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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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Analysis of Distributed Photovoltaic Integration Impact on Distribution network Using MATLAB-Based Newton-Raphson Simulation 24

Analysis of Distributed Photovoltaic Integration Impact on D...

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2nd International conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Li, Yangfan Zhang, Xin Yang, Peihong Zhang, Jihong Zhang, Xiaoming Zhang, Zilei Inner Mongolia University of Science and Technology Key Laboratory of Synthetical Automation for Process Industries Baotou Inner Mongolia Autonomous Region China Inner Mongolia University of Science and Technology Inner Mongolia Autonomous Region Baotou China

ISBN: (纸本)9798400710681

This paper delves into the mathematical foundations of the Newton-Raphson load flow analysis and its implementation within a MATLAB framework for simulating a 33-node distribution network. An admittance matrix is meticulously constructed, encapsulating the impedance characteristics of each branch and the power specifications of generators, thereby laying the groundwork for iterative computational processes. The nodes and branches are precisely defined by their respective contributions in terms of active and reactive power, which facilitates detailed simulations. The study intensively examines the repercussions of integrating distributed photovoltaic (PV) systems into the distribution network. It addresses three distinct dimensions of PV integration: the effects of varying capacities, the impact of different locational deployments within the network, and the influence of diverse power factors. By conducting robust simulation analyses, the research provides both a theoretical framework and a practical toolkit for anticipating and validating the operational performance of power systems under varied configurations and conditions. The insights derived from this analysis prove indispensable for the strategic planning and optimization of power systems, enhancing decision-making capabilities pertinent to system enhancements and the integration of renewable energy sources. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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An Intent-Driven Management Automation for 5G Mobile networks

An Intent-Driven Management Automation for 5G Mobile Network...

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International conference on Information networking

作者： Yoseop Ahn Jaehoon Paul Jeong Department of Computer Science & Engineering Sungkyunkwan University Suwon Republic of Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

This paper introduces a network Management System (NMS) for mobile network services in the area of 5G networks. It offers a structure authorized with Intent-Based networking (IBN). Especially, the NMS supports network intent translator, closed-loop network control, and network audit system. It is a constructive framework with system factors supporting various features in NMS in terms of network management. The structure of the proposed NMS can make the network management be automatic and efficient in 5G mobile networks. Thus, it can perform effectively various services in the 5G mobile networks, such as the data gathering of internet of Things (IoT) devices, the configuration and management of network slicing, and the quality of Service (QoS) in 5G Vehicle-to-Everything (V2X).

关键词： performance evaluation Automation 5G mobile communication network slicing quality of service systems support internet of Things

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Physics-Informed Online Learning by Moving Horizon Estimation: Learning Recurrent Neural networks in Gray-box Models 8

Physics-Informed Online Learning by Moving Horizon Estimatio...

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8th IFAC conference on Nonlinear Model Predictive control (NMPC)

作者： Lowenstein, Kristoffer Fink Bernardini, Daniele Bemporad, Alberto Fagiano, Lorenzo Politecn Milan Dipartimento Elettron Informaz & Bioingn Via Ponzio 34-5 I-20133 Milan Italy ODYS Srl Via Pastrengo 14 I-20159 Milan Italy IMT Sch Adv Studies Piazza San Francesco 19 I-55100 Lucca Italy

In Model Predictive control (MPC) closed-loop performance heavily depends on the quality of the underlying prediction model, where such a model must be accurate and yet simple. A key feature in modern MPC applications is the potential for online model adaptation to cope with time-varying changes, part-to-part variations, and complex features of the system dynamics not caught by models derived from first principles. In this paper, we propose to use a physics-informed, or gray-box, model that extends the physics-based model with a data-driven component, namely a Recurrent Neural network (RNN). Relying on physics-informed models allows for a rather limited size of the RNN, thereby enhancing online applicability compared to pure black-box models. This work presents a method based on Moving Horizon Estimation (MHE) for simultaneous state estimation and learning of the RNN sub-model, a potentially challenging issue due to limited information available in noisy input-output data and lack of knowledge of the internal state of the RNN. We provide a case study on a quadruple tank benchmark showing how the method can cope with part-to-part variations. Copyright (C) 2024 The Authors.

关键词： Learning-based MPC Nonlinear MPC Moving Horizon Estimation Physics-informed learning Adaptive MPC Recurrent Neural network Gated Recurrent Unit

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Simulation Analysis on 5G-TSN Scheduling Algorithms based on OMNeT++ 8

Simulation Analysis on 5G-TSN Scheduling Algorithms based on...

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8th International conference on Computer and Communication systems, ICCCS 2023

作者： Tang, Xiongyan Fu, Qiang Hu, Xinyu Zhu, Yuan Wu, Siyuan Wang, Jianquan Li, Wei China Unicom Joint Research Lab of China Unicom and USTB Beijing China Institute of Industrial Internet University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781665456128

The 5th Generation mobile system was featured as ultra-reliable and low-latency communications, which could support applications for industrial verticals. However, as to industrial control scenarios, it was necessary to ensure the deterministic data transmission. Therefore, 5G Rel.16 proposed Time-Sensitive network technology integrating with the cellular mobile system to enhance the deterministic transmission capability of 5G networks. In the cooperation architecture of 5G and Time-Sensitive network, the 5G system should enhance the capabilities of both the core network and access network to fulfill the cross-network transmission of time-sensitive services. As to the demands of cross 5G and TSN transmission, this paper built simulation environments for 5G-TSN cross-network transmission based on OMNeT++, which was aiming to verify the performance on latency, and throughputs of common-used scheduling algorithms of 5G systems such as proportional fairness, maximum C/I, and earliest deadline first in multi-service scenarios, as well as to verify adaption ability of scheduling to time-sensitive services in 5G system. The simulation results demonstrate that the proportional fairness algorithm can guarantee both the time delay performance of time-sensitive services and the system throughputs, and it becomes a potential scheduling algorithm for 5G-TSN cooperative transmission. © 2023 IEEE.

关键词： Scheduling algorithms

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Towards Deep Learning Based Access control using Hyperledger-Fabric Blockchain for the internet of Things 6

Towards Deep Learning Based Access Control using Hyperledger...

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6th IEEE International conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2023

作者： Rahman, Mahbubur Chen, Liang Loo, Jonathan Jie, Wei University of West London School of Computing and Engineering United Kingdom University of Hertfordshire Department of Computer Science United Kingdom

ISBN: (纸本)9798350303063

The rapid expansion of internet of Things (IoT) devices has resulted in significant progress and developments in various sectors, such as smart healthcare, self-driving vehicles, smart banking, smart home, Industry 4.0, etc. Traditional centralised access control methods are inadequate to deploy in decentralised IoT networks. Although the existing Blockchain-based access control approaches provide a better way of managing access permission for IoT systems, they are ineffective in addressing critical security gaps and preventing unauthorised access while detecting malicious anomalies. To address these two challenges in a single platform, in this paper, we propose a novel approach towards a deep-learning-based authorisation solution that is deployed within the Hyperledger-Fabric (HLF) private Blockchain. Our approach allows smart-contract to define attribute-based access control policies augmented with the Artificial Neural network (ANN) model, which can effectively identify and isolate malicious anomalies and prevent unauthorised access from malicious devices. We run experiments to evaluate our platform in terms of security and performance, and our results show positive indicators essential for addressing security issues in decentralised IoT networks. © 2023 IEEE.

关键词： Anomaly detection

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A Beamforming network for 5G/6G Multibeam Antennas Using the PCB Technology 17

A Beamforming Network for 5G/6G Multibeam Antennas Using the...

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17th European conference on Antennas and Propagation (EuCAP)

作者： Buttazzoni, G. Schettino, G. M. Fanti, A. Marongiu, E. Curreli, N. Babich, F. Comisso, M. Univ Trieste Dept Engn & Architecture Via Valerio 10 I-34127 Trieste Italy Univ Cagliari Dept Elect & Elect Engn Via Marengo 2 I-09123 Cagliari Italy Italian Inst Technol Consorzio Nazl Interuniv Telecomunicaz Via Morego 30 I-16163 Genoa Italy

ISBN: (纸本)9788831299077

This paper presents the design of a 4x4 Blass matrix for enabling beamforming operations in a four-element uniform linear antenna array (ULAA) operating at 3.5 GHz, corresponding to the first frequency band reserved for the forthcoming fifth and sixth generation (5G/6G) systems. To obtain a simple and inexpensive device, the proposed feeding network, which provides to the ULAA beam pointing capabilities towards four different directions, is entirely implemented using printed circuit board technology. The design procedure is realized adopting an extended iterative mathematical framework accounting for losses control and providing the pointing angles and the matrix coefficients. The performance of the conceived architecture is numerically investigated through full-wave simulations. The versatility, low losses, and low price of the developed microstrip network makes it suitable for both 5G/6G air/terrestrial femtocell base stations and internet of Things (IoT) cluster-head sensors/actuators.

关键词： Beamforming

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Sensor-Drift-Aware Time-Series Anomaly Detection for Climate Stations 2

Sensor-Drift-Aware Time-Series Anomaly Detection for Climate...

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2nd IEEE conference on Artificial Intelligence (CAI)

作者： Chen, Bryce Huang, Victoria Wang, Chen Natl Inst Water & Atmospher Res 301 Evans Bay Parade Wellington 6021 New Zealand

ISBN: (纸本)9798350354102;9798350354096

Sensor data collected from climate stations has been used in various scientific applications and environmental monitoring. Maintaining the data quality is essential to guarantee the reliability and accuracy of science outputs, potentially impacting many critical decision making processes. Existing sensor anomaly detection techniques are mostly designed for general purposes, and may not be suitable for climate sensors which require complex handling of seasonality, spatial relationship and sensor interdependency. Current quality control process is deficient in climate sensor drift detection, which is a slow degradation of sensor accuracy over time. Recent development of anomaly detection in climate sensor domain is limited, it's often constrained to particular sensor types, and not focused on drift detection. In this paper, we present a new drift-aware time series anomaly detection framework which leverages the spatial-temporal correlation of the climate sensor network and significantly improves climate sensor drift detection capability. Moreover, the proposed semi-supervised learning approach helps to generalise the solution for various types of sensors and anomalies. Our experiments using real-world dataset have demonstrated promising and competitive performance in regards to sensitivity, false alarm control, and computational efficiency suitable for real-time or near-real-time applications.

关键词： Anomaly Detection Climate Sensor Climate Station control Chart Drift Detection Dynamic Threshold Temporal Fusion Transformer Time Series Time Series Forecasting

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Explainable Transformer-Based Anomaly Detection for internet of Things Security 7th

Explainable Transformer-Based Anomaly Detection for Internet...

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7th EAI International conference on Safety and Security in the internet of Things, SaSeIoT 2023

作者： Saghir, A. Beniwal, H. Tran, K.D. Raza, A. Koehl, L. Zeng, X. Tran, K.P. Mirpur Pakistan International Chair in DS & XAI International Research Institute for Artificial Intelligence and Data Science Dong A University Danang Viet Nam Indian Institute of Technology Gandhinagar Gujarat Gandhinagar India International Chair in DS & XAI International Research Institute for Artificial Intelligence and Data Science Dong A University Danang Viet Nam International Chair in DS & XAI International Research Institute for Artificial Intelligence and Data Science Dong A University Danang Viet Nam University of Lille ENSAIT ULR 2461 - GEMTEX - Génie et Matériaux Textiles Lille France International Chair in DS & XAI International Research Institute for Artificial Intelligence and Data Science Dong A University Danang Viet Nam

ISBN: (纸本)9783031530272

The internet of Things (IoT) combines sensors and other small devices interconnected locally and via the internet. Specifically, IoT devices collect data from the environment through sensors, analyze it, and respond to the actual through controllers. The integration of these devices can be seen in various areas like home appliances, healthcare, control systems, etc. On the other hand, massive digital data can drive system performance, and data security is also a serious concern. Therefore, anomaly detection (AD) is necessary to prevent network security infractions and system attacks. Several Artificial Intelligence (AI)-based anomaly detection methods have been designed with higher detection performance;however, they are still "complex" models that are hard to interpret and explain. This chapter proposes a hybrid learning model for AD in IoT with Explainable Artificial Intelligence to enhance the perspective and explainable results. The proposal’s application uses a well-known traffic traces dataset (https://***/datasets/francoisxa/ds2ostraffictraces). Our code and dataset are added to https://***/himanshubeniwal/ml-xai. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： internet of things

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A Preemption Method for QoS Based on Time-Sensitive network

A Preemption Method for QoS Based on Time-Sensitive Network

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2023 International Technical conference on Circuits/systems, Computers, and Communications, ITC-CSCC 2023

作者： Kim, Jong Hun Do, Young Soo Lee, Jung Ju Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350326413

In-vehicle networks are increasingly using complex functions to enhance user convenience. This has led to a rise in both the number of Electronic control Units (ECUs) and the bandwidth between data traffic. Ethernet communication has been adopted to address this issue in a network where multimedia and control traffic coexists. However, a solution was still required to maintain quality-of-Service (QoS), which led to the emergence of the Time-Sensitive network (TSN). In this situation, TSN, which defines rules for real-time, reliability, etc., is gaining attention. In this paper, we compare the performance of Time Aware Shaper (TAS) and frame preemption methods for real-time scheduling among TSN protocols. Furthermore, we propose a method to enhance QoS by predicting overhead and flexibly selecting preemption size. © 2023 IEEE.

关键词： quality of service

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Optimized Power control Algorithm Based on Differential Game Theory in Cognitive Radio networks 41

Optimized Power Control Algorithm Based on Differential Game...

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第41届中国控制会议

作者： Shasha Zhao Lidan Qin Xiaoyu Zhou Dengyin Zhang Institute of Internet of things Nanjing University of Posts and Telecommunications Tongding Interconnection Information Co. Lt Institute of communications Nanjing University of Posts and Telecommunications

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

An optimized power control algorithm based on differential game theory is *** to the dynamic nature of network,differential game theory is applied to investigate the power control of the cognitive radio network in the proposed *** time continuity of the network is *** power control strategy selections of the secondary users(cognitive users) are modeled as a differential game *** the current income and the long-term significance of the strategy of the users are taken into *** on the proposed model,the open-loop Nash equilibrium solution is *** simulation results show that the proposed algorithm can effectively control the transmit power of the secondary user to reach a steady state,maximize the revenue of the users and improve the performance of the system.

关键词： Cognitive radio network dynamic spectrum sharing differential game power control

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