检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Qiao, Yi-Ling Gao, Lin Lai, Yu-Kun Xia, Shihong Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences School of Computer Science & Informatics Cardiff University

In this paper, we present a novel method for learning to synthesize 3D mesh animation sequences with long short-term memory (LSTM) blocks and mesh-based convolutional neural networks (CNNs). Synthesizing realistic 3D mesh animation sequences is a challenging and important task in computer animation. To achieve this, researchers have long been focusing on shape analysis to develop new interpolation and extrapolation techniques. However, such techniques have limited learning capabilities and therefore can produce unrealistic animation. Deep architectures that operate directly on mesh sequences remain unexplored, due to the following major barriers: meshes with irregular triangles, sequences containing rich temporal information and flexible deformations. To address these, we utilize convolutional neural networks defined on triangular meshes along with a shape deformation representation to extract useful features, followed by LSTM cells that iteratively process the features. To allow completion of a missing mesh sequence from given endpoints, we propose a new weight-shared bidirectional structure. The bidirectional generation loss also helps mitigate error accumulation over iterations. Benefiting from all these technical advances, our approach outperforms existing methods in sequence prediction and completion both qualitatively and quantitatively. Moreover, this network can also generate follow-up frames conditioned on initial shapes and improve the accuracy as more bootstrap models are provided, which other works in the geometry processing domain cannot achieve. Copyright © 2018, The Authors. All rights reserved.

关键词： Mesh generation

来源：评论

学校读者我要写书评

暂无评论

Simple Iterative Clustering on Graphs for Robust Model Fitting

Simple Iterative Clustering on Graphs for Robust Model Fitti...

引用

IEEE Visual Communications and Image Processing (VCIP)

作者： Hailing Luo Guobao Xiao Hanzi Wang Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9781538644591;9781538644584

In this paper, we propose a novel method, simple iterative clustering on graphs (SICG), to deal with robust model fitting problems. Specifically, we first construct a graph, where each vertex denotes a model hypothesis and each edge represents the similarity between two model hypotheses, for model fitting. We then propose a simple iterative clustering algorithm, which adapts the k-medoids clustering algorithm, to intuitively estimate model instances in data. The proposed SICG method is able to effectively fit and segment multiple-structure data contaminated with a large number of outliers and noises. Experimental results show that SICG achieves superior fitting results over several state-of-the-art model fitting methods on real images.

关键词： Computational modeling Clustering algorithms Data models Iterative algorithms Adaptation models Image segmentation Mathematical model

来源：评论

学校读者我要写书评

暂无评论

Prediction of multi-parameters in the inverse heat conduction problems

引用

Journal of Physics: Conference Series 2020年第1期1707卷

作者： Talaat Abdelhamid Rongliang Chen Mahbub Alam Md. Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China. Institute for Turbulence-Noise-Vibration Interaction and Control Shenzhen Graduate School Harbin Institute of Technology Shenzhen China. Physics and Mathematical Engineering Department Faculty of Electronic Engineering Menoufiya University Menouf Egypt. Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing Shenzhen China

This paper studies the prediction of the spatial-dependent-heat transfer coefficient γ(x) and- heat flux q(x), using the modified conjugate gradient method (MCGM). The mathematical formulation of the problem well defined and the existence of the minimizer is investigated. We establish the sensitivity and adjoint equations for computing the gradient with respect to γ(x) and q(x). The proposed algorithm 4.1 is derived for reconstructing γ(x) and q(x) using the MCGM. The numerical experiments are examined to show the efficiency and accuracy of the proposed method. Finally, some conclusions and remarks are given.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

引用

IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Polash Kumar Das Bin Hu Chang Liu Kaixin Cui Prabhat Ranjan Gang Xiong School of Electronic and Information Engineering South China University of Technology Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Science Dongguan China

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.

关键词： Face Videos Feature extraction Databases Face recognition Password

来源：评论

学校读者我要写书评

暂无评论

Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information

arXiv

引用

arXiv 2021年

作者： Huang, Jiahao Ding, Weiping Lv, Jun Yang, Jingwen Dong, Hao Del Ser, Javier Xia, Jun Ren, Tiaojuan Wong, Stephen T. Yang, Guang College of Information Science and Technology Zhejiang Shuren University Hangzhou310015 China National Heart and Lung Institute Imperial College London London United Kingdom School of Information Science and Technology Nantong University Nantong226019 China School of Computer and Control Engineering Yantai University Yantai264005 China Department of Prosthodontics Peking University School Hospital of Stomatology Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Derio48160 Spain Bilbao48013 Spain Department of Radiology Shenzhen Second People’s Hospital The First Affiliated Hospital of Shenzhen University Health Science Center Shenzhen China Systems Medicine and Bioengineering Department Houston Methodist Cancer Center HoustonTX77030 United States Department of Radiology Houston Methodist Hospital Weill Cornell Medicine HoustonTX77030 United States Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom

In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Introducing the soil mineral carbon pump

引用

Nature Reviews Earth & Environment 2023年 506页

作者： Ke-Qing Xiao Oliver W. Moore Alba Otero-Fariña Caroline L. Peacock Yao Zhao Chao Liang Mingyu Zhao Yong-Guan Zhu Karen Johnson School of Earth and Environment University of Leeds Leeds UK State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing China Institute of Applied Ecology Chinese Academy of Sciences Shenyang China Key Laboratory of Cenozoic Geology and Environment Institute of Geology and Geophysics Chinese Academy of Sciences Beijing China Key Laboratory of Urban Environment and Health Institute of Urban Environment Chinese Academy of Sciences Xiamen China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing China Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control CAS Haixi Industrial Technology Innovation Center in Beilun Ningbo China School of Engineering and Computing Sciences Durham University Durham UK

来源：评论

学校读者我要写书评

暂无评论

Path Planning of Humanoid Arm Based on Deep Deterministic Policy Gradient

Path Planning of Humanoid Arm Based on Deep Deterministic Po...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Shuhuan Wen Jianhua Chen Shen Wang Hong Zhang Xueheng Hu department of Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Department of Computing Science University of Alberta Edmonton Canada

ISBN: (纸本)9781728103785;9781728103778

The robot arm with multiple degrees of freedom and working in a 3D space needs to avoid obstacles during the grasping process by its end effector. Path planning to avoid obstacles is very important for accomplishing a grasping task. This paper proposes a new obstacle avoidance algorithm, based on an existing deep reinforcement learning framework called deep deterministic policy gradient (DDPG). Specifically, we propose to use DDPG to plan the trajectory of a robot arm to realize obstacle avoidance. The rewards are designed to overcome the difficulty in convergence of multiple rewards, especially when the rewards are antagonistic with respect to each other. Obstacle avoidance of the robot arm using DDPG is achieved by self-learning, and the convergence problem caused by the high dimension state input and multiple return values is solved. The simulation model of an arm of the Nao robot is built based on the MuJoCo simulation environment. The simulation demonstrates that the proposed algorithm successfully allows the robot arm to avoid obstacles.

关键词： Manipulators Neural networks Robot kinematics Planning Reinforcement learning Trajectory

来源：评论

学校读者我要写书评

暂无评论

Blockchain based Consensus Checking in Cloud Storage

Blockchain based Consensus Checking in Cloud Storage

引用

International Symposium on Autonomous Decentralized Systems (ISADAS)

作者： Guanqiu Qi Zhiqin Zhu Matthew Haner Jaesung Sim Jian Sun Yi Chai Yinong Chen Li Yongfu Mathematics and Computer Information Science Mansfield University of Pennsylvania Mansfield PA 16933 USA College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Electronic and Information Engineering Southwest University Chongqing 400715 China Ministry of Education Key Laboratory of Complex System Safety and Control (Chongqing University) Chongqing 400044 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287 USA

ISBN: (数字)9781728116723

ISBN: (纸本)9781728116730

In cloud computing, data is duplicated to prevent data loss. One way to achieve data consistency in such a distributed computing systems is to use a blockchain. Based on practical Byzantine fault tolerance (PBFT), a specific type of blockchain, this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains data consistency, but also has much higher efficiency than other general blockchain algorithms. We provide experimental results that demonstrate the algorithm's data consistency, efficiency, and reliability.

关键词： Cloud computing Fault tolerance Fault tolerant systems Peer-to-peer computing

来源：评论

学校读者我要写书评

暂无评论

GPRInvNet: Deep learning-based ground penetrating radar data inversion for tunnel lining

arXiv

引用

arXiv 2019年

作者： Liu, Bin Ren, Yuxiao Liu, Hanchi Xu, Hui Wang, Zhengfang Cohn, Anthony G. Jiang, Peng The School of Qilu Transportation Shandong University Jinan250061 China The Geotechnical and Structural Engineering Techniques Research Center Shandong University Jinan250061 China The Data Science Institute Shandong University Jinan250061 China The School of Control Science and Engineering Shandong University Jinan250061 China The School of Computing University of Leeds LeedsLS2 9JT United Kingdom The School of Qilu Transportation Shandong University

A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Collective dynamics and control for multiple unmanned surface vessels

arXiv

引用

arXiv 2019年

作者： Liu, Bin Chen, Zhiyong Zhang, Hai-Tao Wang, Xudong Geng, Tao Su, Housheng Zhao, Jin Guangdong HUST Industrial Technology Research Institute Guangdong Province Key Lab of Digital Manufacturing Equipment Dongguan523808 China School of Electrical Engineering and Computing University of Newcastle CallaghanNSW2308 Australia School of Automation State Key Lab of Digital Manufacturing Equipment and Technology Key Lab of Imaging Processing and Intelligence Control Huazhong University of Science and Technology Wuhan430074 China School of Automation Key Lab of Imaging Processing and Intelligence Control Huazhong University of Science and Technology Wuhan430074 China

A multi-unmanned surface vessel (USV) formation control system is established on a novel platform composed of three 1.2 meter-long hydraulic jet propulsion surface vessels, a differential GPS reference station, and inter-vessel Zigbee communication modules. The system is also equipped with an upper level collective multi-USV protocol and a lower level vessel dynamics controller. The system is capable of chasing and surrounding a target vessel. The results are supported by rigorous theoretical analysis in terms of asymptotical surrounding behavior and trajectory regulation. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed hardware and software architectures. Copyright © 2019, The Authors. All rights reserved.

关键词： Unmanned surface vehicles

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：