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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1353 条记录，以下是121-130 订阅

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Research on Comprehensive Energy Efficiency Control and Optimization Technology for the Internet of Things for Three-Dimensional "Common intelligence" Visualization

Research on Comprehensive Energy Efficiency Control and Opti...

引用

IEEE international conference on Electrical Engineering, big data and Algorithms (EEBDA)

作者： Xie, Yunfu Yang, Jifeng Wen, Yujian State Grid Zhejiang Cixi Power Supply Co Ltd Ningbo 315300 Peoples R China

ISBN: (纸本)9781665416061

This article uses digital twins and edge computing technologies to design virtual models in the digital space and establish the mapping relationship between digital virtual and physical entities to achieve interconnected and interactive digital "twin mirroring" physical models, building multi-source integration, autonomous comprehensive perception, and globalization An integrated small and micro park integrated energy IoT system architecture with optimal coordination, virtualized management and control of physical resources, and deep decoupling of software and hardware. Finally, the typical design of edge computing and edge cloud collaboration of actual power distribution business is listed.

关键词： 3D visualization small and micro parks Internet of Things energy efficiency control

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Cross-Attention Model for Multi-modal Bio-Signal Processing

Cross-Attention Model for Multi-modal Bio-Signal Processing

引用

IEEE international conference on big data and Smart computing (bigComp)

作者： Heesoo, Son Sangseok, Lee Sael, Lee Ajou Univ Dept Artificial Intelligence Suwon South Korea Ajou Univ Software & Comp Engn Suwon South Korea

ISBN: (纸本)9781665421973

In order to comprehensively predict human condition, it is beneficial to analyze various bio-signals obtained from human body. Existing multi-modal deep sequence models are often very complex models that involve significantly more parameters than single-modal models. However, since the number of multi-modal signal data is small compared to single-modal, more efforts is needed to reduce the model complexity of multi-modal models. We introduce a multi-modal sequence classification model based on our cross-attention blocks, which aims to reduce the number of parameters involved in cross-referencing different modes. We compare our method with baseline sequential deep learning models, LSTM and Transformer, and a competitor. We test our methods on two public datasets and a dataset obtained from construction works. We show that our model outperforms compared methods in accuracy and number of parameters when the number of modes increases.

关键词： bio-signals multi-modal analysis cross-attention

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Research on Intelligent Manufacturing System Based on Internet and big data 2

Research on Intelligent Manufacturing System Based on Intern...

引用

2nd international conference on Artificial intelligence, Automation, and High-Performance computing, AIAHPC 2022

作者： Qian, Guiming Sichuan Vocational and Technical College Sichuan Suining629000 China

ISBN: (纸本)9781510657717

At present, with the development of China's manufacturing industry toward high-precision, the original manufacturing system can no longer meet actual needs, and a new intelligent manufacturing system must be built based on Internet big data and intelligent technology. Based on this, the article studies the intelligent manufacturing system based on Internet big data, and focuses on the data analysis and online monitoring modules in the intelligent system construction to improve the level of intelligent manufacturing system construction, and provide reference for future related work. © 2022 SPIE. All rights reserved.

关键词： big data

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Simulation of Distributed big data Intelligent Fusion Algorithm Based on Machine Learning

Simulation of Distributed Big Data Intelligent Fusion Algori...

引用

2022 international conference on Artificial intelligence and Autonomous Robot Systems, AIARS 2022

作者： Tian, Di Henan Finance University Zhengzhou450000 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

Different from intelligent analysis applications in traditional small-scale data scenarios, intelligent analysis applications in big data scenarios are no longer a single AI algorithm model problem, but a fusion of big data, big models, and big computing. The existing data fusion algorithm has problems such as high network energy consumption and short lifetime of network nodes after data fusion. It is necessary to consider algorithm model design, big data processing and efficient distributed parallel computing at the same time, which brings a series of new challenges and problems to the research on the basic theoretical methods and key technologies of big data intelligent analysis. The method used in this paper is machine learning, setting an algorithm to have the machine analysis the data by itself using deep mining. It can largely finish the work that cannot be done manually. We need complex analysis based on machine learning and data mining, strong computing power and storage capacity to achieve massive data analysis. Through the research in this paper, it is concluded that the algorithm in this paper can achieve better time-to-time performance than the traditional algorithm. © 2022 IEEE.

关键词： big data

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Application of big data analysis in enterprises under the background of intelligent manufacturing

Application of big data analysis in enterprises under the ba...

引用

2022 international conference on Artificial intelligence and Intelligent Information Processing, AIIIP 2022

作者： Chen, Zhang Lanzhou University of Technology Gansu Lanzhou China

ISBN: (纸本)9781510660175

With the widespread promotion of intelligent manufacturing and the rise of cloud computing and artificial intelligence, big data analysis plays an increasingly important role in the production and operation of manufacturing enterprises. Mining and visual analysis of the massive data of manufacturing enterprises is conducive to discovering the hidden laws and causal relationships of the data, so as to extract useful information and help enterprises implement better decision-making. This paper expounds big data analysis from three aspects: data life cycle management, big data analysis types and key technologies involved, analyzes the application of big data analysis in the field of intelligent manufacturing, and proposes a big data analysis architecture for manufacturing enterprises. Finally, corresponding countermeasures and suggestions are put forward for the problems existing in big data analysis under the background of intelligent manufacturing. © 2022 SPIE.

关键词： data mining

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Learning Task-Specific Embeddings for Few-Shot Classification via Local Weight Adaptation 24

Learning Task-Specific Embeddings for Few-Shot Classificatio...

引用

16th international conference on Machine Learning and computing (ICMLC)

作者： Gong, Nianru Duan, Pengfei Rong, Yi Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Hubei Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya Peoples R China

ISBN: (纸本)9798400709234

Existing few-shot classification methods ignore the local spatial image features information, as image embeddings extracted globally may not optimally discriminative for the target task. In this paper, we argue that support embeddings supposed to represent image features adaptively for different tasks. Specifically, we extract local features at each spatial locations of images, and introduce an embeddings generator to produce task-specific embeddings of support samples adapt to the task at hand by reweighting local feature weights. The weights optimization is guided by capturing the correlations between intra and inter-class support samples. Then, query images are classified based on the feature maps reconstructed by support embeddings of different classes. We conduct extensive experiments to validate our method and the results indicate that our method improves the performance of few-shot classification on three benchmark datasets.

关键词： few-shot task-specific embeddings reconstruction

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Cognitive Solution for MultimediaAccess 5

Cognitive Solution for MultimediaAccess

引用

5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Periola, A. A. Alonge, A. A. Ogudo, K. A. Univ Johannesburg Dept Elect & Elect Engn Technol Johannesburg South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Subscribers seeking to access multimedia content utilize wireless networks significantly. Multimedia access is influenced and impaired by variability in the availability of wireless access links. The impairment arises due to the occurrence of rain induced attenuation. This results in a multimedia content viewing gap (MCVG). The proposed research addresses the challenge of the MCVG and proposes the incorporation of artificial intelligence with computing and networking entities in enabling the creation of multi - media content. This is done aboard entities located in the subscriber residence instead of entities that were previously out of the subscriber residence. Performance analysis shows that the proposed mechanism outperforms existing mechanism and reduces access costs by at least 22.6% and up to 71% on average. The proposed mechanism also enhances the content access duration by 35.6%.

关键词： Multimedia Content Wireless Networks Access Costs Access Duration Artificial intelligence

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Challenges and Inspirations of AI and Related Technologies in intelligence Analysis 10

Challenges and Inspirations of AI and Related Technologies i...

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10th IEEE Joint international Information Technology and Artificial intelligence conference, ITAIC 2022

作者： Wu, Xu Qin, Daguo Li, Yang Space Engineering University Beijing China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

The special requirements of intelligence analysis on accuracy, timeliness and security limited the application of AI (artificial intelligence) and related technologies (including big data, intelligent algorithms and cloud computing technologies) to a certain extent. Based on the characteristics of intelligence analysis together with AI and related technologies, it is concluded that foggy intelligence analysis caused by big data, unreliability of intelligent algorithm and security risks in cloud computing are three chief challenges. Building application system, surmounting major technical difficulties and accelerating achievements transformation are put forward to push limits in applications of AI and related technologies in intelligence analysis, so as to improve capabilities of intelligence analysis. © 2022 IEEE.

关键词： Cloud computing

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Research and Practice of data Governance in Universities Based on data Middleware 24

Research and Practice of Data Governance in Universities Bas...

引用

2024 international conference on Cloud computing and big data, ICCBD 2024

作者： Lian, Jin Chen, Junfeng School of Artificial Intelligence Jianghan University China Office of Network and Digital Construction Jianghan University China

ISBN: (纸本)9798400710223

With the rapid development of university informatization construction, university big data governance and analysis technology promotes the comprehensive transformation of universities from "digital campuses"to "smart campuses", which further accelerates the construction of university governance modernization. In order to solve the problems of dispersed data resources, lack of data governance, and isolated business systems that still exist in colleges and universities, we have designed and refined the data governance process of colleges and universities by introducing the new structure of data Central Platform to address the problems. Taking Jianghan University as an example, this paper completes the data governance practice based on data Central Platform in accordance with the data governance process of universities, and carries out in-depth analysis of student consumption and scientific research to enhance the effectiveness of data utilization in universities, empower the construction of smart campuses, and improve the level of accurate service, optimize resource construction and realize scientific management in universities. © 2024 ACM.

关键词： data centers

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A New dataset for Intrusion Detection in Industrial Control System: A Gas Pipeline Testbed Study 21

A New Dataset for Intrusion Detection in Industrial Control ...

引用

21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Deng, Longmin Zhang, Xuemin Zheng, Qianrong Zhao, Dongdong Zhou, Junwei Xiang, Jianwen Wuhan University of Technology Hubei Key Laboratory of Transportation of Internet of Things School of Computer Science and Artifical Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China

ISBN: (纸本)9798350329223

The security of Industrial Control Systems (ICS) is a critical issue that has gained increasing attention in recent years. Machine learning based intrusion detection systems (IDS) have shown promise in detecting previously unknown attacks, but their performance depends heavily on the quality of the dataset. However, most existing datasets in the field are either outdated or do not reflect the specific characteristics of ICS. We construct a dataset of ICS network traffic collected from a gas pipeline testbed, which covers a range of normal and abnormal events specific to the domain. We detail our methodology for constructing the dataset based on the gas pipeline testbed. Moreover, the systematic and fundamental comparative experiments are performed, based on the collected dataset. Specifically, we evaluate the performance of five supervised machine learning models on it while comparing their results. Our experimental results show that the dataset can serve as a valuable resource for data-driven IDS research and that Random Forest is an effective model for detecting anomalies in ICS data. © 2023 IEEE.

关键词： Intrusion detection

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