检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Quantum Polynomial Hierarchies: Karp-Lipton, Error Reduction, and Lower Bounds 49

Quantum Polynomial Hierarchies: Karp-Lipton, Error Reduction...

引用

49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Agarwal, Avantika Gharibian, Sevag Koppula, Venkata Rudolph, Dorian David R. Cheriton School of Computer Science Institute for Quantum Computing University of Waterloo Canada Paderborn University Germany Department of Computer Science and Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9783959773355

The Polynomial-Time Hierarchy (PH) is a staple of classical complexity theory, with applications spanning randomized computation to circuit lower bounds to "quantum advantage" analyses for near-term quantum computers. Quantumly, however, despite the fact that at least four definitions of quantum PH exist, it has been challenging to prove analogues for these of even basic facts from PH. This work studies three quantum-verifier based generalizations of PH, two of which are from [Gharibian, Santha, Sikora, Sundaram, Yirka, 2022] and use classical strings (QCPH) and quantum mixed states (QPH) as proofs, and one of which is new to this work, utilizing quantum pure states (pureQPH) as proofs. We first resolve several open problems from [GSSSY22], including a collapse theorem and a Karp-Lipton theorem for QCPH. Then, for our new class pureQPH, we show one-sided error reduction pureQPH, as well as the first bounds relating these quantum variants of PH, namely QCPH ⊆ pureQPH ⊆ EXPPP © Avantika Agarwal, Sevag Gharibian, Venkata Koppula, and Dorian Rudolph.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Quantum-Improved Weather Forecasting: Integrating Quantum Machine Learning for Precise Prediction and Disaster Mitigation

Quantum-Improved Weather Forecasting: Integrating Quantum Ma...

引用

2023 IEEE International Conference on Quantum Technologies, Communications, computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Suhas, S. Divya, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Centre for Advanced Data Science Chennai India

ISBN: (纸本)9798350314946

In numerous industries, weather forecasting is essential for making informed decisions and mitigating the effects of extreme weather events. The complexity and chaos of weather systems, however, place restrictions on standard procedures, leading to errors and significant threats. We suggest the Quantum Improved Weather Forecast framework, which combines quantum machine learning methods with conventional methodologies, to solve these issues. By using quantum algorithms including quantum support vector machines, quantum neural networks, and quantum clustering, the QWF framework seeks to improve accuracy. Despite a few drawbacks, the QWF framework provides a method to revolutionize weather forecasting by enhancing prediction accuracy and facilitating improved catastrophe preparedness. It may prevent fatalities, safeguard critical infrastructure, and promote sustainable growth. © 2023 IEEE.

关键词： Climate change

来源：评论

学校读者我要写书评

暂无评论

Efficient Real-time Breed Classification using YOLOv7 Object Detection Algorithm 14

Efficient Real-time Breed Classification using YOLOv7 Object...

引用

14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Sudharson, S. Deena, S. Reddy, Sappidi Nischinth Vellore Institute of Technology Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India Chennai India

ISBN: (纸本)9798350335095

This work aims to develop an accurate and reliable solution for real-time breed recognition and categorisation of dogs and cats using the YOLO-v7 object detection algorithm. The Oxford IIIT pet dataset, consisting of a large number of images of different dog and cat breeds with various poses and backgrounds, is utilized for training and validation of the model. This model achieved a high F1 score of 85.6% and mAP of 82.57%, superior than other latest models. The findings illustrate the potential of using the YOLO-v7 algorithm for real-time breed detection and classification of dogs and cats, which can be used in a wide range of fields, including veterinary clinics, animal shelters, and pet stores. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

INNSE: Invertible neural network-based DNA image storage with self-correction encoding

引用

Computational and Structural Biotechnology Journal 2025年 27卷 2492-2502页

作者： Zheng, Yanfen Zhang, Xiaokang Sun, Lijun Cao, Ben Wang, Bin Zhang, Qiang Zheng, Pan School of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Liaoning Dalian116622 China Department of Accounting and Information Systems University of Canterbury Upper Riccarton Christchurch8140 New Zealand

DNA storage has become an alternative to silicon-based storage of media. However, when used to store multimodal data such as images, it fails to take full advantage of the characteristics of high correlation and variability present in such data, and currently remains inferior in terms of encoding density and time complexity. It also has the shortcoming of weak performance in terms of error correction methods to address insertion and deletion errors caused by error crosstalk. Therefore, we proposed an invertible neural network-based DNA image storage self-correction encoding method (INNSE) that uses the invertibility of network to perform up-down sampling processing on image and video data, thereby greatly reducing the number of DNA sequences required for data storage. A codebook generator was then designed to encode low-dimensional vectors into DNA sequences that meet various constraints. Finally, a self-correction method was constructed to achieve DNA sequence error correction at the base level without adding any redundant information. Our experimental results showed that, compared to the Yin-Yang algorithm, the encoding density of INNSE was increased by 3×, encoding time complexity was reduced by 95.26%. Under a simulated error rate of 2%, PSNR and SSIM were raised by 80.88% and 63.82%, respectively, proving that INNSE has significant advantages in encoding performance and error correction ability. © 2025

关键词： Network coding

来源：评论

学校读者我要写书评

暂无评论

Leveraging Multi-Model Gradient Boosting Techniques for Workforce Enablement: A Transparent Framework for Task Management and Issue Resolution

Leveraging Multi-Model Gradient Boosting Techniques for Work...

引用

2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Rithani, M. Kumar, Mahendira S. Kumar, Sudeesh V. Kumar, Praveen S. Priyadharshan, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Information and Technology SRM Institute Of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798350374933

In the rapidly shifting workplace environment, fostering an inspiring and dynamic atmosphere is essential for organizational success. This paper introduces an innovative Workforce Enhancement Platform designed to improve transparency, teamwork, and communication within the team. The platform employs state-of-the-art techniques for analyzing textual data, utilizing Gradient Boosting Machines (GBM) such as XGBoost, LightGBM, and CatBoost, to evaluate and categorize worker-submitted issues. It features a comprehensive data cleaning procedure to remove extraneous elements like markup and special symbols, followed by converting the processed text into actionable features using methods such as term frequency and inverse document frequency. Central to the system is the application of GBM algorithms, which detect and classify the emotional tone of the reports, distinguishing between positive and negative sentiments. Furthermore, the platform incorporates advanced data visualization tools to provide an in-depth analysis of model performance, including accuracy metrics, confusion matrices, and feature importance evaluations. By optimizing the identification and classification of workplace issues, the platform facilitates effective resolution, fostering a setting of innovation and teamwork. The main goal of the platform is to boost staff engagement and support by addressing challenges proactively, thereby promoting both individual growth and collective progress. This comprehensive approach ensures that team members’ feedback is acknowledged and valued, ultimately contributing to a more positive and productive organizational environment. © 2024 IEEE.

关键词： Empowerment of personnel

来源：评论

学校读者我要写书评

暂无评论

Prohibited Object Detection using YOLOv7 on X-Ray Images of Airport Luggages (OPIXray) 14

Prohibited Object Detection using YOLOv7 on X-Ray Images of ...

引用

14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Deena, S. Jayaprakash, L.S. Sudharson, S. Amrita Vishwa Vidyapeetham Computer Science and Engineering Amrita School of Computing Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai600127 India

ISBN: (纸本)9798350335095

Ensuring airport security and reducing risks for passengers safe travels is crucial. Conventional security inspection methods involve manual inspection of passengers luggage using X-ray scanners to identify any prohibited items. However, this manual process is prone to human error and can be time-consuming. To address these challenges, there is a need for automated security inspection systems that can enhance efficiency and accuracy. Deep learning techniques, particularly in the field of computer vision, offer potential solutions for autonomous security inspection. One such model is YOLO (You Only Look Once), a real-time object detection system capable of identifying multiple objects in images or video streams. This makes YOLO an ideal choice for airport security applications that require both speed and accuracy. This paper proposes a method for the automatic detection of prohibited objects in X-ray images, aiming to identify potential threats such as weapons, explosives, and other prohibited items in real-time. The proposed system was evaluated by conducting extensive tests on the publicly available benchmark dataset known as OPIXray. This dataset consists of labeled X-ray images that contain prohibited objects. The key component of our method involves the utilization of the YOLOv7 network, which was trained on a subset of the OPIXray dataset. The study demonstrates the effectiveness of this approach in detecting and recognizing prohibited items in OPIXray image datasets. The proposed methodology achieved a precision of 0.73, recall of 0.76, mAP@0.5 of 0.491, and mAP@0.5:0.9 of 0.397 on the OPIXray dataset. The experiments demonstrate the model's high accuracy and speed, thereby indicating its suitability for real-world airport security applications. Comparisons with other methods show that our model outperforms in terms of accuracy and speed. This paper presents a high-accuracy method for real-time airport security applications utilizing the YOLOv7 network. © 2023

关键词： Airport security

来源：评论

学校读者我要写书评

暂无评论

Intelligent Techniques for Nutrition Level Prediction in Guava Plants

Intelligent Techniques for Nutrition Level Prediction in Gua...

引用

2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Nadiger, Rakshith S Suresh, Gautham Shobha Rani, N. Prabhu, Akshatha Sangamesh, M.A. Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science Mysuru India Gitam University Gitam School of Technology Department of Computer Science & Engineering Bengaluru India National Institute of Engineering Department of Chemistry Mysuru India

ISBN: (纸本)9798350361537

Guava, a valuable tropical fruit crop, faces significant defect challenges due to diseases. Manual disease checks are time- consuming and error prone, leading to delayed action. Our research focuses on image processing to quickly and accurately identify guava diseases using deep learning approach with the help of Guava leaf images. This automation minimizes human intervention, enhances accuracy, and aids disease prevention. Early detection of these diseases improves crop quality and sustainability while reducing pesticide use. This Deep learning- driven approach aims to benefit farmers, consumers, and global food security by advancing guava farming practices. By using the YOLOv8 object detection model for 6 classes, we got an accuracy of 87.97%. © 2024 IEEE.

关键词： Agricultural Sustainability Deep Learning Guava Plant Disease Detection Nutrition level prediction YOLOv8

来源：评论

学校读者我要写书评

暂无评论

Soil Analysis Using Deep Learning for Precision Agriculture 1

Soil Analysis Using Deep Learning for Precision Agriculture

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Fotabong, Thierry Forsack Muhammad, Abdullahi Keisham, Murphy Mehrotra, Tushar Singh, Rajneesh Kumar Singh, S. Pratap Agrawal, Arun Prakash Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India Symbiosis Institute of Technology Nagpur Campus Deemed University Pune India

ISBN: (纸本)9798350365016

This research explores the application of deep learning techniques for soil analysis in precision agriculture, focusing on the classification of soil types using convolutional neural networks (CNNs). The study addresses the limitations of traditional soil classification methods, which are often labor-intensive and reliant on expert judgment. The proposed approach involves collecting soil image data, preprocessing it using techniques such as Gabor filtering, and classifying the images with a CNN model. The methodology includes data acquisition, feature extraction, and model training, aiming to accurately predict soil parameters using diverse input data sources like sensor readings and satellite imagery. The potential benefits of this approach include real-time monitoring, optimal fertilizer application, and enhanced crop management, which can contribute to sustainable agricultural practices and food security. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Biotic Stress and Severity Classification of Pear Leaves Disease with Ensemble Learning

Biotic Stress and Severity Classification of Pear Leaves Dis...

引用

2024 IEEE International Conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Sugunadevi, C. Maheswari, B. Uma Cmr Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350390759

Leaf disease frequently arises as pears plants mature. With a particular emphasis on categorizing the pear leaf disease, the proposed research work suggests the implementation of the CNN architecture that we use in this study to create this disease identification and classification method comprised of ResNet50, InceptionV3, VGG16, and VGG19. These neural networks have been pre-trained on the ImageNet dataset. The ensemble methodology that we describe produces final predictions for the test samples based on the recall, precision, accuracy, and f1-score scores of four standard evaluation metrics. Even though there is an increase in the amount of accuracy when the models are ensembled, the remarkable combination is InceptionV3 with VGG16 with an accuracy of 77.25%, precision of 79.34%, recall of 77.25% and F1 score of 76.50% and with the severity classification as accuracy 45.29%, precision 46.88%, recall 45.29% and F1 score 45.50% respectively. © 2024 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：