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Hybrid optimal design method for magnetic field coils under magnetic shielding boundary

SENSORS AND ACTUATORS A-PHYSICAL 2023年第1期359卷

作者： Wu, Zhihong Pang, Haoying Wang, Zhuo Fan, Wenfeng Liu, Feng Pei, Hongyu Quan, Wei Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Ultraweak Magnet Field Measurement Technol Beijing 100191 Peoples R China Hangzhou Innovat Inst Beihang Univ Zhejiang Prov Key Lab Ultraweak Magnet Field Space & Appl Technol Hangzhou 310000 Peoples R China

A novel strategy for designing uniform magnetic field and gradient magnetic field coils considering the boundary effect of magnetic shielding is proposed in this study. This strategy uses intelligent optimization algorithm to skillfully transform the complex coil design problem into the spatial optimization problem of individual population. The mirror method is combined with Green's function to accurately calculate the magnetic field, and the target field method (TFM) is combined with the whale optimization algorithm to optimize the stream functions using adaptive ridge regression. The minimum fitness function is defined as the real-time magnetic field relative error, and the optimal ridge coefficient is continuously obtained by determining the current density coefficient. The proposed method significantly reduces the magnetic field deviation in the target area compared to the conventional TFM, with reductions ranging from 12.2% to 0.11% (�� coil), 0.3% to 0.02% (�� coil), 20% to 10% (d ��/d �� coil) and 2.9% to 2.4% (d ��/d �� coil). The strategy has been effectively validated by analytical simulations and experimental results, and is expected to be applied to the design of uniform field coils and gradient coils for ultra-high precision atomic sensors.

关键词： Uniform field and field gradient coils Target field method Magnetic shielding whale optimization algorithm

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Intelligent Fault Diagnosis Method for Gearboxes Based on Deep Transfer Learning

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PROCESSES 2023年第1期11卷 68页

作者： Wu, Zhenghao Bai, Huajun Yan, Hao Zhan, Xianbiao Guo, Chiming Jia, Xisheng Army Engn Univ PLA Shijiazhuang Campus Shijiazhuang 050003 Peoples R China

The complex operating environment of gearboxes and the easy interference of early fault feature information make fault identification difficult. This paper proposes a fault diagnosis method based on a combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and deep transfer learning. First, the VMD is optimized by using the WOA, and the minimum sample entropy is used as the fitness function to solve for the K value and penalty parameter alpha corresponding to the optimal decomposition of the VMD, and the correlation coefficient is used to reconstruct the signal. Second, the reconstructed signal after reducing noise is used to generate a two-dimensional image using the continuous wavelet transform method as the transfer learning target domain data. Finally, the AlexNet model is used as the transfer object, which is pretrained and fine-tuned with model parameters to make it suitable for early crack fault diagnosis in gearboxes. The experimental results show that the method proposed in this paper can effectively reduce the noise of gearbox vibration signals under a complex working environment, and the fault diagnosis method of using transfer learning is effective and achieves high accuracy of fault diagnosis.

关键词： whale optimization algorithm variational mode decomposition deep transfer learning gearbox fault diagnosis

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A novel framework for integrating solar renewable source into smart cities through digital twin simulations

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SOLAR ENERGY 2023年第1期262卷

作者： Li, Bohan Tan, Weijun Jilin Jianzhu Univ Sch Architecture & Urban Planning Changchun 130119 Peoples R China Tongji Architecture Design Grp Co Ltd Shanghai 200092 Peoples R China

In this study, the challenges of incorporating PV systems and wind energy sources into smart city power grids are addressed. The fluctuating nature of renewable energy is considered, and microgrid energy management is investigated using probabilistic and deterministic assumptions. To enhance energy management, digital twin simulations are employed, providing a virtual representation of the power grid. This enables accurate analysis and prediction of system behavior in different scenarios. Digital twins help simulate the effects of load profiles on microgrids, facilitating better energy planning and decision-making. The proposed framework considers objective functions related to cost reduction, voltage profile improvement, and voltage stability enhancement. The whale optimization algorithm (WOA) is applied to solve the complex and nonlinear energy management problem, aiming to minimize costs and enhance voltage stability. Simulation results show that an optimally installed PV system and wind turbines reduce energy costs and improve overall system efficiency in smart cities. Furthermore, the objective function of WOA outperforms particle swarm optimization (PSO) and differential evolution algorithm (DEA) in achieving the desired outcomes.

关键词： Smart city microgrid Energy management Digital twin whale optimization algorithm Stochastic loads

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One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods

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ENERGIES 2023年第6期16卷 2688页

作者： Huang, Chao-Ming Chen, Shin-Ju Yang, Sung-Pei Chen, Hsin-Jen Kun Shan Univ Dept Elect Engn Tainan 710 Taiwan Kun Shan Univ Dept Green Energy Technol Res Ctr Tainan 710 Taiwan

This paper proposes an optimal ensemble method for one-day-ahead hourly wind power forecasting. The ensemble forecasting method is the most common method of meteorological forecasting. Several different forecasting models are combined to increase forecasting accuracy. The proposed optimal ensemble method has three stages. The first stage uses the k-means method to classify wind power generation data into five distinct categories. In the second stage, five single prediction models, including a K-nearest neighbors (KNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, a support vector regression (SVR) model, and a random forest regression (RFR) model, are used to determine five categories of wind power data to generate a preliminary forecast. The final stage uses an optimal ensemble forecasting method for one-day-ahead hourly forecasting. This stage uses swarm-based intelligence (SBI) algorithms, including the particle swarm optimization (PSO), the salp swarm algorithm (SSA) and the whale optimization algorithm (WOA) to optimize the weight distribution for each single model. The final predicted value is the weighted sum of the integral for each individual model. The proposed method is applied to a 3.6 MW wind power generation system that is located in Changhua, Taiwan. The results show that the proposed optimal ensemble model gives more accurate forecasts than the single prediction models. When comparing to the other ensemble methods such as the least absolute shrinkage and selection operator (LASSO) and ridge regression methods, the proposed SBI algorithm also allows more accurate prediction.

关键词： wind power forecasting ensemble method particle swarm optimization salp swarm algorithm whale optimization algorithm

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Efficient fractional-order modified Harris hawks optimizer for proton exchange membrane fuel cell modeling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 100卷 104193-104193页

作者： Yousri, Dalia Mirjalili, Seyedali Machado, J. A. Tenreiro Thanikanti, Sudhakar Babu Elbaksawi, Osama Fathy, Ahmed Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Polytech Porto Dept Elect Engn Inst Engn Rua Dr Antonio Bernardino de Almeida 431 P-4249015 Porto Portugal Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Jouf Univ Fac Engn Elect Engn Dept Al Jouf Saudi Arabia Port Said Univ Fac Engn Elect Engn Dept Port Said Egypt Zagazig Univ Fac Engn Elect Power Machine Dept Zagazig Egypt

An effective harmony between the exploration and exploitation phases in meta-heuristics is an essential design consideration to provide reliable performance on a wide range of optimization problems. This paper proposes a novel approach to enhance the exploratory behavior of the Harris hawks optimizer (HHO) based on the fractional calculus (FOC) memory concept. In the proposed variant of the HHO, a hawk moves with a fractional order velocity, and the rabbit escaping energy is adaptively tuned based on FOC parameters to avoid premature convergence. As a result, the fractional-order modified Harris hawks optimizer (FMHHO) is proposed. The sensitivity of the algorithm performance vis-a-vis the FOC parameters is addressed, and the best variant is recommended based on twenty-three benchmarks. For validating the quality of the proposed variant, twentyeight benchmarks of CEC2017 are tested. For evaluating the proposed variant against the other present-day techniques, several statistical measures and non-parametric tests are performed. Moreover, to demonstrate the applicability of the proposed technique, the proton exchange membrane fuel cell (PEMFC) model parameters estimation process is handled based on several measured datasets. In this series of experiments, the FMHHO variant is compared with the standard HHO and the other techniques based on intensive statistical metrics, mean convergence curves, and dataset fitting. The overall outcome shows that the FOC memory property improves the performance of the classical HHO and leads to accurate and robust solutions fitting the measured data.

关键词： Fractional-order modified HHO whale optimization algorithm Particle Swarm optimization Harris hawks optimization Salp Swarm algorithm Parameters estimation Grey Wolf Optimizer Fractional calculus Genetic algorithm optimization Fuel cell

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Design of uniform field coils coupled with a magnetic shield cylinder?

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2023年第1期583卷

作者： Huang, Binyue Cao, Qian Chen, Zhengyu Wang, Yuhao Yue, Ziqian Zhai, Yueyang Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Shen Yuan Honors Coll Beijing 100191 Peoples R China

Active magnetic field coils and magnetic shields are widely employed to achieve uniform and low magnetic fields, and the coil generating magnetic field would be influenced as a result of the coupling with the shields. It turns out to increase the magnetic field strength and shrink the uniform area, which is detrimental to atomic devices. An analytical solution that takes into account the coupling effect of shields and coils is established by using the magnetic vector potential. Analyses for three different configurations are first performed, including mu-metals, 2K-MnZn ferrites and 10K-MnZn ferrites. Analytical calculations reveal that the optimal radii of the standard Lee-Whiting coils with high uniformity are all around 0.81 of shield radii. Additionally, adopting the whale optimization algorithm (WOA), a modified coil with improved uniformity and comparable size is proposed. Experimental results show that a better uniformity of 2.68e-5 and a 34% longer uniform area is obtained when compared with the standard coil. The accuracy of the internal magnetic field distributions is compared and tested by simulations and experiments. Situations utilizing shields and coils will be facilitated by this effective application of WOA in uniform field coil design.

关键词： Uniform magnetic field coil Magnetic shield Shield-coil coupling whale optimization algorithm Atomic magnetometer

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Research on IMDB Film Score Prediction Based on Improved whale algorithm

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Procedia Computer Science 2022年 208卷 361-366页

作者： Cong Zhang Film College of Changchun Guanghua University China

In order to improve the prediction precision of film score, the improved whale algorithm is established to predict the IMDB film score. Firstly, the IMDB scoring mechanism is studied, the reason of inaccurate scoring or network "water army" is analyzed, and the score of the meta score, the number of reviews and the popularity index are discussed. Data of the film in system is updated in real time. Secondly, the improved whale optimization algorithm is constructed, the mathematical model of encircling prey, bubble net attacking and search for prey are discussed. The adaptive weight formula is established and the improved adaptive updating formula is constructed, and the corresponding algorithm procedure is designed. Finally, through the analysis of visual results, the key factors affecting film scoring are found out, and suggestions are given for the film recommendation system. One hundred films are selected to carry out prediction analysis, the top 85% films are used as training samples, and the bottom 15% films are used as testing samples. The prediction mode is trained through training samples, and the testing samples are predicted based on trained prediction model. A prediction simulation analysis is carried out using IMDB samples, and results show that the proposed prediction model has high prediction precision.

关键词： IMDB film score whale optimization algorithm prediction model scoring mechanism precision

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Safety Assessment of Liquid Launch Vehicle Structures Based on Interpretable Belief Rule Base

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Computer Systems Science & Engineering 2023年第10期47卷 273-298页

作者： Gang Xiang Xiaoyu Cheng Wei He Peng Han Beihang University School of Automation Science and Electrical Engineering Beijing100083China Beijing Aerospace Automatic Control Institute Beijing100070China School of Computer Science and Information Engineering Harbin Normal UniversityHarbin150025China Rocket Force University of Engineering Xi’an710025China

A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space *** the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment ***,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and ***,an innovative whale optimization algorithm with interpretable constraints is *** experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation *** MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,*** can be seen that the proposed model also shows a better ability to handle small sample ***,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i *** results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.

关键词： Liquid launch vehicle belief rule base with interpretability belief rule base whale optimization algorithm vibration frequency swaying angle

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Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, whale Optimisation algorithm, and Elman Network

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ENERGIES 2022年第9期15卷 3055-3055页

作者： Zhu, Anfeng Zhao, Qiancheng Wang, Xian Zhou, Ling Hunan Univ Sci & Technol Engn Res Ctr Hunan Prov Min & Utilizat Wind Turbi Xiangtan 411201 Peoples R China

Accurate wind power forecasting helps relieve the regulation pressure of a power system, which is of great significance to the power system's operation. However, achieving satisfactory results in wind power forecasting is highly challenging due to the random volatility characteristics of wind power sequences. This study proposes a novel ultra-short-term wind power combined prediction method based on complementary ensemble empirical mode decomposition, the whale optimization algorithm (WOA), and the Elman neural network model. The model can not only solve the phenomenon of easy modal mixing in decomposition but also avoid the problems of reconstruction error and low efficiency in the decomposition process. Furthermore, a new metaheuristic algorithm, WOA, was introduced to optimize the model and improve the accuracy of wind power prediction. Considering a wind farm as an example, several wind turbines were selected to simulate and analyse wind power by using the established prediction model, and the experimental results suggest that the proposed method has a higher prediction accuracy of ultra-short-term wind power than other prediction models.

关键词： ultra-short-term wind power forecast complementary ensemble empirical mode decomposition whale optimization algorithm combination model

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A modified salp swarm algorithm (SSA) combined with a chaotic coupled map lattices (CML) approach for the secured encryption and compression of medical images during data transmission

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期66卷 102465-102465页

作者： Selvi, C. Thirumarai Amudha, J. Sudhakar, R. Sri Krishna Coll Engn & Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Elect & Elect Engn Pollachi Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Elect & Commun Engn Pollachi Tamil Nadu India

Medical image data analysis is a significant data in medical systems to analyze patient's health record. The biomedical image processing techniques and modernization of image devices are enhancing the medical image data. The main advantages of medical imaging data are (i) the medical imaging data are transmitted in the secure network in safe from the hackers (ii) medical imaging data can be shared quickly and easily between health care providers and departments. The chaotic system based different encryption algorithms using researches is analyzed. There are numerous issues in transmission of image such as weak security, small key space and high computational time. In this manuscript, a combined coupled map lattice (CML) with modified salp swarm algorithm is proposed. This algorithm compresses and encrypts the image using coupled map lattice (CML). This CML initially generates the number of encrypted images in modified SSA population. After the initialization, the modified SSA with whale optimization algorithm (WOA) is applied to reduce the computational time and maximize entropy in image cipher. To enhance the security of medical image is encrypted as a cipher image and transmits over the network. The experimental outcomes demonstrate that the proposed technique is more efficient to encrypt the medical image and has capability to resist the different distinct attacks.

关键词： Encryption Compression Coupled map lattice Salp swarm algorithm whale optimization algorithm

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