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检索条件"任意字段=IEEE Conference on Automation Science and Engineering"

共 60728 条记录，以下是221-230 订阅

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A Comprehensive Analysis of Blockchain-Based Cryptographic Framework for Network Security 3

A Comprehensive Analysis of Blockchain-Based Cryptographic F...

引用

3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Vyshnavi, A. Bharathi, B. Singh, Amanveer Aggarwal, Prateek Yadav, Rakesh Kumar Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

Blockchain technology is increasingly developing breakthroughs in secure and decentralized data storage. More recently, it has also garnered interest as a possible solution for network security. In particular, this paper conducts an indepth analysis of a network security cryptographic framework using Blockchain. The framework proposed again adopts the principles of blockchain technology, such as decentralization, immutability and consensus, to build a secure data environment for the network. It uses various cryptographic methods, digital signatures and Encryption and hash functions, to guarantee integrity, confidentiality, and authentication. The framework uses smart contracts to automatically execute security checks that can be enforced with predefined and approved methods. In this analysis, we aim to investigate the proposed scheme's effectiveness, scalability, and practicability in typical network security applications. It will also cover the challenges and future research directions in applying this framework in practical scenarios. This analysis will form the basis for a strong cryptographic system on the Blockchain to provide security reinforcement in networks. © 2025 ieee.

关键词： Network security

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Multivariate Load Forecasting Model Based on MTL-LSTM-TCN

Multivariate Load Forecasting Model Based on MTL-LSTM-TCN

引用

2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Han, Ruicong Jiang, He Wei, Mofan Guo, Rui School of Renewable Energy Shenyang Institute of Engineering Shenyang China School of Electrical Engineering Shenyang University of Technology Shenyang China

ISBN: (纸本)9798331506797

This study introduces a multi-task learning (MTL) model that integrates a temporal convolutional network (TCN) with a long-short-term memory network (LSTM) for short-term multivariate load forecasting. Initially, the input features undergo preprocessing to ensure data quality and consistency. The MTL framework and LSTM are then employed to capture the interdependencies among multivariate loads and the temporal characteristics of load sequences, while TCN is leveraged to extract critical features from local patterns. This approach culminates in the development of the MTL-LSTM-TCN multivariate load forecasting model, aimed at enhancing the precision of multivariate load predictions. A simulation study was performed using a multivariate load dataset from Arizona State University's Tempe campus. The findings confirm that the proposed MTL-LSTM-TCN model delivers outstanding performance across various load forecasting scenarios, significantly boosting both the accuracy and robustness of short-term load predictions. © 2025 ieee.

关键词： integrated energy system load prediction LSTM multi-task learning TCN

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Analysis of Oscillations in Wind Farms Integrated into the Grid Through Flexible Direct Current Transmission Systems

Analysis of Oscillations in Wind Farms Integrated into the G...

引用

2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Liu, Hui Zhang, Yi College of Electrical Engineering North China University of Science and Technology Tangshan China Hebei wind-photovoltaic-Hydrogen-storage system Security Monitoring and Intelligent Operation Center of technology Innovation Tangshan China

ISBN: (纸本)9798331506797

As the share of renewable energy in the power supply grows, power system oscillations are escalating in severity. This paper analyzes the oscillation problem of a direct-drive wind farm connected to the power grid through a voltage source converter based high-voltage direct current transmission system (VSC-HVDC). First, a small signal model is constructed based on modular principles, and the eigenvalue analysis method is applied to solve the state matrix of the entire system to determine the oscillation mode in the system. Secondly, the main factors affecting the subsynchronous oscillations are investigated by the methods of participation factor analysis. Finally, the subsynchronous oscillation is suppressed by optimizing the parameters of the controller at the network side, and the correctness of the theoretical analysis and the effectiveness of the suppression strategy are verified by simulation. © 2025 ieee.

关键词： modular participation factor analysis subsynchronous oscillations VSC-HVDC

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Smart Home automation with MQTT Using ESP32 and Node-RED 4

Smart Home Automation with MQTT Using ESP32 and Node-RED

引用

4th International conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Chouragadey, Pratik Rathore, Bhavna Khare, Gaurav MP Gwalior474005 India Dept. of Electrical Engineering Gati Shakti Vishwavidyalaya Vadodara India

ISBN: (纸本)9798331530402

This paper presents a novel approach to home automation utilizing the MQTT protocol. The system is implemented using an ESP32 node and a Node-RED server running on a Raspberry Pi, allowing users to control the system from the Raspberry Pi as well as client terminals. The proposed system enables users to control and monitor home appliances remotely from anywhere. By using the lightweight MQTT protocol, the system provides real-time communication between the node and the server and ensures responsive control and data monitoring. The implementation of the ESP32 node provides us flexibility and scalability, while Node-RED provides us with a user-friendly interface for system management. Experimental results validate the system's effectiveness in maintaining reliable connectivity between node and server. This work highlights the potential of MQTT-based home automation systems to provide enhanced control and monitoring capabilities in modern smart homes. © 2025 ieee.

关键词： Three term control systems

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An Adaptive Feature Fusion Based Network for Aerial-Ground Point Cloud Place Recognition

An Adaptive Feature Fusion Based Network for Aerial-Ground P...

引用

2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Zhang, Di Song, Rui The School of Control Science and Engineering Shandong University Shandong Province Jinan China

ISBN: (纸本)9798331506797

Point cloud place recognition(PCPR) plays a key role in helping aerial-ground robotic systems navigate and understand their surroundings, especially when GPS signals are unavailable. However, the heterogeneity between point clouds collected by aerial and ground platforms poses challenges and severely affects the robustness and generalization of aerial-ground PCPR. New approaches are required to tackle the differences in how data from various sources are represented and to improve performance in challenging environments. To solve this issue, we introduce a hierarchical adaptive feature fusion network built on the feature pyramid network (FPN), which dynamically adjusts the importance of features at different scales, boosting those critical for place recognition tasks and minimizing the domain gap between diverse point clouds. The experimental results on benchmark datasets highlight the effectiveness of the proposed method. © 2025 ieee.

关键词： feature pyramid network multi-scale features place recognition point cloud

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Integrative automation of Cybersecurity Systems through Advanced Data science Methodologies: Enhancing Predictive Analytics and Real-Time Threat Mitigation 4

Integrative Automation of Cybersecurity Systems through Adva...

引用

4th International conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Das, Subhranil Kumari, Rashmi Singh, Raghwendra Kishore School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of CS & AI SR University Warangal India

ISBN: (纸本)9798331530402

Due to emergence in Corona disease, the digital revolution become more intensified which makes more prevalent in day-to-day activities. As a result, cloud service providers become more frequent. Therefore, system integration is essential throughout a partner ecosystem. Unbelievably many edge devices are sending IoT data to the cloud. The system becomes automated due to the organizational structures which resulted in developing new technologies. This new" Integration ecosystem" is where there is just one platform forall the system. According tothe survey of 4, 500 decisionmakers, data protection-rather than business process optimization or processautomation-was the primary argument for employing AI withinorganizations. Using ML algorithmsto automatically identify cyberattacks is referred to as automating cyber security systems. By leveraging automation, cyber security systems can swiftly identify and respond to attacks, reducing the amount of time needed to avert possible harm. With the considerations in mind, we proposed Automating Cyber Security Systems with Data science Practices in which we will be talking about how various cyber security systems can effectively utilize current data science practices to automate their workflow. © 2025 ieee.

关键词： Predictive analytics

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A Comprehensive Analysis of Hate Speech Detection and Recognition Utilising Advanced Machine Learning Algorithms and Techniques 3

A Comprehensive Analysis of Hate Speech Detection and Recogn...

引用

3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Mannepalli, Praveen Kumar Sharma, Saurabh Rajput, Neha Kalamdhad, Sanjay Chandigarh University Department of Computer Science & Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Punjab India St. Vincent Pallotti College of Engineering and Technology Department of Computer Science & Business System Nagpur India

ISBN: (纸本)9798331542375

Social networking sites have come into demand as a way of sharing ideas and opinions with other people. But unfortunately, people tend to misuse social media to write nasty comments. Victims' lives might be negatively impacted by behaviours that are abusive and humiliating. Social networks give people a great tool to share information on ideas on multiple topics and opinions. However, these platforms are often abused to announce toxic and offensive comments, which eventually negatively affect victims and demographics. This study offers a thorough method for identifying hate speech (HS) on social media by utilising cutting-edge ML algorithms. A publicly available Twitter dataset containing 159,571 tweets classified into six categories, including toxic and identity hatred, was utilised. The methodology comprises data collection, conditioning, and categorisation that employs BiLSTM, which is particularly appropriate for the tasks demanding contextual sensitivity. These operations include symbol removing, stemming and stop-word eliminating, feature extraction using TF-IDF, and using the Bag-of-Words model. For classification of the data between hate and non-hate, the dataset is then split into training and testing datasets and the BiLSTM model used to provide classifications for the text. The evaluation of the BiLSTM model demonstrates superior performance, achieving an accuracy86%, a precision90%, a recall86%, and an F1-score88% • A comparison between the model and other models such as SVM and CNN reveals the strength of the BiLSTM in contextual classification tasks. The results demonstrate that BiLSTM can tackle problems with hate speech detection, providing a strong answer to the problem of harmful content on social media and helping to make the internet a better place for everyone. © 2025 ieee.

关键词： Tweets

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Artificial Intelligence, Intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach 3

Artificial Intelligence, Intelligent Manufacturing, and Sust...

引用

3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shabi, Sathika Begum Mohammad Bhuvaneshwari, M. Kanimozhi, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry. © 2025 ieee.

关键词： Smart manufacturing

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Exploring the Capabilities and Limitations of Generative AI Applications, Challenges, and Future Directions

Exploring the Capabilities and Limitations of Generative AI ...

引用

2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Gulbir Meenakshi Sharma, Poonam Bhardwaj, Anjul Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakhand Nanital India Mmict&bm Mullana Haryana Ambala India Divine Institute of Management & Studies Dept. of Computer Application Ziradei Bihar Siwan India

ISBN: (纸本)9798331508685

Unprecedented capabilities for content generation, predictive analytics, and automation are made available by the introduction of Generative Artificial Intelligence (AI) technologies, which uses in a new age of industrial innovation. The purpose of this study is to investigate the transformational potential of generative AI from the perspective of exploring its capabilities and limitations and highlighting its role in the development of future technological developments. With the help of this study, we want to highlight important unsolved issues in contemporary generative AI paradigms. These challenges need to be addressed in order to further improve the capabilities, variety, and dependability of these concepts. Our goal is to give researchers with significant insights that will allow them to explore successful research areas, which will ultimately lead to the creation of generative AI systems that are more robust and accessible. © 2025 ieee.

关键词： Generative adversarial networks

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MEIC: Re-thinking RTL Debug automation using LLMs 24

MEIC: Re-thinking RTL Debug Automation using LLMs

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43rd International conference on Computer-Aided Design, ICCAD 2024

作者： Xu, Ke Sun, Jialin Hu, Yuchen Fang, Xinwei Shan, Weiwei Wang, Xi Jiang, Zhe National Center of Technology Innovation for EDA School of Integrated Circuits Southeast University China Department of Computer Science University of York United Kingdom

ISBN: (纸本)9798400710773

The deployment of Large Language Models (LLMs) for code debugging (e.g., C and Python) is widespread, benefiting from their ability to understand and interpret intricate concepts. However, in the semiconductor industry, utilising LLMs to debug Register Transfer Level (RTL) code is still insufficient, largely due to the underrepresentation of RTL-specific data in training sets. This work introduces a novel framework, Make Each Iteration Count (MEIC), which contrasts with traditional one-shot LLM-based debugging methods that heavily rely on prompt engineering, model tuning, and model training. MEIC utilises LLMs in an iterative process to overcome the limitation of LLMs in RTL code debugging, which is suitable for identifying and correcting both syntax and function errors, while effectively managing the uncertainties inherent in LLM operations. To evaluate our framework, we provide an open-source dataset comprising 178 common RTL programming errors. The experimental results demonstrate that the proposed debugging framework achieves fix rate of 93% for syntax errors and 78% for function errors, with up to 48x speedup in debugging processes when compared with experienced engineers. The Repo. of dataset and code: https://***/SEU-ACAL/reproduce-MEIC-ICCAD. © 2024 Copyright is held by the owner/author(s).

关键词： Program debugging

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