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14th International Symposium on Antennas Propagation and EM Theory

作者： Tan, Hang Gou, Xiaoming China Res Inst Radiowave Propagat Qingdao Peoples R China

ISBN: (纸本)9798350353129;9798350353136

This article proposes a dot-shaped transmit beamforming algorithm by using interior point (IP) algorithm to design optimized frequency offset (FO). We compared the beam focusing performance of this scheme with linear FO, logarithmic (Log) FO, and genetic algorithm (GA) FO schemes, and provided the convergence process of the algorithm. The simulation results show that the algorithm converges quickly while ensuring good beam focusing effect. We extended it to two-dimensional planar array and further reduced the computational complexity of the algorithm by applying symmetric frequency offset on the array. The effectiveness of the algorithm was verified through numerical simulation

关键词： transmit beamforming FDA optimization algorithm planar FDA

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Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system

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ENERGY 2024年 292卷

作者： Zhang, Yagang Wang, Hui Wang, Jingchao Cheng, Xiaodan Wang, Tong Zhao, Zheng North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Phys & Energy Technol Baoding 071003 Peoples R China North China Elect Power Univ Dept Automat Baoding 071003 Peoples R China Univ South Carolina Interdisciplinary Math Inst Columbia SC 29208 USA North China Elect Power Univ Hebei Key Lab Phys & Energy Technol Box 205 Baoding 071003 Hebei Peoples R China

Wind power is a critical source of renewable energy, but the variability of wind speed can impact the efficiency and reliability of wind power generation. To improve the accuracy of wind speed prediction, this paper proposes a novel system that combines deep learning and decomposition algorithms based on ensemble optimization methods. Firstly, the paper proposes an innovative hybrid modal decomposition (HMD) method that extracts accurate features from wind speed data using Singular Value Decomposition (SVD), modal number selection method, and Variational Mode Decomposition (VMD). Secondly, the variable screening method identifies relevant factors affecting wind speed as fixed inputs. Then, the stationary test method is used to identify the sequences of different features in the mode and establish different prediction models: a bidirectional long-term short-time neural network model (Bi-LSTM) model optimized by artificial hummingbird algorithm (AHA) is used for non-stationary sequences;an ARIMA model is used for stationary sequences. Data from Sotavento and Changma wind farms are used to test this forecasting system. Simulation experiments are carried out from multiple dimensions. The results show that all error indicators are significantly improved, and the proposed prediction system is better than other comparative forecasting schemes.

关键词： Short-term wind forecasts Hybrid mode decomposition optimization algorithm Bi-directional long short-term memory Auto regressive integrated moving average model

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Phased Array Scattering-Cross-Section Reduction Based on Hybrid Scattering Cancellation Technique 17

Phased Array Scattering-Cross-Section Reduction Based on Hyb...

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17th European Conference on Antennas and Propagation (EuCAP)

作者： Li, Peng-Fa Qu, Shi-Wei Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu Peoples R China

ISBN: (纸本)9788831299077

A novel hybrid scattering cancellation approach to reduce scattering cross section (SCS) of phased arrays is proposed in this paper, which not only makes use of cancellations between antenna mode and structural mode scattering fields, but also achieves cancellation of scattering field from different array elements. An 8 x 8 planar microstrip phased array is constructed to validate the concept. By optimizing element loads utilizing particle swarm optimization algorithm, the proposed phased array realizes very impressive SCS reductions of 8.3 dB and 20 dB with respect to its conventional counterpart and the equal-sized metallic plate, respectively, with a balanced radiation performance.

关键词： phased array optimization algorithm scattering cancellation techniques scattering cross section reduction

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Monocular Relative Pose Estimation Pipeline for Uncooperative Resident Space Objects

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2022年第9期19卷 613-632页

作者： Piazza, Massimo Maestrini, Michele Di Lizia, Pierluigi Infin Orbits F-31000 Toulouse France Polytech Univ Milan I-20156 Milan Italy

This paper aims to present a deep learning-based pipeline for estimating the pose of an uncooperative target spacecraft from a single grayscale monocular image. The possibility of enabling autonomous vision-based relative navigation in close proximity to a noncooperative resident space object would be especially appealing for mission scenarios such as on-orbit servicing and active debris removal. The relative pose estimation pipeline proposed in this work leverages state-of-the-art convolutional neural network (CNN) architectures to detect the features of the target spacecraft using monocular vision. Specifically, the overall pipeline is composed of three main subsystems. The input image is first processed using an object detection CNN that localizes the bounding box enclosing our target. This is followed by a second CNN that regresses the location of semantic key points of the spacecraft. Eventually, a geometric optimization algorithm exploits the detected key-point locations to solve for the final relative pose. The proposed pipeline demonstrated centimeter-/degree-level pose accuracy on the spacecraft pose estimation dataset (SPEED), along with considerable robustness to changes in illumination and background conditions. In addition, the architecture showed to generalize well on real images, despite having exclusively exploited synthetic data from the SPEED to train the CNNs.

关键词： Resident Space Object Convolutional Neural Network Target Spacecraft optimization algorithm Earth Quaternions Satellites Machine Learning Euler Angles LIDAR

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Extended model predictive control scheme for smooth path following of autonomous vehicles

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Frontiers of Mechanical Engineering 2022年第1期17卷 34-49页

作者： Qianjie LIU Shuang SONG Huosheng HU Tengchao HUANG Chenyang LI Qingyuan ZHU Department of Mechanical and Electrical Engineering Xiamen UniversityXiamen 361102China School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUK

This paper presents an extended model predictive control(MPC)scheme for implementing optimal path following of autonomous vehicles,which has multiple constraints and an integrated model of vehicle and road *** curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics ***,rollover,and vehicle envelopes are used as multiple constraints in the MPC controller ***,an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path ***,simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC *** indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints,which can improve the path following quality for better ride comfort and road availability of autonomous vehicles.

关键词： autonomous vehicles vehicle dynamic modeling model predictive control path following optimization algorithm

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Disturbance Evaluation in Power System Based on Machine Learning

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Computers, Materials & Continua 2022年第4期71卷 231-254页

作者： Emad M.Ahmed Mohamed A.Ahmed Ziad M.Ali Imran Khan Department of Electrical Engineering College of EngineeringJouf UniversitySakaka72388Al-JoufSaudi Arabia Department of Electrical Engineering College of EngineeringPrince Sattam bin Abdulaziz UniversityWadi Addawaser11991Saudi Arabia Department of Electrical Engineering Aswan Faculty of EngineeringAswan UniversityAswan81542Egypt Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan

The operation complexity of the distribution system increases as a large number of distributed generators(DG)and electric vehicles were introduced,resulting in higher demands for fast online reactive power *** a power system,the characteristic selection criteria for power quality disturbance classification are not *** classification effect and efficiency needs to be improved,as does the generalization *** order to categorize the quality in the power signal disturbance,this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality ***,a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality *** two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine,and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are *** proposed method can be used to classify the single and compound power quality disturbances with improved classification effect,reliability,robustness,and anti-noise performance,according to the experimental *** hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF,SVM and ML-KNN schemes have 0.28,0.23 and 0.22 respectively at a noise intensity of 20 *** average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF,SVM and ML-KNN schemes indicates 0.7,0.77 and 0.78 respectively.

关键词： Optimal power flow optimization algorithm deep learning power systems

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Generalized fractional grey system models: The memory effects perspective

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ISA TRANSACTIONS 2022年 126卷 36-46页

作者： Xie, Wanli Wu, Wen-Ze Liu, Chong Goh, Mark Nanjing Normal Univ Inst EduInfo Sci & Engn Nanjing 210097 Peoples R China Cent China Normal Univ Sch Econ & Business Adm Wuhan 430079 Peoples R China Northeastern Univ Sch Sci Shenyang 110819 Peoples R China Natl Univ Singapore Logist Inst Asia Pacific NUS Business Sch Singapore Singapore

In recent years, grey models based on fractional-order accumulation and/or derivatives have attracted considerable research interest because they offer better performance in handling limited samples with uncertainty than integer-order grey models;however, there remains room for improvement. This paper considers a more flexible and general structure for the fractional grey model by incorporating a generalized fractional-order derivative (GFOD) that complies by memory effects, resulting in the development of a generalized fractional grey model (denoted as GFGM(1,1)). Specifically, we comprehensively analyse the modelling mechanism of the proposed GFGM(1,1) model, involving model parameter estimation and time response function derivation, and discuss the link between the proposed approach and existing special cases. Then, to further improve the efficacy of the proposed approach, four mainstream metaheuristic algorithms are employed to ascertain the orders of fractional accumulation and derivatives. Finally, we carry out a series of simulation studies and a real-world application case to demonstrate the applicability and advantage of the our approach. The numerical results show that GFGM(1,1) outperforms other benchmarks, and some significant insights are obtained from the numerical experiments.

关键词： Fractional -order derivative Grey system model Memory effects optimization algorithm

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Geometric Design of Hypersonic Vehicles for Optimal Mission Performance with High-Fidelity Aerodynamic Models

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JOURNAL OF AIRCRAFT 2022年第3期60卷 870-882页

作者： Coulter, Brian G. Huang, Daning Wang, Zhenbo Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN 37996 USA Penn State Univ Dept Aerosp Engn University Pk PA 16802 USA

Recent advances in efficient optimization algorithms and high-performance computing allow the construction of integrated design frameworks wherein the traditionally segregated disciplines such as airframe design, aerodynamics, and trajectory analysis can be coupled together in order to undertake the design and optimization of vehicles as integrated systems in a larger design space. The particular interest of this paper is a potential approach to incorporating high-fidelity aerodynamic models and trajectory optimization techniques in hypersonic vehicle designs by incrementally varying the geometric parameters of the vehicle to observe induced performance variations and optimize these parameters for specific mission profiles using data-driven optimization. First, the exigency of creating integrated design frameworks considering high-fidelity disciplinary models such as aerodynamics and trajectory modeling is justified. Then, an energy-based problem formulation for hypersonic trajectory optimization is introduced. A panel method based on the modified Newtonian flow theory and Eckert's reference model is used to produce high-fidelity aerodynamic force and heating coefficients, based on which a pseudospectral optimal control package is used to solve for optimal trajectories. Finally, a novel, iterative, data-driven framework employing Bayesian optimization and machine learning is established to integrate these disciplinary models and successively search for the geometry that enables the optimal mission performance. Simulation results demonstrate the feasibility and performance of the developed approach.

关键词： Aerodynamic Characteristics Pseudospectral Optimal Control Bayesian optimization optimization algorithm Aerodynamic Performance

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Kinematic modeling and solution of rigid-flexible and variable-diameter underwater continuous manipulator with load

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ROBOTICA 2022年第4期40卷 1020-1035页

作者： Yang, Chen Xu, He Li, Xin Yu, Fengshu Harbin Engn Univ Coll Mech & Elect Engn Harbin Peoples R China

This paper presents a method to solve the kinematics of a rigid-flexible and variable-diameter continuous manipulator. The multi-segment underwater manipulator is driven by McKibben water hydraulic artificial muscle (WHAM). Considering the effect of elasticity and friction, we optimized the static mathematical model of WHAM. The kinematic model of the manipulator with load is established based on the hypothesis of piecewise constant curvature (PCC). We developed an optimization algorithm to calculate the length of the WHAMs according to the principle of minimum strain energy and obtain the configuration space parameters of the kinematic model. Based on the infinitesimal method, the homogeneous transformation matrices of the variable-diameter bending sections are computed, and the terminal position and attitude are obtained. In this paper, we studied the working space of the manipulator by quantitative analysis of the impact factors including pressure and load. A deep neural network (DNN) with six hidden layers is designed to solve inverse kinematics. The forward kinematic results are used to train and test the DNN, and the correlation coefficient between the output and target samples reaches 0.945. We carried out an underwater experiment and verified the effectiveness of the kinematic modeling and solution method.

关键词： robot kinematics soft actuator hydraulic drive optimization algorithm deep learning

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Industrial Power Load Forecasting Method Based on Reinforcement Learning and PSO-LSSVM

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第2期52卷 1112-1124页

作者： Ge, Quanbo Guo, Chen Jiang, Haoyu Lu, Zhenyu Yao, Gang Zhang, Jianmin Hua, Qiang Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China Southeast Univ Sch Automat Nanjing 211189 Peoples R China Hangzhou Zhongheng Prov Key Enterprise Res Inst P Energy Res Inst Hangzhou 310018 Peoples R China Nanjing Univ Jiangsu Key Lab Meteorol Observat & Informat Proc Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Atmospher Environm & Equip Nanjing 210044 Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China Hangzhou Zhonhen Power Energy Co Ltd Hangzhou 310051 Peoples R China

Influenced by many complex factors, it is very difficult to obtain high-performance industrial power load forecasting. The industrial power load forecasting is deeply studied by fusing some machine-learning methods for industrial enterprise power consumers. As a result, a novel power load forecasting method is proposed by taking into account the variation of load characteristics in different regions, industries, and production patterns. First, through the improved K-means clustering analysis, the historical load data are classified as the production patterns to which they belong. Then, the prediction algorithm combining reinforcement learning with particle swarm optimization and the least-squares support vector machine is proposed. Finally, the improved algorithm in this article is used for short-term load forecasting separately by the load data in different patterns after the above processing. The forecasting method in this article is based on data driven with real datasets. The results of the simulation experiment show that the improved prediction algorithm can distinguish the changes in different production patterns and identify the load characteristics of different regions and industries with high prediction accuracy, which has practical application value.

关键词： Clustering analysis data-driven optimization algorithm power load forecasting reinforcement learning

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