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Investigation of cavitation bubble collapse in hydrophobic concave using the pseudopotential multi-relaxation-time lattice Boltzmann method

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Chinese Physics B 2021年第4期30卷 333-341页

作者： Minglei Shan Yu Yang Xuemeng Zhao Qingbang Han Cheng Yao Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Hohai UniversityChangzhou 213022China College of Computer and Information Hohai UniversityNanjing 210000China

The interaction between cavitation bubble and solid surface is a fundamental topic which is deeply concerned for the utilization or avoidance of cavitation *** complexity of this topic is that the cavitation bubble collapse includes many extreme physical phenomena and variability of different solid surface *** the present work,the cavitation bubble collapse in hydrophobic concave is studied using the pseudopotential multi-relaxation-time lattice Boltzmann model(MRT-LB).The model is modified by involving the piecewise linear equation of state and improved forcing *** fluid-solid interaction in the model is employed to adjust the wettability of solid ***,the validity of the model is verified by comparison with experimental results and grid-independence ***,the cavitation bubble collapse in a hydrophobic concave is studied by investigating density field,pressure field,collapse time,and jet *** superimposed effect of the surface hydrophobicity and concave geometry is analyzed and explained in the framework of the pseudopotential *** study shows that the hydrophobic concave can enhance cavitation effect by decreasing cavitation threshold,accelerating collapse and increasing jet velocity.

关键词： cavitation bubble hydrophobic concave lattice Boltzmann model pseudopotential model

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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Training-free diffusion model adaptation for variable-sized text-to-image synthesis 23

Training-free diffusion model adaptation for variable-sized ...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Zhiyu Jin Xuli Shen Bin Li Xiangyang Xue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University and UniDT Technology

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and evaluated on the images with fixed sizes. However, users are demanding for various images with specific sizes and various aspect ratio. This paper focuses on adapting text-to-image diffusion models to handle such variety while maintaining visual fidelity. First we observe that, during the synthesis, lower resolution images suffer from incomplete object portrayal, while higher resolution images exhibit repetitively disordered presentation. Next, we establish a statistical relationship indicating that attention entropy changes with token quantity, suggesting that models aggregate spatial information in proportion to image resolution. The subsequent interpretation on our observations is that objects are incompletely depicted due to limited spatial information for low resolutions, while repetitively disorganized presentation arises from redundant spatial information for high resolutions. From this perspective, we propose a scaling factor to alleviate the change of attention entropy and mitigate the defective pattern observed. Extensive experimental results validate the efficacy of the proposed scaling factor, enabling models to achieve better visual effects, image quality, and text alignment. Notably, these improvements are achieved without additional training or fine-tuning techniques.

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Decoupling Scattering: Pseudo-Label Guided NeRF for Scenes with Scattering Media 39

Decoupling Scattering: Pseudo-Label Guided NeRF for Scenes w...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Mingyang Zhang, Junkang Fang, Faming Zhang, Guixu School of Computer Science and Technology East China Normal University China Shanghai Key Laboratory of Multidimensional Information Processing China

ISBN: (纸本)157735897X

Neural Radiance Fields (NeRF) has been widely used in computer vision and graphics, achieving impressive results in novel view synthesis and multi-view 3D reconstruction. However, despite its excellent performance under ideal conditions, NeRF struggles in challenging environments such as hazy, foggy, and underwater scenes, primarily due to the difficulty in decoupling objects from the scattering medium. To mitigate this limitation, we proposed a novel approach for NeRF in scenes with scattering media. Specifically, we leverage pseudo-labels during the early stage of training to guide NeRF in decoupling the densities of objects and the scattering medium, guiding the model toward a more appropriate search space. Furthermore, we introduce a Cyclical Progressive Dimensional Optimization Strategy (CPDOS) that focuses on optimizing a single or a few variables during specific periods. Experimental results demonstrate that our method can effectively simulate hazy and underwater scenes, accurately decouple the scattering medium from objects, estimate atmospheric parameters, and outperform existing methods in novel view synthesis and image restoration tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Three dimensional computer graphics

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Offline Reinforcement Learning Based on Next State Supervision

Offline Reinforcement Learning Based on Next State Supervisi...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jie Yan Quan Liu Lihua Zhang School of Computer Science and Technology Soochow University Soochow China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China

Offline reinforcement learning aims to maximize the use of static offline datasets to train agents without interacting with the environment. For the problem of distribution shifts, most approaches avoid out-of-distribution actions through strong constraints, and do not consider generalization and learning within these domains. Based on this problem, we propose a novel method offline reinforcement learning based on next state supervision (NSS), which consists of two main components, the guidance policy and an adaptive coefficient. The guidance policy outputs the next-state with the highest value within a certain range around the current state and an adaptive coefficient regulates the weight of the penalty term in the learned policy. Empirical studies show that the method improves the performance of the baseline method with constraints while having some generalization ability.

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Research on patent recommendation method based on graph neural network 26

Research on patent recommendation method based on graph neur...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Enchao Liu, Weidong Liu, Shi Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation. © 2023 IEEE.

关键词： Patents and inventions

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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A Multitask Learning Approach for Chinese National Instruments Recognition and Timbre Space Regression 9th

A Multitask Learning Approach for Chinese National Instrumen...

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9th Conference on Sound and Music technology, CSMT 2021

作者： Xu, Shenyang Jiang, Yiliang Li, Zijin Sun, Xiaoheng Li, Wei Central Conservatory of Music Beijing100031 China School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Musical instrument recognition is an essential task in the domain of music information retrieval. So far, most existing research are focused on western instruments. In this research, we turn to Chinese national instruments recognition. First, a dataset containing 30 Chinese national instruments is created. Then, a well-designed end-to-end Convolutional Recurrent Neural Network is proposed. Moreover, we combine instrument recognition with instrument timbre space regression using a multitask learning approach to improve performances of both tasks. We conduct experiments in instrument recognition and timbre space regression to evaluate our model and multitask learning approach. Experimental results show that our proposed model outperforms previous algorithms, and the multitask approach can further improve the results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Music

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Offline Reinforcement Learning with Generative Adversarial Networks and Uncertainty Estimation

Offline Reinforcement Learning with Generative Adversarial N...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Lan Wu Quan Liu Lihua Zhang Zhigang Huang School of Computer Science and Technology Soochow University Soochow China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China

In recent years, offline reinforcement learning has attracted considerable attention in artificial intelligence. By generating a static dataset through a behavior policy, it is unable to engage in online interactions with the environment. However, this inevitably leads to states or actions undergoing inherent distribution shifts. To address the above concerns, offline reinforcement learning with generative adversarial networks and uncertainty estimation (GANUE) is proposed. This approach parameterizes the conditional generative adversarial networks and avoids the over-constraint problem that would be introduced when using a distance metric. Estimating the Q-value through ensemble uncertainty not only relaxes the policy constraint strength but also enhances the out-of-distribution generalization ability. The experimental results show that GANUE significantly performs better on the Maze2D and Adroit tasks than multiple baselines. In addition, we perform sensitivity analysis experiments for the parameters of the perturbation model.

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Cognitive Analysis of Medical Decision-Making: An Extended MULTIMOORA-Based Multigranulation Probabilistic Model with Evidential Reasoning

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Cognitive Computation 2024年第6期16卷 3149-3167页

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar Wang, Baoli Li, Wentao School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India School of Mathematics and Information Technology Yuncheng University Shanxi Yuncheng044000 China College of Artificial Intelligence Southwest University Chongqing400715 China

Cognitive computation has leveraged the capabilities of computer algorithms, rendering it an exceptionally efficient approach for addressing multi-attribute group decision-making (MAGDM) problems. Due to the stability of MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) and the capability of evidential reasoning (ER) to combine information from multiple sources, the technique of multigranulation probabilistic rough sets (MG PRSs) holds great promise for solving MAGDM problems. Thus, a new and stable method for MAGDM is proposed. Initially, three forms of multigranulation Pythagorean fuzzy probabilistic rough sets (MG PF PRSs) are constructed using MULTIMOORA approaches. Next, the hierarchical clustering method is employed to cluster similar decision information and consolidate the decision-makers’ preferences. Representatives are chosen from each category to simplify information fusion calculations and reduce complexity by reducing the model’s dimensionality. Following that, the rankings obtained from the three methods are fused using ER. Ultimately, the validity of our method is revealed via a case analysis on chickenpox cases from the UCI data set by employing cognitive analysis. The paper outlines a method for MAGDM that provides significant advantages. Specifically, the use of MULTIMOORA improves the stability of decision results, while the incorporation of ER reduces the overall uncertainty of entire decision processes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Granular computing

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