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Prediction of Time Delay in eLoran data acquisition systems 3

Prediction of Time Delay in eLoran Data Acquisition Systems

引用

3rd international conference on Cloud computing, Big data Application and Software Engineering, CBASE 2024

作者： Li, Xuyin Software Engineering Institute Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331506582

This study examines the influence of six meteorological factors on eLoran signal propagation delay and develops a predictive model using four machine learning approaches: BP neural networks, random forests, support vector regression, and gradient boosting. Optimization of these models led to significant reductions in root mean square error (RMSE) and mean absolute error (MAE). Results demonstrate that the gradient boosting model offers superior accuracy in predicting propagation delay, satisfying the eLoran system's timing requirement of 100 ns. This research provides valuable theoretical insights and practical guidance for enhancing the eLoran system's application in interference-resistant environments. © 2024 ieee.

关键词： Prediction models

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Recommendation System using Emotion Detection 3

Recommendation System using Emotion Detection

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susindra, S. Sweta, B. Keerthikha Shri, R. Brindha, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems. © 2025 ieee.

关键词： Emotion Recognition

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Semantic Slicing across the Distributed intelligent 6G Wireless Networks 20

Semantic Slicing across the Distributed Intelligent 6G Wirel...

引用

20th Annual ieee international conference on Sensing, Communication, and Networking (SECON)

作者： Loven, Lauri Shahid, Hafiz Faheem Le Nguyen Harjula, Erkki Silven, Olli Pirttikangas, Susanna Lopez, Miguel Bordallo Univ Oulu Ctr Ubiquitous Comp Oulu Finland Univ Oulu Ctr Wireless Commun Oulu Finland Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland

ISBN: (纸本)9798350300529

In the age of the Internet of Things (IoT) and the expanding computing continuum, it's crucial to manage and share resources at the edges of networks. This position paper presents a new concept known as 'semantic slicing'. This approach harnesses the power of artificial intelligence (AI), wireless networks, edge computing, and sensing technologies to enable novel applications, optimize resource allocation, and streamline data processing and decision-making across complex systems spanning the computing continuum. Semantic slicing applies a deep understanding of the data and specific application requirements to intelligently allocate resources and distribute processing tasks in the computing continuum. This strategy allows for the creation of systems that are not only more efficient and responsive, but also better equipped to adapt to a variety of applications and services.

关键词： Semantics

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Optimizing LLM Architectures for Real-Time Applications in Full-Stack Development 3

Optimizing LLM Architectures for Real-Time Applications in F...

引用

3rd international conference on Automation, computing and Renewable systems, ICACRS 2024

作者： Kannadasan, Tamilarasan Frisco TX United States

ISBN: (纸本)9798331532420

As real-time applications become increasingly integral to full-stack development, the demand for efficient and scalable Large Language Model (LLM) architectures has surged. This study explores optimization strategies for LLMs tailored to meet the stringent performance and latency requirements of real-time environments. We investigate architectural enhancements, including model pruning, quantization, and parallel processing techniques, to reduce computational overhead without compromising accuracy. To validate the proposed approach, we employ the Common Crawl Corpus dataset as a comprehensive case study, leveraging its extensive and diverse textual data to simulate real-world application scenarios. Our experiments demonstrate significant improvements in response times and resource utilization, enabling seamless integration of LLMs into full-stack frameworks. Additionally, we address challenges related to data handling and model adaptability, ensuring that optimized architectures maintain robustness across dynamic workloads. The findings highlight the potential of LLM optimizations to bridge the gap between advanced natural language processing capabilities and the immediate demands of real-time application development. This work provides a foundational framework for developers aiming to harness the power of LLMs in full-stack projects, paving way for more responsive and intelligent web and software solutions. © 2024 ieee.

关键词： Natural language processing systems

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Research on the Application of Big data in Transient Stability Simulation of Power systems 4

Research on the Application of Big Data in Transient Stabili...

引用

4th international conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Zhang, Jie Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou510663 China National Energy Power Grid Technology R&d Centre Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou510663 China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou5106633 China

ISBN: (纸本)9798331518066

With the rapid expansion of smart grids, there is a growing complexity in power system dynamics, which increases the availability of real-time operational data and underscores the need for advanced transient stability simulations. Traditional transient stability analysis methods are constrained by their computational demands and may compromise accuracy. Furthermore, deep learning-based methods often focus on the application of a single algorithm, lacking a systematic consideration of the entire simulation process. To address these challenges, this study proposes a novel approach that leverages big data to enhance transient stability simulations. By integrating deep learning and ensemble learning techniques, the model significantly improves the accuracy and efficiency of predicting transient instabilities. The methodology utilizes a multi-source data acquisition and fusion scheme to ensure data comprehensiveness and a distributed storage architecture to handle large-scale datasets effectively. advanced visualization technologies are also employed to enhance the interpretability of the simulation results. This comprehensive framework aims to support the safe and stable operation of power systems amid increasing complexity and data availability. © 2024 ieee.

关键词： Transient stability

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Enhancing Disaster Recovery Mechanism in SCADA using Multichain Blockchain 2

Enhancing Disaster Recovery Mechanism in SCADA using Multich...

引用

2nd ieee international conference on Device Intelligence, computing and Communication Technologies, DICCT 2024

作者： Bhatia, Kunal Khanna, Anirudh Sharma, Ishu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Primus Global Services Data Protection Tower Lead Plano United States

ISBN: (纸本)9798350372847

The disaster recovery mechanism is employed in various industries but in critical infrastructure scenarios, the process should be reliable and resilient. This research paper proposes a novel framework for disaster recovery methods for as Supervisory control and data acquisition systems using communication channels created by blockchain technology. The permission blockchain technology is integrated with as Supervisory control and data acquisition devices for configuring the data and event-sharing techniques. The research work presents the process of configuring and implementing the disaster recovery process of risk assessment, backup creation and event logging. Further, the events in the critical infrastructure are controlled by smart contract automation as part of the multichain blockchain. The consensus for fault tolerance is achieved by keeping 90% of the nodes operational in the network. The proposed solution is simulated by creating a private communication channel among as Supervisory control and data acquisition devices and the performance is evaluated with successful execution rate and average time of block confirmation by the disaster recovery process. The mining diversity, number of required active nodes, creation of stream and creation of distributed ledger is controlled by single administrative node in the proposed architecture which can be configured according to the requirements of the SCADA network architecture. © 2024 ieee.

关键词： Blockchain

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The Interaction of Big data and AI intelligent Technology in Visual Design systems 2

The Interaction of Big Data and AI Intelligent Technology in...

引用

2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Zhu, Yuanji School of Art Suzhou Vocational University Suzhou China

ISBN: (纸本)9798350382952

With the rapid development of big data and artificial intelligence (AI) technologies, their wide application in various fields has become possible. Traditional visual design systems are usually based on static rules and human experience, which are difficult to meet the personalized needs of users, resulting in low universality of design works. This article solves the problems existing in traditional research through in-depth research on the interaction of big data and AI in visual design systems, and promotes the development of more intelligent, personalized and scientific development in the field of visual design. This article takes the Generative Adversarial Network (GAN) algorithm model as the research object, and performs algorithm fusion on its generative fusion model A-VAE (Attention-Variational Autoencoder) based on variational autoencoders and attention mechanisms, and proposes a new fusion model algorithm. Through research, it has been found that the merged algorithm exhibits better image generation performance, accuracy, clarity, and diversity in visual design compared to other algorithms. © 2024 ieee.

关键词： Generative adversarial networks

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data Privacy and Cybersecurity Law: Regulatory Challenges and Compliance Strategies

Data Privacy and Cybersecurity Law: Regulatory Challenges an...

引用

2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Trikha, Akash Das, Rahul Chandra, Sayani Kumar, Ramnwesh Muduli, Himanshu Sekhar Soa National Institute of Law Soa university Odisha Bhubaneswar India

ISBN: (纸本)9798331543617

It is now more crucial than ever to safeguard sensitive information and prevent unauthorized access, given the rapid expansion of data collection, storage, and processing capabilities facilitated by modern technology. This essay investigates the obstacles associated with complying with regulations and implementing strategies to guarantee cybersecurity and data privacy. This study delves into the ever-evolving and dynamic realm of Regulations regarding the protection of personal information, with a particular emphasis on the California Consumer Privacy Act (CCPA), GDPR, by examining the effects on both organizations and individuals. The essay emphasizes the importance of instituting comprehensive cybersecurity measures and discusses the escalating prevalence of cyberattacks. Risk assessments, data encryption, incident response strategies, and staff training are identified as essential components of compliance. The objective of this publication is to help enterprises navigate the complexities of data privacy and cybersecurity regulation by providing a comprehensive analysis of the current regulatory landscape and suggesting practical solutions for compliance. © 2024 ieee.

关键词： Cybersecurity

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Research on Natural Language Generation System of BERT Model Computer Big data Technology 6

Research on Natural Language Generation System of BERT Model...

引用

6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Zhang, Jianqin Jiangsu Union Technical Institute Nanjing Engineering Branch Nanjing China

ISBN: (纸本)9798350374315

This paper focuses on the application and research of pre-trained model BERT in natural language generation system to meet the needs of natural language processing tasks in the era of big data. With the emergence of massive data, it is of great significance to use big data technology to understand and generate natural language efficiently. This study aims to build and optimize a natural language generation system based on BERT model, which can fully tap and utilize large-scale unannotated text data and capture complex context dependencies through deep two-way Transformer architecture to achieve accurate and smooth natural language generation. First, this paper discusses the core mechanism of BERT model and its superiority in natural language understanding, and analyzes its ability to effectively learn massive text resources in big data environment. Then, we design a BERT model extension scheme that ADAPTS to big data environment. By combining distributed computing framework and targeted model optimization strategy, the training efficiency and generalization performance of the model can be improved when processing massive data. In the experimental part, we conduct a detailed evaluation of the constructed natural language generation system using large-scale real-world data sets, compare multiple benchmark models, and demonstrate significant effects on specific tasks such as news summary generation and conversation response generation. Experiments show that the natural language generation system based on BERT model effectively utilizes the advantages of big data while maintaining high language quality, and improves the relevance and accuracy of generated content. © 2024 ieee.

关键词： data accuracy

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High-speed Train intelligent Maintenance Pattern and Edge-computing Perception Model 23

High-speed Train Intelligent Maintenance Pattern and Edge-co...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Sun, Peng Zhang, Weijiao Jia, Zhikai Institute of Computing Technology China Academy of Railway Sciences Beijing China

ISBN: (纸本)9781665494571

Aiming at the key requirements for the advanced security risks prevention and the smart-precise maintenance management of high-speed trains, given the EMU (Electric Multiple Units) trains management characteristics such as complex dynamic and static scenarios, business collaboration, and cross-specialty cooperation in conurbation transportation, a 'Cloud-Edge-Terminal' architecture of intelligent EMU maintenance, an IoT data collection system, and its maintenance organization patterns are researched;an FSM-based (Finite State Machine based) intelligent maintenance edge-perception model and related state transition definition are proposed;which support big-data analysis and intelligent maintenance decision for EMU. A simulation experiment was performed to verify the efficiency, stability, and availability of the system and method in this paper. It has important significance for railway rolling stocks maintenance and other vertical industries that need intelligent reengineering. © 2021 ieee.

关键词： Systematics Computational modeling Transportation Organizations data models Stability analysis Rail transportation

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