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Hybrid Network Model Based on Data Enhancement for Short-term Power Prediction of New PV Plants

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 77-88页

作者： Shangpeng zhong Xiaoming Wang Bin Xu Hongbin Wu Ming Ding the anhui Province Key Laboratory of Renewable Energy Utilization and Energy Saving Hefei University of TechnologyHefei 230009China the State Grid anhui Electric Power Research Institute Hefei 230061China

This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV ***,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training ***,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction ***,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction *** simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.

关键词： New photovoltaic(PV)plant short-term predic tion time-series generative adversarial network(TimeGAN) hy brid network hyperparameter

Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

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Chinese Journal of Chemical Engineering 2024年第3期67卷 182-194页

作者： Shan Liu Wenqi zhong Li Sun Xi Chen Rafal Madonski School of Energy and Environment Key Laboratory of Energy Thermal Conversion and Control of Ministry of EducationSoutheast UniversityNanjing 210096China Faculty of Automatic Control Electronics and Computer ScienceSilesian University of TechnologyGliwise 44-100Poland

Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution *** desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system *** address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this *** UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant *** viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear *** obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled *** the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and ***,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.

关键词： Desulphurization system Disturbance rejection Model predictive control Uncertainty and disturbance estimator Nonlinear system

Robust-augmentation nonlinear dynamic inversion control of over-actuated coaxial high-speed helicopter in transition mode

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Chinese Journal of Aeronautics 2024年第8期37卷 404-420页

作者： Yuqing QIU Yan LI Jinxi LANG Yuxian LIU zhong WANG School of Automation Northwestern Polytechnical UniversityXi’an 710129China

As the elevator and rudder can be used actively for control,in addition to the rotors,Coaxial High-speed Helicopters(CHHs)have the problems of control redundancy and changing control authority in the transition *** paper presents a robust-augmentation transitioning flight control design for a CHH under the adverse conditions of parametric uncertainties and external ***,based on control characteristic analysis,an Adaptive Filtered Nonlinear Dynamic Inversion(AFNDI)controller is proposed for the angular rate to handle the effect of unknown unstructured uncertainties and external *** analysis proves that the presented angular rate controller can guarantee steady-state and transient ***,the attitude angle and velocity controllers are also ***,an Incremental-based Nonlinear Prioritizing Control Allocation(INPCA)method is designed to take into account control surface transition and changing control authority,which efficiently distributes the required moments between coaxial rotors and aero-surfaces,and avoids the control reversal problem of the yaw *** the proposed control architecture,the low-pass filter is introduced to alleviate the adverse influence of time delay and measurement ***,the effectiveness of the proposed controller is demonstrated through nonlinear numerical simulations,and is compared with existing *** results show that the proposed control law can improve both capabilities of disturbance rejection and fast response,and works satisfactorily for the CHH transitioning control characteristic.

关键词： Coaxial high-speed helicopter Transition mode Attitude control Control allocation Robust adaptive control

Optimized Selection Method of Air Combat Course of Action under Stochastic Uncertainty

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Journal of Systems Science and Systems Engineering 2024年第4期33卷 494-518页

作者： Yun zhong Jieyong Zhang Peng Sun Lujun Wan Kepeng Wang Information and Navigation College Air Force Engineering UniversityXi’an710077China Equipment Management and Unmanned Aerial Vehicle Engineering College Air Force Engineering UniversityXi’an710051China Air Traffic Control and Navigation College Air Force Engineering UniversityXi’an710051China Unit 94040 of PLA Korla841000China

Aiming at the design problem of aviation swarm combat course of action(COA),considering the influence of stochastic parameters in the causal relationship model and optimization problem model,according to the dynamic influence net(DIN)theory,stochastic simulation technique,feedforward neural network(FNN)function approximation technique and multi-objective artificial fish school algorithm(MOAFSA),this paper proposed a COA optimized method based on DIN and multi-objective stochastic chance constraint optimization for aviation swarm ***,on the basis of establishing the overall framework of the model and defining the elements of causal relationship modeling,the static and dynamic causal relationship modeling and optimization problem modeling were carried out ***,the probability propagation mechanism of DIN was established,which mainly included two aspects,i.e.,the overall process and the specific ***,input and output data were generated based on stochastic *** to these data,FNN was adopted for function approximation,and MOAFSA was adopted for iterative ***,the rationality of the model,and the effectiveness and superiority of the algorithm were verified through multiple sets of simulation cases.

关键词： Stochastic uncertainty aviation swarm stochastic simulation course of action feedforward neural network multi-objective artificial fish school algorithm

A bifunctional POM-based Cu-viologen complex with mixed octamolybdate clusters for rapid oxidation desulfurization and effective photogeneration of hydrogen

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Rare Metals 2024年第1期43卷 236-246页

作者： Lin Yang zhong Zhang Chao-Nan Zhang Xiu-Li Wang College of Chemistry and Materials Engineering Bohai UniversityJinzhou 121013China Professional Technology Innovation Center of Liaoning Province for Conversion Materials of Solar Cell Bohai UniversityJinzhou 121013China

The design and synthesis of catalysts for the oxidation desulfurization and production of hydrogen are extremely important for solving environmental pollution and energy ***,a novel bifunctional [α-Mo_(8)O_(26)]^(4-)/[β-Mo_(8)O_(26)]^(4-)-based Cu-viologen complex H_(4)[Cu_(2)~ⅠCl_(2)(Hbcbpy)_(4)] [α-Mo_(8)O_(26)] [β-Mo8O_(26)]·H_(2)O(BHU-2,Hbcbpy=1-(4-carboxybenzyl)-4,4'-bipyridinium) was synthesized and characterized by single-crystal X-ray diffraction(XRD),infrared radiation spectra,powder X-ray diffraction(PXRD) and X-ray photoelectron spectroscopy(XPS) *** structural characteristic of BHU-2 is the presence of two types of octamolybdate clusters [α-Mo_(8)O_(26)]^(4-)/[β-Mo_(8)O_(26)]^(4-)and a new binuclear Cu~Ⅰ-Hbcbpy complex linked by Cl-bridges[Cu_(2)ⅠCl_(2)(Hbcbpy)_(4)]^(4+).BHU-2 as a heterogeneous catalyst exhibits excellent activities to the oxidation desulfurization and photocatalytic hydrogen *** room temperature,BHU-2 can catalyze 96% conversion of methyl phenyl sulfide with 98% selectivity,and the process obeys the pseudo-first-order reaction kinetic with the half-life of9.6 *** notorious 2-chloroethyl ethyl sulfide can achieve 99% conversion with 98% selectivity within only1 min at the presence of BHU-2,and the turnover frequency(TOF) is up to 7400 h^(-1).BHU-2 also exhibits high catalytic activity for the oxidation of other aromatic and aliphatic thioethers within short time at room ***,BHU-2 shows a high catalytic activity for visible-light-driven hydrogen evolution with an H_(2) evolution rate of 1677.85 μmol·g^(-1)·h^(-1) within 10 ***,the catalytic activities do not decrease evidently after three cycles,revealing the prominent structural stability and recyclability.

关键词： Polyoxometalate Metal-organic complex Viologen Oxidation desulfurization Photocatalytic hydrogen production

REEs recovery from molten salt electrolytic slag:Challenges and opportunities for environmentally friendly techniques

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Journal of Rare Earths 2024年第6期42卷 1009-1019,I0001页

作者： Ziyan Yang Faxin Xiao Shuchen Sun Heng zhong Ganfeng Tu School of Metallurgy Northeastern UniversityShenyang110819China Key Laboratory for Recycling of Nonferrous Metal Resources(Shenyang) Shenyang110819China

Due to the unique physical and chemical properties,rare earth elements(REEs)play a significant role in the high-tech *** the past few decades,the rare earth reserve in China has been gradually decreasing and more pressure has been exerted on the global rare earth supply for the increasing demand of REEs,which indicates that it is essential to recycle secondary resources to meet the rare earth *** for rare earth molten salt electrolytic slag(REMES),although its high rare earth content has potential huge economic value,its high fluorine content of approximately 10 wt%-20 wt%can pollute the *** methods are used to treat *** metallurgical and pyro-hydrometallurgical methods have gotten a big success for solving most of the hydrometallurgical problems,while some problems,like long route and waste water,need to be *** distillation is a new and promising method with a short process due to its harmlessness and high efficiency,but has shortcomings such as high energy consumption and material *** review presents these above three treatment methods,and the challenges and chances of using the recovery technique of REMES in an environmentally friendly way.

关键词： Rare earths Molten salt electrolytic slag Hydrometallurgy Pyro-hydrometallurgy Vacuum distillation Recovery

Reliable calculations of nuclear binding energies by the Gaussian process of machine learning

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Nuclear Science and Techniques 2024年第6期35卷 130-144页

作者： Zi-Yi Yuan Dong Bai Zhen Wang zhong-Zhou Ren School of Physics Science and Engineering Tongji UniversityShanghai 200092China College of Science Hohai UniversityNanjing 211100China Key Laboratory of Advanced Micro-Structure Materials Ministry of EducationShanghai 200092China

Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.

关键词： Nuclear binding energies decay Machine learning Gaussian process

Staring-imaging satellite pointing estimation based on sequential ISAR images

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Chinese Journal of Aeronautics 2024年第8期37卷 261-276页

作者： Canyu WANG Libing JIANG Weijun zhong Xiaoyuan REN Zhuang WANG National Key Laboratory of Automatic Target Recognition National University of Defense TechnologyChangsha 410073China Xi’an Satellite Control Center Xi’an 710600China

Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar(ISAR)images plays a significant role in space situational awareness and ***,feature extraction and cross-range scaling of ISAR images create bottlenecks that limit performances of current estimation ***,the emergence of staring imaging satellites,characterized by complex kinematic behaviors,presents a novel challenge to this *** address these issues,this article proposes a pointing estimation method based on Convolutional Neural Networks(CNNs)and a numerical optimization algorithm.A satellite’s main axis,which is extracted from ISAR images by a proposed Semantic Axis Region Regression Net(SARRN),is chosen for investigation in this article due to its unique ***,considering the kinematic characteristic of the staring satellite,an ISAR imaging model is established to bridge the target pointing and the extracted *** on the imaging model,pointing estimation and cross-range scaling can be described as a maximum likelihood estimation problem,and an iterative optimization algorithm modified by using the strategy of random sampling-consistency check and weighted least squares is proposed to solve this ***,the pointing of targets and the cross-range scaling factors of ISAR images are *** experiments based on actual satellite orbital parameters verify the effectiveness of the proposed *** work can improve the performance of satellite reconnaissance warning,while accurate cross-range scaling can provide a basis for subsequent data processes such as 3D reconstruction and attitude estimation.

关键词： Staring-imaging Agile satellite Inverse Synthetic Aperture Radar(ISAR) Pointing estimation Cross-range scaling

A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition

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Tsinghua Science and Technology 2024年第1期29卷 99-111页

作者： Yuxuan Cao Difei Zhang Shaoqi Ding Weiyi zhong Chao Yan School of Computer Science Qufu Normal UniversityRizhao 276826China School of Mathematical Sciences Qufu Normal UniversityQufu 273165China School of Computer Science Qufu Normal UniversityRizhao 270826China College of Economic and Management Shandong University of Science and TechnologyQingdao 250307China

Air pollution is a severe environmental problem in urban *** air quality prediction can help governments and individuals make proper decisions to cope with potential air *** a classic time series forecasting model,the AutoRegressive Integrated Moving Average(ARIMA)has been widely adopted in air quality ***,because of the volatility of air quality and the lack of additional context information,i.e.,the spatial relationships among monitor stations,traditional ARIMA models suffer from unstable prediction *** some deep networks can achieve higher accuracy,a mass of training data,heavy computing,and time cost are *** this paper,we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring *** proposed model consists of three components:(1)an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations;(2)the Empirical Mode Decomposition(EMD)to decompose the air quality time series data into multiple smooth sub-series;and(3)the truncated Singular Value Decomposition(SvD)to compress and denoise the expanded *** results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.

关键词： air quality prediction Empirical Mode Decomposition(EMD) Singular Value Decomposition(SVD) AutoRegressive Integrated Moving Average(ARIMA)