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10th International conference on Applied System Innovation, ICASI 2024

作者： Alzahrani, Ahmad Najran University Faculty of Engineering Electrical Engineering Saudi Arabia

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394924

This paper presents a novel frequency control method for static wireless power transfer (WPT) systems using neural networks. Static WPT, where transmitter and receiver distance is fixed, achieves optimal power at the resonant frequency. The study utilizes a feedforward neural network to predict and adjust frequency for maximum power output, improving control precision, response time, and accuracy over traditional methods. Validation through simulations confirms its effectiveness in enhancing WPT system performance by integrating electromagnetic principles with computational intelligence for better efficiency and control. © 2024 IEEE.

关键词： Energy transfer

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Wear reliability analysis of pantograph systems based on an optimized radial basis function neural network

Wear reliability analysis of pantograph systems based on an ...

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2023 IEEE International conference on control, Electronics and Computer Technology, ICCECT 2023

作者： Luo, Xiaojun Research and Development Institute of Northwestern Polytechnical University Shenzhen China

ISBN: (纸本)9798350399028

The wear reliability analysis model of pantograph systems is built based on a radial basis function neural network in this paper, complicated and strong nonlinear wear characteristics of pantograph systems are obtained, and the wear variational laws with external parameters are analyzed. By Comparing the optimized radial basis function neural network performance in relative errors and learning speed with BP neural network and ordinary radial basis function neural network, the optimized radial basis function neural network has strong ability of nonlinear mapping and functional approach, and obtains high prediction accuracy. The study provides a simple and feasible method for wear characteristic analysis and wear reliability design of pantograph systems in electric railroad. © 2023 IEEE.

关键词： Pantographs

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Enhanced Neural network Model Predictive control based on TLBO algorithm: Applied to speed control of PMSM

Enhanced Neural Network Model Predictive control based on TL...

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2024 International conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Stiti, Chafea Aouaichia, Abdelhadi Benrabah, Mohamed Kara, Kamel Blida1 University Laboratoire des systèmes électriques et télécommande Blida Algeria Usthb University Laboratoire de robotique Parallélisme et systemes embarqués Alger Algeria

ISBN: (纸本)9798350353754

This paper proposes an enhanced neural network model predictive control based on the teaching learning-based optimization algorithm, for the speed control of the permanent magnet synchronous motor. The objectives of this strategy are firstly, to solve the online computational problem and the nonconvex solution of model predictive control, using neural network identification and online metaheuristic optimization, secondly, to improve computational efficiency, real-time applicability and tracking accuracy of the conventional neural network model predictive control based on the teaching learning-based optimization algorithm using the proposed contribution of adding a sequence of reduction weighting coefficients to its cost function, over the prediction horizon. To assess the efficiency of the proposed control algorithm, a comparative study with the conventional neural network model predictive controller based on the teaching learning algorithm, is done. The mean square error, mean absolute error, root mean square error and computing time of the speed response of the PMSM are calculated and analyzed for the both *** obtained results demonstrate that the proposed control algorithm gives better control performance than the other controller. © 2024 IEEE.

关键词： Predictive control systems

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Interval Adaptive Predictive control of Nonlinear systems Based on LSTM network 42

Interval Adaptive Predictive Control of Nonlinear Systems Ba...

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42nd Chinese control conference, CCC 2023

作者： Guan, Shouping Zhao, Qixin Wang, Xinzong College of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9789887581543

This paper presents a formulation of interval control for nonlinear systems based on Long Short-Term Memory network (LSTM) and using predictive control to solve the nonlinear interval control problem. The gradient descent with momentum is used as the learning algorithm to train the LSTM, which is used as the nonlinear system prediction model. The set interval is introduced into the optimized performance index and the gradient descent with momentum is applied to obtain the control variable in the performance index to realize the interval control, and the LSTM online learning algorithm is used to replace the feedback correction to realize the adaptive control. Furthermore, the system stability for the proposed formulation is proved based on Lyapunov theorem. The effectiveness of the proposed algorithm is proved by simulation and comparison experiments. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Long short-term memory

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Estimation of Inverse Kinematics Solutions of a 2D Planar Robotic Manipulator using Feed-Forward Neural network 5

Estimation of Inverse Kinematics Solutions of a 2D Planar Ro...

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5th International conference on IoT Based control networks and Intelligent systems, ICICNIS 2024

作者： Navya, M. Muralidhara Jain, Nirmith Pai, Vaishnavi Rao, Meghana Rao, Aparna Nitte Karkala India Nitte Karkala India

ISBN: (纸本)9798331518097

This study explores the application of machine learning algorithms, including Linear Regression, Decision Trees, and a Feed-Forward Neural network, to solve the inverse kinematics problem for a 2R planar robotic arm. The dataset was generated using inverse kinematics equations for various end-effector positions within a rectangular workspace, resulting in 10,011 data points. The Neural network model, consisting of an input layer, a hidden layer, and an output layer, was trained to predict joint angles from the end-effector coordinates. The performance of the Neural network model was compared to traditional regression models using metrics such as Mean Square Error, Root Mean Square Error, Mean Absolute Error, and R2. The Neural network model outperformed both Linear Regression and Decision Trees, achieving a Mean Square Error of 0.000031 on the validation set. The model was tested on new data from a semicircular path, demonstrating strong generalization capabilities. This method presents a promising approach to real-time, accurate robotic control without the need for explicit inverse kinematic equation derivation. © 2024 IEEE.

关键词： Mean square error

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Transmission control for Stability of Aloha networks with Multiple Transmitter-Receiver Pairs 59

Transmission Control for Stability of Aloha Networks with Mu...

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59th Annual IEEE International conference on Communications (IEEE ICC)

作者： Yang, Yunshan Dai, Lin City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9781728190549

Slotted Aloha has been widely adopted in various communication systems, while its stability performance has long been observed as being sensitive to the setting of transmission probabilities. For stability analysis of Aloha networks with multiple transmitter-receiver (T-R) pairs, the focus of previous studies has been placed on characterization of the maximum input rate of T-R pairs, below which the network can be stabilized under any given topology. With a fixed and identical transmission probability for all T-R pairs, nevertheless, network stability is often found to be unachievable, as part of transmitters would become unstable regardless of how small the traffic input rate is. As we will demonstrate in this paper, to stabilize the whole network, transmission probabilities of T-R pairs should be properly adjusted according to their traffic input rates and locations. Specifically, by establishing the fixed-point equations of the steady-state probabilities of successful transmissions of Head-of-Line (HOL) packets, the service rates of transmitters' queues can be obtained, based on which the operating region of transmission probabilities for achieving stability can further be characterized. Simulation results corroborate that all the T-R pairs can be stabilized by choosing transmission probabilities from the region, which highlights the importance of proper transmission control based on the traffic input rates and network topology for Aloha networks.

关键词： Aloha random access stability multiple transmitter-receiver pairs transmission control ad-hoc networks

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Enhanced Decision Mechanism for RAN Subslicing in Management Closed control Loop 8

Enhanced Decision Mechanism for RAN Subslicing in Management...

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8th IEEE International conference on Fog and Mobile Edge Computing (FMEC)

作者： Kulmar, Marika Muursepp, Ivo Alam, Muhammad Mahtab Tallinn Univ Technol Thomas Johann Seebeck Dept Elect Tallinn Estonia

ISBN: (纸本)9798350316971

5G radio access network (RAN) slicing enables better satisfaction of different quality of service (QoS) requirements than without network slicing. A handful of slice types are specified for various service verticals;however, the number and size of those slices is unspecified. Subslicing refers to grouping slice users into smaller groups, subpartitioning slice bandwidth, and allocating smaller bandwidth parts to smaller user groups. State of the art subslicing has been done to better satisfy the QoS requirements inside the service vertical. Slice performance improvement was not the purpose of subslicing, but the positive effect was noticeable. In this paper, the subslicing decision is done with the aim of improving slice performance. The decision mechanism for management closed control loop is proposed. The input dataset consists of 6 key performance indicators, namely slice bandwidth utilization, slice goodput (application level throughput) per one allocated resource block (RB), and slice block error ratio (BLER), both in uplink and downlink. This dataset is clustered, and the result is learned by a classifier to decide whether the slice is too large and should be split or too small and should be merged with another too small slice or subslice. The results show that by knowing the slice utilization and goodput per one allocated RB, the slice reconfiguration action regarding subslicing can be determined using machine learning tools.

关键词： subslicing performance clustering neural network

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HAQM: A Hybrid SDN controller-based Architecture for effective QoS Management in Edge-IoT network 3

HAQM: A Hybrid SDN controller-based Architecture for effecti...

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3rd International conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Lo, Nogaye Mbacke Babou, Cheikh Saliou Niang, Ibrahima Cheikh Anta Diop University Laboratoire Informatique de Dakar Dakar Senegal Tokyo184-8795 Japan

ISBN: (纸本)9798331523893

The rapid development of the Internet of Things (IoT) has led to an exponential increase in data volumes offloaded to edge computing for rapid processing and analysis close to IoT devices. One of the major challenges of edge computing is better use of network performance in terms of latency, bandwidth, reliability, and security. Most IoT applications such as autonomous vehicles, connected health systems, augmented reality, industrial monitoring, and smart cities operate in real-time and generally have quality of service (QoS) constraints when transmitting data in the network. In addition, the Software-Defined networking (SDN) paradigm with its centralized control and configuration of network devices can make decision-making dynamic on the transmission policy of data flows in the Edge-IoT system. However, these traditional SDN approaches based on a centralized controller have shown their limitations in terms of scalability, efficiency, and speed. To address these QoS issues, we propose a novel hybrid SDN controller-based architecture for effective QoS management in Edge-IoT network (HAQM) which is a promising solution for real-time IoT applications that are latency-sensitive, providing centralized control while using local resources to reduce latency. This sophisticated architecture marks a major advancement in edge-IoT network management, meeting the performance requirements of modern IoT applications. © 2025 IEEE.

关键词： Quality of service

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A two-stage algorithm for vehicle routing problem with charging relief in post-disaster

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IET INTELLIGENT TRANSPORT systems 2023年第8期17卷 1525-1543页

作者： Liu, Qixing Xu, Peng Wu, Yuhu Shen, Tielong Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Sophia Univ Dept Elect & Comp Engn Dept Mech Engn Tokyo Japan

This paper first investigates emergency transportation for power recovery in post-disaster. The problem is formulated as a mixed-integer linear programming model called vehicle routing problem with charging relief (VRPCR). The battery state of charge (SoC$SoC$) implies the working hours that the battery can provide. The goal is to make a set of shelters charge before the battery SoC$SoC$ of shelters reaches the minimum bound over time. To this end, a two-stage algorithm is developed to deal with the problem. In stage I, a reduced road network is obtained from a leading road network by the A-star search algorithm. Subsequently, to determine the order of power delivery with charging operations at shelters by enhanced genetic algorithm (EGA) in stage ii. To evaluate this strategy, the detailed complexity analysis of the three algorithms and results tested on a realistic disaster scenario shows the performance of the A-star search algorithm for VRPCR that outperforms the Dijkstra and Floyd algorithms. In addition, the EGA is applied to Solomon's benchmarks compared with the state-of-the-art heuristic algorithms, which indicates a better performance of EGA. A real case obtained from a disaster scenario in Ichihara City, Japan is also conducted. Simulation results demonstrate that the method can achieve satisfactory solutions.

关键词： optimisation vehicle routing

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Few-Shot Class-Incremental Learning for network Intrusion Detection systems

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 6736-6757页

作者： Di Monda, Davide Montieri, Antonio Persico, Valerio Voria, Pasquale De Ieso, Matteo Pescape, Antonio Univ Napoli Federico II Dipartimento Ingn Elettr & Tecnol Informaz I-80125 Naples Italy IMT Sch Adv Studies I-55100 Lucca Italy

In today's digital landscape, critical services are increasingly dependent on network connectivity, thus cybersecurity has become paramount. Indeed, the constant escalation of cyberattacks, including zero-day exploits, poses a significant threat. While network Intrusion Detection systems (NIDSs) leveraging machine-learning and deep-learning models have proven effective in recent studies, they encounter limitations such as the need for abundant samples of malicious traffic and full retraining upon encountering new attacks. These limitations hinder their adaptability in real-world scenarios. To address these challenges, we design a novel NIDS capable of promptly adapting to classify new attacks and provide timely predictions. Our proposal for attack-traffic classification adopts Few-Shot Class-Incremental Learning (FSCIL) and is based on the Rethinking Few-Shot (RFS) approach, which we experimentally prove to overcome other FSCIL state-of-the-art alternatives based on either meta-learning or transfer learning. We evaluate the proposed NIDS across a wide array of cyberattacks whose traffic is collected in recent publicly available datasets to demonstrate its robustness across diverse network-attack scenarios, including malicious activities in an Internet-of-Things context and cyberattacks targeting servers. We validate various design choices as well, involving the number of traffic samples per attack available, the impact of the features used to represent the traffic objects, and the time to deliver the classification verdict. Experimental results witness that our proposed NIDS effectively retains previously acquired knowledge (with over 94% F1-score) while adapting to new attacks with only few samples available (with over 98% F1-score). Thus, it outperforms non-FSCIL state of the art in terms of classification effectiveness and adaptation time. Moreover, our NIDS exhibits high performance even with traffic collected within short time frames, achieving 95% F1-score whi

关键词： Power capacitors Training Metalearning Computer crime Denial-of-service attack Transfer learning network intrusion detection Telecommunication traffic Vectors Traffic control Attack-traffic classification deep learning few-shot class-incremental learning network intrusion detection system network security

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