. The common empirical risk estimator obtained by averaging values of a loss function over all possible pairs of observations is essentially a U-statistic. One well-known problem with minimizing U-statistic type empirical risks, is that the computational complexity of U-statistics increases quadratically with the sample size. When faced with big data, this poses computational challenges as the colossal number of observation pairs virtually prohibits centralized computing to be performed on a single machine. This paper addresses this problem by developing two computationally and statistically efficient methods based on the divide-and-conquer strategy on a decentralized computing system, whereby the data are distributed among machines to perform the tasks. One of these methods is based on a surrogate of the empirical risk, while the other method extends the one-step updating scheme in classical M-estimation to the case of pairwise loss. We show that the proposed estimators are as asymptotically efficient as the benchmark global U-estimator obtained under centralized computing. As well, we introduce two distributed iterative algorithms to facilitate the implementation of the proposed methods, and conduct extensive numerical experiments to demonstrate their merit.

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Semi-supervised inference for block-wise missing data without imputation

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2024年第1期25卷 4902-4937页

作者： Shanshan Song Yuanyuan Lin Yong Zhou School of Mathematical Sciences and School of Economics and Management Tongji University Shanghai China Department of Statistics The Chinese University of Hong Kong Hong Kong China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE Academy of Statistics and Interdisciplinary Sciences and School of Statistics East China Normal University Shanghai China

We consider statistical inference for single or low-dimensional parameters in a high-dimensional linear model under a semi-supervised setting, wherein the data are a combination of a labelled block-wise missing data set of a relatively small size and a large unlabelled data set. The proposed method utilises both labelled and unlabelled data without any imputation or removal of the missing observations. The asymptotic properties of the estimator are established under regularity conditions. Hypothesis testing for low-dimensional coefficients are also studied. Extensive simulations are conducted to examine the theoretical results. The method is evaluated on the Alzheimer's Disease Neuroimaging Initiative data.

关键词： block-missing data confidence intervals hypothesis testing semi-supervised inference

A hybrid deep learning method for controlled stochastic Kolmogorov systems with regime-switching

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A hybrid deep learning method for controlled stochastic Kolm...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Yu Zhang Zhuo Jin Jiaqin Wei School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China Department of Actuarial Studies and Business Analytics Macquarie University NSW Australia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In this paper, we employ numerical methods based on deep learning algorithms for solving controlled stochastic Kolmogorov systems with regime-switching. Different from classical control problems, each component of the state in controlled Kolmogorov systems is nonnegative. Due to the nonlinearity and complexity of the controlled stochastic Kolmogorov systems, we develop a hybrid deep learning method to numerically solve the optimal controls under this system. Subsequently, we apply the hybrid deep learning method to solve a specific case of a controlled stochastic Kolmogorov system, specifically controlled SIS (susceptible-infected-susceptible) systems. Finally, the effectiveness of the proposed hybrid deep learning method is verified through numerical results.

关键词： Deep learning Closed-form solutions Neural networks Optimal control Aerospace electronics Control systems Complexity theory Approximation methods Information technology Method of moments

Spatially Clustered Varying Coefficient Model

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arXiv 2020年

作者： Lin, Fangzheng Tang, Yanlin Zhu, Huichen Zhu, Zhongyi Department of Statistics Fudan University China Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE School of Statistics East China Normal University China Department of Mathematics Hong Kong University of Science and Technology China

In various applications with large spatial regions, the relationship between the response variable and the covariates is expected to exhibit complex spatial patterns. We propose a spatially clustered varying coefficient model, where the regression coefficients are allowed to vary smoothly within each cluster but change abruptly across the boundaries of adjacent clusters, and we develop a unified approach for simultaneous coefficient estimation and cluster identification. The varying coefficients are approximated by penalized splines, and the clusters are identified through a fused concave penalty on differences in neighboring locations, where the spatial neighbors are specified by the minimum spanning tree (MST). The optimization is solved efficiently based on the alternating direction method of multipliers, utilizing the sparsity structure from MST. Furthermore, we establish the oracle property of the proposed method considering the structure of MST. Numerical studies show that the proposed method can efficiently incorporate spatial neighborhood information and automatically detect possible spatially clustered patterns in the regression coefficients. An empirical study in oceanography illustrates that the proposed method is promising to provide informative results. Copyright © 2020, The Authors. All rights reserved.

关键词： Constrained optimization