检索结果-内蒙古大学图书馆

6th International conference on Geographical Information Systems Theory, Applications and Management (GISTAM)

作者： Vithlani, Hemang Narendra Dogotari, Marcel Lam, Olee Hoi Ying Pruem, Moritz Melville, Bethany Zimmer, Frank Becker, Rolf Rhine Waal Univ Appl Sci Fac Commun & Environm D-47475 Kamp Lintfort Germany Astron Environm Serv 129 Royal St East Perth WA 6004 Australia

ISBN: (纸本)9789897584251

Aerial images acquired using drone-based imaging sensors can be processed by photogrammetry toolkits to create geometrically corrected 2D orthophoto and/or 3D models. This is a crucial step for many of the ever-evolving civil applications of drones such as precision agriculture and surveying. Nevertheless, limited computational resources become bottleneck in providing these results quickly. Cloud computing helps in such scenarios because of its value-added features, namely virtualization, elasticity, high performance and distributed computing for the web-based image processing. The containerization approach plays a vital role in cloud computing by providing operational efficiency. Container orchestration engine, Kubernetes, not only provides template-based or GUI-based service deployment but also better monitoring, log querying and autoscaling. The present work displays a scalable photogrammetry service, deployed on a Kubernetes-orchestrated on-premise cluster. This reference implementation on Kubernetes enables the parallel processing of large datasets in less time than a single computer using the free and open-source toolkit OpenDroneMap.

关键词： Drone imagery Opendronemap Photogrammetry Open-source Kubernetes Cloud Computing

来源：评论

学校读者我要写书评

暂无评论

Kubeflow-based Automatic Data processing Service for Data Center of State Grid Scenario

Kubeflow-based Automatic Data Processing Service for Data Ce...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Chongyou Xu Guangxian Lv Jian Du Lei Chen Yu Huang Wang Zhou State Grid Ningbo Electric Power Supply Company Ningbo China State Grid Shanghai Energy Interconnection Research Institute Co. Ltd Shanghai China State Grid Zhejiang Electric Power Co. LTD. Hangzhou China National Engineering Research Center for Software Engineering Peking University Beijing China China Southern Power Grid Digital Grid Research Institute Co. Ltd Guangzhou China

With the rapid development of machine learning and deep learning, more and more machine learning and deep learning have appeared in the power grid business. The data processing in the State Grid business is very complicated, the data processing is very cumbersome, and the reuse rate of the data processing code is also very low. In order to solve these problems, this paper proposes an efficient automated data processing service-EADP (Efficient Automated Data processing). EADP service is built on Kubeflow. Kubeflow/Pipeline is Google’s open source workflow for building end-to-end services. Users can build the code as Pipeline/Component for use by Kubeflow/Pipeline. But Kubeflow's Component and Pipeline construction is extremely cumbersome and lacks management of Component and Pipeline. In order to solve these problems, EADP provides the function of automatically constructing Component and data processing DAG. There is a one-to-one correspondence between Component and Docker/image. Docker/image contains code blocks for data processing, which can be run after instantiation. The data processing flow can be constructed as a data processing DAG. The data processing DAG is composed of Component, and each node in the DAG corresponds to a Component. EADP uses a topological sorting algorithm to convert the data processing DAG into Kubeflow/Pipeline, thereby realizing automated data processing. On the surface of the experiment, EADP has high stability and convenience, which can greatly shorten the time-consuming data processing.

关键词： Deep learning Codes Pipelines Buildings Power system stability Data processing Power grids

来源：评论

学校读者我要写书评

暂无评论

QUBIQ: Uncertainty Quantification for Biomedical image Segmentation Challenge

arXiv

引用

arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical image Quantification Challenge (QUBIQ), which was organized in conjunction with International conferences on Medical image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

An Efficient HEVC Intra Frame Coding Based on Deep Convolutional Neural Network

An Efficient HEVC Intra Frame Coding Based on Deep Convoluti...

引用

ACIS International conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Tien-Yang Hsu Yueh-Ju Lu Tung-Hung Hsieh Chou-Chen Wang I-Shou University Kaohsiung Taiwan

ISBN: (纸本)9781665447324

High efficiency video coding (HEVC) is a very popular video coding standard. The HEVC can achieve high coding efficiency with a lower bitrate for intra frame coding. However, it still needs many bits to finish best rate-distortion (R-D) curve. Since there are only 35 directions prediction modes provided in intra prediction module (IPM), HEVC occurs a large distortion when the image contents are out of these prediction directions. In order to obtain a better R-D curve, Zhang et al. [3] recently proposed a simple convolutional neural network (S-CNN) to improve the encoding performance of HEVC. However, S-CNN has to consume more time to encode intra frame coding since it needs to perform more CNN enhancement mode. In order to further speed up S-CNN based intra frame coding, we propose an early termination algorithm to skip CNN. Because the natural images are generally homogenous, we find the mean square errors (MSE) of reconstructed CTU exist high spatial correlation at HEVC encoder. Therefore, a dynamic threshold of MSE is set according to three neighboring encoded CTU blocks to evaluate whether the current reconstructed CTU is useful for the CNN enhancement mode. Simulation results show that the proposed method can achieve faster HEVC encoding process than S-CNN by reducing time increase ratio (TIR) about 12% on an average.

关键词： Simulation Heuristic algorithms Rate-distortion Mean square error methods Encoding Convolutional neural networks image reconstruction

来源：评论

学校读者我要写书评

暂无评论

DVFS RK: Performance and Energy Modeling of Frequency-Scaled Multithreaded Runge-Kutta methods 27

DVFS RK: Performance and Energy Modeling of Frequency-Scaled...

引用

27th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Rauber, Thomas Ruenger, Gudula Univ Bayreuth Bayreuth Germany Tech Univ Chemnitz Chemnitz Germany

ISBN: (纸本)9781728116440

Runge-Kutta ( RK) methods are popular and well-known methods for simulations in scientific computing based on ordinary differential equations ( ODEs). RK-based simulations can stem from space-discretized time-dependent partial differential equations ( PDEs), which can model a large variety of phenomena in natural science or engineering. This article investigates several multithreaded versions of RK methods and studies their performance and energy consumption on a recent multicore processor. The emphasis of the analysis is on the performance and energy behavior using dynamic voltage and frequency scaling ( DVFS) and the modeling of the time and energy behavior based on power models.

关键词： Runge-Kutta methods multithreaded programs systems of ordinary differential equations DVFS performance power and energy modeling

来源：评论

学校读者我要写书评

暂无评论

DeepPFCN: Deep parallel Feature Consensus Network for Person Re-identification 7th

DeepPFCN: Deep Parallel Feature Consensus Network for Person...

引用

7th National conference on Computer Vision, Pattern Recognition, image processing, and Graphics, NCVPRIPG 2019

作者： Singh, Shubham Kumar Miyapuram, Krishna P. Raman, Shanmuganathan Indian Institute of Technology Gandhinagar PalajGujarat382355 India

ISBN: (纸本)9789811586965

Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is highly time consuming and erroneous. Automated person re-identification is required due to the extensive quantity of visual data produced by rapid inflation of large scale distributed multi-camera systems. The state-of-the-art works focus on learning and factorize person appearance features into latent discriminative factors at multiple semantic levels. We propose Deep parallel Feature Consensus Network (DeepPFCN), a novel network architecture that learns multi-scale person appearance features using convolutional neural networks. This model factorizes the visual appearance of a person into latent discriminative factors at multiple semantic levels. Finally consensus is built. The feature representations learned by DeepPFCN are more robust for the person re-identification task, as we learn discriminative scale-specific features and maximize multi-scale feature fusion selections in multi-scale image inputs. We further exploit average and max pooling in separate scale for person-specific task to discriminate features globally and locally. We demonstrate the re-identification advantages of the proposed DeepPFCN model over the state-of-the-art re-identification methods on three benchmark datasets - Market1501, DukeMTMCreID, and CUHK03. We have achieved mAP results of 75.8%, 64.3%, and 52.6% respectively on these benchmark datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Method research on ship detection in remote sensing image based on Yolo algorithm

Method research on ship detection in remote sensing image ba...

引用

2020 International conference on Engineering Management of Communication and Technology, EMCTECH 2020

作者： Li, Xi Cai, Kaiyu College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665404488

Small target recognition is a classic challenge in the field of target recognition. In this paper, an improved algorithm based on Yolo V3 is proposed for ship detection in remote sensing images. In order to solve the problem that the input satellite image size is too large, the window sliding segmentation technology is used to cut the large image into several small images;because the detection target only has a single kind of ship, K-means algorithm is used to re cluster anchor Box, to improve the detection accuracy of the algorithm;in view of the small ship target in the ocean background, the multi-level feature extraction method is used to highlight the feature vector of small target to improve the recognition ability of small target. Through the above-mentioned methods, using the data set of Airbus ship detection provided by kaggle, the ship target detection has achieved a better comprehensive accuracy rate, which is higher than the accuracy and recall rate of similar target detection algorithms. © 2020 IEEE.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

parallelization of Fast Monte Carlo Dose Calculation for Radiotherapy Treatment Planning on the ARMv8 Architecture

Parallelization of Fast Monte Carlo Dose Calculation for Rad...

引用

International conference on Information Science and Technology (ICIST)

作者： Chi Jin Qinglin Wang Yang Zhao Yong Dou Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Monte Carlo (MC) simulation plays a key role in radiotherapy. Since the simulation time of the MC program cannot fully meet the clinical requirements, we use the ARM-based FT-2000+ multi-core processor for parallelization, which provides an effective solution for accelerating MC dose calculation. In this paper, we implement and verify FT-DPM, which is an OpenMP-based MC Dose Planning Method on FT-2000+. FT-DPM utilizes the parallelism of MC simulation and the advantage of ARM architecture to achieve the parallelization on ARM architecture. Meanwhile, we optimize the original DPM program in terms of memory allocation, data structure and data type. The experiments show that, compared with the original DPM code, FT-DPM obtains very accurate results and reaches the maximum speedups of 155.94 times for the electron case. The parallel program based on FT-2000+ only takes 44.1 seconds to simulate the particle transport of 100 million times, showing good clinical application potential. In addition, the speedup and efficiency of FT-DPM running on different core counts are also discussed.

关键词： Monte Carlo methods Multicore processing Simulation Scalability parallel processing Data structures Load management

来源：评论

学校读者我要写书评

暂无评论

AMF-CSR: Adaptive Multi-Row Folding of CSR for SpMV on GPU

AMF-CSR: Adaptive Multi-Row Folding of CSR for SpMV on GPU

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Jianhua Gao Weixing Ji Jie Liu Senhao Shao Yizhuo Wang Feng Shi School of Computer Science and Technology Beijing Institute of Technology China

ISBN: (纸本)9781665408790

SpMV is a cost-dominant operation used in many iterative methods for solving large-scale sparse linear systems. However, irregular memory access of SpMV to the multiplied vector leads to low data locality and then harms the performance. This paper presents an adaptive multi-row folding of CSR (AMF-CSR) format for SpMV calculation on GPU. This new storage format supports the folding of the variable number of rows in order to achieve better load balancing in computation. AMF-CSR not only increases the density of non-zero elements in a folded row, thereby improving the access locality of the multiplied vector, but also merges an approximately equal number of nonzero elements in a folded row, hence achieving load balancing. The performance evaluation using 28 sparse matrices shows that the proposed SpMV algorithm based on AMF-CSR achieves the highest speedup of 4.11x and 3.62x on GTX 1080 Ti and Tesla V100 respectively against a fixed multi-row folding-based SpMV algorithm. Evaluation results using 450 regular sparse matrices and 450 irregular sparse matrices also show that AMF-CSR is superior to other SpMV implementations.

关键词： Performance evaluation Linear systems conferences Graphics processing units Load management Approximation algorithms Sparse matrices

来源：评论

学校读者我要写书评

暂无评论

Automatic Fine-Grained Localization of Utility Pole Landmarks on distributed Acoustic Sensing Traces Based on Bilinear Resnets

Automatic Fine-Grained Localization of Utility Pole Landmark...

引用

IEEE International conference on Acoustics, Speech and Signal processing

作者： You Lu Yue Tian Shaobo Han Eric Cosatto Sarper Ozharar Yangmin Ding Virginia Tech Blacksburg VA USA

In distributed acoustic sensing (DAS) on aerial fiber-optic cables, utility pole localization is a prerequisite for any subsequent event detection. Currently, localizing the utility poles on DAS traces relies on human experts who manually label the poles' locations by examining DAS signal patterns generated in response to hammer knocks on the poles. This process is inefficient, error-prone and expensive, thus impractical and non-scalable for industrial applications. In this paper, we propose two machine learning approaches to automate this procedure for large-scale implementation. In particular, we investigate both unsupervised and supervised methods for fine-grained pole localization. Our methods are tested on two real-world datasets from field trials, and demonstrate successful estimation of pole locations at the same level of accuracy as human experts and strong robustness to label noises.

关键词： Location awareness image edge detection Estimation Optical fiber cables Machine learning Signal processing Acoustics

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：