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

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Lightweight Authentication and Key Establishment Scheme for Cloud-network-End Integrated System

Lightweight Authentication and Key Establishment Scheme for ...

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International conference on Future Communications and networks (FCN)

作者： Tian, Chuang Zhang, Junwei Li, Teng Ma, Zhuo Shen, Yulong Ma, Jianfeng Xidian Univ Sch Cyber Engn Xian Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9798350396041;9798350396034

The cloud-network-end integrated system is the inevitable result of integrating cloud-based Internet of Things (IoT) systems and new-generation network communication architectures. It can achieve wide-area coverage to enable efficient control and secure communication between various manned/unmanned end systems. For example, as a typical application in such systems, cloud-based Unmanned aerial vehicles (UAVs) surpass the payload limitations of traditional unmanned end systems by leveraging the computing and storage capabilities of cloud servers. This enables a significant enhancement in the diversity and complexity of various types of manned/unmanned end systems, leading to promising development prospects. In particular, the establishment and development of the space-air-ground integrated network (SAGIN) has greatly expanded and improved the communication range and quality between end systems. With a control paradigm centered on cloud servers, it can effectively meet the control requirements of various end systems in all areas and at all times. And further achieve secure data sharing among multiple end systems, as well as secure collaboration between multiple end systems. This paper proposes two lightweight authentication and key establishment (AKE) protocols for two typical scenarios of cloud-network-end integration systems to ensure secure communication between end systems based on SM2. We briefly analyze the correctness of our schemes and conduct a basic performance analysis. The results show that our schemes are reliable, effective, and very suitable for use in cloud-network integrated systems.

关键词： Cloud-network-End Integrated System Authentication and Key Establishment Protocol SM2

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In-Situ Model Validation for Continuous Processes Using In-network Computing 7

In-Situ Model Validation for Continuous Processes Using In-N...

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IEEE 7th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Kunze, Ike Scheurenberg, Dominik Tirpitz, Liam Geisler, Sandra Wehrle, Klaus Rhein Westfal TH Aachen Commun & Distributed Syst Aachen Germany Rhein Westfal TH Aachen Inst Automat Control Aachen Germany Rhein Westfal TH Aachen Data Stream Management & Anal Aachen Germany

ISBN: (纸本)9798350363029;9798350363012

The advancing industrial digitalization enables evolved process control schemes that rely on accurate models learned through data-driven approaches. While they provide high control performance and are robust to smaller deviations, a larger change in process behavior can pose significant challenges, in the worst case even leading to a damaged process plant. Hence, it is important to frequently assess the fit between the model and the actual process behavior. As the number of controlled processes and associated data volumes increase, the need for lightweight and fast reacting assessment solutions also increases. In this paper, we propose CIVIC, an in-network computing-based solution for Continuous In-situ Validation of Industrial control models. In short, CIVIC monitors relevant process variables and detects different process states through comparison with a priori knowledge about the desired process behavior. This detection can then be leveraged to, e.g., shut down the process or trigger a reconfiguration. We prototype CIVIC on an Intel Tofino-based switch and apply it to a lab-scale water treatment plant. Our results show that we can achieve a high detection accuracy, proving that such monitoring systems are feasible and sensible.

关键词： Plant shutdowns

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Heuristic Evolutionary Optimization for control and Management of Renewable-Based Hybrid Microgrids 6

Heuristic Evolutionary Optimization for Control and Manageme...

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IEEE 6th International conference on Direct Current Microgrids (ICDCM)

作者： Moghassemi, Ali Timilsina, Laxman Scruggs, Douglas Arsalan, Ali Rahman, S. M. Imrat Khan, Asif Ahmed Ciftci, Okan Papari, Behnaz Ozkan, Gokhan Edrington, Christopher S. Clemson Univ Holcombe Dept Elect & Comp Engn Clemson SC 29634 USA Clemson Univ Dept Automot Engn Greenville SC 29607 USA Clemson Univ Dominion Energy Innovat Ctr N Charleston SC 29405 USA

ISBN: (纸本)9798350363647;9798350363630

The surge in renewable energy and distributed generation necessitates advanced control systems for network performance enhancement. However, the lack of a standardized evaluation framework hampers comparisons, especially in complex hybrid networks. Hybrid energy systems offer greener and more reliable networks but require sophisticated control methods. To address this challenge, research explores novel linear and nonlinear techniques. This overview focuses on heuristic evolutionary optimization methods for Microgrids, covering AC, DC, and hybrid AC-DC systems, highlighting current research and future control requirements.

关键词： Microgrids Energy Storage Devices Uncertainty Optimization control Management System

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BICC: Optimizing Sensor network performance Via an Efficient Bioinspired Iterative Approach with Congestion control 3rd

BICC: Optimizing Sensor Network Performance Via an Efficient...

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3rd International conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Mutneja, Lovely S. Harkut, Dinesh G. Thakar, Prachi D. PRMCEAM Badnera India

ISBN: (纸本)9789819719457

Sensor networks are the backbone of emerging Internet of Things (IoT) ecosystems, serving as critical components in various applications ranging from environmental monitoring to smart cities. However, the Quality of Service (QoS) in sensor networks is compromised by issues such as communication delay, energy consumption, and throughput limitations. To address these challenges, this paper introduces a novel optimization algorithm called Ant Lion Grey Wolf Optimizer (ALGWO). Inspired by the foraging behaviour of ant lions and the hunting strategies of grey wolves, ALGWO aims to optimize both temporal and spatial routing in sensor networks. It uses spatial node features, and temporal network parameters to optimize routing performance under real-time scenarios. The algorithm deploys bioinspired techniques for iterative search and congestion control mechanisms to enhance network performance. Our experiments demonstrate remarkable improvements in QoS parameters: an 8.3% reduction in communication delay, a 4.9% decrease in energy consumption, and a 10.4% boost in throughput when compared to recently proposed routing models. The findings indicate that ALGWO provides a robust and efficient framework for optimizing sensor network performance, holding significant promise for real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Congestion control (communication)

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LyZNet with control: Physics-Informed Neural network control of Nonlinear systems with Formal Guarantees 8

LyZNet with Control: Physics-Informed Neural Network Control...

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8th IFAC conference on Analysis and Design of Hybrid systems (ADHS)

作者： Liu, Jun Meng, Yiming Zhou, Ruikun Univ Waterloo Dept Appl Math Waterloo ON Canada Univ Illinois Coordinated Sci Lab Urbana IL USA

Optimal control for high-dimensional nonlinear systems remains a fundamental challenge. One bottleneck is that classical approaches for solving the Hamilton-Jacobi-Bellman (HJB) equation suffer from the curse of dimensionality. Recently, physics -informed neural networks have demonstrated potential in overcoming the curse of dimensionality in solving certain classes of PDEs, including special cases of HJB equations. However, one perceived limitation of neural networks is their lack of formal guarantees in the solutions they provide. To address this issue, we have built LyZNet, a Python tool that combines physics-informed learning with formal verification. The previous version of the tool demonstrated the capability for stability analysis and region of attraction estimates. In this paper, we present the tool for solving optimal control problems. We expand the functionalities of the tool to support the formulation and solving of optimal control problems for control-affine systems via physics-informed neural network policy iteration (PINN-PI). We outline the methodology that enables the learning and verification of PINN for optimal stabilization tasks. We demonstrate with a classical control example that the learned optimal controller indeed has significantly improved performance and verifiable regions of attraction. Copyright (c) 2024 The Authors.

关键词： Neural networks formal verification nonlinear control optimal control policy iteration

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Development and Assessment of Resource Management Solutions for Throughput Enhancement in a RIS-aided Mobile network

Development and Assessment of Resource Management Solutions ...

引用

Joint European conference on networks and Communications & 6G Summit (EuCNC/6G Summit)

作者： Agarwal, Sakshi Das, Kallol Litjens, Remco TNO Dept Networks The Hague Netherlands Delft Univ Technol Fac EEMCS Delft Netherlands

ISBN: (纸本)9798350344998;9798350345001

Reconfigurable Intelligent Surfaces (RIS) stand out among the key technologies driving 6G mobile network development. In this paper, we develop and assess radio resource management solutions aimed to exploit the potential of RIS deployments for coverage and throughput enhancement for indoor users in 6G mobile networks. We introduce two heuristic algorithms that jointly control the cell-RIS-user association, user scheduling, transmit beamforming and the RIS's reflective configuration, and compare these algorithms against a RIS-free benchmark. Simulation results are presented to (i) demonstrate the promising potential of RIS deployments in multi-cell/multiuser scenarios;(ii) reveal the inherent trade-off between coverage and throughput enhancement;and (iii) show the performance impact of distinct RIS deployment locations. Our study provides valuable insights for efficiently leveraging RIS in evolving mobile network architectures.

关键词： Wireless networks

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Implementation of Neural network controller for Grid Connected Wind-Solar PV Charging Station 2

Implementation of Neural Network Controller for Grid Connect...

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2nd International conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Gorla, Koteswara Rao Krishnamoorthy, Vinoth Ramesh, S. Karunanithi, K. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Sch Elect & Elect Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

This work proposes a novel approach to enhance grid-connected wind-solar PV charging stations that face challenges like fluctuating energy supply, inefficient resource usage, and the necessity for adaptive real-time control. Traditional control methods like PI controllers, often fall short in optimizing the system performance under the dynamic conditions, resulting in inadequate power supply for EV charging. To tackle these challenges, this study proposes a novel approach by employing Neural network (NN) controllers to enhance grid-connected wind-solar PV charging stations' operation. NN controllers dynamically adjust charging station operations based on real-time data inputs, offering superior adaptability and efficiency. By integrating wind and solar power generation with intelligent NN control mechanisms, the system adeptly responds to varying environmental conditions and grid demands, ensuring more effective utilization of renewable energy sources. The proposed NN controller-based system targets enhancing the reliability, sustainability, and economic feasibility of grid-connected charging stations. Simulations showcase the effectiveness and stability of this approach in integrating renewable energy into transportation infrastructure. performance evaluation can be conducted using Matlab/Simulink Software.

关键词： Solar Wind Grid Electric Vehicle Battery Inverter DC to DC converters PI controller NN controller

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An Optimized Inverse Neural network control Augmented with Feedback PI controller for Time-Varying systems 3

An Optimized Inverse Neural Network Control Augmented with F...

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3rd International conference on Communication, control, and Intelligent systems, CCIS 2024

作者： Daksh, ManMahendra Singh Mishra, Puneet Birla Institute of Technology and Science Department of Electrical and Electronics Engineering Rajasthan Pilani333031 India

ISBN: (纸本)9798331528201

Linear proportional-integral-derivative (PID) controllers often fail to provide adequate control performance for nonlinear and time-varying systems due to their constant parametric nature. To address the challenges posed by nonlinear and time-varying systems in their regulation, intelligent controllers using neural networks (NN) and fuzzy logic have been vastly investigated. Out of these techniques, NN-based controllers are more popular for the design of indirect adaptive control schemes owing to their immense learning capability. Yet, these NN controllers may fail to perform well if their hyperparameters, typically learning rate and initial weights, are not designed properly. The control performance may worsen if these NNs fail to mimic or learn the system's dynamics. To address this issue, the current work explores the possibility of employing bio-inspired optimization to tune the essential hyperparameters of NN, along with the inclusion of a PI controller in the loop to compensate for the unidentified dynamics. Through the extensive simulation studies carried out in this work, it was found that the learning capability of the INN assisted by a feedback PI controller, i.e. (INN+PI) controller, could achieve superior performance compared to INN only. This paper also compares the ability of the designed INN+PI controller with only the PI controller in terms of set point tracking, the controller's output on the basis of significant decrement in MSE and IAE, and elucidates the simulation results. © 2024 IEEE.

关键词： Time varying control systems

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EFFICIENT HIGH-performance BARK-SCALE NEURAL network FOR RESIDUAL ECHO AND NOISE SUPPRESSION 49

EFFICIENT HIGH-PERFORMANCE BARK-SCALE NEURAL NETWORK FOR RES...

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49th IEEE International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Seidel, Ernst Mowlaee, Pejman Fingscheidt, Tim Tech Univ Carolo Wilhelmina Braunschweig Inst Commun Technol Schleinitzstr 22 D-38106 Braunschweig Germany GN Audio AS Lautrupbjerg 7 DK-2750 Ballerup Denmark

ISBN: (纸本)9798350344868;9798350344851

In recent years, the introduction of neural networks (NNs) into the field of speech enhancement has brought significant improvements. However, many of the proposed methods are quite demanding in terms of computational complexity and memory footprint. For the application in dedicated communication devices, such as speakerphones, hands-free car systems, or smartphones, efficiency plays a major role along with performance. In this context, we present an efficient, high-performance hybrid joint acoustic echo control and noise suppression system, whereby our main contribution is the post-filter NN, performing both noise and residual echo suppression. The preservation of nearend speech is improved by a Bark-scale auditory filterbank for the NN postfilter. The proposed hybrid method is benchmarked with state-of-the-art methods and its effectiveness is demonstrated on the ICASSP 2023 AEC Challenge blind test set. We demonstrate that it offers high-quality nearend speech preservation during both double-talk and nearend speech conditions. At the same time, it is capable of efficient removal of echo leaks, achieving a comparable performance to already small state-of-the-art models such as the end-to-end DeepVQE-S, while requiring only around 10% of its computational complexity. This makes it easily realtime implementable on a speakerphone device.

关键词： speech enhancement acoustic echo control deep neural network residual echo suppression noise reduction

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From Unstable Electrode Contacts to Reliable control: A Deep Learning Approach for HD-sEMG in Neurorobotics

From Unstable Electrode Contacts to Reliable Control: A Deep...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Tyacke, Eion Gupta, Kunal Patel, Jay Katoch, Raghav Atashzar, S. Farokh Carnegie Mellon Univ Pittsburgh PA 15213 USA NYU New York NY 10012 USA

ISBN: (纸本)9798350384581;9798350384574

In the past decade, there has been significant advancement in designing wearable neural interfaces for controlling neurorobotic systems, particularly bionic limbs. These interfaces function by decoding signals captured non-invasively from the skin's surface. Portable high-density surface electromyography (HD-sEMG) modules combined with deep learning decoding have attracted interest by achieving excellent gesture prediction and myoelectric control of prosthetic systems and neurorobots. However, factors like small electrode size and unstable electrode-skin contacts make HD-sEMG susceptible to pixel electrode drops. The sparse electrode-skin disconnections rooted in issues such as low adhesion, sweating, hair blockage, and skin stretch challenge the reliability and scalability of these modules as the perception unit for neurorobotic systems. This paper proposes a novel deep-learning model providing resiliency for HD-sEMG modules, which can be used in the wearable interfaces of neurorobots. The proposed 3D Dilated Efficient CapsNet model trains on an augmented input space to computationally 'force' the network to learn channel dropout variations and thus learn robustness to channel dropout. The proposed framework maintained high performance under a sensor dropout reliability study conducted. Results show conventional models' performance significantly degrades with dropout and is recovered using the proposed architecture and the training paradigm.

关键词： Myoelectrically controlled prosthetics

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