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Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment

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SENSORS 2022年第19期22卷 7249-7249页

作者： Huo, Zimin Wang, Fuchao Shen, Honghai Sun, Xin Zhang, Jingzhong Li, Yaobin Chu, Hairong Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Airborne Opt Imaging & Measurement Changchun 130033 Peoples R China Forest Protect Res Inst Heilongjiang Prov Harbin 150040 Peoples R China

Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the particle swarm optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively;compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times.

关键词： MEMS gyroscope convolutional denoising autoencoder temporal convolutional network attention mechanism particle swarm optimization algorithm Kalman filter

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Optimal design and dynamic performance analysis of a fractional-order electrical network-based vehicle mechatronic ISD suspension

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年 184卷

作者： Shen, Yujie Hua, Jie Fan, Wei Liu, Yanling Yang, Xiaofeng Chen, Long Jiangsu Univ Res Inst Automot Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China

To further explore the beneficial effects of the vibration isolation performance of a new vehicle ISD (inerter-spring-damper) suspension using a mechatronic inerter, this paper proposes a novel optimal design methodology for a vehicle mechatronic ISD suspension system based on a fractional-order electrical network is proposed. In view of difficulties in the engineering imple-mentation of fractional-order mechanical network elements, this paper first studies the corre-sponding relationship and analytical expression of fractional-order mechanical and electrical network elements respectively. Under the instruction of fractional calculus, the fractional-order electrical network elements are used to realize equivalent fractional-order mechanical network elements by adopting a ball-screw mechatronic inerter. Then, a quarter car dynamic model of vehicle mechatronic ISD suspension is constructed, and the parameters of the fractional-order electrical network and the integer-order electrical network are obtained by means of particle swarm optimization algorithm. The performance advantages of vehicle mechatronic ISD sus-pension using a fractional-order electrical network are verified by numerical simulations and experiments. The results show that, compared with the application of an integer-order electrical network, the vibration suppression performance of a vehicle mechatronic ISD suspension can be further enhanced by using a fractional-order electrical network.

关键词： Vehicle suspension Mechatronic inerter Fractional-order electrical network particle swarm optimization algorithm Optimal design

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A simulation-based optimization for deploying multiple kinds road rescue vehicles in urban road networks

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期181卷

作者： Tan, Zheyi Zhang, Qian Deng, Weiliang Zhen, Lu Shao, Wei Shanghai Univ Sch Management Shang Da Rd 99 Shanghai 200444 Peoples R China Shanghai Univ Sci Res Management Dept Shang Da Rd 99 Shanghai 200444 Peoples R China

In the rescue process of urban road traffic accidents, the decision-making support system is of great importance since it will affect the rescue response time, and the response time is used as an evaluation index of the system performance. A shorter response time can ensure that casualties are rescued timely and can help restore the road as soon as possible. This paper provides a decision tool for traffic accident rescue vehicles' integrated deployment problem in urban areas. In order to better simulate the stochastic rescue requests, the time and space distribution of traffic, and the dynamic rescue process, a simulation optimization model is established. This model determines the optimal deployment plan for three kinds of road rescue vehicles, i.e., ambulances, wreckers, and sweepers, in several time intervals in one day. To find a better solution, we incorporate a particle swarm optimization (PSO) method into the simulation and optimization procedures. The proposed method is validated by a case study in Shanghai. Additionally, sensitivity analysis is included to study the effects of various parameters and to confirm effectiveness and efficiency of the proposed method. The numerical experiments indicate that simply increasing the number of road rescue resources does not always improve the efficiency of road rescue.

关键词： Traffic accident rescue Vehicle deployment Simulation optimization particle swarm optimization algorithm

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particle swarm-differential evolution algorithm with multiple random mutation

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APPLIED SOFT COMPUTING 2022年第0期120卷

作者： Lin, Meijin Wang, Zhenyu Chen, Danfeng Zheng, Weijia Foshan Univ Sch Mechatron Engn & Automat Foshan 528000 Peoples R China

Differential evolution (DE) algorithm has attracted considerable attention because of its effectiveness and simplicity. However, previous studies have validated that DE still suffers from some limitations such as premature convergence and slow convergence especially dealing with multimodal optimization problems. To address these concerning issues, we propose an innovative optimization method named particle swarm-differential evolution algorithm with multiple random mutation (MRPSODE) in this paper. The proposed MRPSODE algorithm is based on multiple random mutation framework cooperating with mean particle swarm mutation strategy, DE/current-to-rand/1 mutation strategy and disturbance strategy. Firstly, we incorporate the modified mean particle swarm mutation strategy into DE algorithm to improve the global convergence ability. Secondly, DE/current-to-rand/1 mutation strategy is adopted to increase the population diversity and produce perturbations to avoid the algorithm trapping into a local optimum. Thirdly, we propose a disturbance strategy to help the population escape from local optima, so as to enhance the exploration ability. Finally, to ensure that the proposed algorithm can get satisfactory solutions with a fast convergence speed, we design a multiple random mutation framework, in which these three mutation strategies can effectively play their advantages and make up for the shortcomings of others. To evaluate the performance of the proposed algorithm, three different experiments are constructed on twenty-nine classical benchmark functions. The simulation results demonstrate that, (1) MRPSODE significantly outperforms conventional PSO and DE algorithms, (2) MRPSODE can achieve better performance than nine well-known DE variants in terms of solution quality and robustness, (3) MRPSODE is superior to nine latest heuristic-based algorithms. Furthermore, MRPSODE is successfully applied to seven typical constrained optimization problems and performs better than a

关键词： particle swarm optimization algorithm Differential evolution Disturbance strategy Multiple random mutation Constrained optimization problems

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Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model

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AGRICULTURAL WATER MANAGEMENT 2020年 227卷 105846-000页

作者： Jin, Xiuliang Li, Zhenhai Feng, Haikuan Ren, Zhibin Li, Shaokun Chinese Acad Agr Sci Inst Crop Sci Minist Agr Key Lab Crop Physiol & Ecol Beijing 100081 Peoples R China Beijing Acad Agr & Forestry Sci Beijing Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Peoples R China

The accurate and timely estimation of temporal and spatial changes in crop growth and yield before harvesting is essential for ensuring global food security. The integration of remote sensing data and crop models is a potential approach for the estimation of key crop growth parameters and crop yields. Therefore, the aim of this study was to assimilate biomass and canopy cover (CC) derived from vegetation indices into the AquaCrop model using the particle swarm optimization (PSO) algorithm in order to obtain a more accurate estimation of CC, biomass, and yield for maize. The results show that, compared to other vegetation indices, the enhanced vegetation index (EVI) and the three-band water index (TBWI) can be used to obtain a better estimation of CC (R-2 = 0.78 and root-mean-square error (RMSE) =9.84%) and biomass (R-2 = 0.76 and RMSE = 2.84 ton/ha), respectively. Additionally, it was found that the data assimilation approaches in which only CC was used as a state variable (scheme SVcc) and only biomass was used as a state variable (scheme SVbio) can be used to obtain more accurate estimations of CC (R-2 = 0.83 and RMSE = 8.12%) and biomass (R-2 = 0.81 and RMSE = 2.51 ton/ha), respectively;however, larger differences were found between the measured and estimated values of one variable (i.e., CC or biomass) when the other variable (i.e., biomass or CC) was used as the only state variable during the data assimilation. The data assimilation approach in which both CC and biomass were used as state variables (scheme SVcc+bio) produced a robust result, with the estimation accuracy being fairly close to that obtained using the single-variable (SVcc or SVbio) data assimilation approaches. The estimation accuracy for maize yield was slightly better when using a double-variable data assimilation approach (R-2 = 0.78 and RMSE = 1.44 ton/ha) than when using a single-variable data assimilation approach. In summary, this study presents a robust approach for increasing the estimat

关键词： Maize yield Canopy cover Biomass particle swarm optimization algorithm Double-state variables Data assimilation

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Expeditious COVID-19 similarity measure tool based on consolidated SCA algorithm with mutation and opposition operators

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APPLIED SOFT COMPUTING 2021年 104卷 107197-107197页

作者： Issa, Mohamed Zagazig Univ Fac Engn Comp & Syst Dept Zagazig Egypt Nahda Univ Fac Comp & Informat Bani Suwayf Egypt

COVID-19 is a global pandemic that aroused the interest of scientists to prevent it and design a drug for it. Nowadays, presenting intelligent biological data analysis tools at a low cost is important to analyze the biological structure of COVID-19. The global alignment algorithm is one of the important bioinformatics tools that measure the most accurate similarity between a pair of biological sequences. The huge time consumption of the standard global alignment algorithm is its main limitation especially for sequences with huge lengths. This work proposed a fast global alignment tool (G-Aligner) based on meta-heuristic algorithms that estimate similarity measurements near the exact ones at a reasonable time with low cost. The huge length of sequences leads G-Aligner based on standard Sine-Cosine optimization algorithm (SCA) to trap in local minima. Therefore, an improved version of SCA was presented in this work that is based on integration with PSO. Besides, mutation and opposition operators are applied to enhance the exploration capability and avoiding trapping in local minima. The performance of the improved SCA algorithm (SP-MO) was evaluated on a set of IEEE CEC functions. Besides, G-Aligner based on the SP-MO algorithm was tested to measure the similarity of real biological sequence. It was used also to measure the similarity of the COVID-19 virus with the other 13 viruses to validate its performance. The tests concluded that the SP-MO algorithm has superiority over the relevant studies in the literature and produce the highest average similarity measurements 75% of the exact one. (C) 2021 Elsevier B.V. All rights reserved.

关键词： COVID-19 Bioinformatics Pairwise global alignment Sine-Cosine optimization algorithm particle swarm optimization algorithm

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Peak shaving and heat supply flexibility of thermal power plants

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APPLIED THERMAL ENGINEERING 2021年 193卷 117030-117030页

作者： Wang, Congyu Song, Jiwei Zhu, Lingkai Zheng, Wei Liu, Zhaozhao Lin, Chengkun Shandong Univ Inst Adv Technol 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Univ Inst Thermal Sci & Technol 17923 Jingshi Rd Jinan 250061 Peoples R China State Grid Corp China Shandong Elect Power Res In 2000 Wangyue Rd Jinan 250003 Peoples R China

The operational flexibility of thermal power plants is important to consume renewable energy generation, especially in the regions where combined heat and power (CHP) units account for a high proportion. Focusing on the relationship between peak-shaving capacity of CHP units and the consumption of renewable energy generation, the problem about operational flexibility of CHP plants is analyzed in this paper. From the perspective of entire CHP plants rather than the renovation of one CHP unit, the problem regarding the operation scheduling without cost or risk is addressed. A plant-level operation domain model is established, which can improve the operational flexibility of CHP plants. The heat and power adjustable ranges of CHP plants were obtained by particle swarm optimization. Also the CHP plant located in northern China was taken as an example. The power load downward adjustment range of four periods in the heating season are 9.21%, 20.44%, 14.09% and 5.31% respectively, compared with the entire plant actual power under the same heat load. Moreover, the operational flexibility of CHP plants can be enlarged by decreasing the pressure of heating extraction. Compared with the entire plant actual operating conditions, the consumption of renewable energy generation could be increased by 268.56 million kWh approximately and about 0.22 million tons CO2 emissions can be reduced per heating season if the reference plant operates with minimum power load constantly.

关键词： CHP Operation domain Flexibility Renewable energy generation particle swarm optimization algorithm

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Recovery trajectory planning for the reusable launch vehicle

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AEROSPACE SCIENCE AND TECHNOLOGY 2021年 117卷 106965-106965页

作者： Cheng, Guanghui Jing, Wuxing Gao, Changsheng Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China

Trajectory planning method for the entire recovery process of the reusable launch vehicle is studied. Firstly, a revised trajectory correction method (RTCM) is proposed to reduce the maximum normal aerodynamic load in the endo-atmosphere unpowered descent phase and improve the vehicle's landing accuracy on the influence of wind field. It consists of two parts, namely moving the ignition point of the vertical landing phase on the horizontal plane and introducing preset steady wind field into trajectory planning. Secondly, a compound trajectory planning method, which combines particle swarm optimization algorithm (PSO) with Polynomial Guidance Law (PGL), is proposed to complete the optimization task of the entire recovery process. Employing PSO algorithm can avoid the complex calculation of additional angle of attack caused by the preset steady wind field in traditional trajectory correction. To overcome the premature convergence of basic PSO, the traditional re-initialization mechanism is improved. Finally, the guidance simulation of two phases greatly affected by wind is accomplished. The effectiveness of the proposed methods is demonstrated with some scenarios and Monte Carlo simulation. (C) 2021 Elsevier Masson SAS. All rights reserved.

关键词： Reusable launch vehicle Trajectory correction Compound trajectory planning method particle swarm optimization algorithm Polynomial guidance law

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Optimal Time of Use Electricity Pricing Model and Its Application to Electrical Distribution System

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IEEE ACCESS 2019年 7卷 123558-123568页

作者： Yang, Hejun Wang, Lei Ma, Yinghao Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China

Because the time of use (TOU) strategies can directly affect the power flow distribution of electrical distribution system, this paper investigates the optimal TOU electricity pricing model and its functions for improving the power quality and reducing the power loss of electrical distribution system. Firstly, an optimal period partitioning algorithm based on a moving boundary technique is proposed for dividing an entire day to the different periods. Secondly, an optimal TOU electricity pricing model is proposed through minimizing the peak-valley difference, the voltage fluctuation, and the power loss. The particle swarm optimization (PSO) algorithm is adopted to solve the proposed optimization problem, and the multi-objective constrained optimization problem is transformed into a single objective unconstrained optimization problem. Thirdly, two novel indices of describing the voltage variation and the power loss are defined for considering the impact of TOU strategies and improving the power quality and reducing the power loss. Finally, an IEEE 14-bus system is applied to verifying the correctness and effectiveness of the proposed method. The results prove that the algorithm which proposed in this paper has a great significance in improving the power quality and the economic benefits of electrical distribution system.

关键词： Time of use optimal period partitioning moving boundary technique particle swarm optimization algorithm electrical distribution system

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Spatial Consistency Calibration Based on Phase Difference Minimization for Parallel Slightly Off-Axis Digital Holographic Microscopy

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2022年第3期14卷 1页

作者： Jin, Chuan He, Yu Tang, Yan Liu, Junbo Sun, Haifeng Hu, Song Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Sichuan Peoples R China

In this study, we propose a spatial consistency calibration method for slightly off-axis digital holographic microscopy (SO-DHM). The acquisition of relative position errors is summarized into a solution of a nonlinear optimization problem in which the root mean squared error of phase aberration of holograms is minimized. The particle swarm optimization algorithm is chosen to solve this optimization problem due to its simple structure, high convergence efficiency, and robust global search ability. Phase-only wavefronts based on phase aberration, which remove the influence of noise, are used for calibration. The simulation result indicates that the proposed method has subpixel-level precision, and the experiment demonstrates the effectiveness of the proposed method in the SO-DHM system.

关键词： Calibration optimization Charge coupled devices Optical attenuators Layout particle swarm optimization Optical polarization Digital holographic microscope particle swarm optimization algorithm slightly off-axis spatial consistency calibration

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