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Numerical calculation on propeller-induced pressure fluctuation of a river-to-sea ship 27

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Numerical calculation on propeller-induced pressure fluctuat...

27th International Ocean and Polar Engineering Conference, ISOPE 2017

作者： Wu, Weiguo Wei, Ji Ezheng Lin, Yongshui Qin, Shengjie Yang, Tao 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 Wuhan University of Technology Wuhan Hubei China Department of Technology National Deep Sea Center Qingdao Shandong China

ISBN: (纸本)9781880653975

The propeller is one of main vibration sources and cabin noise of a ship. This study utilized numerical simulation of CFD to analyze the characteristics of pressure fluctuation induced by propeller of a new generation of river-to-sea ship beyond rules. The pressure fluctuation in the area of D ×D above propeller and the effects of ship speed and propeller rotational speed are calculated and analyzed. The results show that maximum pressure fluctuation in the direction of length is in front of propeller approximately 0.1D and the magnitude is determined by amplitude of blade frequency. Centered on propeller, the amplitude in front of propeller is greater than that behind propeller and the amplitude on the outside of propeller is greater than that inside. It's also found that propeller rotational speed has a great influence on pressure fluctuation, and the effect of ship speed is small. This study provides a numerical computation method for pressure fluctuation induced by propeller of new generation of river-to-sea ship beyond rules, and has a great significance for the design of reducing vibration and noise. Copyright © 2017 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Rivers

Thermoelectric Properties of Ag2te Prepared by One-Step Hot-Pressing Method

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SSRN

SSRN 2023年

作者： Xu, Yulun Yan, Minghao Jiang, ErZhuo Zheng, Zedong Wang, Hongtao Duan, Bo Li, Guodong Zhai, Pengcheng Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Wuhan University of Technology No. 122 Luoshi Road Wuhan430070 China

In this work, a series of Ag2Te samples were successfully prepared by the one-step hot-pressing method (OS-HP). The results showed that the microstructure and thermoelectric properties of the samples were greatly affected by temperature and pressure. X-ray diffraction revealed that pure phase samples of Ag2Te can be obtained by OS-HP with a temperature of 673K and pressure of 10~20MPa in 2 h. The 673K-20 sample illustrated the largest ZT values owing to the relatively high Seebeck coefficient and low thermal conductivity. Overall, the OS-HP with appropriate temperature and pressure could serve as a route to efficiently prepare Ag2Te thermoelectric materials. © 2023, The Authors. All rights reserved.

关键词： Hot pressing

EFFECTS OF WATER IMMERSION ON THE PENETRATION RESISTANCE OF GRAPHITE/EPOXY LAMINATES IMPACTED BY A PROJECTILE WITH A CONICAL TUP

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EFFECTS OF WATER IMMERSION ON THE PENETRATION RESISTANCE OF ...

第二十一届国际复合材料大会

作者： ZHAGN Jiangtao GU Weimin DUAN Qingfeng LIU Lisheng ZHAI Pengcheng Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Wuhan University of Technology Department of engineering structure and mechanics Wuhan University of Technology

Many applications of fiber-reinforced polymer composite materials involve impact or dynamic loading in humid or water-immersion environments. Since composite materials are known to degrade when absorbed moisture, a lot of studies have been carried out to help understand and improve the impact resistance of composite materials and structures in humid and water-immersion ***, much work has been done to characterize the influence of moisture absorption on the low velocity impact properties of polymer-based composites, the penetration resistance and perforation mechanisms of composite materials have not been extensively studied.

关键词： Graphite/epoxy composite laminate Impact Water immersion Penetration

NOVEL ENSEMBLE ALGORITHMS FOR RANDOM TWO-DOMAIN PARABOLIC PROBLEMS

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arXiv 2023年

作者： Sun, Yizhong Wang, Jiangshan Zheng, Haibiao School of Mathematical Sciences East China Normal University Shanghai China School of Mathematical Sciences East China Normal University Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice Key Laboratory of Advanced Theory and Application in Statistics and Data Science Shanghai China

In this paper, three efficient ensemble algorithms are proposed for fast-solving the random fluid-fluid interaction model. Such a model can be simplified as coupling two heat equations with random diffusion coefficients and a friction parameter due to its complexity and uncertainty. We utilize the Monte Carlo method for the coupled model with random inputs to derive some deterministic fluid-fluid numerical models and use the ensemble idea to realize the fast computation of multiple problems. Our remarkable feature of these algorithms is employing the same coefficient matrix for multiple linear systems, significantly reducing the computational cost. By data-passing partitioned techniques, we can decouple the numerical models into two smaller sub-domain problems and achieve parallel computation. Theoretically, we derive that both algorithms are unconditionally stable and convergent. Finally, numerical experiments are conducted not only to support the theoretical results but also to validate the exclusive feature of the proposed algorithms. © 2023, CC BY-NC-SA.

关键词： Numerical models

CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning

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CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cr...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lianggangxu Chen Xuejiao Wang Jiale Lu Shaohui Lin Changbo Wang Gaoqi He School of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal University Chongqing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

3D Scene Graph Generation (3DSGG) aims to classify objects and their predicates within 3D point cloud scenes. However, current 3DSGG methods struggle with two main challenges. 1) The dependency on labor-intensive ground-truth annotations. 2) Closed-set classes training hampers the recognition of novel objects and predicates. Addressing these issues, our idea is to extract cross-modality features by CLIP from text and image data naturally related to 3D point clouds. Cross-modality features are used to train a robust 3D scene graph (3DSG)feature extractor. Specifically, we propose a novel Cross-Modality Contrastive Learning 3DSGG (CCL-3DSGG) method. Firstly, to align the text with 3DSG, the text is parsed into word level that are consistent with the 3DSG annotation. To enhance robustness during the alignment, adjectives are exchanged for different objects as negative samples. Then, to align the image with 3DSG, the camera view is treated as a positive sample and other views as negatives. Lastly, the recognition of novel object and predicate classes is achieved by calculating the cosine similarity between prompts and 3DSG features. Our rigorous experiments confirm the superior open-vocabulary capability and applicability of CCL-3DSGG in real-world contexts.

关键词： Point cloud compression Training Computer vision Three-dimensional displays Annotations Contrastive learning Feature extraction

Rethinking Centered Kernel Alignment in Knowledge Distillation

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arXiv 2024年

作者： Zhou, Zikai Shen, Yunhang Shao, Shitong Gong, Linrui Lin, Shaohui School of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE China Youtu Lab Tencent Shanghai China Hunan University China

Knowledge distillation has emerged as a highly effective method for bridging the representation discrepancy between large-scale models and lightweight models. Prevalent approaches involve leveraging appropriate metrics to minimize the divergence or distance between the knowledge extracted from the teacher model and the knowledge learned by the student model. Centered Kernel Alignment (CKA) is widely used to measure representation similarity and has been applied in several knowledge distillation methods. However, these methods are complex and fail to uncover the essence of CKA, thus not answering the question of how to use CKA to achieve simple and effective distillation properly. This paper first provides a theoretical perspective to illustrate the effectiveness of CKA, which decouples CKA to the upper bound of Maximum Mean Discrepancy (MMD) and a constant term. Drawing from this, we propose a novel Relation-Centered Kernel Alignment (RCKA) framework, which practically establishes a connection between CKA and MMD. Furthermore, we dynamically customize the application of CKA based on the characteristics of each task, with less computational source yet comparable performance than the previous methods. The extensive experiments on the CIFAR-100, ImageNet-1k, and MS-COCO demonstrate that our method achieves state-of-the-art performance on almost all teacher-student pairs for image classification and object detection, validating the effectiveness of our approaches. Our code is available in https://***/Klayand/PCKA. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

Assessment of Effects of Age and Gender on the Incubation Period of COVID-19 with a Mixture Regression Model

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Journal of Data Science 2021年第1期19卷 p1-16页

作者： SIMING ZHENG JING QIN YONG ZHOU Academy of Mathematics and Systems Science University of Chinese Academy of Sciences Beijing China National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda Maryland USA Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE Academy of Statistics and Interdisciplinary Sciences Faculty of Economics and Management East China Normal University Shanghai China

Following the outbreak of COVID-19, various containment measures have been taken, including the use of quarantine. At present, the quarantine period is the same for everyone, since it is implicitly assumed that the incubation period distribution of COVID-19 is the same regardless of age or gender. For testing the effects of age and gender on the incubation period of COVID-19, a novel two-component mixture regression model is proposed. An expectation-maximization (EM) algorithm is adopted to obtain estimates of the parameters of interest, and the simulation results show that the proposed method outperforms the simple regression method and has robustness. The proposed method is applied to a Zhejiang COVID-19 dataset, and it is found that age and gender statistically have no effect on the incubation period of COVID-19, which indicates that the quarantine measure currently in operation is reasonable. [ABSTRACT FROM AUTHOR]

关键词： EM algorithm incubation period length-biased data mixture model

An efficient semismooth Newton method for adaptive sparse signal recovery problems

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arXiv 2021年

作者： Ding, Yanyun Zhang, Haibin Li, Peili Xiao, Yunhai Department of Operations Research and Information Engineering Beijing University of Technology Beijing100124 China School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai200062 China School of Mathematics and Statistics Henan University Kaifeng475000 China

We know that compressive sensing can establish stable sparse recovery results from highly undersampled data under a restricted isometry property condition. In reality, however, numerous problems are coherent, and vast majority conventional methods might work not so well. Recently, it was shown that using the difference between l1- and l2-norm as a regularization always has superior performance. In this paper, we propose an adaptive lp-l1−2 model where the lp-norm with p ≥ 1 measures the data fidelity and the l1−2-term measures the sparsity. This proposed model has the ability to deal with different types of noises and extract the sparse property even under high coherent condition. We use a proximal majorization-minimization technique to handle the nonconvex regularization term and then employ a semismooth Newton method to solve the corresponding convex relaxation subproblem. We prove that the sequence generated by the semismooth Newton method admits fast local convergence rate to the subproblem under some technical assumptions. Finally, we do some numerical experiments to demonstrate the superiority of the proposed model and the progressiveness of the proposed algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressed sensing

Cross Attention Based Style Distribution for Controllable Person Image Synthesis

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arXiv 2022年

作者： Zhou, Xinyue Yin, Mingyu Chen, Xinyuan Sun, Li Gao, Changxin Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China Shanghai AI Laboratory Shanghai China Huazhong University of Science and Technology Wuhan China

Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The module intentionally selects the style represented by each semantic and distributes them according to the target pose. The attention matrix in cross attention expresses the dynamic similarities between the target pose and the source styles for all semantics. Therefore, it can be utilized to route the color and texture from the source image, and is further constrained by the target parsing map to achieve a clearer objective. At the same time, to encode the source appearance accurately, the self attention among different semantic styles is also added. The effectiveness of our model is validated quantitatively and qualitatively on pose transfer and virtual try-on tasks. Codes are available at https://***/xyzhouo/CASD. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics