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In-Field Calibration of Triaxial Accelerometer Based on Beetle Swarm Antenna Search algorithm

SENSORS 2020年第3期20卷 947-947页

作者： Wang, Pengfei Gao, Yanbin Wu, Menghao Zhang, Fan Li, Guangchun Harbin Engn Univ Collage Automat Harbin 150001 Peoples R China

Traditional calibration method is usually performed with expensive equipments such as three-axis turntable in a laboratory environment. However in practice, in order to ensure the accuracy and stability of the inertial navigation system (INS), it is usually necessary to recalibrate the inertial measurement unit (IMU) without external equipment in the field. In this paper, a new in-field recalibration method for triaxial accelerometer based on beetle swarm antenna search (BSAS) algorithm is proposed. Firstly, as a new intelligent optimization algorithm, BSAS algorithm and its improvements based on basic beetle antennae search (BAS) algorithm are introduced in detail. Secondly, the nonlinear mathematical model of triaxial accelerometer is established for higher calibration accuracy, and then 24 optimal measurement positions are designed by theoretical analysis. In addition, the calibration procedures are improved according to the characteristics of BSAS algorithm, then 15 calibration parameters in the nonlinear method are optimized by BSAS algorithm. Besides, the results of BSAS algorithm and basic BAS algorithm are compared by simulation, which shows the priority of BSAS algorithm in calibration field. Finally, two experiments demonstrate that the proposed method can achieve high precision in-field calibration without any external equipment, and meet the accuracy requirements of the INS.

关键词： calibration inertial measurement unit triaxial accelerometer intelligent optimization algorithm beetle swarm antenna search algorithm

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Concurrent fault diagnosis of modular multilevel converter with Kalman filter and optimized support vector machine

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2019年第3期7卷 43-53页

作者： Zhang, Yong Hu, Huanzhen Liu, Zhenxing Zhao, Min Cheng, Lei Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Hubei Peoples R China Minist Educ Engn Res Ctr Met Automat & Measurement Technol Wuhan Hubei Peoples R China

In this paper, concurrent fault diagnosis problem of modular multilevel converter (MMC) with Kalman filter and optimized support vector machine (SVM) is investigated. The state space model by synthesizing the circulating current and the output current is first established. Recurring to the Kalman filtering theory, the estimation on circulating and output current is realized, the residual is achieved by using the innovation which involved the predicted and measured current. Based on the obtained residual, the residual evaluation function and its threshold are constructed. Then, the fault can be detected according to the proposed fault detection strategy. Once the fault is detected, the fault localization unit is triggered and the residual data is adopted as data set. By employing the optimized SVM with genetic algorithm, the concurrent and intermittent fault localization of MMC can be accomplished. Finally, an 11-level MMC simulation systems with concurrent fault and intermittent fault are set up in MATLAB/Simulink, and the effectiveness of the proposed fault detection and localization method is verified.

关键词： Modular multilevel converter concurrent fault diagnosis Kalman filter support vector machine intelligent optimization algorithm

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Real-time monitoring of the corrosion behaviour of the 304SS in HCl solution using BPNN with joint image recognition and electrochemical noise

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CORROSION SCIENCE 2024年 228卷

作者： Zhang, Haofeng Wu, Zhiqin Chen, Yang Feng, Kaixuan Yan, Hong Song, Honggun Luo, Chao Hu, Zhi Nanchang Univ Sch Adv Mfg Nanchang 330031 Peoples R China Nanchang Univ Inst Adv Study Nanchang 330031 Peoples R China

Electrochemical noise has an important reference role in the study of early corrosion;however, a direct correlation between the microscopic morphology and the electrochemical noise of the corrosion process is lacking. This study uses a back propagation artificial neural network (BPNN) optimized by three intelligent optimization algorithms to real-time monitor the local corrosion of 304 stainless steel in HCl solution with joint recognition and feature extraction of corrosion images and electrochemical noise. The results show that the corrosion monitoring model established by optimized BPNN with genetic algorithms exhibits the highest convergence speed and accuracy.

关键词： 3 04SS Electrochemical noise In -situ observation Artificial Neural Networks intelligent optimization algorithm Localized corrosion

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Optimal Operation and Output Oscillations Reduction of PEMFC by using an intelligent Strategy

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INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2014年第8期9卷 4172-4189页

作者： Sarvi, M. Parpaei, M. Bagheri, H. Kojoori, M. R. Alkaei Imam Khomeini Int Univ Tech & Engn Fac Qazvin Iran

This paper presents a novel maximum power point control of stand-alone proton exchange membrane fuel cell (PEMFC) system using a novel optimization strategy based on the eagle strategy (ES) coupled with cuckoo optimization algorithm (COA) and particle swarm optimization (PSO) in order to track the maximum power point of PEMFC. The proposed method presents a new combination of optimization algorithms. According to this strategy, the advantages of optimization algorithms beside each other lead to reach better results in term of the convergence speed and the accuracy of the optimum operation of the system. Furthermore, the standard deviation of data produced by the proposed method is very lower than other conventional optimization algorithms;PSO and COA algorithms, so it leads to decrease the output power oscillation. To validate these advantages, the proposed tracker applied to the PEMFC system in MATLAB/SIMULINK environment. The simulation results confirm the effectiveness and accuracy of the proposed approach.

关键词： Maximum power point tracking intelligent optimization algorithm PEMFC Particle swarm optimization Cuckoo optimization algorithm

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An improved particle swarm optimization algorithm with adaptive weighted delay velocity

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2021年第1期9卷 188-197页

作者： Xu, Lin Song, Baoye Cao, Maoyong Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China

An improved particle swarm optimization (PSO) with adaptive weighted delay velocity (PSO-AWDV) is proposed in this paper. A new scheme blending weighted delay velocity is firstly presented for a new PSO with weighted delay velocity (PSO-WDV) algorithm. Then, to adaptively update the velocity inertia weight, an adaptive PSO-AWDV algorithm is developed based on the evolutionary state of the particle swarm evaluated via a new estimation method. The newly proposed adaptive PSO-AWDV algorithm is tested based on some famous benchmark functions, which can confirm that the performance of PSO-AWDV is superior to several well-known PSO variants and intelligent optimization algorithms in literature.

关键词： PSO particle swarm optimization adaptive weighted delay velocity intelligent optimization algorithm

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A novel fractional neural grey system model with discrete q-derivative

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COMPLEX & intelligent SYSTEMS 2024年第5期10卷 6409-6420页

作者： Xu, Zhenguo Liu, Caixia Liang, Tingting Qufu Normal Univ Sch Commun Rizhao Peoples R China Jiangsu Normal Univ Coll Intelligent Educ Xuzhou Peoples R China

The challenge of predicting time series with limited data has evolved over time due to nonlinearity, complexity, and limited information. It can be perceived as a mapping of dynamical systems in one-dimensional space. This article proposes a neural grey system to tackle this challenge. The system enhances its ability to fit nonlinearity by employing polynomials, captures complexity through a fractional-order cumulant operator, and resolves information-poor uncertainty by utilizing grey system modeling techniques. The model effectively integrates research findings from neural computing, uncertainty theory, and complexity theory at a theoretical level. It accurately describes dynamic processes of complex systems. Additionally, we have reduced the complexity of calculations in the algorithm design. We selected a dataset of total retail sales of consumer goods to test the model's validity and applicability. Our experiments demonstrate that the newly proposed grey forecasting model can effectively forecast time series with small samples, offering good forecasting outcomes and generalization ability.

关键词： Grey model Fractional derivative Artificial neural network intelligent optimization algorithm

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WSN node location based on beetle antennae search to improve the gray wolf algorithm

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WIRELESS NETWORKS 2022年第2期28卷 539-549页

作者： Yu, Xiu-wu Huang, Lu-ping Liu, Yong Zhang, Ke Li, Pei Li, Ying Univ South China Hengyang Peoples R China Hunan Engn Res Ctr Uranium Tailings Decommiss & T Hengyang Peoples R China Wuhan Univ Technol Wuhan Peoples R China

With the rapid development of the Internet, more and more people pay attention to wireless sensor networks. Localization technology plays a vital role in wireless sensor networks. To reduce the localization error and improve the localization stability, a gray wolf localization algorithm based on beetle antennae search (BASGWO) is proposed, transforming the node localization problem into function constrained optimization. Firstly, the excellent point set method is used to initialize the gray wolf population, improving the richness. Secondly, the beetle antennae search mechanism with good global search ability is introduced into the gray wolf algorithm to avoid the gray wolf algorithm falling into local optimization in the late iteration. The gray wolf is the beetle antennae in search of excellence. The location of the gray wolf was updated according to the fitness value of the gray wolf and beetle antennae. The optimal global solution can be obtained, and then the unknown node coordinates can be obtained. The improved gray wolf algorithm improves the localization accuracy by 24% through simulation comparison and reduces the localization error fluctuation by 23%. Compared with the classical localization algorithm of WSN, the solution ability and localization accuracy of the BASGWO algorithm are improved.

关键词： Wireless sensor network Beetle antennae search Gray wolf algorithm Node positioning Good point set intelligent optimization algorithm Positioning accuracy

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CORPORATE FRAUD DETECTION BASED ON IMPROVED BP NEURAL NETWORK

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COMPUTING AND INFORMATICS 2024年第3期43卷 611-632页

作者： Liu, Wei Liu, MingMing Yan, Chun Qi, Man Zhang, LuLu Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Canterbury Christ Church Univ Sch Engn Technol & Design Canterbury CT1 1QU England

Corporate fraud risk detection is a branch of fraud. It may exist in various industries and cause economic problems. Effective identification of corporate fraud can protect the safety of funds for investors in some sense. This paper proposes a classifier model of a fractional -order immune BP neural network based on the self -attention mechanism to improve efficiency. The improved artificial immune algorithm with dynamic region contraction strategy is used to optimize the initialization process of the BP neural network. Furthermore, it combines the self -attention mechanism to design the input layer. Finally, Caputo fractional noncausal calculus is used to optimize the parameter updating process in BP neural network. The experiment results indicate that our model has fast convergence rate and powerful capacity of detection, and performs efficiently in detecting fraud behaviors.

关键词： intelligent optimization algorithm self-attention mechanism BP neural network fractional-order fraud detection

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intelligent Combined Prediction of Wind Power Based on Numerical Weather Prediction and Fuzzy Clustering

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IFAC-PapersOnLine 2015年第28期48卷 538-543页

作者： Jiaran, Yang Xingcheng, Wang Xiaofen, Luo Cheng, Jiang Information Science and Technology College Dalian Maritime University Dalian116026 China Huaneng Weihai Power Generation CO. LTD Weihai264205 China Beijing Electric Power Company Beijing China

Wind power prediction accuracy has important implications for the scheduling and stable operation of the power system. An intelligent Combined Prediction algorithm of Wind Power Based on Numerical Weather Prediction and Fuzzy Clustering is proposed in the paper. Based on numerical weather prediction (NWP) data and using the method of fuzzy clustering subtraction, the original NWP data is divided into several typical weather patterns; T-S fuzzy model, time-series model, multiple linear regression model and gray model are established respectively according to different whether types; the combination of multi-model is optimized using intelligent optimization algorithms and the optimal combination prediction model is obtained. Prediction results of a domestic wind farm indicate that the proposed combination prediction method is valid and effective in short-term wind power prediction, with better prediction accuracy. © 2015

关键词： Weather forecasting Fuzzy clustering Linear regression Numerical methods Wind power Combination predictions intelligent optimization intelligent optimization algorithm Multiple linear regression models Numerical weather prediction Prediction algorithms Short Term wind power predictions Wind power predictions

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optimization Modeling for Vibration of Composite Conical Shell Suitable for A Wide Range of Complex Boundaries: Used for Discontinuous Winding Structure

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2024年第SUPPL 1期12卷 531-556页

作者： Wang, Ting Fan, Ye Zhao, Yang Li, Xinghong Wang, Hui Lu, Sheng Chongqing Univ Posts & Telecommun Inst Adv Sci Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Coll Adv Mfg Engn Chongqing 400065 Peoples R China Xi An Jiao Tong Univ State Key Lab Strength & Vibrat Mech Struct Xian 710049 Peoples R China

BackgroundThe study of shell vibration also has clear engineering significance and scientific *** practical engineering, the spring stiffness configuration of the classical boundary is difficult to simulate the actual boundary situation, which in turn requires the use of elastic boundaries for *** this, this paper is a pioneering proposal to optimize the spring stiffness setting by introducing an intelligent optimization algorithm. Firstly, the vibration model of an isotropic truncated conical shell is established by applying a modified Fourier series method. Then the new characteristic equation is derived by setting the artificial spring stiffness as the parameter to be optimized. The objective function is then obtained from the deviations of the modal parameters to build the optimization model. Subsequently, the optimized model is optimized using an improved particle swarm algorithm and an immune algorithm. Finally, the optimized semi-analytical solution is compared with the finite element analysis results for *** results show that the method in this paper can find a better spring stiffness configuration to simulate complex boundary *** optimization algorithm can be applied to the cone, column, spherical shell, and other shells and combination shells. The stator end winding is used as an example. Similarly, and characteristic equation under the theory of composite materials are innovatively established;the objective function is extended to the optimization model of characteristic equation for stator-winding. To solve the vibration optimization model of complex structural constraints, the pathological characteristics of data introduced by the numerical difference between the small vibration and the order of magnitude of constraint stiffness are solved. The spring stiffness of small-diameter end of the boundary spring for the stator-winding model has been optimized.

关键词： Energy method Conical shell Complex boundary intelligent optimization algorithm Artificial spring Stator winding

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