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共 35128 条记录，以下是701-710 订阅

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A Model-Predictive-Enabled Equivalent-Input-Disturbance Approach for Disturbance Rejection 6

A Model-Predictive-Enabled Equivalent-Input-Disturbance Appr...

引用

ieee 6th international conference on Industrial cyber-Physical systems (ICPS)

作者： Zhou, Yujian She, Jinhua Wang, Feng Iwasaki, Makoto China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Tokyo Univ Technol Sch Engn Hachioji Tokyo 1920982 Japan Nagoya Inst Technol Dept Elect & Mech Engn Nagoya 4668555 Japan

ISBN: (纸本)9798350311259

This paper presents a model-predictive-enabled equivalent-input-disturbance (MPEID) method for disturbance rejection. An EID estimator with a state observer estimates the effect of disturbances in a system. The disturbance estimation will not be directly added to the input channel for disturbance rejection. A cost function that considers the disturbance estimation is designed. An MPC controller calculates an optimal control input by minimizing the cost function. The stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Simulation results show that our method has better disturbance-rejection performance than an MPC method when the control input is similar.

关键词： Equivalent input disturbance model predictive control disturbance rejection

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Research on Design Optimization and Simulation of Electrical automation System Assisted by Artificial Intelligence 6

Research on Design Optimization and Simulation of Electrical...

引用

6th ieee international conference on Civil Aviation Safety and Information technology, ICCASIT 2024

作者： Hu, Zixuan Du, Yu School of Electrical and Electronic Engineering Beijing China School of International Education Baoding China

ISBN: (纸本)9798350389418

This research investigates the implementation of artificial intelligence in the design optimization of electrical automation systems, with the objective of enhancing system configuration efficiency and minimizing energy usage. By integrating the differential evolution algorithm with the specific requirements of electrical automation, an intelligent optimization framework is developed to adaptively modify system configurations. This framework thoroughly assesses critical elements such as equipment selection, energy management, and response time throughout the design phase and employs artificial intelligence techniques to fine-tune algorithm parameters for optimal configuration. In the simulation phase, multi-scenario testing is employed to validate the real-world efficacy of the optimization framework. The simulation outcomes indicate that under high-load scenarios, the optimized framework achieves a 15% reduction in energy consumption and a 20% decrease in response time. Compared to traditional design approaches, the optimized system demonstrates superior performance in energy efficiency and response time, with the capability for dynamic adjustments based on real-time requirements, thereby showcasing the strengths of artificial intelligence in the design of complex systems. Detailed data analysis further confirms the effectiveness of the optimization framework, offering robust support for the intelligent design of electrical automation systems. © 2024 ieee.

关键词： Energy utilization

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SEAGuard: A blockchain-based security framework for IoT maritime transportation systems

SEAGuard: A blockchain-based security framework for IoT mari...

引用

4th ieee Annual international conference on cyber Security and Resilience (ieee CSR)

作者： Leonis, Panagiotis Ntouros, Konstantinos Mazilu, Antonio, I Brotsis, Sotirios Kolokotronis, Nicholas Univ Peloponnese Tripolis 22131 Greece

ISBN: (纸本)9798350375367

Although the modernized Maritime Transportation systems (MTS) have experienced meaningful advancements and benefits from the adoption of Internet of Things (IoT) technology, security, reliability and privacy issues have increased extensively. The federation of multiple maritime stakeholders in the flow of transportation management and schedule exacerbates these issues, with the MTS to encounter escalating attacks dealing with data manipulation and unauthorized access that pose risks of high severity in MTS. Therefore, the maritime IoT infrastructures require to transition from centralized to distributed networks with access control policies, strong identity authentication and private communication systems that cannot be manipulated by unauthorized stakeholders. Blockchain has recently emerged as a key technology for addressing data security and privacy issues. However, this integration can introduce substantial delays in maritime services. In this paper, we propose the SEAGuard framework, particularly designed and developed for addressing security and privacy issues specific to MTS. A virtualized testbed is built to evaluate the SEAGuard framework that is developed on-top of Hyperledger Fabric and the InterPlanetary File System (IPFS). The results illustrate exceptional performance metrics with minimum latency and high throughput, less than 4 ms and more than 70 transactions per second, respectively.

关键词： Security and privacy blockchain Internet of things maritime transportation systems

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Skills Composition Framework for Reconfigurable cyber-Physical Production Modules 29

Skills Composition Framework for Reconfigurable Cyber-Physic...

引用

29th ieee international conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Sidorenko, Aleksandr Wagner, Achim Ruskowski, Martin German Research Center for Artificial Intelligence Innovative Factory System Kaiserslautern Germany

ISBN: (纸本)9798350361230

While the benefits of reconfigurable manufacturing systems (RMS) are well-known, there are still challenges to their development, including, among others, a modular software architecture that enables rapid reconfiguration without much reprogramming effort. Skill-based engineering improves software modularity and increases the reconfiguration potential of RMS. Nevertheless, a skills' composition framework with a focus on frequent and rapid software changes is still missing. The behavior trees (BTs) framework is a novel approach that enables the intuitive design of modular hierarchical control structures. BTs have been mostly explored from the AI and robotics perspectives, and little work has been done in investigating their potential for composing skills in the manufacturing domain. This paper proposes a framework for skills' composition and execution in skill-based reconfigurable cyber-physical production modules (RCPPMs). It is based on distributed BTs and provides good integration between low-level devices' specific code and AI-based task-oriented frameworks. We have implemented the provided models for the IEC 61499-based distributed automation controllers. This shows the instantiation of the proposed framework with specific industrial technology to enable its evaluation by the automation community. © 2024 ieee.

关键词： Reconfigurable architectures

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Lightweight CNN-BiLSTM based Intrusion Detection systems for Resource-Constrained IoT Devices 20

Lightweight CNN-BiLSTM based Intrusion Detection Systems for...

引用

20th ieee international Wireless Communications and Mobile Computing (ieee IWCMC) - Green and intelligent Communications

作者： Jouhari, Mohammed Guizani, Mohsen Moroccan Sch Engn Sci EMSI LSIA Lab Tanger Morocco Mohamed Bin Zayed Univ Artificial Intelligence MB Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350361261;9798350361278

Intrusion Detection systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. It is not surprising that the same technology is being applied to secure Internet of Things (IoT) networks from cyber threats. The limited computational resources available on IoT devices make it challenging to deploy conventional computing-based IDSs. The IDSs designed for IoT environments must also demonstrate high classification performance, utilize low-complexity models, and be of a small size. Despite significant progress in IoT-based intrusion detection, developing models that both achieve high classification performance and maintain reduced complexity remains challenging. In this study, we propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset. The proposed model is specifically designed to run onboard resource-constrained IoT devices and meet their computation capability requirements. Despite the complexity of designing a model that fits the requirements of IoT devices and achieves higher accuracy, our proposed model outperforms the existing research efforts in the literature by achieving an accuracy of 97.28% for binary classification and 96.91% for multiclassification.

关键词： Intrusion Detection systems Deep Learning Convolutional Neural Networks Long-Short-Term-Memory UNSW-NB15

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Smart Grid Testbed Architectures: A Comprehensive Survey

Smart Grid Testbed Architectures: A Comprehensive Survey

引用

2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Singar, Mahendra Kumar Singh, Rakesh Kumar Jebaraj, N.R. Solomon Vivekananda Global University Department of Civil Engineering Jaipur India Noida Institute of Engineering & Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India

ISBN: (纸本)9798350357769

The development of smart grids has accelerated due to the combination of advanced information technology and improved control approaches. Building an effective, dependable, and adaptable communication network is essential for enabling real-time data sharing across different conventional and intelligent grid components. The power grid's performance and efficiency are increased by the integration of communication connections, intelligent automation, sophisticated sensors, and information technology. While there are many benefits associated with smart grid technologies in terms of economics, society, and the environment, testing heterogeneity and present cyber-Physical-Smart Grids (CP-SGs) among traditional technology presents considerable hurdles. Thorough analysis of the software and hardware parts of the smart grid's (SG) system is necessary prior to real-time deployment, which means that a prototype that accurately and suitably simulates real-world operating circumstances must be created. Therefore, it is crucial to conduct a thorough analysis of cutting-edge industrial control system testbeds, assessing new technologies and security flaws to spur creative thinking and design. This article offers a comprehensive survey of the development of CP-SGs and the testbeds that support them, including a range of testing approaches. CP-SG testing facility layouts, their uses, main vulnerabilities, needs, limitations, and applications are all covered in the conversation. In this ever-evolving discipline, the conclusion delineates future research objectives and identifies emerging patterns. © 2023 ieee.

关键词： CP-SG Testing cyber-Physical-Smart Grids cybersecurity Industrial control systems Performance Studies Power Grid Security Verification Smart Grids Testbed Applications and Testbed Interconnectivity Vulnerability Testing

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Mitigating Denial of Service Attacks through Machine Learning-based Intrusion Detection

Mitigating Denial of Service Attacks through Machine Learnin...

引用

2024 ieee international conference on intelligent systems and Advanced Applications, ICISAA 2024

作者： Hussian, Mohammed Ashraf Vasantha, S. Ala, Sowmya Uma Mageswari, R. Pendam, Keerthi Rame, Swetha Vardhaman College of Engineering Department of CSE Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India

ISBN: (纸本)9798331539948

This research examines the application of twelve leading Machine Learning (ML) techniques, utilizing the Pycaret module, to effectively analyze Distributed Denial of Service (DDoS) attacks. The objective is to develop a highly efficient methodology that facilitates the rapid detection, mitigation, and prevention of such cyber-attacks by thoroughly analyzing incoming network traffic and packet patterns. In comparison to conventional ML models like Random Forest, Decision Tree, and Gradient Boosting, the study's findings highlight that Pycaret, especially when integrated with the XGBoost model, significantly improves the accuracy and speed of DDoS detection. This approach plays a crucial role in preserving the availability, integrity, and confidentiality of cloud services, thereby minimizing financial losses, reputational damage, and strengthening overall cybersecurity in today's fast-paced digital world. © 2024 ieee.

关键词： cyber attacks

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Fuzzy Based Web automation for Smart Power Supply controller 3

Fuzzy Based Web Automation for Smart Power Supply Controller

引用

3rd international conference on Communication, control, and intelligent systems, CCIS 2024

作者： Singhai, Ankur Pandey, Sudhakar Agrawal, Deepika Nit Raipur C.G Information Technology India

ISBN: (纸本)9798331528201

Home automation is becoming increasingly important in today's world, with new technologies focusing on providing better user experience and reduced energy consumption. Also, the opportunity of working from home is becoming more common in professional settings, creating a greater need for home automation. An important aspect of such systems is represented by the lighting system, which provides enhanced convenience and efficiency. The present paper focuses on the automation of lighting as a development process that can be done both locally and via the web. The fuzzy logic-based local automation requires three key inputs: the previous light intensity is between 0% - 100%, the relative work mode is between 0 to 24H, and the temperature is between 0°C to 40°C. Time is categorized into working, relaxing, and sleeping mode. The system optimizes lighting based on user behavior and environmental factors to increase energy efficiency. As lighting devices proliferate, power consumption rises. This paper designs an intelligent system using fuzzy logic to manage lighting in smart buildings. Inputs include light intensity measured by an LDR sensor, work culture based on daily phases, and human presence detected by a PIR sensor. The system increases and decreases lighting adjustment when the intensity and the work culture values differ and it sends the data to an ESP8266 Node Wi-Fi microcontroller through a monitoring that is used by the real-time monitoring via an IoT based Web Monitoring tool. Therefore, this system facilitates early warnings of changes in remote parameters, which allow adjustment of the lighting. It can be concluded from the results that the latter are moderate. Technological progress opens the possibility of creating customizable and automated home systems. Smart lighting is an essential part of such systems as it has many advantages, such as being automatic, customizable and user-friendly. The given study is devoted to the issue of the optimization of a home

关键词： Microcontrollers

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Abnormal Network Traffic Detection Algorithm Based on Improved Convolutional Neural Network

Abnormal Network Traffic Detection Algorithm Based on Improv...

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2024 international conference on Interactive intelligent systems and Techniques, IIST 2024

作者： Chen, Lu Zhang, Tao Ma, Yuanyuan Chen, Mu State Grid Smart Grid Research Institute Co. Ltd Protection and Monitoring Technology Nanjing210003 China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China

ISBN: (纸本)9798350374421

With the increasing complexity, automation, and intelligence of network attacks, new types of attacks are constantly emerging in the network, posing great challenges to network attack detection and timely response based on prior rules. In order to more effectively and accurately identify abnormal traffic, a network abnormal traffic detection algorithm based on improved convolutional neural networks is proposed. The algorithm first inputs the key features of traffic anomaly monitoring, converts them into color images through visualization technology, extracts higher dimensional features, and then uses neural structure search technology to find a lightweight convolutional network structure with high accuracy and less time consumption. The optimal model is used to output the type of traffic anomaly. The results indicate that the algorithm has a higher accuracy and the shortest classification time for a single image. © 2024 ieee.

关键词： Convolutional neural networks

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A Review of Monocular Visual Localization 1

A Review of Monocular Visual Localization

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1st international conference on cyber-Energy systems and intelligent Energy, ICCSIE 2022

作者： Li, Ran Song, Ruizhuo University of Science and Technology School of Automation and Electrical Engineering Beijing China

ISBN: (纸本)9781665452151

Visual measurement and positioning by camera has been widely used in all kinds of places, positioning effect has been closely paid attention to by researchers. Visual localization can be roughly divided into two categories: monocular vision and binocular vision. In the part of monocular vision, the depth research method of monocular vision is mainly introduced. The deep learning method based on unsupervised model is the most mainstream research method in the academic field at present, and it is also widely used. The problems encountered in today's widely used mainstream methods will have to be solved in the future. © 2023 ieee.

关键词： Binocular vision

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