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Multi-fidelity deep neural network surrogate model for aerodynamic shape prediction based on multi-task learning

Multi-fidelity deep neural network surrogate model for aerod...

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Energy and Power Engineering, control Engineering (EPECE), International conference on

作者： Pin Wu Zhitao Liu Zhu Zhou Chao Song School of Computer Engineering and Science Shanghai University Shanghai China State Key Laboratory of Aerodynamics China Aerodynamics Research and Development Center Mianyang Sichuan China

ISBN: (数字)9798350366099

ISBN: (纸本)9798350366105

With the increasing demands of refined aerodynamic shape design for modern aircraft, it is very essential for aerodynamic shape optimization to obtain more accurate aerodynamic data. The widely used high-fidelity simulation is usually accurate but extremely time-consuming. Therefore, we propose an innovative multi-fidelity model based on multi-task learning for aerodynamic shape prediction. This model consists of Bézier-Auxiliary Classifier GAN (Bézier-ACGAN), Multi-gate Mixture-of-Experts and Multi-Fidelity (MMoE-MF). Firstly, Bézier-ACGAN is used to construct the subsonic and transonic datasets and is used as an intelligent parameterization method. Secondly, The MMOE-MF model is coupled with the parameters of Bezier-ACGAN to predict different-fidelity of aerodynamic data. The results show that the predicted results of the optimal airfoils agree with the results of high-fidelity simulation well. This method is a promising approach that can convert from low-fidelity data to high-fidelity data in a few seconds.

关键词： Accuracy Shape Atmospheric modeling Transforms Predictive models Aerodynamics Multitasking

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Active Distribution network Day-Ahead Day-In Collaborative Dispatching Method Based on Optimal Economic Operation Area

Active Distribution Network Day-Ahead Day-In Collaborative D...

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Intelligent Power and Systems (ICIPS), International conference on

作者： Hui Zhang Jiye Wang Xiongwen Zhao Xiaohui Yuan School of Electric and Electronic Engineering North China Electric Power University Beijing China Beijing Fibrlink Commnications Co. Ltd. State Grid Information & Telecommunication Group Co. Ltd. Beijing China State Grid Digital Technology Holding Co. Ltd. Beijing China

The conventional day-ahead and intraday coordinated scheduling typically employs minimizing the deviation from the day-ahead temporal planning curve as the intraday objective function. However, when there is a significant disparity between the intraday renewable energy output forecasts and the day-ahead plans, energy storage systems (ESS), and other controllable devices have limited room for intraday adjustments due to their time-coupled constraints. This limitation leads to reduced economic efficiency and flexibility. In response to this challenge, this paper introduces an Active Distribution network (ADN) day-ahead and intraday coordinated scheduling method based on the Optimal Economic Operation Region (OEOR). In the day-ahead stage, a linearized ADN scheduling model is developed. It leverages a large number of optimized curves for control devices under various random scenarios generated using Latin Hypercube Sampling. The objective is to maximize the number of optimized results within the boundaries of the control device output levels over the entire time span. The model also considers adjacent time constraints on energy storage state-of-charge and the ramp rate of micro gas turbines, thus establishing an OEOR generation model. In the intraday stage, control devices are continuously optimized within the OEOR. When the optimization value for a particular time period approaches the OEOR boundary, constraints on output from adjacent time periods are considered. This extends the OEOR to an Optimal Economic Extreme Operation Region (E-OEOR). The results of the case study indicate that the proposed method offers improved economic efficiency for the distribution network compared to traditional approaches. If you need further revisions or have additional content to translate, please let me know.

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Intelligent Agricultural Greenhouse Data Acquisition and Transmission and Equipment control System Based on IOT

Intelligent Agricultural Greenhouse Data Acquisition and Tra...

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3rd International conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2024

作者： Qiu, Jiejin Chen, Zhenwen Qi, Wang Chen, Xiaoyi Huang, Zhanghan Meng, Yuexin Mu, Guangrui Bi, Lvqing College of Physics and Telecommunication Engineering Yulin Normal University Guangxi Yulin China Guangxi Center for Applied Mathematics Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin China

ISBN: (纸本)9789819627660

With the popularization of Internet of Things technology, smart agriculture is gradually becoming a new driving force to promote the development of agricultural modernization. Aiming at environmental detection and equipment control of agricultural greenhouses, this paper introduces a data acquisition, transmission and equipment control system based on Internet of Things technology. The system uses sensors to collect various environmental parameters in the greenhouse, and realizes the wireless upload of data to Baidu Cloud platform through intelligent judgment, and presents it in a visual way. Through Web technology, the equipment in the greenhouse can be controlled remotely. The test shows that the system integrates intelligent control, real-time data transmission and visualization functions, and uses IOT, automation and MQTT protocol network technology to achieve automatic collection, analysis and feedback of environmental parameters, and complete data wireless transmission, visualization and human–computer interaction. This provides an efficient and convenient solution for the management of smart agricultural greenhouses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental testing

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Comprehensive Analysis of EV Aspects and its Industrial Integration for Vehicle to Home Applications

Comprehensive Analysis of EV Aspects and its Industrial Inte...

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Automation, Computing and Renewable Systems (ICACRS), International conference on

作者： Vaasu Gupta Parveen Kumar Surbhi Maurya Arun Kumar Maurya Dushyant Singh Chauhan Kanak Gahlaut Electrical and Electronics Ajay Kumar Garg Engineering College Ghaziabad India Bachelor of Science Invertis Institute of AS&H Bareilly India Electronics & Communication Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Electric vehicle mobility system are potential resources utilized in the modern power system network as energy storage device and find application involving Vehicle to grid, vehicle to Home/load systems for power optimization and management. In this paper various key aspects of electric vehicle energy storage system are analyzed, discussed and simulation of PR controller based integrated vehicle to Home/Load energizing system is performed. The converter provides regulated sinusoidal output load characteristics with minimum total harmonic distortion (only 1.58%) leading to sustainable Dc/Ac power conversion. An intervening Dc bus is also stabilized and sustained at 400 V using PI controller for better grid synchronization.

关键词： Electric potential Total harmonic distortion Renewable energy sources Power quality Power system stability Electric vehicles Synchronization Voltage control Standards Energy storage

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Regret Bounds for Adaptive Nonlinear control 3

Regret Bounds for Adaptive Nonlinear Control

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

作者： Boffi, Nicholas M. Tu, Stephen Slotine, Jean-Jacques E. Harvard Univ Cambridge MA 02138 USA Google Brain Robot Mountain View CA USA MIT Cambridge MA 02139 USA

We study the problem of adaptively controlling a known discrete-time nonlinear system subject to unmodeled disturbances. We prove the first finite-time regret bounds for adaptive nonlinear control with matched uncertainty in the stochastic setting, showing that the regret suffered by certainty equivalence adaptive control, compared to an oracle controller with perfect knowledge of the unmodeled disturbances, is upper bounded by (O) over tilde(root T) in expectation. Furthermore, we show that when the input is subject to a k timestep delay, the regret degrades to (O) over tilde (k root T). Our analysis draws connections between classical stability notions in nonlinear control theory (Lyapunov stability and contraction theory) and modern regret analysis from online convex optimization. The use of stability theory allows us to analyze the challenging infinite-horizon single trajectory setting.

关键词： Adaptive control online convex optimization matched uncertainty

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Parameter Identification of Phase-Locked Loop in DFIG-Based Wind Turbine

Parameter Identification of Phase-Locked Loop in DFIG-Based ...

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Energy Engineering and Power Systems (EEPS), International conference on

作者： Pengyu Gao Yuqing Jin Huan Xie Beihua Liang Qian Chen Ping Ju School of Electrical and Power Engineering Hohai University Nanjing China Flexible Resource-Network-Demand-Storage Cooperation and Control Laboratory State Grid Jibei Electric Power Research Institute Beijing China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

As the proportion of wind power in the global electricity supply continues to increase, comprehensive and accurate parameter identification for wind turbine generators becomes crucial. This paper introduces a method for identifying phase-locked loop (PLL) parameters in doubly-fed induction generators (DFIG) using virtual measurement excitation (VME) technology. This method applies virtual frequency and phase disturbances on the secondary side voltage to effectively excite the PLL dynamics. By calculating the average trajectory sensitivities, appropriate disturbance forms and observation variables are selected. Identifiability analysis is then performed on PLL parameters to confirm that all parameters are uniquely identifiable, followed by parameter identification using the particle swarm optimization. Simulation results demonstrate that this method can accurately identify PLL parameters.

关键词： Parameter estimation Sensitivity Accuracy Systems operation Simulation Doubly fed induction generators Wind power generation Wind turbines Trajectory Phase locked loops

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Certifying Incremental Quadratic Constraints for Neural networks via Convex optimization 3

Certifying Incremental Quadratic Constraints for Neural Netw...

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

作者： Hashemi, Navid Ruths, Justin Fazlyab, Mahyar Univ Texas Dallas Mech Engn Dallas TX 75080 USA Johns Hopkins Univ Math Inst Data Sci Baltimore MD USA

The Abstracting neural networks with constraints they impose on their inputs and outputs can be very useful in the analysis of neural network classifiers and to derive optimization-based algorithms for certification of stability and robustness of feedback systems involving neural networks. In this paper, we propose a convex program, in the form of a Linear Matrix Inequality (LMI), to certify incremental quadratic constraints on the map of neural networks over a region of interest. These certificates can capture several useful properties such as (local) Lipschitz continuity, onesided Lipschitz continuity, invertibility, and contraction. We illustrate the utility of our approach in two different settings. First, we develop a semidefinite program to compute guaranteed and sharp upper bounds on the local Lipschitz constant of neural networks and illustrate the results on random networks as well as networks trained on MNIST. Second, we consider a linear time-invariant system in feedback with an approximate model predictive controller parameterized by a neural network. We then turn the stability analysis into a semidefinite feasibility program and estimate an ellipsoidal invariant set for the closed-loop system.

关键词： Neural networks Convex optimization Linear Matrix Inequalities Semidefinite Programming

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Qasync: A Dual network Architecture for Value Based Multi-Agent Reinforcement Learning

Qasync: A Dual Network Architecture for Value Based Multi-Ag...

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Automation, Robotics and Computer Engineering (ICARCE), International conference on

作者： Du Xu Lu Jia Yewei Wang Qian Liu Yong Zhang School of Computer Science and Information Engineering Hefei University of technology Hefei China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

In this work, we propose Qasync, a novel dual network framework for value-based multi-agent reinforcement learning, aimed at addressing the control decision-making issues of Connected Autonomous Vehicles (CAVs) in merge scenario. The core contributions of Qasync include the following two aspects: 1) Qasync extends QMIX by integrating a feedforward network, which reduces the limitations of QMIX's linearity assumption. 2) Qasync introduces a periodic mixing mechanism that combines Q-values from both networks, enabling Qasync to better capture non-linear relationships and enhance agents' exploration of diverse strategies. We evaluated Qasync using the SMARTS platform in a mixed traffic merging scenario, where autonomous vehicles must merge onto a two-lane road from a slip road, interacting with vehicles that randomly generated at the starting point to simulate realistic traffic conditions. Qasync was compared against several value-based baselines, including VDN, QMIX, and QTRAN, achieving average merging success rates of 80%, 89%, and 94%, respectively. Qasync achieved an overall merging success rate of 95%, demonstrating its ability to effectively handle complex traffic interactions and highlighting its potential for enhancing autonomous driving systems.

关键词： Roads Robot kinematics Merging Decision making Linearity Reinforcement learning network architecture Feedforward systems Autonomous vehicles optimization

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optimization of machining parameters using artificial Intelligence techniques 3

Optimization of machining parameters using artificial Intell...

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3rd International conference on Materials, Manufacturing and Modelling (ICMMM)

作者： Muthuram, N. Frank, F. Christo PSG Coll Technol Dept Prod Engn Coimbatore 641004 Tamil Nadu India

In the industrial scenario, there is a requirement for robust tools to estimate the parameters of machining processes precisely. This can be achieved by employing optimization techniques in conjunction with state of the art prediction models to predict the best set of parameters to perform the operation. In this work, the turning operation on titanium alloy is optimized for improved surface roughness while mitigating the effects on the material removal rate by employing non-traditional optimization algorithm. The optimization algorithm demands for a prediction model that establishes an empirical relationship between the parameters in the machining operation and its response variables. The prediction models are employed on turning of Ti-6-Al 4-V Titanium alloy dataset and were contrasted by prediction metrics to determine the best prediction technique. The Artificial Neural network (ANN) outperformed other prediction models by achieving a mean absolute percentage error of 1.08% as it could learn the non-linear and interaction effects in the data. The algorithms have been implemented in the functional programming paradigm in python (general purpose programming language). The accuracy of prediction model has been improved in contrast to the existing ANN models in the literature by fine tuning the ANN to determine the best architecture and hyper parameters. The ANN was then integrated with the genetic algorithm, a nontraditional meta-heuristic optimization algorithm to arrive at the best set of combination that minimizes the surface roughness while maximizing the material removal rate. A bias factor was introduced to control the optimization between the contradictory objectives. The optimized solutions offered the best combination of operating parameters to achieve the best performance in the given operation. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International conference on Mater

关键词： optimization Artificial intelligence Turning operation Genetic algorithm

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Research on Traffic Dispatching Scheme of Multi-service Data Center for 5G Smart Grid 3

Research on Traffic Dispatching Scheme of Multi-service Data...

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2020 3rd International conference on Modeling, Simulation and optimization Technologies and Applications, MSOTA 2020

作者： Sun, Yanzhi Liu, Yuming Tian, Feng Wen, Quan Jiang, Liqiong Yunnan Electric Power Dispatching Control Center Kunming Yunnan China

Aiming at the flow scheduling problem of 5G power network multi-service data center, this paper designs and proposes a multi-service-oriented multi-factor flow scheduling scheme. Based on the shortest path algorithm, this paper improves and perfects the traffic scheduling strategy, and introduces a set of scoring mechanism. The number of hops, the number of large flows and the remaining bandwidth of each path are used as scoring indicators, and the path with the highest comprehensive score is selected. As the forwarding path of traffic. Based on the Mininet simulation platform, use Ryu to control to generate probability model traffic to simulate data center network traffic. The experimental results show that the scheme improves the network throughput and bandwidth utilization, and improves the performance of the data center network. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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