检索结果-内蒙古大学图书馆

international conference on Communication Systems and Network Technologies (CSNT)

作者： Ashwini Malviya Chandra Prakash Lora A Kannagi Department of uGDX ATLAS SkillTech University Mumbai Maharashtra India Department of Chemistry Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Web3.0 apps, with their emphasis on decentralization, real-time processing, and low latency, hold the key to MEC's bright future. this calls for an all-encompassing, future-oriented strategy in MEC architecture and design. the suggested technique is a multi-stage strategy for molding the future of MEC via cutting-edge architectures and designs for Web3.0 over 5G/6G networks. In the first stage, you'll examine the needs and goals of Web3.0 in detail. Real-time, lowlatency interactions are essential, as is familiarity with the dynamics of distributed apps and blockchain technology. In the second stage, we examine the current MEC setup, from edge server dispersion to data center density to network topology. the technique takes use of the low latency, high throughput, and huge device connection offered by 5G and impending 6G networks to guarantee preparedness for the future. the suggested technique is based on novel building layouts as its foundation. Latency, throughput, resource usage, scalability, and security compliance are only few of the metrics that may be evaluated to guarantee that the design is effective. the difficulties of actual deployment are also discussed; they include issues like where to put edge servers and how to scale resources according to demand. To ensure that the process remains in step with developing technologies and user preferences, it is recommended that improvements be made in a series of iterations.

关键词： Heuristic algorithms Computer architecture throughput Real-time systems User experience Blockchains Servers

来源：评论

学校读者我要写书评

暂无评论

Unpaired thick Cloud Removal with Multiscale SAR-optical Data Fusion

Unpaired Thick Cloud Removal with Multiscale SAR-optical Dat...

引用

Computer Vision, Image and Deep Learning (CVIDL), international conference on

作者： Xuanyu Xiang Yihua Tan National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Cloud removal is vital for the analysis of optical satellite images. To alleviate the impact of thick clouds, recent advances integrate deep learning withmultimodal data. To relax the requirements of paired training samples, existing methods utilized cycle-consistent architecture to learn the relation between cloudy and cloud-free images based on unpaired training samples. When considering thick cloud removal based on unpaired training data, the relevant studies are insufficient and they face two challenges: 1) the information in non-cloudy areas is not preserved well after cloud removal; 2) the recovered information lacks consistency with the global textures and structures. Based on optical-SAR fusion and cycle-consistent training, this paper proposes Multiscale Cycle-consistent Fusion (MCF) model. MCF designs preservation loss to overcome the first challenge, and proposes global-local discriminator combined with parallel Dilated Channel-weighted Module (PDCM) for the second challenge. MCF is evaluated both on a simulated dataset and a real dataset, which demonstrates its effectiveness.

关键词： Training Deep learning Optical losses Clouds Training data Optical imaging Data models

来源：评论

学校读者我要写书评

暂无评论

Big Data architectures Benchmark for Forecasting Electricity Consumption

Big Data Architectures Benchmark for Forecasting Electricity...

引用

international conference on Cloud Technologies and Applications (CloudTech)

作者： Houda Daki Asmaa El Hannani Hassan Ouahmane Laboratory of Information Technologies National School of Applied Sciences University of Chouaib Doukkali El Jadida Morocco

ISBN: (数字)9781728161754

ISBN: (纸本)9781728161761

Now a day, educational institutions present one of the highest power consuming sector due to their new activities and occupancy pattern. this enormous amount of energy consumption at the university need a huge effort to reduce it. Smart grid is among the efficient solution to save energy and balance supply and demand. For the same purpose, the National School of Applied Sciences of El Jadida-Morocco wants take advantage from smart grid to maintain the balance between energy production and consumption. Despite of all added value of this smart grid solution for the school, it has the issue of managing energy production surplus, because it cannot inject it into Moroccan electrical infrastructure neither store it using storage devices. So, to overcome this challenge the system need to predict electrical consumption to be able to produce exactly the same value. Recently, Big Data contributed very well in analysing electrical consumption data using many tools and advanced techniques. It process, interprets and analyzes huge quantity of data to make it more profitable and valuable. For that reason, the school will take refuge in Big data technology to implement a custom system to predict electrical energy consumption by analyze all factors that influence electrical energy use. In this paper, we propose a benchmark of the main Big Data architectures in the field and that will cover all electrical energy data processing from data collection, data storage, data analytic and data visualization. the aim of this benchmark is to choose the optimal architecture in term of fault tolerance, resource management, data storage and data modelling to forecast electricity consumption in educational institutions.

关键词： Data analysis Memory Computer architecture Big Data Benchmark testing Smart grids Resource management

来源：评论

学校读者我要写书评

暂无评论

Research on CUDA-based Multidimensional Time Series Motif Mining

Research on CUDA-based Multidimensional Time Series Motif Mi...

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Jimin Wang Shuaiwei Wang Junru Wang College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Traditional serial motif mining methods struggle to quickly identify motif information in large-scale time series data. A CUDA-based multidimensional motif mining algorithm is proposed to discover motifs in multidimensional time series. the algorithm introduces a multidimensional kNN Matrix Profile (kNN mMP) structure to address the limitations of the multidimensional mMP. parallel computation of kNN mMP using the CUDA architecture enables rapid motif discovery in massive datasets and facilitates detection of additional motif features, such as strong and weak motifs, as well as identification of anomalous motifs. Experiments on public datasets demonstrate that using CUDA for kNN mMP structure achieves a significant speedup; on NVIDIA GeForce RTX 4060, the kNN-mSTOMP-GPU algorithm exhibits a speedup of over 220 times compared to the STOMP algorithm utilizing only kNN mMP when the sequence length is 200,000.

关键词： Time series analysis Graphics processing units Computer architecture Big Data Feature extraction Data mining Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

LION: Real-Time I/O Transfer Control for Massively parallel Processor Arrays

LION: Real-Time I/O Transfer Control for Massively Parallel ...

引用

ACM and IEEE international conference on Formal Methods and Models for Co-Design (MEMOCODE)

作者： Dominik Walter Jü rgen Teich Friedrich-Alexander-Universit&#x00E4 t Erlangen-N&#x00FC rnberg Hardware/Software Co-Design Erlangen Germany

the performance of many accelerator architectures depends on the communication with external memory. During execution, new I/O data is continuously fetched forth and back to memory. this data exchange is very often performance-critical and a careful orchestration thus vital. To satisfy the I/O demand for accelerators of loop nests, it was shown that the individual reads and writes can be merged into larger blocks, which are subsequently transferred by a single DMA transfer. Furthermore, the order in which such DMA transfers must be issued, was shown to be reducible to a real-time task scheduling problem to be solved at run time. Rather than just concepts, we investigate in this paper efficient algorithms, data structures and their implementation in hardware of such a programmable Loop I/O Controller architecture called LION that only needs to be synthesized once for each processor array size and I/O buffer configuration, thus supporting a large class of processor arrays. Based on a proposed heap-based priority queue, LION is able to issue every 6 cycles a new DMA request to a memory bus. Even on a simple FPGA prototype running at just 200 MHz, this allows for more than 33 million DMA requests to be issued per second. Since the execution time of a typical DMA request is in general at least one order of magnitude longer, we can conclude that this rate is sufficient to fully utilize a given memory interface. Finally, we present implementations on FPGA and also 22nm FDX ASIC showing that the overall overhead of a LION typically amounts to less than 5% of an overall processor array design.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cloud-Driven AI: Enhancing Machine Learning Performance through Distributed Computing

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

引用

international conference on ICT in Business Industry & Government (ICTBIG)

作者： Kunal Dixit Himani Parul Bhardwaj Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

the exponential data volume and complexity of Machine Learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. this research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application’s efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. this framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5–12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. these results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today’s AI applications while opening the door to new advancements in ML throughout different domains.

关键词： Training Machine learning algorithms Protocols Scalability Government Partitioning algorithms Synchronization Resource management Distributed computing Random forests

来源：评论

学校读者我要写书评

暂无评论

Optimal Analysis for Enterprise Financial Management Based on Artificial Intelligence and parallel Computing Method

Optimal Analysis for Enterprise Financial Management Based o...

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： M. Rajeswari N. Kumar P. Raman Pramod Kumar Patjoshi Vaishali Singh Sumit Pundir Department of Management Studies SRM Institute of Science and Technology Vadapalani Chennai TamilNadu College of Management SRM Institute of Science and Technology Chennai Kattankulathur TamilNadu Department of MBA Panimalar Engineering College Chennai TamilNadu India School of Management Centurion University of Technology and Management Bhubaneswar Odisha Department of Computer Science and Engineering Ambalika Institute of Management & Technology Luknow Uttar Pradesh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

To function in today’s society, people need access to information technology for every aspect of their lives, from personal interactions to the creation of goods and services. As the Internet and the In tandem with the expansion of the country’s information industry, an increasing number of businesses are opting to use an Automation of Financial Data Management with AI. Artificial intelligence (AI) is a new technological science that aims to mimic, extend, and increase human intellect via the study and development of ideas, methodologies, technologies, and application systems. Artificial intelligence (AI), which may be seen as a method of teaching computers to replicate and simulate human thinking patterns and behaviours, has been the primary focus of data processing in recent years. To better grasp the complexity of many challenges in the financial services industry, AI has become a significant solution for creating accuracy and accountability. this paper begins with a discussion of basic technology’s blueprint and shape, optimises it, investigates the real-world requirements of SMEs in terms of financial management, concludes with system architecture and development architecture, and finally concludes with performing hypothetical tests testing the system’s functional performance. the test results show that the system accomplishes its intended purposes and performs its primary operations, but that it still needs some tweaks and additions. Because of this, it is very important that all the information used in managing money is exact, correct, and reliable.

关键词： Computers Shape Systems architecture Production Financial management Software Reliability

来源：评论

学校读者我要写书评

暂无评论

HARDWARE SOFTWARE FIRMWARE TRADE-OFFS. IEEE international COMPUTER SOCIETY conference, conference DIGEST, PROCEEDINGS, ANNUAL, 5th, BOSTON, MASS, SEP 22-24 1971.

引用

1971年

作者： Hancock, Robert J. Apfelbaum, H. Oppenheimer, G. Bauerle, David A. Sumner, F.H. McGrew, Mark J. Arndt, Fred R. Oliver, George M. Cantrell, Harry N. Nuspl, S.J. Johnson, M.D. Meilander, W.C. Gall, R.G. Curtis, R.C.

Folloing is a continuation of the list of titles and authors: Microprogrammed Disk Peripheral Control Units. By Robert J. Hancock. Design of Virtual Memory Systems. By H. Apfelbaum and G. Oppenheimer. Addressing Structure of the Burroughs B6700. By David A. Bauerle. Operand Accessing in the MU5 Computer. By F. H. Sumner. Measuring for Design. By Mark J. McGrew. Hardware Monitoring of Real-Time Computer System Performance. By Fred R. Arndt and George N. Oliver. Designing for Measurement. By Harry N. Cantrell. Effect of I/O Characteristics on the Performance of a parallel Processor. By S. J. Nuspl and M. D. Johnson. Associative processing in an Air Traffic Control environment. By W. C. Meilander and R. G. Gall. Management of High Speed Memory in the Star-100 Computer. By R. C. Curtis.

关键词： Computers

来源：评论

学校读者我要写书评

暂无评论

Single Node Acceleration of Generative Adversarial Networks using HPC for Image Analytics 22

Single Node Acceleration of Generative Adversarial Networks ...

引用

Proceedings of the 2022 5th international conference on Computational Intelligence and Intelligent Systems

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of Technology India

ISBN: (纸本)9781450397612

Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial Networks (DCGAN) architecture by using its parallelism and executing it on High-Performance Computing platforms. the effective analysis of a DCGAN in Graphic processing Unit and Tensor processing Unit platforms in which each layer execution pattern is analyzed. the bottleneck is identified for the GAN structure for each execution platforms. the Central processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic processing Unit in contrast, side, are a hundred times quicker than CPUs for Neural Networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well.

关键词： Generative Adversarial Network High-Performance Computing Graphic processing Unit Tensor processing Unit

来源：评论

学校读者我要写书评

暂无评论

A Low-Cost Positioning System for parallel Tracking Applications of Agricultural Vehicles by Using Kalman Filter

A Low-Cost Positioning System for Parallel Tracking Applicat...

引用

第五届国际计算机与计算技术在农业中的应用研讨会

作者： Fangming Zhang Ximing Feng Yuan Li Xiuqin Rao Di Cui Ningbo Institute of Technology Zhejiang UniversitySchool of Mechanical and Energy Engineering

ISBN: (纸本)9783642272806

A position-velocity(PV) model and a multi-sensor system,consisted of a consumer application GPS,a MEMS gyro,two encoders,and a turning angle sensor,was constructed for the positioning *** two encoders augmented the positioning accuracy greatly that the fluctuation of vehicle position was greatly smoothed comparing with a GPS-only *** minimal fluctuation was falling from 2.21 m to 0.52 m(east direction),from0.68 m to 0.23 m(north direction).the maximum XTE was reduced from 2.5m to 0.77 m,and the RMS value was improved to 0.22 *** GPS bias error was the major difficulty to produce better performance.

关键词： Positioning system GPS Kalman filter parallel tracking low-cost

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：