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Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

Computational Visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

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A computer-aided diagnosis system for breast pathology: a deep learning approach with model interpretability from pathological perspective

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Service Oriented Computing and Applications 2023年第2期18卷 183-193页

作者： Hsu, Wei-Wen Wu, Yongfang Chen, Chung-Hao Hao, Chang Hou, Yu-Ling Gao, Xiang Shao, Yun Zhang, Xueli He, Tao Tai, Yanhong Department of Computer Science and Information Technology National Taitung University Taitung Taiwan Department of Pathology The Fifth Medical Center of Chinese PLA General Hospital Beijing China Department of Electrical and Computer Engineering Old Dominion University Norfolk VA United States Institute of Big Data Technology Bright Oceans Corporation Beijing China

In this study, a computer-aided diagnosis (CAD) system for whole-slide images (WSIs) with breast cancer was proposed to achieve lesion detection and classification. Different from the previously proposed works that performed segmentation directly and applied the segmentation metrics for performance evaluation, here we computed the accuracy in patch-level, region-level, and slide-level to access our proposed framework to meet the clinical requirements, instead of simply regarding it as an AI segmentation task. To achieve that for WSIs, lesion detection was conducted at high magnification (× 40) first for region proposal by patching using the modified AlexNet model;and then, several patches were sampled from the detected regions at lower magnification (× 10) to observe cells’ patterns for lesion classification by the model of ResNet50. With such fashion, the deep features being distinguishing in lesion classification from the convolutional neural networks (CNN) were further analyzed in this work to provide comprehensive interpretability for the proposed CAD system based on pathological knowledge. For our experiments, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). The slide-level accuracy rate for the three-category classification reached 90.8% (99/109) through a fivefold cross-validation and achieved 94.8% (73/77) on the testing set. In addition, the learned features reflect the clinical insights in lesion classification for explainable AI, which enhanced the reliability and validity of our proposed system. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： computer aided diagnosis

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A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

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computers, Materials & Continua 2023年第1期74卷 1279-1302页

作者： Yu Fan Jingbing Li Uzair Aslam Bhatti Chunyan Shao Cheng Gong Jieren Cheng Yenwei Chen School of Information and Communication Engineering Hainan UniversityHaikou570100China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570100China School of Computer Science and Technology Hainan UniversityHaikou570100China Graduate School of Information Science and Engineering Ritsumeikan UniversityKyoto5258577Japan Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570100China TJ-YZ School of Network Science Haikou University of EconomicsHaikou571127China

Medical images are a critical component of the diagnostic process for *** the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical *** watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient *** addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are *** paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)Inception V3 and the discrete cosine transform(DCT)to address above mentioned ***,the medical image is input into the Inception V3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution ***,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical *** last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third *** encrypted watermarks and the original image features are performed logical operations to realize the embedding of *** the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed *** NC values for all the images are more than 90%accurate which shows the robustness of the *** experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

关键词： Inception V3 multi-watermarking DCT watermark encryption robustness

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Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1909-1923页

作者： Jiasen Wang Jun Wang Qing-Long Han IEEE the Future Network Research Center Purple Mountain LaboratoriesNanjing 211111China the Department of Computer Science the School of Data ScienceCity University of Hong KongHong KongChina the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal *** feasibility of the formulated optimization problem is guaranteed under derived *** optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/*** results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.

关键词： Collaborative neurodynamic optimization receding-horizon planning trajectory planning under-actuated vehicles

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Efficient Resource Allocation in Vehicular networks Using Embedding-Enhanced MAPPO 10

Efficient Resource Allocation in Vehicular Networks Using Em...

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10th IEEE Smart World Congress, SWC 2024

作者： Qiu, Wen Shao, Xun Zhao, Wei Masui, Hiroshi Kitami Institute of Technology Information and Communication Engineering Hokkaido Japan Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Toyohashi Japan Anhui University of Technology School of Computer Science and Technology Anhui China Kitami Institute of Technology Information Processing Center Hokkaido Japan

ISBN: (纸本)9798331520861

With the swift advancement of automotive technology, the need for efficient allocation of resources in V2V (vehicle-to-vehicle) communications has grown significantly. However, the dynamic nature of channels in vehicular networks introduces considerable uncertainty in resource allocation. This research presents a novel approach to addressing these challenges by utilizing multi-agent deep reinforcement learning (MADRL). By leveraging shared resource blocks (RBs) for V2V and vehicle-to-infrastructure (V2I) communications, our approach minimizes interference with V2I links and ensures optimal V2V performance. Unlike traditional methods that select actions sequentially, our approach treats every V2V link as an agent and simultaneously determines all agents' actions to improve coordination and efficiency. To accelerate the training of the multi-agent critic network, we integrate an embedding module to simplify and compress the network input dimensions. Through the use of our embedding multi-agent proximal policy optimization (E-MAPPO) algorithm, the agents collaboratively optimize their transmission strategies to efficiently share spectrum with V2I links. Simulation results show that our method greatly minimizes interference with V2I links while meeting the stringent latency and reliability requirements of V 2 V communications, surpassing baseline approaches. © 2024 IEEE.

关键词： Deep reinforcement learning

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Heart Disease Prediction Using Machine Learning 2

Heart Disease Prediction Using Machine Learning

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ghazal, Taher M. Ibrahim, Amer Akram, Ali Sheraz Qaisar, Zahid Hussain Munir, Sundus Islam, Shanza Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Skyline University College University City School of Information Technology Sharjah1797 United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NFC Institute of Engineering and Technology Department of Computer Science Multan Pakistan Lahore Garrison University Lahore Department of Computer Science Lahore Pakistan Lahore Garrion University Lahore Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc. © 2023 IEEE.

关键词： Support vector machines

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Competition on robust deep learning

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National science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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Early Detection of Autism in Children Using Transfer Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 11-22页

作者： Taher M.Ghazal Sundus Munir Sagheer Abbas Atifa Athar Hamza Alrababah Muhammad Adnan Khan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Lahore Garrison University Lahore54000Pakistan Department of Computer Science COMSATS University Islamabad–Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnamGyeonggido13120Korea

Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking *** early detection of ASD is essential for delivering effective,timely *** facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early *** study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)*** model achieved a detection accuracy of 87.7%and performed better than other established ASD detection ***,this model could facilitate the early detection of ASD in clinical practice.

关键词： Autism spectrum disorder convolutional neural network loss rate transfer learning AlexNet deep learning

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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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