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A numerical investigation on interaction effects between two underwater vehicles in close proximity

Journal of Ocean engineering and Science 2025年

作者： Zheng, Zi-Qiang Zou, Lu Wang, Xuan Zou, Zao-Jian School of Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University Shanghai 200240 China China Ship Scientific Research Center Wuxi 214082 China

Underwater vehicles operating in close proximity suffer from significant hydrodynamic interactions, especially when there is a prominent vehicle size disparity. To gain profound insights into the physical mechanisms of interaction effects, this paper investigates the hydrodynamic interactions between a submarine model (SUBOFF) and a much smaller Unmanned Underwater Vehicle (UUV) model using the Reynolds Averaged Navier–Stokes (RANS) method. Firstly, the resistance of SUBOFF is predicted for Verification and Validation (V&V) purpose, from which satisfactory numerical accuracies are achieved. Subsequently, hydrodynamic forces and moments on the two vehicles are predicted, considering 60 layouts with different relative vehicle positions. The simulated flow fields indicate that remarkable impacts on the UUV longitudinal force are due to the distinct flow disturbances and shelter effects caused by the submarine sail. In general, the presence of submarine causes considerable flow variations, resulting in notable vertical forces and pitch moments on UUV. The results further suggest that placing the UUV above the SUBOFF middle-body in a limited region is more favorable. Finally, it is found that keeping the UUV pitch angle below 2° is helpful to avoid apparent increases in vertical force and pitch moment on UUV. © 2025

关键词： Factor interpolation Flow field analysis Hydrodynamic quantities Interaction effects Position layout Underwater vehicles

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Implementation of Positive Operator-Valued Measure in Passive Faraday Mirror Attack

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Communications in Theoretical Physics 2015年第3期63卷 296-302页

作者：王卫龙高明马智 State Key Laboratory of Mathematical Engineering and Advanced Computing

Passive Faraday-mirror(PFM) attack is based on imperfect Faraday mirrors in practical quantum cryptography systems and a set of three-dimensional Positive Operator-Valued Measure(POVM) operators plays an important role in this *** this paper,we propose a simple scheme to implement the POVM in PFM attack on an FaradayMichelson quantum cryptography *** the POVM can not be implemented directly with previous methods,in this scheme it needs to expand the states sent by Alice and the POVM operators in the attack into four-dimensional Hilbert space first,without changing the attacking effect by *** on the methods proposed by Ahnert and Payne,the linear-optical setup for implementing the POVM operators is *** last,the complete setup for realizing the PFM attack is presented with all ***,our scheme can also be applied to realize PFM attack on a plug-and-play system by changing the parameters in the setup.

关键词： Faraday mirrors passive Faraday mirror attack POVM linear optics

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UZAWA TYPE ALGORITHM BASED ON DUAL MIXED VARIATIONAL FORMULATION

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Applied Mathematics and Mechanics(English Edition) 2002年第7期23卷 765-772页

作者：王光辉王烈衡 State Key Laboratory of Acoustics Institute of Acoustics Chinese Academy of Sciences 100080 Beijing P R China State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Chinese Academy of Sciences 100080 Beijing P R China

Based on the dual mixed variational formulation with three variants (stress, displacement, displacement on contact boundary) and the unilateral beaming problem of finite element discretization, an Uzawa type iterative algorithm is presented. The convergence of this iterative algorithm is proved, and then the efficiency of the algorithm is tested by a numerical example.

关键词： Continuum mechanics Elasticity

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A NEW FAMILY OF TRUST REGION ALGORITHMS FOR UNCONSTRAINED OPTIMIZATION

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Journal of Computational Mathematics 2003年第2期21卷 221-228页

作者： Yuhong Dai Dachuan Xu(state key laboratory of scientific/engineering computing, Institute of Computational Mathematicsand scientific/engineering computing, Academy of Mathematics and System Sciences, ChineseAcademy of Sciences, P.O. Box 2719, Beijing 100080, China) Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences State Key Laboratory of Scientific/Engineering Computing 北京 100080

Trust region (TR) algorithms are a class of recently developed algorithms for nonlinear optimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously, differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive.

关键词： trust region method global convergence quasi-Newton method unconstrained optimization nonlinear programming.

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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

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IEEE Transactions on Fuzzy Systems 2025年

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still challenging, such as only 20 points labeled in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlabeled data learning through reliable long-range supervision propagation from labeled superpoints to unlabeled superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlabeled superpoints to clusters centered on labeled superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% labeled points. Our code is available at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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LINEAR SYSTEMS ASSOCIATED WITH NUMERICAL METHODS FOR CONSTRAINED OPITMIZATION

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Journal of Computational Mathematics 2003年第1期21卷 71-84页

作者： Y. Yuan(state key laboratory of scientific and engineering computing, Institute of Computational Mathematics and scientific/engineering computing, Chinese Academy of Sciences, P. O. Box 2719,Beijing 100080, China.) Institute of Computational Mathematics and Scientific/Engineering Computing Chinese Academy of Sciences P. 0. Box 2719 State Key Laboratory of Scientific and Engineering Computing

Linear systems associated with numerical methods for constrained optimization are discussed in this paper. It is shown that the corresponding subproblems arise in most well-known methods, no matter line search methods or trust region methods for constrained optimization can be expressed as similar systems of linear equations. All these linear systems can be viewed as some kinds of approximation to the linear system derived by the Lagrange-Newton method. Some properties of these linear systems are analyzed.

关键词： Constrained optimization Linear equations Lagrange-Newton method Trust region Line search

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Convergence properties of Beale-Powell restart algorithm

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Science China Mathematics 1998年第11期41卷 1142-1150页

作者：戴彧虹袁亚湘 1. State Key Laboratory of Scientific and Engineering Computing ICMSEC Chinese Academy of Sciences 100080 Beijing China

The Beale\|Powell restart algorithm is highly useful for large\|scale unconstrained optimization. An example is taken to show that the algorithm may fail to converge. The global convergence of a slightly modified algo... 详细信息

关键词： unconstrained optimization conjugate gradient restart line search global convergence.

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TDiffSal: Text-Guided Diffusion Saliency Prediction Model for Images 27th

TDiffSal: Text-Guided Diffusion Saliency Prediction Model fo...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Xiong, Min Zhu, Dandan Zhu, Kun Zhai, Guangtao School of Computer Science and Technology Donghua University No. 2999 Renmin North Road Songjiang District Shanghai201620 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Institute of AI Education Shanghai East China Normal University No. 3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No. 4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781858

Existing visual saliency prediction methods mainly focus on single-modal visual saliency prediction, while ignoring the significant impact of text on visual saliency. To more comprehensively explore the influence of text on human attention in images, we propose a text-guided diffusion saliency prediction model, named TDiffSal. In specific, recent studies on stable diffusion models have shown promising performance in unifying tasks due to their inherent generalization ability. Inspired by this, a novel diffusion model for generalized visual-text saliency prediction is proposed, which formulates the prediction issue as a conditional generative task of the saliency map by employing input visual and text as the conditions. Meanwhile, we introduce a multi-head fusion module to effectively integrate text features and image features, which can efficiently guide the image denoising process and progressively refine the generated saliency map to make it semantically relevant to the text. Additionally, we employ an efficient pre-training strategy to enhance the robustness and generalization of the proposed model. We conduct extensive experiments on benchmark datasets to demonstrate its superior performance compared to other state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Auto-GAN: GAN-Based Self-Supervised Collaborative Learning for Robust Spatio-Temporal Trajectory Classification in IoT

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IEEE Internet of Things Journal 2025年

作者： Jia, Jia Li, Linghui Li, Ximing Wang, Ning Cai, Binsi Zhang, Xu Qiu, Pengfei Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China Research Institute of China Mobile Communications Corporation Beijing100032 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China China Academy of Information and Communications Technology Beijing China Nanjing University School of Electronic Science and Engineering Nanjing210023 China

With the rapid proliferation of crowd mobility data produced by ubiquitous mobile devices equipped with spatial positioning modules, deep neural networks (DNNs) have become widely applied in spatio-temporal trajectory modeling. However, recent studies have shown that DNNs are vulnerable to adversarial examples with strong transferability, which are crafted by introducing small perturbations to original examples but can cause catastrophic mistakes. To mitigate this vulnerability and enhance model robustness, we propose a novel self-supervised collaborative learning framework named Auto-GAN that consists of a generator for automatically learning robust latent features and a discriminator for providing comprehensive guidance to the generator. By leveraging the collaboration between the generator and discriminator, our proposed method significantly improves the denoising performance. Moreover, we combine point-level and feature-level constraints into training processes between original example reconstruction and adversarial example denoising, thereby effectively suppressing the potential "error amplification effect". Extensive experiments conducted on two representative real-world mobility datasets show that our proposed method can significantly enhance the model’s robustness against various adversarial attacks, while preserving the model’s prediction accuracy on original examples. © 2014 IEEE.

关键词： Self-supervised learning

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In situ fully vectorial tomography and pupil function retrieval of tightly focused fields

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Nature Communications 2025年第1期16.0卷 1-10页

作者： Liu, Xin Tu, Shijie Hu, Yiwen Peng, Yifan Han, Yubing Kuang, Cuifang Liu, Xu Hao, Xiang State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China

Tightly focused optical fields are essential in nano-optics, but their applications have been limited by the challenges of accurate yet efficient characterization. In this article, we develop an in situ method for reconstructing the fully vectorial information of tightly focused fields in 3D space, while simultaneously retrieving the pupil functions. Our approach encodes these fields using phase-modulated focusing and polarization-split detection, followed by decoding through an algorithm based on least-sampling matrix-based Fourier transform and analytically derived gradient. We further employ a focus scanning strategy. When combined with our decoding algorithm, this strategy mitigates the imperfections in the detection path. This approach requires only 10 frames of 2D measurements to realize approximately 90% accuracy in tomography and pupil function retrieval within 10 s. Thus, it serves as a robust and convenient tool for the precise characterization and optimization of light at the nanoscale. We apply this technique to fully vectorial field manipulation, adaptive-optics-assisted nanoscopy, and addressing mixed-state problems. © The Author(s) 2025.

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