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3rd International Conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Chen, Xiaojuan Cai, Xiaohui Qu, Chang Li, Guohua Changchun University of Science and Technology School of Electronic Information Changchun China Changchun University of Science and Technology School of Artificial Intelligence Changchun China Changchun Power Supply Company of State Grid Jilin Electric Power Co Changchun China

ISBN: (纸本)9798350316674

The pulse signal is a valuable source of pathological information for TCM diagnosis. However, traditional TCM pulse diagnosis heavily relies on the doctor's experience and lacks objectivity. Therefore, it is crucial to study the objectivity of pulse diagnosis. This paper proposes a method that utilizes the improved Beluga optimization algorithm (BWO) to optimize the extreme gradient boosting tree algorithm (XGBoost) for identifying pulse signals. Additionally, the XGBoost parameters are optimized using the improved BWO algorithm, leading to the establishment of a pulse signal recognition model. This model takes into account the time-domain and frequency-domain characteristics of the four pulse signals (Pingmai, Huamai, Xianmai, and Shumai) as inputs to obtain recognition results. Experimental and simulation results demonstrate that the accuracy rate of this model is 95.58%. © 2023 IEEE.

关键词： extreme gradient boosting pulse signal whale optimization algorithm

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Seepage characteristics of core rockfill dam foundation with double cutoff walls in deep overburden: A case study

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 21卷

作者： Huang, Zhangxin Shen, Zhenzhong Xu, Liqun Sun, Yiqing Li, Haoxuan Liu, Detan Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China Minist Transport Key Lab Transport Ind Comprehens Transportat Theor Nanjing Modern Multimodal Transportat Lab Nanjing 211100 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing 210024 Peoples R China Datang Hydropower Sci & Technol Res Inst Co Ltd Chengdu 610074 Peoples R China

Some high rockfill dams, built on deep overburden, utilize double concrete cutoff walls for seepage control, thereby introducing increased complexity to seepage characteristics in the dam foundation. This study focuses on a core rockfill dam located on the Dadu River in China, investigating the impact of dam body and foundation permeability coefficients on the percentages of hydraulic head reduction by the double cutoff walls. A multifactor sensitivity analysis, based on finite element method, is conducted to analyze this influence. Subsequently, an inversion model, integrating support vector regression and whale optimization algorithm, is proposed to identify parameters that align with seepage monitoring data. Results indicate that the permeability coefficients of the surface bedrock and the main grouting curtain exert the most significant influence. Parameters derived from the inversion model yield finite element calculations consistent with monitoring data. Moreover, upstream water level minimally affects the distribution of hydraulic head reduction percentages by the double cutoff walls. And the seepage gradient (G) in the dam foundation overburden remains below the permissible limit, with its relationship to the upstream water level (L) fitted as G = -1.03318 + 6.96E - 4 x *** study may offer valuable insights for the construction and operational management of similar projects employing double cutoff walls.

关键词： High rockfill dam Deep overburden Double concrete cutoff walls Seepage characteristics Support vector regression whale optimization algorithm

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Optimal design of a BLDC motor using African vulture optimization algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 7卷

作者： Tripathy, Surya Narayan Kundu, Shubhasri Pradhan, Arjyadhara Samal, Padarbinda School of Electrical Engineering KIIT Deemed to be University India

BLDC motors are extensively favoured in robots, electric vehicles, and many industrial applications due to their superior torque, efficiency, and speed control features as compared to traditional motors. In this paper, collective intelligence-based optimization approaches are used to improve the cost-effectiveness, volume, and total loss of a BLDC motor. The geometric dimensions of the motor are considered the optimization parameters. The study employs several optimization algorithms, such as the whale optimization algorithm (WOA), Grey Wolf optimization (GWO), Cuckoo Search algorithm (COA), Genetic algorithm (GA), Particle Swarm optimization (PSO), and the African Vulture optimization algorithm (AVOA), used for the optimal BLDC motor design. The investigation can provide data on the convergence rates and optimal design parameters of a BLDC motor through optimization approaches. The standard deviation has also been calculated for each optimization technique to assess the stability and consistency of finding the optimum solution. The paper also includes a sensitivity analysis of the AVOA regulating parameter to obtain the best solutions. In comparison to GA, PSO, COA, GWO, and WOA, the AVOA technique achieves faster convergence. Additionally, the ANOVA test is employed as a statistical test to validate the efficacy of the AVOA. According to the results, AVOA outperforms all other optimization algorithms for the design of the BLDC motor. © 2024 The Author(s)

关键词： Brush-less DC motors Cuckoo search algorithm Particle swarm optimization algorithm whale optimization algorithm

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Research on High-Frequency Torsional Oscillation Identification Using TSWOA-SVM Based on Downhole Parameters

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PROCESSES 2024年第10期12卷 2153-2153页

作者： Zhang, Tao Zhang, Wenjie Meng, Zhuoran Li, Jun Wang, Miaorui Beijing Univ Informat Sci & Technol Beijing Key Lab High Dynam Nav Technol Beijing 100101 Peoples R China China Univ Petr Sch Petr Engn Beijing 102249 Peoples R China Harbin Inst Informat Engn Sch Software Harbin 150431 Peoples R China

The occurrence of downhole high-frequency torsional oscillations (HFTO) can lead to the significant damage of drilling tools and can adversely affect drilling efficiency. Therefore, establishing a reliable HFTO identification model is crucial. This paper proposes an improved whale algorithm optimization support vector machine (TSWOA-SVM) for accurate HFTO identification. Initially, the population is initialized using Fuch chaotic mapping and a reverse learning strategy to enhance population quality and accelerate the whale optimization algorithm (WOA) convergence. Subsequently, the hyperbolic tangent function is introduced to dynamically adjust the inertia weight coefficient, balancing the global search and local exploration capabilities of WOA. A simulated annealing strategy is incorporated to guide the population in accepting suboptimal solutions with a certain probability, based on the Metropolis criterion and temperature, ensuring the algorithm can escape local optima. Finally, the optimized whale optimization algorithm is applied to enhance the support vector machine, leading to the establishment of the HFTO identification model. Experimental results demonstrate that the TSWOA-SVM model significantly outperforms the genetic algorithm-SVM (GA-SVM), gray wolf algorithm-SVM (GWO-SVM), and whale optimization algorithm-SVM (WOA-SVM) models in HFTO identification, achieving a classification accuracy exceeding 97%. And the 5-fold crossover experiment showed that the TSWOA-SVM model had the highest average accuracy and the smallest accuracy variance. Overall, the non-parametric TSWOA-SVM algorithm effectively mitigates uncertainties introduced by modeling errors and enhances the accuracy and speed of HFTO identification. By integrating advanced optimization techniques, this method minimizes the influence of initial parameter values and balances global exploration with local exploitation. The findings of this study can serve as a practical guide for managing near-bit st

关键词： high-frequency torsional oscillation time-frequency domain analysis support vector machines whale optimization algorithm near-bit measurement

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Combined Terrestrial Evapotranspiration Index prediction using a hybrid artificial intelligence paradigm integrated with relief algorithm-based feature selection

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 193卷 106687-106687页

作者： Jamei, Mehdi Elbeltagi, Ahmed Maroufpoor, Saman Karbasi, Masoud Jamei, Mozhdeh Jalali, Mohammadnabi Najafzadeh, Negin Shahid Chamran Univ Ahvaz Shohadaye Hoveizeh Campus Technol Fac Engn Dashte Azadegan Iran Mansoura Univ Fac Agr Agr Engn Dept Mansoura 35516 Egypt Zhejiang Univ Coll Environm & Resource Sci Hangzhou 310058 Peoples R China Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran Univ Zanjan Fac Agr Zanjan Iran Ferdowsi Univ Mashhad Water Sci & Engn Dept Mashhad Razavi Khorasan Iran Khuzestan Water & Power Author Ahvaz Iran Univ Tehran Dept Water Engn Aburaihan Campus Tehran Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran

Drought is a common environmental disaster strongly influenced by the potential production of agricultural products, lack of water resources, and yields destructive effects on the economy. In this study, the prediction of a novel monthly Combined Terrestrial Evapotranspiration Index (CTEI) was considered as a measure of all three types of drought (meteorology, hydrology, and agriculture) in the Ganga river basin (GRB) over the period of 14 years. For this purpose, a combination of hydro-meteorological and satellite-based data, including 11 input variables, was implemented. A new Artificial Neural Network (ANN) integrated with a whale optimization algorithm (WOA-ANN) and a Relief algorithm-based Feature Selection (FS) method was applied to simulate the monthly CTEI index and find the optimum input combinations. Besides, the standalone ANN and Least Square Support Vector Regression (LSSVR) models were examined to validate the WOA-ANN performance. The results indicated that WOA-ANN with (R = 0.9391), (RMSE = 0.241), (WI = 0.968), and (U-95% = 0.669) had a high ability to predict CTEI and reduced the RMSE in the ANN and LSSVR models by 27% and 30%, respectively. WOA-ANN had the best predictive performance, followed by LSSVR and ANN models, respectively, concerning various graphical validations and diagnostic analyses. Besides, the outcome of this research, despite fewer parameters (seven parameters), considerably outperformed the study of (Elbeltagi et al., 2021) with (R = 0.9055 and RMSE = 0.33) accuracy employing 11 inputs.

关键词： Terrestrial Evapotranspiration Index Drought whale optimization algorithm Relief algorithm ANN LSSVR

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Calibration and frequency estimation in sensors for electrical parameter measurement using regression and metaheuristic based models

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EXPERT SYSTEMS 2023年第3期40卷 e13208-e13208页

作者： Ranasingh, Soumyaranjan Pradhan, Tapan Dhenuvakonda, Koteswara Raju Natl Inst Technol Dept Elect Engn Silchar India

Calibration is the backbone of any sensor and measurement philosophy. The conventional calibration techniques for electrical parameter measurement using nonlinear sensors are affected by the repeating analogue signals and lead to errors in measurement. This research paper investigates different regression-based mathematical models to calibrate the Hall sensor for measuring RMS and the fundamental frequency. The novelty of this research work lies in the feature-based input modelling to measure RMS current and frequency with an error of 3.37e-12% and 7.61e-9%, respectively. The conventional Fourier transform method is compared with six different bio-inspired metaheuristic algorithms to estimate the frequency components of the analogue signal received from the measurement setup. Apart from the conventional sine waveforms, this paper investigates sawtooth and square waveforms as the periodic signals for determining the frequency components. The results from the comparison study show that the whale optimization algorithm exhibits 1.25% lesser error whilst predicting the features in measured frequency components. Apart from this, the paper identifies a unique combination of features that effectively measures the instantaneous electrical parameter with RMSE, NRMSE, and R2 value of 158.8, 0.51, and 0.44, respectively. Experimentally it is found that the decision tree and random forest regression models calibrate the Hall sensor with 93% less error than their linear and polynomial counterparts.

关键词： calibration current sensor decision tree regression frequency estimation regression models whale optimization algorithm

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Research on Financial Risk Crisis Prediction of Listed Companies Based on IWOA-BP Neural Network

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JOURNAL OF INTERNET TECHNOLOGY 2022年第5期23卷 955-965页

作者： Li, Sha Chen, Xuan Zhejiang Ind Polytech Coll Finance Branch Shaoxing Peoples R China Zhejiang Ind Polytech Coll Design & Art Branch Shaoxing Peoples R China

To avoid the risk brought by the financial crisis, the Improved whale optimization algorithm-Back Propagation neural network (IWOA-BP) financial crisis early warning model is proposed. This paper selects the data from financial statements of some of the listed Chinese manufacturing companies from 2015-2019 as the research sample. First, the financial data of enterprises are screened by principal component analysis, and the early warning model is constructed from the financial and nonfinancial factors of six indicators: solvency, operating capacity, profitability, development capacity, cash flow and risk level factors. Second, the whale optimization algorithm is optimized by the chaos strategy, as well as by the dynamic weight and sine cosine algorithm. Finally, the improved whale algorithm is optimized for BP neural network parameters. In the simulation experiments, the performance of the improved whale optimization algorithm is substantially improved. In addition, in the empirical analysis, compared to the prediction model with other algorithms, the prediction model of this paper has better results in terms of prediction accuracy.

关键词： Financial risk Crisis prediction whale optimization algorithm BP neural network

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A novel metaheuristic approach to scale the economic impact of grid participation on a microgrid system

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartA期53卷

作者： Dey, Bishwajit Garcia Marquez, Fausto Pedro Panigrahi, Pratap Kumar Bhattacharyya, Biplab IIT ISM Dept Elect Engn Dhanbad Bihar India GIET Univ Dept Elect & Elect Engn Gunupur Odisha India Univ Castilla La Mancha Ingenium Res Grp Ciudad Real Spain

Microgrids are having more importance in the power system network, favouring the use of renewable energy sources (RES), thus, reducing the emission of the toxic gases. Grid-connected mode of microgrid operations is emphasized due to its economic and technical advantages, resulting in diminished generation cost of the system. Six scenarios of grid participation are studied in this paper, with the generation cost of the microgrid system being minimized in each scenario. These scenarios are modelled in a way such that they exhibit various paradigms of connections and disconnections between the grid and microgrid system. A novel hybrid of whale optimization algorithm (WOA) and sine-cosine algorithm (SCA) is proposed as the optimization tool to minimize the generation cost for all the scenarios. Costs obtained by the approach are analyzed with the costs given by differential evolution, whale optimization and sine-cosine algorithms. The generation cost surged approximately 4%, 7% and 11% when the grid is disconnected from the microgrid system for 8, 10 and 12 h respectively. The generation cost, during passive participation of the grid, is as high as 7.5% more than the ideal case. Numerical results and non-parametric statistical analysis such as Friedman and Quade tests, validate the results obtained by the novel hybrid approach.

关键词： Grid participation Microgrid Energy management whale optimization algorithm Sine cosine algorithm

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Research on renewable energy power demand forecasting method based on IWOA-SA-BILSTM modeling

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FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Wang, Minghu Xia, Yushuo Zhang, Xinsheng Xian Univ Architecture & Technol Sch Management Xian Peoples R China

This paper introduces a novel coupling method to enhance the precision of short- and medium-term renewable energy power load demand forecasting. Firstly, the Tent chaotic mapping incorporates the standard WOA and modifies its internal convergence factor to a nonlinear convergence mode, resulting in an improved IWOA. It is used for the weight optimization part of BILSTM. Then, the SA is introduced to optimize the learning rate, the number of nodes in hidden layers 1 and 2, and the number of iterations of BILSTM, constructing an IWOA-SA-BILSTM prediction model. Finally, through case analysis, the prediction model proposed in this paper has the highest improvement of 76.7%, 74.5%, and 45.9% in terms of Mean Absolute Error, Root Mean Square Error, and R2, respectively, compared to other optimal benchmark models, proving the effectiveness of the model.

关键词： bi-directional long and short-term memory neural networks whale optimization algorithm multidimensional feature analysis simulated annealing algorithm load demand forecasting

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Swarm intelligence RFID network planning using multi-antenna readers for asset tracking in hospital environments

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COMPUTER NETWORKS 2021年 198卷 108427-108427页

作者： Shokouhifar, Mohammad Shahid Beheshti Univ Dept Elect & Comp Engn Tehran Iran

Radio frequency identification (RFID) systems have been widely used for the inventory management and asset tracking in hospital industry. Although the total cost of an RFID system relies mostly on the number of readers, existing RFID network planning (RNP) techniques focused on single-port readers. This paper introduces a swarm intelligence RFID network planning model with multi-antenna readers (named RNP-MAR) to efficiently track medical assets in hospital environments. Our motivation is to utilize multi-port RFID readers, in order to minimize the total cost of the RFID network, and simultaneously maximizing the network coverage. To achieve this purpose, we consider three reader types with one, two, and four antenna ports. Moreover, different antenna models with different coverage characteristics are utilized to be allocated on the different ports of RFID readers. The aim of the RNP-MAR is not only to determine the optimal placements of the RFID readers in the hospital plan, but also to design the multi-antenna readers and their antennas. To efficiently solve the RNP-MAR model, a combined global-local linear-nonlinear metaheuristic based on whale optimization algorithm and simulated annealing (named WOA-SA) is presented. The multi-objective function of the WOA-SA is to maximize the network coverage, while minimizing the total cost, interference, collision, and power consumption. Simulation results show that the proposed method provides 39.57% saving (on average) in the total cost of the RFID network through efficiently utilizing multi-antenna RFID readers.

关键词： RFID network planning Asset tracking Directional antennas Multi-antenna readers whale optimization algorithm Simulated annealing

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