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A hybrid cluster-borderline SMOTE method for imbalanced data of rock groutability classification

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2022年第1期81卷 1-15页

作者： Li, Kai Ren, Bingyu Guan, Tao Wang, Jiajun Yu, Jia Wang, Kexiang Huang, Jicun Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety 92 Weijin Rd Tianjin 300072 Peoples R China China Three Gorges Construct Engn Corp Yichang Peoples R China

Groutability classification is highly important for guaranteeing the safety and quality of grouting projects. However, the precision of groutability classification is inevitably influenced by imbalanced data, in which most fractured rock masses are groutable. Current studies cannot realize high-precision classification for minority classes without considering imbalanced data. Although synthetic minority oversampling technique (SMOTE) is the most influential oversampling method, it produces redundant samples and noise labels. To address these issues, a hybrid cluster-borderline SMOTE method (HCBS) is proposed in this paper. The weights of samples near the minority class center and border were improved to present the category feature, thereby solving the redundant samples problem of SMOTE. To restrain noise, the total samples were divided into different clusters by k-means, and a cluster with an imbalance ratio of more than one was selected to generate new samples. The negative majority samples near the minority class border were removed by variant borderline SMOTE to clean the dataset from noisy instances, and the original and produced minority samples were reduced to a certain ratio according to the majority samples. Finally, the classification precision of the proposed HCBS method was verified using random forest (RF) in grouting engineering, and the number of trees and split variables of the tree nodes were optimized using the grey wolf optimization (GWO) algorithm. The proposed HCBS-GRF method outperformed RF, GWO-optimized RF (GRF), SMOTE-GRF, density SMOTE-GRF, borderline density SMOTE-GRF, and other competitive methods, thereby providing the highest groutability classification accuracy with the least number of generated instances.

关键词： Groutability classification Imbalanced data SMOTE Oversampling k-means clustering Random forest grey wolf optimization algorithm

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A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2021年第2期66卷 181-200页

作者： Cetinkaya, Mehmet Bahadir Duran, Hakan Univ Erciyes Fac Engn Dept Mechatron Engn TR-38039 Kayseri Turkey

Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.

关键词： artificial bee colony algorithm differential evolution algorithm firefly algorithm grey wolf optimization algorithm harmony search algorithm particle swarm optimization algorithm retinal vessel segmentation teaching learning based optimization algorithm

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An EEMD-SVD method based on gray wolf optimization algorithm for lidar signal noise reduction

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第17期44卷 5448-5472页

作者： Li, Shun Mao, Jiandong Li, Zhiyuan North Minzu Univ Sch Elect & Informat Engn Yinchuan Peoples R China Key Lab Atmospher Environm Remote Sensing Ningxia Yinchuan Peoples R China

Atmospheric lidar is susceptible to light attenuation, sky background light and detector dark current during detection, which results in a lot of noise in the lidar return signal. In order to improve the SNR and extract useful signals, this paper proposes a new joint denoising method EEMD-GWO-SVD, which includes empirical mode decomposition (EEMD), grey wolf optimization (GWO) and singular value decomposition (SVD). Firstly, the grey wolf optimization algorithm was used to optimize two parameters of EEMD algorithm according to moderate values: the standard deviation Nstd of adding Gaussian white noise to the signal and the number NE of adding Gaussian white noise. Secondly, the mode components obtained by EEMD-GWO decomposition are screened and reconstructed according to the correlation coefficient method. Finally, the SVD algorithm with strong noise reduction ability was used to further remove the noise in the reconstructed signal, and the lidar return signal with high SNR was obtained. In order to verify the effectiveness of the proposed method, the proposed method was compared with empirical mode decomposition (EMD), complete ensemble empirical modal decomposition (CEEMDAN), wavelet packet decomposition and EEMD-SVD-lifting wavelet transform (EEMD-SVD-LWT). The results show that the noise reduction effect of the proposed method was better than that of the other four methods. This method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. In fact, the denoised signal is not distorted, the waveform is smooth, the far-field noise interference can be suppressed and the denoised signal is closer to the real signal with higher accuracy, which indicates the feasibility and practicability of the proposed method.

关键词： Lidar grey wolf optimization algorithm singular value decomposition empirical modal decomposition noise reduction

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Predictive control of microwave hot-air coupled drying model based on GWO-BP neural network

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DRYING TECHNOLOGY 2023年第7期41卷 1148-1158页

作者： Bai, Haoran Chu, Ziyi Wang, Dongwei Bao, Yan Qin, Liyang Zheng, Yuhui Li, Fengmei Qingdao Agr Univ Coll Mech & Elect Engn 700 Great Wall Rd Qingdao 266109 Peoples R China China Construct Zhongxin Construct Engn Co Ltd Chengdu Peoples R China

A GB-MPC control algorithm (GWO-BP-MPC) was proposed to solve the problem of precise temperature control of fruit and vegetable coupling drying devices. Firstly, the BP (Back Propagation) neural network was improved using the grey wolf Optimizer (GWO) algorithm to increase the relevance and accuracy of the prediction model. By means of an improved neural network, we developed a high-accuracy predictive model for temperature control of drying units. Secondly, the projection conjugate gradient method was proposed for nonlinear optimization of the control system to improve the solving speed and accuracy of the optimal solution. The GB-MPC control algorithm was compared with the PID controller. The experimental results shown that the convergence speed of GB-MPC control was faster, the time took to reach a steady state in a single stage was shortened by 47 seconds compared with PID control. In the control process, the temperature change range of the GB-MPC control algorithm was smaller and there was no overshoot problem, which gave a better control effect than PID.

关键词： grey wolf optimization algorithm BP neural network predictive Control projection conjugate gradient method fruit and vegetable drying

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Group learning algorithm: a new metaheuristic algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第19期35卷 14013-14028页

作者： Rahman, Chnoor M. Charmo Univ Coll Sci Comp Dept Sulaymaniyah Iraq Amer Univ Iraq Sulaimani Informat Technol Dept Sulaymaniyah Iraq

Metaheuristics are intelligent optimization techniques that lead the searching procedure through utilizing exploitation and exploration. Increasing the number of hard problems with big data sets has encouraged researchers to implement novel metaheuristics and hybrid the existing ones to improve their performance. Hence, in this work, a novel metaheuristic called group learning algorithm is proposed. The main inspiration of the algorithm emerged from the way individuals inside a group affect each other, and the effect of group leader on group members. The two main steps of optimization, exploration and exploitation are outlined through integrating the behaviors of group members and the group leader to complete the assigned task. The proposed work is evaluated against a number of benchmarks. The produced results of classical benchmarks are compared against PSO, GWO, TLBO, BA, ALO, and SSA. In general, compared to other participated algorithms, out of 19 classical benchmarks, the proposed work showed better results in 11. However, the second best algorithm which is SSA performed better in 4 out of 19 benchmarks. To further evaluate the ability of the algorithm to optimize large scale optimization problems CEC-C06 2019 benchmarks are utilized. In comparison to other participated algorithms, the proposed work produced better results in most of the cases. Additionally, the statistical tests confirmed the significance of the produced results. The results are evidence that the proposed algorithm has the ability to optimize various types of problems including large scale optimization problems.

关键词： optimization Single objective optimization Benchmark Particle swarm optimization grey wolf optimization algorithm Teaching learning based optimization Population based algorithm Evolutionary algorithm

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Optimized Controller Gains Using grey wolf algorithm for Grid Tied Solar Power Generation with Improved Dynamics and Power Quality

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Chinese Journal of Electrical Engineering 2022年第2期8卷 75-85页

作者： Veramalla Rajagopal Danthurthi Sharath Gundeboina Vishwas Jampana Bangarraju Sabha Raj Arya Challa Venkatesh Department of Electrical and Electronics Engineering Kakatiya Institute of Technology and Science WarangalTelangana 506015India Department of Electrical and Electronics Engineering B V Raju Institute of TechnologyTelangana 502313India Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology SuratGujarat 395007India

This study proposes a control algorithm based on synchronous reference frame theory with unit templates instead of a phase locked loop for grid-connected photovoltaic(PV)solar system,comprising solar PV panels,DC-DC converter,controller for maximum power point tracking,resistance capacitance ripple filter,insulated-gate bipolar transistor based controller,interfacing inductor,linear and nonlinear *** dynamic performance of the grid connected solar system depends on the effect operation of the control algorithm,comprising two proportional-integral *** controllers estimate the reference solar-grid currents,which in turn generate pulses for the three-leg voltage source *** grey wolf optimization algorithm is used to optimize the controller gains of the proportional-integral controllers,resulting in excellent performance compared to that of existing optimization *** compensation for neutral current is provided by a star-delta transformer(non-isolated),and the proposed solar PV grid system provides zero voltage regulation and eliminates harmonics,in addition to load *** power extraction from the solar panel is achieved using the incremental conductance algorithm for the DC-DC converter supplying solar power to the DC bus capacitor,which in turn supplies this power to the grid with improved dynamics and *** solar system along with the control algorithm and controller is modeled using Simulink in Matlab 2019.

关键词： Control algorithm solar power generation DC-DC converter star-delta transformer maximum power point tracking power quality grey wolf optimization algorithm

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US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model

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ENERGY 2020年第0期194卷 116905-000页

作者： Lu, Hongfang Ma, Xin Azimi, Mohammadamin Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploita Chengdu 610500 Peoples R China Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Louisiana Tech Univ Trenchless Technol Ctr Ruston LA 71270 USA

Natural gas (NG) is a vital energy in the energy structure transition, and its consumption prediction is a significant issue in energy structure management and energy security. As the second largest energy consumer and producer in the world, the status of NG in the United States (US) energy system has been increasing since the "An America First Energy Plan" was proposed in 2017. Accurate prediction of natural gas consumption (NGC) can provide an effective reference for decision-makers, policymakers, and energy companies. This paper proposes an improved kernel-based nonlinear extension of the Arps decline model (KNEA) to forecast NGC in the US. The grey wolf optimization (GWO) algorithm is used to optimize the regularization parameter and kernel width in the KNEA model, and applies the hybrid model to the NGC datasets of different sectors (including lease and plant fuel usage, pipeline and distribution usage, residential users, commercial users, industrial users, vehicle fuels users, and power generation users) in the US. Compared with the prediction results of five benchmark models, it is shown that the GWO-KNEA model has the best performance in each dataset, and the range of mean absolute percentage error is less than 5%. By comparing the computational time and memory occupancy of the model, it can be concluded that the time and space complexity of the GWO-KNEA model is greater than that of the original KNEA model, but lower than that of other benchmark models. Moreover, this paper uses the newly proposed model to predict the NGC and consumption mix of the US from 2019 to 2025. The main conclusions are drawn: (1) NGC in the US will show a slow growth trend (the average annual growth rate is only 1.2%);(2) The proportion of NGC in power generation will increase significantly, reaching about 39% in 2025;(3) The proportion of residential, commercial and industrial NGC will decline slightly. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： United States Natural gas consumption Prediction KNEA grey wolf optimization algorithm Kernel method

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Learner Emotional Value Recognition Technology: An Artificial Neural Network Optimized by the grey wolf algorithm

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IEEE ACCESS 2023年 11卷 127689-127696页

作者： Chen, Qi Yang En Univ Law Sch Quanzhou 362014 Peoples R China

In cross-cultural communication, multimedia animation is crucial in defining a nation's image and cultural form. It is a key vehicle for cultural diffusion and a tool for film and television to convey national culture and highlight regional culture. Animation's particular charm, position, and function in cultural dissemination are further highlighted by the special cinematic language used to portray emotions, and it also reflects the medium's unique significance in the rapidly evolving modern society. To better understand the emotions generated by various multimedia animations, in-depth research is needed. To investigate these issues, this article explores the use of the Sigma-pi artificial neural network (SP-ANN) algorithm based on the grey wolf optimization algorithm (GWOA) to identify emotional states. Compared with traditional Sigma n-artificial neural network algorithms, a training process that does not require complex derivative calculations in derivative-based algorithms is performed. Sigma n-networks can benefit from the proposed learning algorithms. This algorithm has high approximation accuracy and is particularly suitable for real-time approximation of nonlinear processes. The test results indicate that the proposed algorithm can work as expected.

关键词： Animation Artificial neural networks Computational modeling Clustering algorithms Approximation algorithms Training Mathematical models Cultural aspects Multimedia systems Multimedia animation grey wolf optimization algorithm sigma pi artificial neural network algorithms

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Seasonal waste, geotherm, nuclear, wood net power generations forecasting using a novel hybrid grey model with seasonally buffered and time-varying effect

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APPLIED ENERGY 2024年 368卷

作者： Li, Xuemei Shi, Yansong Zhao, Yufeng Wu, Yajie Zhou, Shiwei Ocean Univ China Sch Econ Qingdao 266100 Peoples R China Ocean Univ China Inst Marine Dev Qingdao 266100 Peoples R China Ocean Univ China Sch Management Qingdao 266100 Peoples R China Ocean Univ China Coll Engn Qingdao 266100 Peoples R China

Seasonal volatility data is often disturbed by uncertain external shocks, making accurate forecasting particularly strenuous. This paper proposes a progressive adaptive prediction framework of data preprocessing, feature recognition, and seasonal prediction, namely SAWBO-TNGBM (1,1) model. Specifically, the seasonal full information variable weight weakening buffering operator is employed to effectively smooth the nonlinear fluctuation data. Furthermore, the grey Bernoulli model is extended by considering the time-varying effect, and grey wolf optimization algorithm improves the overall prediction efficiency. Necessarily, the Convertibility, Unbiasedness, and Recursiveness are fully derived and proven, which undoubtedly improves the reliability and the ability to capture seasonal information. Empirically, from a data-driven perspective, US seasonal clean energy net generations with diverse fluctuating characteristics are utilized to validate the predicted performance, including quarterly series (waste, geotherm) and monthly series (nuclear, wood). Results obtained from comprehensive experimental comparative analyses show that the fitting ability of the SAWBO-TNGBM (1,1) model exceeds that of other models, demonstrating its flexibility, universality, and high precision. Lastly, innovative robustness testing and extended analysis ensure that the novel model provides an effective tool for seasonal forecasting in clean energy generation.

关键词： Seasonal clean energy generation Seasonal weakening buffer operator grey prediction model grey wolf optimization algorithm Time-varying effect

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Effective virtual inertia control using inverter optimization method in renewable energy generation

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ENERGY EXPLORATION & EXPLOITATION 2021年第6期39卷 2103-2125页

作者： Niu, Shuanbao Qu, Linan Lin, Hsiung-Cheng Fang, Wanliang Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China Northwest Branch State Grid Corp China Xian Peoples R China China Elect Power Res Inst Nanjing Nanjing Peoples R China Hebei Univ Technol Sch Elect Engn Tianjin Peoples R China Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China

The high-level penetration of intermittent renewable power generation may limit power system inertia, resulting in system frequency instability in increasing power converter-based energy sources. To resolve this problem, virtual inertia control using distributed gray wolf optimization (DGWO) method in a synchronous generator is simulated under a distinct output fluctuation condition. First, the DGWO algorithm was established to achieve a local and global balance solution, and standard test functions were employed to verify the model convergence. Second, the key parameters that determine the effect of the virtual inertia controller in the power grid were analyzed. A DGWO-based optimization strategy to stabilize inertia was also developed. Finally, simulation results using step and random loads under a high permeability level are provided to verify the effectiveness of the proposed model. In the step load disturbance, the system can recover from the disturbance point to the stable point after 3 s under the regulation of the proposed control strategy, which is reduced by 18 s compared with the traditional control method. In the random load test, it takes only 12 s, 63 s less than the traditional one. Accordingly, the power system frequency can be stabilized more quickly from a disturbance state to a stable stage.

关键词： Virtual synchronous generator renewable energy generation grey wolf optimization algorithm frequency regulation parameter optimization

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