检索结果-内蒙古大学图书馆

Multi-scale change monitoring of water environment using cloud computing in optimal resolution remote sensing images

引用

ENERGY REPORTS 2022年 8卷 13610-13620页

作者： Feng, Lei You, Yu Liao, Weiling Pang, Jiawei Hu, Ronghao Feng, Li Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Super Resolut Opt Res Ctr Chongqing 400714 Peoples R China Chongqing Environm & Ecol Big Data Applicat Ctr Chongqing 401121 Peoples R China Chongqing Acad Ecol & Environm Sci Water Environm Engn Technol Innovat Ctr Chongqing 401120 Peoples R China Chongqing Pulusi Environm Protect Technol Dev Co L Chongqing 401121 Peoples R China Chongqing Univ Posts & Telecommun Coll Optoelect Engn Chongqing 400065 Peoples R China

Water is the source of life and a very important part of life. However, the current water resources security is facing global challenges. Affected by climate change and human factors, how to quickly realize the continuous monitoring of water resources changes is crucial to the sustainable management of water resources. In recent years, the amount of multi-temporal remote sensing data has grown rapidly, which has caused problems such as weak single-computer processing for change detection and difficulty in mass image storage management. Traditional single-computer processing methods can no longer meet the needs of remote sensing big data change detection. It provides cutting-edge methods and means for monitoring large-scale changes in water resources. Therefore, a distributed parallel method for remote sensing image change detection based on cloud computing is proposed. Through this method, the change detection efficiency of massive remote sensing images is improved, and the high-resolution remote sensing image change detection based on fully connected conditional random fields is deeply analyzed. By summarizing the experimental data of this method and comparing with other data, it can be seen that when the parallelism is higher, the advantage of running time is more obvious. The performance improvement is also as high as 89.49%. Therefore, distributed parallel processing through cloud computing can complete the efficient change detection of massive remote sensing images without affecting the accuracy. This helps us to better monitor and monitor water resources at multiple scales.

关键词： Cloud computing Remote sensing image Water resource Environmental monitoring

来源：评论

学校读者我要写书评

暂无评论

Optimization of the Functional Decomposition of parallel and distributed Computations in Graph Coloring With the Use of High-Performance Computing

引用

IEEE ACCESS 2022年 10卷 34996-35011页

作者： Skrinarova, Jarmila Dudas, Adam Matej Bel Univ Fac Nat Sci Dept Comp Sci Banska Bystrica 97401 Slovakia

This article presents methods for correct decomposition for high performance computations related to large sets of graphs. These computations contain large number of calls of sequential, recursive algorithm for NP-complete problem - proper edge coloring of graph. Decomposition of this computational problem is not trivial, since the number of recursions in various parts of the computation is different and causes a high load and time imbalance. We designed, implemented and experimentally verified a new decomposition method that significantly reduces the computational time for a large set of graphs (up to 404 million graphs). This method ensures the same duration of computational time for partial subtasks and thus eliminates the need to wait for synchronization of parallel computations.

关键词： Task analysis parallel processing image color analysis Resource management Program processors NP-complete problem Load management Decomposition distributed computing graph coloring parallel computing snark

来源：评论

学校读者我要写书评

暂无评论

CDCP: A Framework for Accelerating Dynamic Channel Pruning methods 29

CDCP: A Framework for Accelerating Dynamic Channel Pruning M...

引用

29th IEEE International Conference on parallel and distributed Systems, ICPADS 2023

作者： Xiang, Zhaohong Luo, Yigui Xie, Yin She, Haihong Liang, Weiming Zhao, Laigang Tongji University School of Software Engineering Shanghai China Company Limited China

ISBN: (纸本)9798350330717

Dynamic channel pruning is a technique aimed at reducing the theoretical computational complexity and inference latency of convolutional neural networks. Dynamic channel pruning methods introduce complex additional modules for dynamically selecting channels for images. Due to the additional modules, dynamic channel pruning methods never achieve optimal acceleration effect in real world. To address this problem, we propose Consecutive Dynamic Channel Pruning (CDCP), a novel dynamic channel pruning framework unified for almost all dynamic pruning methods designed for continuous image processing. The core idea of CDCP stems from our observation that adjusting the network for all frames in semantically continuous scenes is unnecessary since adjacent frames often share similar network structures in dynamic channel pruning. CDCP introduces a simple binary classifier to determine whether the network structure needs to be adjusted for a new frame. Our method can also be used for semantically non-continuous image processing tasks with a slightly lower probability of model reuse. We validate the effectiveness of CDCP on three dynamic channel pruning methods and better acceleration effects are achieved when applied them with CDCP to the semantically continuous Waymo dataset, the nuScenes dataset, and the semantically discontinuous COCO dataset. © 2023 IEEE.

关键词： dynamic channel pruning model acceleration network structure

来源：评论

学校读者我要写书评

暂无评论

Ancient Chinese Character image Retrieval Based on Fusing Deep Features and Skeleton Features 29

Ancient Chinese Character Image Retrieval Based on Fusing De...

引用

29th IEEE International Conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Xinhui Tian, Xuedong Hebei University School of Cyber Security and Computer Baoding071002 China Hebei University Institute of Intelligent Image and Document Information Processing Baoding071002 China

ISBN: (纸本)9798350330717

The complex glyph structures and diverse writing styles of ancient Chinese character images lead to suboptimal performance when existing image retrieval methods are directly applied to datasets of these images. Addressing the impact on retrieval caused by the complex glyph structure and rich detail information characteristic of ancient Chinese character images, a multi-layer feature adaptive fusion model for ancient Chinese character image retrieval is designed. Firstly, a local feature extraction module is constructed to obtain low-level feature maps with different spatial receptive fields. Secondly, to better fuse features of varying scales, an improved ASFF(Adaptively Spatial Feature Fusion) method is employed to build the PSASFF(Pixel Shuffle Adaptively Spatial Feature Fusion) module for adaptive fusion of multi-layer features extracted by the designed network. Finally, in order to reduce the influence of the writing style of ancient Chinese characters on retrieval, the cosine similarity scores of image retrieval before and after fine processing are weighted and used as the ultimate similarity scores. The retrieval method proposed achieves an average retrieval accuracy of mAP@50 and mAP@30 of 0.8537 and 0.9576 respectively on the ancient Chinese character image dataset. Experimental results demonstrate the effectiveness of this method for ancient Chinese character image retrieval. © 2023 IEEE.

关键词： adaptively feature fusion ancient Chinese character image retrieval local feature extraction weighted similarity scores

来源：评论

学校读者我要写书评

暂无评论

RAMMM: A Rapid Attention-Based Multimodal Modification Model for Massive image Generation 29

RAMMM: A Rapid Attention-Based Multimodal Modification Model...

引用

29th IEEE International Conference on parallel and distributed Systems, ICPADS 2023

作者： Xu, Zhenyuan Chen, Xuewen Wang, Taotao Quan, Wei Zhu, Yi Nanjing Audit University Nanjing211815 China

ISBN: (纸本)9798350330717

Nowadays, deep learning (DL) techniques have found extensive applications in the Internet of Things (IoT) community, such as autonomous driving and medical diagnosis. Despite these successful implementations, elevated data collection expenses, data confidentiality concerns, and privacy issues all contribute to the complexity and costliness of data acquisition in DL-based image processing. image modification methods based on diffusion models can subtly modify images to generate new images with similar content to the original image but with unique differences, providing a powerful tool for data augmentation. Nonetheless, diffusion models exhibit constraints in cross-modal image modification, such as sensitivity to prompt, model complexity, and user-friendliness. To address these issues, this paper proposes a rapid attention-based multimodal modification model (RAMMM) to facilitate straightforward and efficient text-guided image modification. Our proposed RAMMM primarily enhances the text processing and image generation procedures via an attention mechanism, thus improving the ability in capturing semantic and contextual information within the text. Consequently, RAMMM excels in generating high-quality sample images aligned with the description provided for the modified text. In addition, by utilizing text-guided image modification, RAMMM is capable of generating batches of image samples systematically to augment the dataset size. Evaluation results demonstrate that RAMMM can improve the performance and generalization capabilities of diffusion models by enhancing both the quality and quantity of the dataset. When employed for data augmentation on the CIFAR10 dataset, RAMMM achieves a 1.5% increase in target recognition accuracy. © 2023 IEEE.

关键词： attention mechanism data augmentation deep learning image modification

来源：评论

学校读者我要写书评

暂无评论

Energy Efficiency Impact of processing in Memory: A Comprehensive Review of Workloads on the UPMEM Architecture

Energy Efficiency Impact of Processing in Memory: A Comprehe...

引用

29th International Conference on parallel and distributed Computing (Euro-Par)

作者： Falevoz, Yann Legriel, Julien UPMEM 6 Pl Robert Schuman F-38000 Grenoble France

ISBN: (纸本)9783031488023;9783031488030

processing-in-Memory (PIM) architectures have emerged as a promising solution for data-intensive applications, providing significant speedup by processing data directly within the memory. However, the impact of PIM on energy efficiency is not well characterized. In this paper, we provide a comprehensive review of workloads ported to the first PIM product available on the market, namely the UPMEM architecture, and quantify the impact on each workload in terms of energy efficiency. Less than the half of the reviewed papers provide insights on the impact of PIM on energy efficiency, and the evaluation methods differ from one paper to the other. To provide a comprehensive overview, we propose a methodology for estimating energy consumption and efficiency for both the PIM and baseline systems at data center level, enabling a direct comparison of the two systems. Our results show that PIM can provide significant energy savings for data intensive workloads. We also identify key factors that impact the energy efficiency of UPMEM PIM, including the workload characteristics. Overall, this paper provides valuable insights for researchers and practitioners looking to optimize energy efficiency in data-intensive applications using UPMEM PIM architecture.

关键词： processing in memory (PIM) UPMEM architecture Data-centric architectures Workload optimization High-performance computing Computational efficiency Energy consumption Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Exploring Load Forecasting: Bridging Statistical methods to Deep Learning Techniques in Real-World Environment 20

Exploring Load Forecasting: Bridging Statistical Methods to ...

引用

20th Annual International Conference on distributed Computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Makris, Ioannis Moschos, Nikolaos Radoglou-Grammatikis, PaPanagiotis Andriopoulos, Nikolaos Tzanis, Nikolaos Malamaki, Kyriaki-Nefeli Fotopoulou, Maria Kaousias, Konstantinos Tompros, George Lampsidis Sarigiannidis, Panagiotis MetaMind Innovat Kozani Greece Ctr Res & Technol Hellas CERTH Athens Greece Hellen Elect Distribut Network Operator HEDNO Athens Greece Univ Western Macedonia Dept Elect & Comp Engn Kozani Greece K3Y Ltd Sofia Bulgaria

ISBN: (纸本)9798350369458;9798350369441

Load forecasting has a significant impact on energy management and planning, facilitating efficient allocation of resources and grid operations. In this study, a comparative analysis of traditional statistical methods and deep learning techniques is conducted utilizing a real-world dataset from the Ikaria islanded grid. This paper focuses on four different forecasting approaches: Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN). Through the appropriate processing of the data, extensive experimentation took place, aiming to capture the complex and nonlinear patterns of the dataset. The results indicated that LSTM and DNN outperformed both ARIMA and SARIMAX in all three evaluation metrics, achieving 0.13, 0.09, and 2.11%, RMSE, MAE, and MAPE, respectively. As a result, this study validates the superiority of deep learning techniques in real-world islanded grid environments being capable of accurately predicting future load values based on historical data.

关键词： Deep Neural Networks Electricity Demand Prediction Load Forecasting Long Short-term Memory Time Series Analysis

来源：评论

学校读者我要写书评

暂无评论

Dissecting Convolutional Neural Networks for Runtime and Scalability Prediction 24

Dissecting Convolutional Neural Networks for Runtime and Sca...

引用

53rd International Conference on parallel processing (ICPP)

作者： Beringer, Tim Mazaheri, Arya Stock, Jakob Wolf, Felix Tech Univ Darmstadt Darmstadt Hesse Germany

ISBN: (纸本)9798400717932

Given the computational complexity of deep neural networks (DNN), accurate prediction of their training and inference time using performance modeling is crucial for efficient infrastructure planning and DNN development. However, existing methods often predict only the inference time and rely on exhaustive benchmarking and fine tuning, making them time consuming and restricted in scope. As a remedy, we propose ConvMeter, a novel yet simple performance model that considers the inherent characteristics of DNNs, such as architecture, dataset, and target hardware, which strongly affect their runtime and scalability. Our performance model, which has been thoroughly tested on convolutional neural networks (ConvNets), a class of DNNs widely used for image analysis, offers the prediction of inference and training time, the latter on one or more compute nodes. Experiments with various ConvNets demonstrate that our runtime predictions of inference and training phases achieved an average error rate of less than 20% and 18%, respectively, making the assessment of ConvNets regarding efficiency and scalability straightforward.

关键词： Artificial intelligence deep neural networks convolution performance modeling distributed training scalability

来源：评论

学校读者我要写书评

暂无评论

Exploration on Facial image Recognition and processing Algorithms under Hadoop

Exploration on Facial Image Recognition and Processing Algor...

引用

2023 International Conference on Integrated Intelligence and Communication Systems, ICiiCS 2023

作者： Gu, Meifang Tang, Hailin Li, Lili Guangdong Baiyun University Institutional Research Center Guangdong Guangzhou China Guangdong Baiyun University Faculty of Intelligent Manufacturing Engineering Guangdong Guangzhou China Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou China

ISBN: (纸本)9798350315455

Face recognition is an important technology in the field of digital image processing, which has a crucial impact on subsequent work. This article introduced an example of facial recognition based on the combination of Hadoop algorithm and facial feature extraction. The experimental results were validated in the MATLAB environment and trained using neural networks to improve system performance and adaptability. The test results indicated that the processing speed was between 0.84 and 0.97;the highest resource utilization rate was 0.06, and the lowest was 0.02;the throughput range of facial data was between 13598 and 15479, and the throughput range of user data was between 5037 and 5879. This indicated that the system had fast recognition speed and high-precision image output, and exhibited good fault tolerance and robustness compared to other methods. Exploring facial image recognition and processing algorithms in the Hadoop environment can utilize Hadoop's distributed computing capabilities to accelerate the processing of large-scale facial image data, achieve fast and efficient processing of large-scale facial image data, accelerate algorithm operation, and possess distributed computing capabilities. Multiple computing nodes can be used for parallel processing to improve algorithm performance. © 2023 IEEE.

关键词： algorithm research facial recognition Hadoop algorithm image processing

来源：评论

学校读者我要写书评

暂无评论

FedGG: Leveraging Generative Adversarial Networks and Gradient Smoothing for Privacy Protection in Federated Learning 30th

FedGG: Leveraging Generative Adversarial Networks and Gradie...

引用

30th European Conference on parallel and distributed processing (Euro-Par)

作者： Lv, Jiguang Xu, Shuchun Zhan, Xiaodong Liu, Tao Man, Dapeng Yang, Wu Harbin Engn Univ Harbin Heilongjiang Peoples R China Changan Commun Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9783031697654;9783031697661

Gradient leakage attack allow attackers to infer Privacy data, which raises concerns about data leakage. To solve this problem, a series of methods have been proposed, while previously proposed methods have two weaknesses. First, adding noise (e.g., Differential privacy) to client-shared gradients reduces Privacy data leaks but harms performance of model and leaves room for data recovery attack(e.g., Gradient leak attacks). Second, encrypting shared gradients (e.g., Homomorphic encryption) enhances security but demands high computational costs, making it impractical for resource-constrained edge devices. This work proposes a novel federated learning method that leverages generative adversarial networks and gradient smoothing, which generates pseudodata through Wasserstein GAN(WGAN) and retains classification characteristics. Gradient smoothing can suppress gradients with high frequency changes;To improve the diversity of training data, launching data augmentation by mixup. Experiments show that compared with common defense methods, the MES-I of noise and gradient clipping are 0.5278 and 0.1036, respectively, while the MES-I of FedGG is 0.6422.

关键词： Federated Learning Privacy Protection parallel computing Generate adversarial networks

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：