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检索条件"任意字段=Conference on Internet Quality and Performance and Control of Network Systems"

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Fault Diagnosis and Fault Tolerant control Algorithm based on Neural network in Modular Multi-level Converter (MMC) 3

Fault Diagnosis and Fault Tolerant Control Algorithm based o...

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3rd International conference on Electronics and Renewable systems, ICEARS 2025

作者： Xie, Jun Qian, Xiaodong Huang, Qiong Anhui Vocational and Technical College Anhui Hefei230011 China

ISBN: (纸本)9798331509675

The state-of-the-art modulation strategy of MMC is closely related to the quality of its output voltage waveform and system loss, and is related to whether the entire MMC system can maintain efficient and stable operation. In fact, all multi-level modulation technologies can be applied in MMC systems. To deal with the challenges, this study proposes a novel neural network-based MMC fault diagnosis (FD) and fault-tolerant efficient control (FTEC) algorithm. First, initial step for the modeling is to conduct the comprehensive analysis of the MMC architecture characteristics and prone faults. In particular, considering the schemes of open circuit (OC) and short circuit (SC) related faults, a model based on recurrent neural network (RNN) is established to extract and classify fault features, and the network structure of deep learning is improved. At the same time, an improved CNN model is applied to process the voltage signal of MMC sub-module. In improved part, efficient Softmax function is used to realize the probability distribution of multi-fault classification. The entire fault detection and analysis framework includes the following points: data collection, data pre-processing, network parameter initialization, fault feature training and classification decision. The experiment uses a self-built MMC simulation model to test the performance. At same time, in specific experiment, high voltage and low voltage are displayed independently to achieve a comprehensive analysis. © 2025 IEEE.

关键词： Recurrent neural networks

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Input-to-State Stable Coupled Oscillator networks for Closed-form Model-based control in Latent Space 38

Input-to-State Stable Coupled Oscillator Networks for Closed...

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38th conference on Neural Information Processing systems, NeurIPS 2024

作者： Stölzle, Maximilian Della Santina, Cosimo Department of Cognitive Robotics Delft University of Technology Netherlands

Even though a variety of methods have been proposed in the literature, efficient and effective latent-space control (i.e., control in a learned low-dimensional space) of physical systems remains an open challenge. We argue that a promising avenue is to leverage powerful and well-understood closed-form strategies from control theory literature in combination with learned dynamics, such as potential-energy shaping. We identify three fundamental shortcomings in existing latent-space models that have so far prevented this powerful combination: (i) they lack the mathematical structure of a physical system, (ii) they do not inherently conserve the stability properties of the real systems, (iii) these methods do not have an invertible mapping between input and latent-space forcing. This work proposes a novel Coupled Oscillator network (CON) model that simultaneously tackles all these issues. More specifically, (i) we show analytically that CON is a Lagrangian system - i.e., it possesses well-defined potential and kinetic energy terms. Then, (ii) we provide formal proof of global Input-to-State stability using Lyapunov arguments. Moving to the experimental side, we demonstrate that CON reaches SoA performance when learning complex nonlinear dynamics of mechanical systems directly from images. An additional methodological innovation contributing to achieving this third goal is an approximated closed-form solution for efficient integration of network dynamics, which eases efficient training. We tackle (iii) by approximating the forcing-to-input mapping with a decoder that is trained to reconstruct the input based on the encoded latent space force. Finally, we leverage these three properties and show that they enable latent-space control. We use an integral-saturated PID with potential force compensation and demonstrate high-quality performance on a soft robot using raw pixels as the only feedback information. © 2024 Neural information processing systems foundation. All rights

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5th International conference on Electrical and Electronics Engineering, ICEEE 2024

5th International Conference on Electrical and Electronics E...

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5th International conference on Electrical and Electronics Engineering, ICEEE 2024

ISBN: (纸本)9789819790364

The proceedings contain 81 papers. The special focus in this conference is on Electrical and Electronics Engineering. The topics include: Design of a Single-Axis Solar Tracking System with a PI controller Implemented in MATLAB/Simulink;Reduce Multiple Power quality Issues with UPQC by Using UVTG and PQ Current control Techniques Based on Snake Optimization;Utilize UPQC to Reduce Power quality Issues by SRF control Strategies Based on Snake Optimization;Integrating Industrial PV-Battery systems with Utility Grid: Techno Economic Analysis for an Industry;AI Based Phase Identification to Integrate Distributed Energy Resources in Distribution network;optimal Nearest Level control Modulation Technique for 25-Level Asymmetrical Hybrid Multilevel Inverter Topologies with Reduced Switch Stress;performance Investigation of a Vacuum-Enhanced Direct Contact Membrane Distillation Coupled with a Photovoltaic-Thermal System;utilization of Redundant Switching States in Asymmetric Cascaded H-bridge Multilevel Inverter;Modular DC-DC Converter for Battery Bank Interfacing in DC Microgrid;the performance Effect of Seasonality Feature in Solar Photovoltaic Power Prediction Using Machine Learning;Hybrid Power System Simulation and Modeling for PV and Wind;Analysis of Different Energy Storage Characteristic Under Load Using BLDC Motor of Electric Scooter;advanced Federated Spectrum Busting: Federated Learning-Based Transmitter Identification System for Conventional Warfare and Counter Terrorism;Leveraging Synthetic Data and LSTM networks for Reliable RUl Estimation of EV battery;Thermal Analysis of HTS Tape for SMES-Integrated Wind Turbines During Voltage Dips;maximizing Customer Satisfaction and Grid Resilience Through a Three-Tiered Energy Bidding Framework;Techniques to Rapid and Economic Prototyping of PWM Inverters;Analyzing the Impacts on Stability of IBR-Dominated Power systems Under Evolving Composite Loads.

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performance Impact of control Flow Enforcement Technology (CET) 25

Performance Impact of Control Flow Enforcement Technology (C...

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25th conference on Innovation in Clouds, internet and networks (ICIN)

作者： Kucab, Michal Borylo, Piotr Cholda, Piotr AGH Univ Sci & Technol Akamai Technol Poland Krakow Poland AGH Univ Sci & Technol Inst Telecommun Krakow Poland

ISBN: (纸本)9781728186887

The application flow integrity plays a critical role in proper system behavior. Unfortunately, taking over the control of the flow is still difficult to detect. We evaluate the performance of hardware support for the application flow integrity control. A solution is based on platform capabilities and is said to overcome existing limitations met in similar systems. Our results show that overhead is acceptable and with minimal effort, the solution can be successfully used in practical applications.

关键词： CET ROP CFI CFG

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quality of Service Enhancement for IoT-Based Smart Office System Using Ad Hoc On-Demand Distance Vector-Smart control Ration control Algorithm (AODV-SRCA) 17th

Quality of Service Enhancement for IoT-Based Smart Office Sy...

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17th International conference on Computational Intelligence in Security for Information systems, CISIS 2024 and the 15th International conference on EUropean Transnational Education, ICEUTE 2024

作者： CheSuh, Lawrence Nforh Fernández-Díaz, Ramón Ángel Aveleria-Mata, Jose Bayón-Gutiérrez, Martín Martín, Sergio Rubio Alaiz-Moretón, Héctor Department of Electric Systems and Automatics Engineering Universidad de Leon Campus de Vegazana S/N León24071 Spain RIASC Research Institute of Applied Sciences in Cybersecurity Universidad de León. MIC Campus de Vegazana S/N León24071 Spain

ISBN: (纸本)9783031750151

The performance of an application in IoT settings heavily relies on the quality of service (QoS) parameters, which play a crucial role. Due to their diverse nature, attaining optimal QoS parameters presents a significant challenge. This study presents an innovative approach to enhance QoS parameters in IoT by leveraging machine learning (ML) and Blockchain methodologies. A simulation of the IoT network integrated with Blockchain technology is conducted using an NS2 simulator to improve QoS. Various QoS parameters, including delay, throughput, packet delivery ratio, and packet drop, are thoroughly examined. Subsequently, the QoS data is analysed using different ML algorithms such as Naive Bayes (NB), Decision Tree (DT), and Ensemble learning techniques. The findings indicate that the Ensemble classifier achieves the highest classification accuracy of 83.74% compared to NB and DT classifiers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： quality of service

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Advanced Process control in Manufacturing Using IoT Devices and Artificial Neural networks 4

Advanced Process Control in Manufacturing Using IoT Devices ...

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4th International conference on Sustainable Expert systems, ICSES 2024

作者： Sumathi, V. Ramesh, S. Basavaraddi, Chethan Chandra S. Visumathi, J. Ishwarya, M.V. Srinivasan, S. Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India R&d Don Bosco Institute of Technology Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Optimization of industrial activities is significantly helped by Advanced Process control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural networks (ANNs) and the internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. Recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems. © 2024 IEEE.

关键词： Adaptive control systems

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A Survey on IoT-Based Monitoring and control Technologies 17

A Survey on IoT-Based Monitoring and Control Technologies

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17th IEEE International Scientific conference on Informatics, INFORMATICS 2024

作者： Witczak, Dawid Szymoniak, Sabina Czestochowa University of Technology Department of Computer Science Czestochowa Poland

ISBN: (纸本)9798350387674

The internet of Things (IoT) is an ever-advancing technology that connects heterogeneous products and systems to a worldwide network and makes almost every aspect of daily life. The system has also become integral to several sectors, including domestic appliances, transport, healthcare, and agriculture. IoT systems use different communication protocols. The wireless standards are Wi-Fi, Bluetooth, and Zigbee, while the wired protocols are Modbus and CAN. GSM allows for remote monitoring and device management installed at sites. On the other hand, 5G provides higher bandwidths with low latencies, which is why it is ideal for IoT applications. The IoT systems will play an increasingly important role in resource optimisation, cost reduction, and improving quality of life. This work examines the IoT-based monitoring and management systems literature, comparing solutions and identifying possible future development paths. The article aims to closely analyse the current status of IoT technology, identify the most effective solutions, and solve the problems to enable a better future of this technology, benefiting society and the environment. Identifying future development trajectories will help us focus research and investment on the most promising sectors, benefiting the technology industry, society, and the natural environment. © 2024 IEEE.

关键词： Cost reduction

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5th International conference on Electrical and Electronics Engineering, ICEEE 2024

5th International Conference on Electrical and Electronics E...

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5th International conference on Electrical and Electronics Engineering, ICEEE 2024

ISBN: (纸本)9789819791118

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Deep Causal Domain Generalization network for Human Action Recognition in internet of Behaviors 21

Deep Causal Domain Generalization Network for Human Action R...

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21st International conference on networking, Sensing and control, ICNSC 2024

作者： Zhong, Ruirui Feng, Yixiong Hu, Bingtao Zhang, Zhifeng Song, Xiuju Tan, Jianrong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798350365221

The internet of Behaviors (IoB) aims to leverage advanced sensing and computing technologies to understand human behaviors, and human action recognition is an essential task in IoB. However, recognizing human actions across varied and unseen domains remains a challenging problem, largely due to domain shifts that degrade the performance of conventional models. To address the aforementioned issue, a Deep Causal Domain Generalization network (DCDGNet) is designed to enhance the generalizability of the human action recognition model. Specifically, a structured causal model for the data generation of human motion is constructed, where human motion signals are decoupled into action class-related causal factors and domain shift-related non-causal factors. DCDGNet integrates a human domain encoder, causal encoder, domain discriminator, motion reconstruction decoder, and action classifier, contributing to the model's ability to disentangle causal factors from non-causal ones from domain-specific features and achieve domain-invariant representations. Comprehensive experiments confirm that DCDGNet significantly outperforms existing state-of-the-art methods in terms of both accuracy and generalizability. © 2024 IEEE.

关键词： Signal encoding

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ImageFed: Practical Privacy Preserving Intrusion Detection System for In-Vehicle CAN Bus Protocol 9

ImageFed: Practical Privacy Preserving Intrusion Detection S...

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9th IEEE International conference on Big Data Security on Cloud (BigDataSecurity) / 9th IEEE International conference on High performance and Smart Computing (IEEE HPSC) / 9th IEEE International conference on Intelligent Data and Security (IEEE IDS)

作者： Taslimasa, Hamideh Dadkhah, Sajjad Pinto Neto, Euclides Carlos Xiong, Pulei Iqbal, Shahrear Ray, Suprio Ghorbani, Ali A. UNB Fac Comp Sci Fredericton NB Canada Natl Res Council Canada Cybersecur Ottawa ON Canada Natl Res Council Fredericton New Brunswick NJ USA

ISBN: (纸本)9798350312935

In vehiclenetworks handle the communication among Electronic control Units (ECUs) and sensors with a serial protocol called controller Area network (CAN), which takes advantage of a broadcast mechanism liar transferring data. However, the lack of authentication and encryption in CAN bus might lead to the vehicle being compromised by an untrusted agent. Meanwhile, AI -based network Intrusion Detection systems (NIDS) introduced in the literature require a centralized cloud server to undertake the resource -consuming task of processing network data, which also occupies the bandwidth while transferring data from vehicles to the remote server. Moreover, many car manufacturers avoid sharing information with the remote server to prevent further exposure of vehicles' private data. We utilize Federated Learning (FL) as a suitable solution to previous challenges, where data is trained locally on the vehicle side, and partially trained models are sent to the server. Nevertheless, private training inside the vehicle requires low memory utilization while achieving low latency and high performance compared to Centralized Learning (CL) approaches. In this study, we propose a practical privacy-preserving IDS (ImageFed) approach by employing federated Convolutional Neural network (CNN). To evaluate the robustness of ImageFed in real -world applications, we investigate two scenarios that may cause performance degradation in FL, such as non-independent and identically distributed (non-iid) clients and scarcity of training data during the process. The resulting analysis proves the robustness of ImageFed while delivering an average 99.54% fl -score, 99.87% accuracy, and low detection latency.

关键词： Terms internet of Vehicles (IoV) Security Intrusion Detection systems (IDS) Federated Learning (FL) I)eep Learning (DL) Convolutional Neural networks (CNN)

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