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检索条件"任意字段=3rd Euro-NF Conference on Network Control and Optimization"

共 1644 条记录，以下是621-630 订阅

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3rd International conference on Futuristic Trends in network and Communication Technologies, FTNCT 2020

3rd International Conference on Futuristic Trends in Network...

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3rd International conference on Futuristic Trends in network and Communication Technologies, FTNCT 2020

ISBN: (纸本)9789811614798

The proceedings contain 80 papers. The special focus in this conference is on Futuristic Trends in network and Communication Technologies. The topics include: Unmanned Vehicles: Safety Management Systems and Safety Functions;probabilistic Characteristics of a Two-Channel Detector with Two Inertial Single-Photon Photoemission Devices and an Electronic Adder;energy and Spectrum-Aware Cluster-Based Routing in Cognitive Radio Sensor networks;The Hybrid Approach for the Partitioning of VLSI Circuits;maximization of IoT network Lifetime Using Efficient Clustering Technique;a Hybrid Metaheuristic to Solve Capacitated Vehicle Routing Problem;Energy Conservation in IOT: A Survey;identification of Implicit Threats Based on Analysis of User Activity in the Internet Space;EERO: Energy Efficient Route optimization Technique for IoT network;representing a Quantum Fourier Transform, Based on a Discrete Model of a Quantum-Mechanical System;forecasting Non-Stationary Time Series Using Kernel Regression for control Problems;A Smart Waste Management System Based on LoRaWAN;simulation of the Semantic network of Knowledge Representation in Intelligent Assistant Systems Based on Ontological Approach;A Deep Learning Approach for Autonomous Navigation of UAV;framework for Processing Medical Data and Secure Machine Learning in Internet of Medical Things;path Planning for Autonomous Robot Navigation: Present Approaches;Development of a Routing Protocol Based on Clustering in MANET;threat Model for Trusted Sensory Information Collection and Processing Platform;autonomous Navigation of Mobile Robot with Obstacle Avoidance: A Review;Design of a Distributed Debit Management network of Operating Wells of Deposits of the CMW Region;design of U-Shaped Multiline Microstrip Patch Antenna for Advanced Wireless Applications.

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Deep Reinforcement Learning-Based Scheduling optimization of a Wind-solar Coupled CHP Unit System Considering Source-load Uncertainty

Deep Reinforcement Learning-Based Scheduling Optimization of...

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Fully Actuated System Theory and Applications (CFASTA), conference on

作者： Baohua Wang Xinglong Gao Xianlian Wang Zhixiao Wang Li Sun Shan Hua State Key Laboratory of Low-Carbon Smart Coal-Fired Power Generation and Ultra-Clean Emission China Energy Science and Technology Research Institute Co.Ltd. Nanjing China National Energy Changzhou Power Generation Co. Ltd. Changzhou China National Engineering Research Center of Power Generation Control and Safety School of Energy and Environment Southeast University Nanjing China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

Promoting the electric-thermal decoupling in combined heat and power (CHP) units is an effective way to enhance their peak-regulation capability and facilitate the integration of renewable energy. In order to address the problems of flexibility and source-load uncertainty in the operation of the CHP unit, this paper studies the deep reinforcement learning (DRL)-based efficient scheduling problem of a wind-solar coupled CHP unit system. The problem is optimized under the objective of comprehensive income for energy supply and the constraints of safe operation and supply-demand balance. Based on system operation characteristics in uncertain environments, a Markov decision process (MDP) model is designed so as to transform the scheduling optimization into a sequential decision problem. To achieve efficient and intelligent scheduling of the CHP unit, the co-optimization of electricity and heat decisions is achieved by a continuous DRL algorithm, twin delayed deep deterministic policy gradient (TD3). Simulation results under a typical day scenario in the heating season show that TD3 algorithm can promote the consumption of renewable energy and ensure the sustainable scheduling capability of the thermal storage device while satisfying power balances. Furthermore, based on the well-trained neural network, the system exhibits near-theoretically optimal results with rapid single-step scheduling duration, effectively enhancing the system’s adaptability to uncertainty.

关键词： Renewable energy sources Uncertainty Cogeneration Electricity Simulation Transforms Scheduling

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Channel Modelling for UAV Air-to-Ground Communication

Channel Modelling for UAV Air-to-Ground Communication

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Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM), 2020 3rd International conference on

作者： Valencia Lala Andrea F. Ndreveloarisoa Wang Desheng R. Feno Heriniaina Nour Mohammad Murad Glauco Fontgalland Blaise Ravelo Electronic Information and Communications HUST Wuhan China National Engineering School of Tunis Tunis Tunisia Studio A Antananarivo Madagascar PIMENT IUT University of La Réunion Saint Pierre La Reunion School of Engineering University of Mount Union Alliance USA NUIST Nanjing China

ISBN: (数字)9798331532475

ISBN: (纸本)9798331532482

After a brief literature review, this paper presents the main advantages and drawbacks of UAV and IoT cellular network communication. Nowadays, UAVs are increasingly exploited in urban environments. However, optimizing the performance of UAV-BS cellular networks for coverage remains a significant challenge for communication system design engineers. For this reason, the air-to-ground (A2G) and ground-to-air (G2A) wireless propagation channels are analytically modeled by identifying the optimal geometrical position. This paper investigates channel communication modeling in urban environments. Different cases parameterized by the coverage zone radius, assumed as the A2G channel for two different scenarios, are simulated by assessing the line-of-sight (LOS) probability (PLOS) and path loss. The obtained results enable the identification of the optimal elevation angle corresponding to the expected PLOS and the optimal altitude for minimal path loss. UAV-BS cellular network performance is illustrated, emphasizing that the UAV cellular network is potentially useful for monitoring and remote control.

关键词： Cellular networks Analytical models Atmospheric modeling Wireless networks Urban areas Autonomous aerial vehicles Air to ground communication Trajectory optimization Systematic literature review Sports

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Maximum Likelihood Signal Matrix Model for Data-Driven Predictive control 3

Maximum Likelihood Signal Matrix Model for Data-Driven Predi...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Yin, Mingzhou Iannelli, Andrea Smith, Roy S. Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland

The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data. This signal matrix model is derived by maximum likelihood estimation with noise-corrupted data. By linearizing online, the implicit model can be used as a linear constraint to characterize possible trajectories of the system in receding horizon control. The signal matrix can also be updated online with new measurements. This algorithm can be applied to large datasets and slowly time-varying systems, possibly with high noise levels. An additional regularization term on the prediction error can be introduced to enhance the predictability and thus the control performance. Numerical results demonstrate that the proposed signal matrix model predictive control algorithm is effective in multiple applications and performs better than existing data-driven predictive control algorithm.

关键词： Predictive control data-driven optimization maximum likelihood estimation

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3rd International conference on Futuristic Trends in network and Communication Technologies, FTNCT 2020

3rd International Conference on Futuristic Trends in Network...

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3rd International conference on Futuristic Trends in network and Communication Technologies, FTNCT 2020

ISBN: (纸本)9789811614828

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Reinforcement Learning-Based Optimal Power Flow control in Islanded Microgrids 3

Reinforcement Learning-Based Optimal Power Flow Control in I...

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3rd IEEE International conference on Automation and Computation, AUTOCOM 2025

作者： Agarwal, Ankita Kumar, Amit Maity, Dipankar Ojha, Varun Nalini Sunil, M. Dore, Aruna Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798331542375

One of the popular paradigms for control problems in different applications is Reinforcement Learning. In this paper, we develop an RL-based technique for OPF control in islanded micro grids. Micro grids are small power systems for remote or rural regions that can function independently of the national grid and serve as a dependable alternative. The dependence on their decentralized power generation over DERs is a major complication for micro grid system stability and noteworthy optimization of reactive power flow. The operation of micro grids is both dynamic and stochastic;as a result, operational models based on traditional control methods at the model level or rule-based conflicts with optimized controllers that need to work in an environment where the future long-term forecasts remain uncertain. RL is suggested as a data-driven and adaptive control method to address these issues and optimize power flow in islanded micro grids. The RL-based control proposed in this paper uses realtime data from the micro grid to learn an optimal policy. The presented micro grid environment is for the reinforcement learning agent with which it interacts, and on success or failure of its actions, it will be given a reward signal to learn. The RL agent, through its interactions and learning process by taking actions during the operation of islanded micro grids, can optimize power flow in a distributed manner given DERs availability, load demand dynamics, and network constraints. © 2025 IEEE.

关键词： Reinforcement learning

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Research on Modular Cooperative Optimal control of Low Altitude Approach Booted Aircraft 3

Research on Modular Cooperative Optimal Control of Low Altit...

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3rd IEEE International conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Li, Jiusheng Kong, Depeng Dai, Shaoqing Gu, Weisi Chen, Xinyue Kong, Dehui The Center of Robot Education Research Zhejiang University of Technology Hangzhou China Cyberspace Security Academy Jiaxing Vocation Technical College Jiaxing China College of Education Zhejiang University of Technology Hangzhou China English Teaching Group Youlan Junior High School Tanghe China

ISBN: (纸本)9781665425186

According to the needs of flight, based on the automatic control technology, the steering gear and the vertical gyroscope are used to realize the sliding approach, free elevation and attitude maintenance, and the structured design of the shoe-type aircraft. The fine adjustment of the rising rudder angle and the different rotation force of the pitch angle can carry out error tracking, realize the stable adjustment of different states, and also achieve the purpose of rapid response. The accuracy of the cooperation between the two boots also depends on the optimization strategy of the system and the control of the rising threshold. The deficiencies in the initial setting model can be adjusted through the advanced filtering and lagging network correction of the model to achieve the precise service of the system And seamless cooperation. This article gives the automatic control design steps and optimized paths for precise adjustment. © 2021 IEEE

关键词： Automation

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Intrusion Detection Study of Nuclear Power Plant Electronic Systems Based on Machine Learning optimization Algorithms

Intrusion Detection Study of Nuclear Power Plant Electronic ...

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Electronic Information Engineering and Computer Communication (EIECC), International conference on

作者： Xiaoyan Zhang School of Management Shanghai University Shanghai China

In modern society, the rapid development of information technology makes a variety of electronic devices are widely used, and the electronic systems in these devices are often the key to the control system, and their stability and security are crucial to the operation of the whole system. This paper studies and compares the prediction ability of various machine learning algorithm models in the intrusion detection of electronic systems in nuclear power plants. The results show that the decision tree model and random forest model have the best prediction ability, which is far better than the other models. The linear regression model and KNN model have poor prediction ability, while the BP neural network model has poor prediction effect and is not suitable for intrusion detection of nuclear power plant electronic system. Intrusion detection in electronic systems of nuclear power plants is an important security problem, so it is important to study the prediction ability of different machine learning models for this problem. In this paper, through the experimental comparison of multiple machine learning models, we found that the decision tree model and random forest model have the best prediction ability, which indicates that these two models have high prediction ability and accuracy in nuclear power plant electronic system intrusion detection. The linear regression model and KNN model have poorer predictive ability, which may be due to the inability of these two models to capture the complex relationships in the intrusion detection of nuclear power plant electronic systems. And the BP neural network model has poor predictive effect, which may be due to the fact that the model is prone to overfitting phenomenon, resulting in inaccurate prediction results. The findings of this paper show that these conclusions provide valuable references for intrusion detection in electronic systems of nuclear power plants.

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Safe Reinforcement Learning of control-Affine Systems with Vertex networks 3

Safe Reinforcement Learning of Control-Affine Systems with V...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Zheng, Liyuan Shi, Yuanyuan Ratliff, Lillian J. Zhang, Baosen Univ Washington Seattle WA 98195 USA Univ Calif San Diego San Diego CA 92103 USA

This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints. Despite its success in many domains, reinforcement learning is challenging to apply to problems with hard constraints, especially if both the state variables and actions are constrained. Previous works seeking to ensure constraint satisfaction, or safety, have focused on adding a projection step to the policy during learning. Yet, this approach requires solving an optimization problem at every policy execution step, which can lead to significant computational costs and has no safety guarantee with the projection step removed after training. To tackle this problem, this paper proposes a new approach, termed Vertex networks (VNs), with guarantees on next step safety during both the exploration and execution stage, by incorporating the safety constraints into the policy network architecture. Leveraging the geometric property that all points within a convex set can be represented as the convex combination of its vertices, the proposed algorithm first learns the convex combination weights and then uses these weights along with the pre-calculated vertices to output an action. Numerical examples illustrate that the proposed VN algorithm outperforms projection-based reinforcement learning methods.

关键词： safe reinforcement learning network architecture convex polytope

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QoS- aware Multi-objective PSO-FA based Optimizer for Uplink Radio Resource Management of LTE-A network 3

QoS- aware Multi-objective PSO-FA based Optimizer for Uplink...

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3rd International conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Paul, M.Mano Raja Perarasi, T. Moses, M. Leeban Rahul, P. Nehru Institute of Engineering and Technology Department of Eee Coimbatore India Bannari Amman Institute of Technology Department Ece Erode India MET'S School of Engineering Department of Cse Thrissur India

ISBN: (纸本)9780738146270

Long Term Evolution Advanced (LTE-A), is a leading-edge technology that provides higher peak data rates, more consumers per cell, lower control plane latency and delivers variety of Quality of Service (QoS) options which include Throughput, Packet Loss Rate, Packet Delay and the delay variation. The communication on the LTE air interface occurs between the Evolved Node B (eNB) and User Equipment (UE) and it is exceptionally imperative to deal with the resources because of the uninterrupted allocation of resources on the LTE networks. From the 3GPPLTE principles, while there is no coordination in uplink, at that juncture there would be a disputation based random access and it would be very tough administration would be a difficult job due to asymmetrical and mammoth UE transmissions. To surmount this challenge, a QoS-aware Multi-objective PSO-FA based Optimizer for Uplink Radio Resource Management of LTE-A network is suggested in our research which eliminates the multi-objective Knapsack issue with limited modification in the class-based resource allocation scenario. The first phase of the work would be the allocation of resources to the users in the network by calculating the metric which is based upon QCI and priority function. The second phase of the work includes a QoS-aware optimizer with three objective functions, which can guarantee QoS service among users. Hybrid Optimizer is proposed in this work which integrates Firefly Algorithm (FFA) and Particle Swarm optimization (PSO) by enhancing the fairness index and throughput. © 2021 IEEE.

关键词： Resource allocation

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