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Two-stage hybrid model for hydrological series prediction based on a new method of partitioning datasets

JOURNAL OF HYDROLOGY 2022年第PartA期612卷

作者： Xu, Hanbing Song, Songbai Guo, Tianli Wang, Huimin Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Area Minist Educ Yangling 712100 Peoples R China

Obtaining accurate point estimates and reliable interval prediction results for rainfall and runoff series is important to aid in water resource decision-making and planning management in a changing environment. In this paper, we propose a two-stage hybrid model (P-CVEE-LDBNN). The model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods for data preprocessing and a Bayesian neural network (LDBNN) based on Langevin dynamics as a prediction model. Then, a new dataset partitioning method based on a particle swarm optimization algorithm, which is different from the traditional dataset partitioning method, is applied. A one-stage hybrid model (T-CEELDBNN) that integrates the CEEMDAN method and the traditional method of partitioning datasets, a one-stage hybrid model (P-CEE-LDBNN) that integrates the CEEMDAN method and the new method of partitioning datasets, and the two-stage hybrid P-CVEE-LDBNN model were compared. These models were applied to monthly runoff and monthly precipitation series from seven hydrological and meteorological stations in the Yellow River basin. The mean absolute error (MAE), absolute root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R) were used to evaluate the predictive ability of the models. The containment ratio (CR), the average bandwidth (IW), and the average asymmetry degree (S) were used to evaluate the interval prediction results of the models. The results show that (1) the subset obtained with the new method of data partitioning considering statistical properties is more favorable for model prediction. (2) The second decomposition approach based on VMD is a reliable method that can significantly improve the prediction accuracy of the final model. (3) Compared with traditional neural networks that can only obtain deterministic point prediction results, Bayesian methods can provide intervals with prediction resu

关键词： Two -stage hybrid model Langevin dynamics Bayesian neural networks Data portioning particle swarm optimization algorithm Yellow River basin

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船用固体氧化物燃料电池与二冲程低速双燃料发动机集成动力系统的设计与优化

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哈尔滨工程大学学报（英文版） 2023年第4期22卷 837-849页

作者：曲金博冯永明吴云金朱元清邬斌扬肖忠旭 College of Power and Energy Engineering Harbin Engineering UniversityHarbin 150001China State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this ***,a 0-D model of SOFC and a 1-D model of ICE are built as agent ***,parameter analysis of the system is conducted based on SOFC and ICE *** show that the number of cells,current density,and fuel utilization can influence SOFC and ***,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this *** results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power ***,promising improvements in efficiency of 5.1%are achieved compared to the original ***,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.

关键词： Combined system SOFC-ICE integrated cycle Data-driven model particle swarm optimization algorithm

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Multi-objective time-energy-impact optimization for robotic excavator trajectory planning

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AUTOMATION IN CONSTRUCTION 2023年 156卷

作者： Feng, Hao Jiang, Jinye Ding, Nan Shen, Fangping Yin, Chenbo Cao, Donghui Li, Chunbiao Liu, Tao Xie, Jiaxue Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Wuxi Cosmo Suspended Platform Co Ltd Wuxi 214128 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Changwang Sch Honors Nanjing 210044 Peoples R China Nanjing Tech Univ United Inst Excavator Key Technol Nanjing 211816 Peoples R China SANY Grp Co Ltd Suzhou 215300 Peoples R China

Single-objective optimal trajectory cannot adapt to the complex requirements of excavator construction. A comprehensive optimal trajectory planning method is proposed to optimize the working time, energy consumption, and operational impact of robotic excavators. Without fusing any performance indexes, a normalized multi-objective function and an improved particle swarm optimization algorithm are established to achieve a comprehensive optimization of multiple objectives, while considering joint angle, velocity, acceleration, and quadratic acceleration constraints. Typical deep pit excavation simulation and experimental results show that the multi-objective optimization method is feasible, can balance multi-objective constraints, and can avoid falling into extremely long working times or large impacts. This method offers a more efficient and effective solution for multi-objective trajectory planning and provides a method for planning excavation trajectories based on different operating scenarios and objectives.

关键词： Excavator Hydraulic system Trajectory planning particle swarm optimization algorithm

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A parameter determination method of unscented transformation and its approximate ability analysis in the precision estimation of nonlinear measurement adjustment

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MEASUREMENT 2020年 166卷 108065-108065页

作者： Wang, Leyang Ding, Rui East China Univ Technol Fac Geomat Nanchang 330013 Jiangxi Peoples R China Shandong Jianzhu Univ Sch Surveying & Geoinformat Jinan 250101 Peoples R China

Unscented transformation (UT) is the key technology of unscented Kalman filter, which can approximate probability distribution to estimate second-order approximate mean and covariance matrix of nonlinear function. In parameter selection of various sampling strategies in the framework of the unscented transformation (UT), there are few studies on the determination method of sampling parameters. In this study, the particle swarm optimization (PSO) algorithm is introduced into UT for high convergence efficiency and few control parameters. The parameter selection of several commonly used sampling strategies is analyzed from the numerical point of view, and a method to determine the sampling parameters is proposed. Furthermore, the higher-order information ignored by the recommended parameters of UT is considered, and the approximation ability of UT is tapped and improved by the obtained parameters. For the first time, the minimal skew simplex sampling (MSS) and the scaled minimal skew simplex sampling (SMSS) are applied to the precision estimation of nonlinear measurement adjustment. Considering the influence of non-normal distribution data on nonlinear error propagation in geodetic measurement, this study analyzes the PSO-UT model in a case study from the geodetic field. Experiments show that PSO can effectively determine the parameters of UT and improve its approximation ability, which enriches UT theory. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Nonlinear measurement adjustment Precision estimation Unscented transformation particle swarm optimization algorithm Parameter selection Non-normal distribution

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Traveling wave inversion method of power line faults

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年第1期149卷

作者： Xia, Yixiang Li, Zewen Wang, Shuai Sun, Jian Guo, Xinyu Changsha Univ Sci & Technol Elect & Informat Engn Dept Changsha Peoples R China

In substations, the fault signal at the measuring point includes a variety of signal aliasing. This is partly due to the short length of the substation lines and the exitance of multiple types of equipment that cause the reflection and refraction of the traveling waves. Compared with the incident wave, the distortion in the fault signal is often significant hence interfering with the fault identification and location. To address this issue, we propose an inversion method of the incident wave. To do this, we first investigate the impact of multiple outlets of the substation bus, equivalent stray capacitance of the bus to the ground, transformers, line trap, and other types of equipment on traveling wave transmission. Modeling the aliasing effect of the traveling wave measuring point signal we then propose a fault signal filtering algorithm based on the improved Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT). Using this algorithm we then formulate an incident wave inversion model based on substation component parameters. The component parameters in the equivalent circuit model of substation are then accurately identified to achieve an accurate inversion of the incident waves. Identification of parameters is based on an optimization model of substation component parameters based on the particle swarm optimization algorithm (PSO). Our results show that compared with the measured mixed wave signal, the fault characteristics of the inverted incident waves are more accurate, hence improving the fault location accuracy.

关键词： Waveform inversion Incident wave Substation particle swarm optimization algorithm

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Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour

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ENERGY 2022年第0期243卷 122976-122976页

作者： Li, Bin Dong, Xujun Wen, Jianghui Changan Univ Sch Automobile Xian 710064 Shaanxi Peoples R China Wuhan Univ Technol Sch Sci Wuhan 430063 Peoples R China

Recently, electric vehicles (EVs) have become an important traffic tool in connected and automated environments due to their own advantages. However, the optimal driving control strategies of EVs and charging modes of wireless charging lanes are complex since wireless charging technology is limited when EVs are moving and traffic conditions are random. This paper constructs a scenario in which wireless charging facilities are deployed in part of a single lane and proposes a cooperative control method of mixed fleets including EVs and fuel vehicles based on the four objectives of driving safety, traffic efficiency, passenger comfort and energy management. Combined with the particle swarm optimization algorithm, the average driving velocity, lane-changing frequency and location of vehicles under different deployment lengths of charging facilities and mixing ratios are compared to discuss the influence of deployment length on the control effect of energy consumption and traffic efficiency. The numerical results indicate that when the lane length is 1500 m, with the increasing proportion of EVs, the optimal deployment lengths are approximately 700 m, 550 m and 400 m in the case of low traffic density and approximately 550 m, 500 m and 450 m in the case of high traffic density.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Cooperative control method of mixed fleet particle swarm optimization algorithm Optimal deployment Energy consumption

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Fast basis search for adaptive Fourier decomposition

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2018年第1期2018卷 1页

作者： Wang, Ze Wan, Feng Wong, Chi Man Qian, Tao Univ Macau Dept Elect & Comp Engn Fac Sci & Technol Ave Univ Taipa Macau Peoples R China Univ Macau Dept Math Fac Sci & Technol Ave Univ Taipa Macau Peoples R China

The adaptive Fourier decomposition (AFD) uses an adaptive basis instead of a fixed basis in the rational analytic function and thus achieves a fast energy convergence rate. At each decomposition level, an important step is to determine a new basis element from a dictionary to maximize the extracted energy. The existing basis searching method, however, is only the exhaustive searching method that is rather inefficient. This paper proposes four methods to accelerate the AFD algorithm based on four typical optimization techniques including the unscented Kalman filter (UKF) method, the Nelder-Mead (NM) algorithm, the genetic algorithm (GA), and the particle swarm optimization (PSO) algorithm. In the simulation of decomposing four representative signals and real ECG signals, compared with the existing exhaustive search method, the proposed schemes can achieve much higher computation speed with a fast energy convergence, that is, in particular, to make the AFD possible for real-time applications.

关键词： Adaptive Fourier decomposition Unscented Kalman filter Nelder-Mead algorithm Genetic algorithm particle swarm optimization algorithm

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Evolutionary dynamics in spatial threshold public goods game with the asymmetric return rate mechanism

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CHAOS SOLITONS & FRACTALS 2020年 136卷 109819-109819页

作者： Wang, Xianjia Chen, Wenman Wuhan Univ Econ & Management Sch Wuhan 430070 Peoples R China Wuhan Univ Inst Syst & Engineer Wuhan 430070 Peoples R China

In reality, cooperators often are provided a higher return rate for their contributions. Inspired by the reality, this paper introduces the asymmetric return rate mechanism, where the return rate is asymmetric between cooperators and defectors. This paper mainly studies how the asymmetric return rate mechanism influences the evolutionary dynamics in spatial threshold public goods game on two different complex networks, the namely square lattice and Barabási-Albert scale-free network. The simulation results show that increasing the sensitivity for the spread of cooperation is more effective than increasing that for the spread of defection not only to promote cooperation, but also to elevate the provision of the public goods. In addition, a moderate value of threshold is the best to elevate both the promotion of cooperation and the provision of the public goods.

关键词： Evolutionary dynamics Spatial threshold public goods game Asymmetric return rate particle swarm optimization algorithm

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A tristable nonlinear energy sink

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2023年 238卷

作者： Zeng, You-cheng Ding, Hu Shanghai Univ Shanghai Inst Appl Math & Mech Sch Mech & Engn Sci Shanghai Key Lab Mech Energy Engn Yangchang Rd 149189 Shanghai 200444 Peoples R China

The tristable characteristics are mostly used in the field of energy harvesting and vibration isolator, but are rarely devoted to in the field of vibration absorption. In this paper, a tristable nonlinear energy sink (TNES) model is proposed for vibration absorption. The TNES is constructed by fixing a magnet in the middle of the clamping pre -pressure beam, and setting a magnet on both sides of the fixed magnet. By adjusting the magnet position, the proposed TNES can be transformed into bistable NES (BNES) and monostable NES (MNES). The TNES is coupled with the linear oscillator (LO), and the differential equation of motion of the coupled system is obtained with the Hamiltonian principle. The approximate analytical method and the numerical method are mutually verified. The vibration reduction efficiency of the TNES under harmonic excitation is presented. The influence of the pa-rameters of the TNES on the dynamics and vibration reduction efficiency is studied. The optimal parameters of the TNES are obtained by the particle swarm optimization (PSO) algorithm. Compared with the corresponding bistable NES and monostable NES, it shows that the tristable NES has obvious advantages in vibration sup-pression efficiency. The effects of the TNES barrier depths and the excitation amplitude on the vibration reduction efficiency are revealed. The TNES can dissipate energy through chaotic inter-well oscillation between three stable positions, and achieve the purpose of effectively suppressing vibration.

关键词： Nonlinear energy sink Tristable Vibration control Harmonic balance method particle swarm optimization algorithm

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Wavefront sensing method for diffraction optical system based on phase diversity

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INFRARED PHYSICS & TECHNOLOGY 2019年 102卷 102980-000页

作者： Zhi, Xiyang Gong, Jinnan Yu, Di Wang, Dawei Niu, Ruize Harbin Inst Technol Res Ctr Space Opt Engn Harbin 150001 Heilongjiang Peoples R China

In this paper, we present a novel wavefront sensing method for diffraction optical system based on phase diversity. Based on the physical-imaging mechanism of diffractive optical system, the wavefront characteristics of the diffraction optical system are characterized by using diffraction efficiency. On this basis, a novel modified phase diversity (PD) wavefront sensing method is established based on the blocking idea. After that, the global optimization method of corresponding method based on particle swarm optimization algorithm is proposed. Finally, some experiment results are achieved with the experiment on a membrane diffraction optical system. Experimental results indicate that the proposed algorithm performs well on both wavefront reconstruction and image restoration and is far superior to traditional methods in diffraction optical systems. The accuracy of our proposed PD method is at least two orders of magnitude higher than the traditional method. Taking the 0.01 degrees field of view for example, our modified PD method can achieve the accuracy of 2.5 x 10(-4) wavelength when the diffraction efficiency is higher than 0.6. This proposed method can be applied to improve the image quality of the diffraction optical system and further support the on-orbit application of ultra-large aperture membrane imaging technology.

关键词： Wavefront sensing Phase diversity Diffraction optical system particle swarm optimization algorithm

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