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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Fengyang Han Runmin Li Dianwei Qian School of Control and Computer Engineering North China Electric Power University Beijing China

Short-term wind speed forecasting plays an important role in the daily power system operation. Therefore, this paper presents a novel model based on spiking neural network (SNN) used spike response model (SRM). Further, to achieve both smaller training errors and higher precision forecasting, the basic SpikeProp learning algorithm is improved by adaptively adjusting the learning rate and adding momentum items. Then, this paper selects the actual sampling data from a wind farm to verify the effectiveness and advantages of the proposed model.

关键词： Neurons Wind speed Biological neural networks Forecasting Training Encoding Delays

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Quantum repeaters: From quantum networks to the quantum internet

arXiv

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arXiv 2022年

作者： Azuma, Koji Economou, Sophia E. Elkouss, David Hilaire, Paul Jiang, Liang Lo, Hoi-Kwong Tzitrin, Ilan NTT Basic Research Laboratories NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan NTT Research Center for Theoretical Quantum Physics NTT Corporation 3-1 Morinosato-Wakamiya Kanagawa Atsugi243-0198 Japan Department of Physics Virginia Tech BlacksburgVA24061 United States QuTech Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands Networked Quantum Devices Unit Okinawa Institute of Science and Technology Graduate University Okinawa Japan Quandela SAS 10 Boulevard Thomas Gobert Palaiseau91120 France Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Quantum Bridge Technologies Inc. 100 College Street TorontoONM5G 1L5 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Center for Quantum Information and Quantum Control Department of Physics Department of Electrical & Computer Engineering University of Toronto TorontoM5S 3G4 Canada Department of Physics University of Toronto Toronto Canada

A quantum internet is the holy grail of quantum information processing, enabling the deployment of a broad range of quantum technologies and protocols on a global scale. However, numerous challenges exist before the quantum internet can become a reality. Perhaps the most crucial of these is the realization of a quantum repeater, an essential component in the long-distance transmission of quantum information. As the analog of a classical repeater, extender, or booster, the quantum repeater works to overcome loss and noise in the quantum channels comprising a quantum network. Here, we review the conceptual frameworks and architectures for quantum repeaters, as well as the experimental progress towards their realization. We also discuss the various near-term proposals to overcome the limits to the communication rates set by point-to-point quantum communication. Finally, we overview how quantum repeaters fit within the broader challenge of designing and implementing a quantum internet. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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Modeling and Simulation of Indoor Environment Parameters Based on Least Squares 29

Modeling and Simulation of Indoor Environment Parameters Bas...

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第29届中国控制与决策会议

作者： ZENG Deliang JIAN Yifan DENG Zhiguang YANG Yang North China Electric Power University School of Control and Computer Engineering

ISBN: (纸本)9781509046584

Indoor building environment for energy saving and human health has important significance and the main criterion for evaluating its quality is environmental *** experimental scheme for obtaining the mathematical model of the main parameters is designed by analyzing the parameters that affect the environmental *** scheme establishes a discrete bilinear model with parameters and control variables,and uses the the least squares method to obtain the model parameters based on the experimental *** simulation results show that the experimental scheme of this paper has strong universality and adaptability,and the model with the deviation of the real system is small,which can be used as the model basis for optimizing the control of the indoor building environment.

关键词： indoor building environment environmental comfort discrete bilinear model least squares Optimization control

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Wind Turbine Spindle Condition Monitoring Based on Operational Data 29

Wind Turbine Spindle Condition Monitoring Based on Operation...

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第29届中国控制与决策会议

作者： Zhao-guang WANG Peng GUO School of Control and Computer Engineering North China Electric Power University

ISBN: (纸本)9781509046584

Wind turbine spindle is the connecting part of impeller and *** has the function of transmitting torque and energy in the transmission chain of the *** needs to bear the bending moment and thrust of the wind wheel,so the failure rate of spindle is ***,the condition monitoring of the spindle is directly related to the stability of wind turbine and power *** spindle temperature model is established and used to predict by the nonlinear state estimate technology under the normal operating condition of the *** the spindle fails,the observation vector of the model input changes obviously,resulting in a significant change of the prediction *** order to improve the sensitivity and reliability of the spindle anomaly early warning,using the double moving window calculate the statistic properties of the residual sequence based on the Lewitt *** the residual mean or standard deviation exceeds the set fault alarm threshold,an alarm message will be *** the actual operating data of wind turbine spindles verify the validity of this method.

关键词： spindle condition monitoring nonlinear state estimate technology residual Lewitt criteria double moving window

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Region based Ensemble Learning Network for Fine-grained Classification

arXiv

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arXiv 2019年

作者： Li, Weikuang Wang, Tian Wang, Chuanyun Shan, Guangcun Zhang, Mengyi Snoussi, Hichem School of Computer Science Shenyang Aerospace University Shenyang China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Instrumentation Science and Opto-electronics Engineering Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China Institue Charles Delaunay-LM2S-UMR STMR 6279 CNRS University of Technology of Troyes Troyes France

As an important research topic in computer vision, fine-grained classification which aims to recognition subordinate-level categories has attracted significant attention. We propose a novel region based ensemble learning network for fine-grained classification. Our approach contains a detection module and a module for classification. The detection module is based on the faster R-CNN framework to locate the semantic regions of the object. The classification module using an ensemble learning method, which trains a set of sub-classifiers for different semantic regions and combines them together to get a stronger classifier. In the evaluation, we implement experiments on the CUB-2011 dataset and the result of experiments proves our method’s efficient for fine-grained classification. We also extend our approach to remote scene recognition and evaluate it on the NWPU-RESISC45 dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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Predictive Function control of Output Probability Density Function

Predictive Function Control of Output Probability Density Fu...

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Chinese Automation Congress (CAC)

作者： Jinfang Zhang Ruoxuan Tian Di Wu School of Control and Computer Engineering North China Electric Power University BeiJing China

Randomness is common in many real industrial processes, its modeling problem has always been an important research content in the field of industrial control. Probability density function(PDF) is an effective means to solve the problem of stochastic distributed system modeling This paper based on Bspline basis function sets up the model of output and input data. Due to the fact that there are often many constraints in the actual industrial process and high requirements for real-time and accuracy, this paper proposes to combine the PDF model with predictive control to achieve effective tracking of system output setpoints. Predictive control optimization is more complicated and computationally intensive, so the predictive functional control (PFC) strategy is adopted to reduce the degree of freedom of the optimization problem. Applying PFC based on PDF modeling to the molecular weight distribution (MWD) of the polymerization process, and compared with generalized predictive control (GPC) algorithm, the validity of the proposed method is verified.

关键词： Predictive models Probability density function Predictive control Splines (mathematics) Prediction algorithms Mathematical model

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Solar Photovoltaic Power Prediction Based on Similar Day Approach 36

Solar Photovoltaic Power Prediction Based on Similar Day App...

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第36届中国控制会议

作者： Xu Zhang Bo Jiang Xiaoning Zhang Fang Fang Zhengping Gao Tao Feng The School of Control and Computer Engineering North China Electric Power University

ISBN: (纸本)9781538629185

The output power of photovoltaic(PV) power generation system is related to solar irradiance,temperature,humidity and other meteorological *** output powers in the similar days,which are much alike in meteorological conditions and social activities,will be more likely *** this paper,a prediction algorithm combining the time series and similar day is proposed to predict the power output of a PV *** this algorithm,the meteorological conditions of target days are predicted by the time series analysis,and the subjective weight and the entropy weight method are used to select the similar *** that,the target day's power output will be forecasted by tuning the weights of similar days' power *** on the actual data collected from an experimental system,the accuracy of prediction method is verified.

关键词： Photovoltaic(PV) power prediction similar days time series analysis

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SMK-means:An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data

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computers, Materials & Continua 2018年第9期56卷 365-379页

作者： Bo Xiao Zhen Wang Qi Liu Xiaodong Liu Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Centre of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computer and Software Nanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computing Edinburgh Napier University10 Colinton RoadEdinburghEH105DTUK

In recent years,the rapid development of big data technology has also been favored by more and more *** data storage and calculation problems have also been *** the same time,outlier detection problems in mass data have also come along with ***,more research work has been devoted to the problem of outlier detection in big ***,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect *** this paper,an improved algorithm is *** SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of *** this paper,several experiments are performed to compare and analyze multiple performances of the *** analysis,we know that the proposed algorithm is superior to the existing algorithms.

关键词： Big data outlier detection SMK-means Mini Batch K-means simulated annealing

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Mathematical Model for an Electrode-Type Electric Boiler Based on GSA-optimized Neural Network

Mathematical Model for an Electrode-Type Electric Boiler Bas...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Ma Liangyu Zhang Peiyao Ma Jin School of Control and Computer Engineering North China Electric Power University Baoding China

ISBN: (纸本)9781538680988;9781538680971

Taking an electrode-type electric boiler as the object, the factors influencing the thermal load of the electric boiler are analyzed. A prediction model for the thermal load is established with neural network, and the model parameters are optimized by Gravitational Search Algorithm (GSA)to improve the convergence performance of its training process. The results show that, the neural network model can fit the nonlinear characteristics among input and output variables, and GSA significantly improves the training speed of the neural network model. The model can predict the electric boiler thermal load accurately under various conditions. It can also participate in peak regulation and frequency regulation of power grid combining with thermal power units, which can improve the operational flexibility of thermal power unit.

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Application of Improved Gravitational Search Algorithm in PID control for Boiler Drum Water Level 29

Application of Improved Gravitational Search Algorithm in PI...

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第29届中国控制与决策会议

作者： Le Wei Binbin Ma School of Control and Computer Engineering North China Electric Power University

ISBN: (纸本)9781509046584

The boiler drum water level reflects the equilibrium relationship between the steam load and the water supply of the drum boiler,which is an important monitoring parameter in boiler *** object has the characteristics of non-self-balancing and pure delay,and its control quality directly affects the safe operation of a boiler-turbine *** on the improved gravitational search algorithm,the parameter optimization method of the main controller is put forward according to the wide-used cascade control structure of drum water level in power *** gravitational search algorithm has the advantages of simple process,little parameter setting,strong universality of the algorithm,but easy to fall into local *** with the memory function of particle swarm optimization algorithm and the high precision of other improved algorithms,the formulas of position and velocity are improved,an improved gravitational search algorithm(IGSA) is *** applying this method to the optimization control of the drum water level,and comparing with trial-and-error method(TEM),genetic algorithm(GA),and the standard algorithm(GSA),simulation tests are carried out to verify its effectiveness.

关键词： Improved Gravitational Search Algorithm PID control drum water level

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