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Application of Deep Reinforcement Learning in Quantum control 3

Application of Deep Reinforcement Learning in Quantum Contro...

引用

3rd International conference on Computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Cheng, Yiming Yin, Yixi Lin, Jiaying Wang, Yaobin Officers College of PAP Sichuan Chengdu610213 China School of Science Beijing University of Posts and Telecommunications Beijing100876 China Southwest University of Science and Technology Sichuan621010 China

Machine learning technology based on artificial neural network has been successfully applied to solve many scientific problems. One of the most interesting areas of machine learning is reinforcement learning, which has natural applicability to optimization problems in physics. In the quantum control task, it is necessary to find a set of optimal control functions to transfer a quantum system from the initial state to the target state with the highest fidelity possible, which is essentially an optimization task. In this paper, we use Deep Deterministic Policy Gradient algorithm (DDPG) to study the quantum control tasks. We use the algorithm to control the transfer of several quantum systems from one state to another. The results show that DDPG algorithm can find a control strategy to make the fidelity of the final state and the target state of the quantum system be maximum value 1. The results show the potential of DDPG in quantum control. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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A Study on Search and Correction of Error Dominant Regions in Simulation Based on Measured Trajectories 3

A Study on Search and Correction of Error Dominant Regions i...

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3rd International conference on Power Systems and Electrical Technology, PSET 2024

作者： Wang, Lei Pan, Ruxiang Lv, Rui Li, Junxian Yue, Xiaoyu Liao, Siyang Laboratory of Hydro-Wind-Solar Multi-Energy Control Coordination China Yangtze Power Co. Ltd. Wuhan China Wuhan University Laboratory of Hydro-Wind-Solar Multi-Energy Control Coordination Hubei Engineering and Technology Research Center. for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan China Co. Ltd. Beijing China

ISBN: (纸本)9798350353204

With the proposal of the carbon peak and carbon neutrality goal, a significant influx of new energy sources with random and fluctuating characteristics is being integrated into the power grid. Consequently, the adaptability of the traditional four-parameter model, as well as new energy models, to the actual power grid is becoming increasingly prominent. Conversely, simulations often rely solely on the accuracy of models and algorithms and are based on predefined conditions. To address the aforementioned challenges, this paper utilizes PMU-measured data to identify error-dominant regions. Initially, it employs a hybrid dynamic simulation method to partition the power grid simulation system into blocks. Concurrently, it establishes a simulation credibility evaluation system and employs comprehensive simulation credibility to identify error-dominant regions. Subsequently, it iteratively decouples sub-regions to systematically identify error elements. Subsequently, the perturbation method was employed to analyze the trajectory sensitivity of each parameter of the element. Parameters with low sensitivity were assigned typical values, while those with high trajectory sensitivity were chosen for identification. The genetic algorithm was then utilized for online parameter verification and optimization. Finally, the proposed method was validated using RTDS real-time simulation software. Experimental results demonstrate that the calibrated model simulation results exhibit better alignment with measured data, highlighting the accuracy and reliability of the proposed approach. © 2024 IEEE.

关键词： Perturbation techniques

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A multi-time scale grid scheduling optimization method considering demand-side resources 3

A multi-time scale grid scheduling optimization method consi...

引用

2024 3rd International conference on Power System and Energy Technology, ICPSET 2024

作者： Jiang, Chi Wang, Chaoliang Lu, Chunguang Yan, Huajiang State Grid Zhejiang Electric Power Co. Ltd. Hangzhou310000 China

This research introduces a pioneering strategy for optimizing power grid scheduling across various time scales, addressing a previously unmet need in the field of energy regulation. Initially, we amassed and categorized tunable resources on the demand side into three distinct temporal categories—long-term, short-term, and immediate—reflecting their respective response attributes. This taxonomy informed the development of a cohesive scheduling framework, encompassing pre-emptive, daily, and instantaneous sub-models, designed to facilitate precise control over grid operations. The deployment of this framework has led to marked enhancements in both the efficiency and dependability of power grid management. The merits of this methodology were exemplified through its application to the IEEE33 network model, showcasing its proficiency in the acquisition of optimized scheduling parameters. This multi-tiered modeling and optimization technique offers an innovative pathway for the dynamic and effective management of smart grids. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Power grids

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Trust-based Performance optimization for Human-Swarm Collaboration 3

Trust-based Performance Optimization for Human-Swarm Collabo...

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3rd Modeling, Estimation and control conference (MECC)

作者： Zanone, R. Oliver Velni, Javad Mohammadpour Clemson Univ Dept Mech Engn Clemson SC 29631 USA

ISBN: (纸本)9781713872344

This work develops a trust inference model to address scenarios where agents in a swarm collaborate to achieve the coverage control task. To gather empirical data from human subjects for the probabilistic model development, we build various simulation tools and user interfaces. Using our visual training tool, we train a single-agent model and then extend that to create our multi-agent model. These models utilize a dynamic Bayesian network and produce stochastic predictions. We then apply these models to our Voronoi-based area coverage problem in real time, where agents adjust their behavior to maximize the team performance and hence human trust. As a result of this research, multi-agent teams will be able to increase their individual trust levels thereby enhancing team performance and efficiency. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Trust modeling multi-agent systems dynamic Bayesian network expectation maximization

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Deep Learning and Machine Learning Techniques Applied to Speaker Identification on Small Datasets 3rd

Deep Learning and Machine Learning Techniques Applied to Spe...

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3rd International conference on optimization, Learning Algorithms and Applications (OL2A)

作者： Manfron, Enrico Teixeira, Joao Paulo Minetto, Rodrigo Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Associate Lab Sustainabil & Technol SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana BR-80230901 Curitiba Parana Brazil

ISBN: (纸本)9783031530357;9783031530364

In this study, we explore the capabilities of speaker recognition technology for biometric authentication developing speaker recognition-based access control systems and serving as a resource for future research and improvements in secure and efficient speaker identification solutions. We focused on developing and evaluating machine learning and deep learning models for speaker identification. The models were trained and tested on private datasets with 32 speakers and public datasets with 1251 to 6112 speakers. The Gaussian Mixture Model performed well with our private datasets, with 93,10%, and 95% accuracy in correctly identifying the speakers. The Multilayer Perceptron achieved a peak accuracy of 93.33% on the Framed Trim private dataset. The VGGM model, after initial training on larger datasets, achieved an accuracy of 90.34% and 98.33% on our private datasets. At last, the model ResNet50 slightly outperformed the other models on two versions of our private dataset, achieving accuracies of 97.93% and 100%.

关键词： Speaker Identification Convolutional Neural network Deep Learning

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A study on the model predictive control based on convolutional neural network 3

A study on the model predictive control based on convolution...

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3rd International conference on Signal Processing and Machine Learning, COnf-SPML 2023

作者： Wang, Hongyu University of Michigan 500 S. State St Ann ArborMI48109 United States

As one of the most widely used rolling optimization methods, model predictive control (MPC) can effectively deal with constrained problems with multivariate. However, MPC relies on the accurate system dynamics model, which means that the nonlinear terms in the system model such as some irregular disturbances or noises will weaken the control effect. On the other hand, machine learning has been shown to work well on black-box systems, so the idea of using deep learning to handle nonlinear terms is feasible. This paper studies a method for applying deep learning to MPC, which is to use convolutional neural networks to fit nonlinear terms of the system model and thus remove their effects by using methods such as precise compensation. The paper also compares traditional and convolutional neural networks and the simulation results show that this method can achieve good control effect. © Published under licence by IOP Publishing Ltd.

关键词： Model predictive control

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Distributed network Welfare Maximization DSM-DG Strategy 3

Distributed Network Welfare Maximization DSM-DG Strategy

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3rd International conference on Power, Energy, control and Transmission Systems, ICPECTS 2022

作者： Mohan, S. Nayanatara, C. Baskaran, J. Sri Sairam Engineering College Department of Electrical and Electronics Engineering Chennai600044 India Psg Institute of Technology and Applied Research Coimbatore641062 India

ISBN: (纸本)9781665462754

This methodology aims in regulating the parameters of demand response management (DRM) to limit the usage of consumer energy. Therefore, this will increase the stability and minimizes the cost of generation. Synchronized optimization of IoT based distributed generation (DG) parameters with DRM programs ensure the trapping of the obtainable power and maintain the welfare maximization. Comprehensive cases are taken into consideration by optimizing the DG parameters. This control proposes a strawberry (SBY) optimization to effectively manage the resources. To justify the integration of dynamic generation and demand parameters, a standard IEEE 33bus system is considered and the results show that the proposed optimization technique improves the system performance. © 2022 IEEE.

关键词： Distributed power generation

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Proceedings - 2024 FORTEI-International conference on Electrical Engineering: Empowering Innovations : Navigating The Future Of Semiconductor Industry, FORTEI-ICEE 2024

Proceedings - 2024 FORTEI-International Conference on Electr...

引用

3rd FORTEI-International conference on Electrical Engineering, FORTEI-ICEE 2024

ISBN: (纸本)9798331542207

The proceedings contain 49 papers. The topics discussed include: implementation of ADAS for electric vehicle safety for pediatric patients based on fuzzy logic;the influence of parallel multigap rod electrodes on the breakdown probability distribution;navigation system in dynamic indoor environment using deep reinforcement learning with actor-critic;application of carbon nanoparticles with arc-discharge synthesis as an anode material in lithium-ion batteries;frequency control of interconnected power system utilizing novel optimization approaches;river detection using fast Fourier transform, contour detection, and Hough line transform;LTE-advanced network planning with inter-band non-contiguous carrier aggregation in Mampang Prapatan;and harmonic distortion reduction analysis of railway static inverter output using passive filter and PI controller with MATLAB Simulink.

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A network Reconfiguration Method of Distribution networks Considering Dynamic Characteristics of The Distributed Renewable Energy Sources 12

A Network Reconfiguration Method of Distribution Networks Co...

引用

12th International conference on Renewable Power Generation, RPG 2023

作者： Li, Jing Yu, Xijuan Yang, Bin Li, Xinming Xu, Wanyu Xuan, Zhenwen State Grid Beijing Electric Power Research Institute 34 South 3rd Ring Road Middle Road Fengtai District Beijing China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Department of Electrical Engineering 5340 Xiping Road Beichen District Tianjin China

The existing research on distribution network restoration control lacks the consideration of the dynamic recovery characteristics of distributed renewable energy sources (dRESs). By taking the output of dRESs as invariant, the voltage of network could be excessive low, which leads to the failure of system restoration. Given this, a restoration control method of distribution network considering the output fluctuation of dRESs is proposed in this paper. Firstly, the dynamic restoration control model of distribution network with dRESs integration is constructed. Then, the Gaussian mutation, which can effectively improve the global search ability and computing speed, are introduced into the traditional Binary Particle Swarm optimization (BPSO) algorithm. The examples validate that the proposed method can calculate the optimal reconstruction scheme of the dRESs-penetrated distribution network within dozens of seconds. The off-normal voltage and failure reconnection of dRESs can be avoided, boosting the restoration of distribution system. © The Institution of Engineering & Technology 2023.

关键词： Electric power distribution

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Vehicular Ad Hoc network performance enhancement using VOA based Road Side Unit Deployment 3

Vehicular Ad Hoc Network performance enhancement using VOA b...

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3rd International conference on Intelligent Computing, Instrumentation and control Technologies, ICICICT 2022

作者： Sharma, Ayushi Pandey, Kavita Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Road side unit (RSU) is the key element in VANET architecture. It is used to improve communication range, driving awareness, traffic safety, signal acknowledgement and violation. However, VANET faces major challenges in RSU deployment. To achieve the objectives of RSU deployment problem i.e. minimizing the cost of deployment and maximizing network coverage;swarm intelligence algorithm have been used in the literature. In our work, Virus optimization algorithm (VOA) has been applied on VANET to generate optimal solutions and is compared with swarm family algorithms such as Particle Swarm optimization (PSO) and Genetic Algorithm (GA). The outcome generated shows the expertise of the suggested algorithm i.e. VOA in terms of optimal RSU deployment and also on various performance metrics like overlapping rate, average transmission time and network connectivity. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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