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Prediction of GHG emissions from Chengdu Metro in the construction stage based on WOA-DELM

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2023年第1期139卷

作者： Chen, Zheng Guo, Yalin Guo, Chun Southwest Jiaotong Univ Sch Civil Engn MOE Key Lab Transportat Tunnel Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Civil Engn Key Lab Transportat Tunnel Engn Minist Educ Chengdu Sichuan Peoples R China

With the mass construction of urban subways, the global greenhouse gas (GHG) emissions have been on the rise. This paper provides statistical evidence to support the infrastructure of subway emissions reduction through a study of GHG emissions during the construction stage of 6 stations and 7 sections of Chengdu Metro Line 18. Using the emission coefficient method, the GHG emissions from building material production, transportation and site construction in subway stations and shield sections were calculated, and a subway GHG emissions prediction model dependent on deep extreme learning machine (DELM) with whale optimization algorithm (WOA) was established(i.e., WOA-DELM). Compared with some optimized DELMs, namely wind driven optimizer (WDO) -DELM, grey wolf optimizer (GWO) -DELM, particle swarm optimizer (PSO) -DELM, artificial bee colony (ABC) -DELM, multi verse optimizer (MVO) -DELM, and atom search optimizer (ASO) -DELM, and some non-optimized algorithm models, namely back propagation neural network (BPNN), kernel extreme learning machine (KELM) and DELM, the correlation consistency of WOA-DELM algorithm prediction results (0.757) was found to be slightly higher. Through sensitivity analysis of the main input variables of subway GHG emissions with the WOADELM algorithm model, it was determined that the key influencing factors of station GHG emissions prediction were the station length and the depth of track surface, with relative change rates of corresponding variables of GHG emissions at 30.1% and 23.1% respectively. Finally, a rough prediction formula of GHG emissions from Chengdu Metro stations and shield sections were fitted based on the key influencing factors of GHG emissions. This study provides a practical and effective reference for reducing GHG emissions in subway construction and operation.

关键词： GHG emissions Emission coefficient method Subway construction Deep extreme learning machine whale optimization algorithm

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Single dendritic neural classification with an effective spherical search-based whale learning algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第4期20卷 7594-7632页

作者： Yu, Hang Shi, Jiarui Qian, Jin Wang, Shi Li, Sheng Taizhou Univ Coll Comp Sci & Technol Taizhou 225300 Peoples R China Wesoft Co Ltd Dept Engn Kawasaki 2100024 Japan

McCulloch-Pitts neuron-based neural networks have been the mainstream deep learning methods, achieving breakthrough in various real-world applications. However, McCulloch-Pitts neuron is also under longtime criticism of being overly simplistic. To alleviate this issue, the dendritic neuron model (DNM), which employs non-linear information processing capabilities of dendrites, has been widely used for prediction and classification tasks. In this study, we innovatively propose a hybrid approach to co-evolve DNM in contrast to back propagation (BP) techniques, which are sensitive to initial circumstances and readily fall into local minima. The whale optimization algorithm is improved by spherical search learning to perform co-evolution through dynamic hybridizing. Eleven classification datasets were selected from the well-known UCI Machine Learning Repository. Its efficiency in our model was verified by statistical analysis of convergence speed and Wilcoxon sign -rank tests, with receiver operating characteristic curves and the calculation of area under the curve. In terms of classification accuracy, the proposed co-evolution method beats 10 existing cutting-edge non-BP methods and BP, suggesting that well-learned DNMs are computationally significantly more potent than conventional McCulloch-Pitts types and can be employed as the building blocks for the next-generation deep learning methods.

关键词： evolutionary algorithms optimization methods whale optimization algorithm back-propagation artificial neural networks deep learning dendritic neuron model

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Short-term wind power forecasting and uncertainty analysis based on FCM-WOA-ELM-GMM

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ENERGY REPORTS 2023年 9卷 807-819页

作者： Gu, Bo Hu, Hao Zhao, Jian Zhang, Hongtao Liu, Xinyu North China Univ Water Resources & Elect Power Sch Elect Engn Zhengzhou 450011 Peoples R China Yellow River Conservancy Tech Inst Kaifeng 475000 Peoples R China State Grid Henan Elect Power Co Elect Power Res Inst Zhengzhou 450002 Peoples R China

With large-scale wind power connected to the power grid, accurate short-term wind power forecasting has become a key technology for safe, economic power grid operation. Therefore, a short-term wind power forecasting and uncertainty analysis method based on the FCM-WOA-ELM-GMM was proposed. Fuzzy C-means (FCM) was used to cluster the numerical weather prediction (NWP) and wind farm power data, and the data points with similar meteorological information are classified into one class. Using the rapid convergence and high convergence accuracy of the whale optimization algorithm (WOA), the input weight and hidden layer threshold of the extreme learning machine (ELM) model were optimized to improve the ELM calculation speed and forecasting accuracy. The WOA-ELM model was trained using the clustered NWP and wind farm power data and the short-term wind power was projected using the trained forecasting model. To accurately calculate the forecasting error probability density distribution, the Gaussian mixture model (GMM) was applied and the wind power forecast confidence intervals under different climatic conditions and time scales were calculated. The forecasting accuracies of the WOA-ELM, ELM, PSO-LSSVM, LSSVM, LSTM, PSO-BP, and WNN models were compared and analyzed, and the RMSE values of the 4-h forecasting results in April were as follows: WOA-ELM, 5.95%;ELM, 26.73%;PSO-LSSVM, 3.78%;LSSVM, 5.19%;LSTM, 23.71%;PSO-BP, 15.63%;and WNN, 23.23%. The RMSE values of 24-h forecasting results in April were: WOA-ELM, 6.62%;ELM, 19.86%;PSO-LSSVM, 9.91%;LSSVM, 13.73%;LSTM, 23.69%;PSO-BP, 14.08%;and WNN, 20.11%. The RMSE values of 72-h forecasting results in April were as follows: WOA-ELM, 5.24%;ELM, 13.64%;PSO-LSSVM, 12.03%;LSSVM, 13.67%;LSTM, 16.61%;PSO-BP, 15.46%;and WNN, 20.22%. According to the calculation results, under different climatic conditions and forecasting time scales, the forecasting accuracy of the FCM-WOA-ELM-GMM model is higher than those of the other models.(c) 202

关键词： Fuzzy C-means clustering whale optimization algorithm Extreme learning machine Wind power forecasting

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Selective removal of antibiotics over MgAl2O4/C3N4/YMnO3 photocatalysts: Performance prediction and mechanism insight

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JOURNAL OF THE AMERICAN CERAMIC SOCIETY 2023年第4期106卷 2420-2442页

作者： Li, Maoyuan Wang, Shifa Gao, Huajing Yin, Zijuan Chen, Chaoli Yang, Hua Fang, Leiming Veerabhadrappa, Jagadeesha Angadi Yi, Zao Li, Dengfeng Chongqing Three Gorges Univ Chongqing Key Lab Geol Environm Monitoring & Disas Chongqing Peoples R China Lanzhou Univ Technol Sch Sci Lanzhou Peoples R China China Acad Engn Phys Inst Nucl Phys & Chem Mianyang Sichuan Peoples R China PC Jabin Sci Coll Dept Phys Hubballi Karnataka India Southwest Univ Sci & Technol Joint Lab Extreme Condit Matter Properties Mianyang Peoples R China Chongqing Univ Posts & Telecommun Sch Sci Chongqing Peoples R China Chongqing Three Gorges Univ Chongqing Key Lab Geol Environm Monitoring & Disas Chongqing 404000 Peoples R China

A simple polyacrylamide gel method combined with low temperature sintering technology has been used to synthesize the C-O functional groups grafted MgAl2O4/C3N4/YMnO3 (MAO-CN-YMO) heterojunction photocatalysts with enhanced visible-light-induced photodegradation toward oxytetracycline hydrochloride (OTC-HCl). A variety of characterization methods are used to gain insight into the phase purity, crystal structure, microstructure, functional group information, elemental composition, surface defect, light response capability, and photocatalytic activity of the as-synthesized samples. The influences of the mass ratios of m(CN)/m(YMO), m(CN)/m(MAO), and m(MAO)/(m(CN) + m(YMO)) in CN-YMO, CN-MAO, and MAO-CN-YMO heterojunction photocatalysts on the photocatalytic activity for the degradation of OTC-HCl was also discussed, and the optimal mass ratio of m(MAO)/(m(CN) + m(YMO)) is identified as 15 wt%. The photocatalytic experiments confirmed that the MAO-CN-YMO heterojunction photocatalysts had high selectivity for the degradation of antibiotics. The prediction of the photocatalytic activity of the MAO-CN-YMO heterojunction photocatalysts for the degradation of OTC-HCl was made by a variety of intelligent algorithm models. The results of the whale optimization algorithm are highly consistent with the experimental results. Combined with the energy band theory and the characterization results of high-performance liquid chromatography-tandem mass spectrometry, the free radicals in the reaction solution preferentially attacked the -CH3, -NCH2, and -OH of OTC-HCl during the degradation of OTC-HCl by MAO-CN-YMO heterostructure photocatalysts, and then attack -C=O and -C=O-NH2, and finally perform ring-opening reaction to degrade OTC-HCl into nontoxic and harmless products of small molecules such as CO2, H2O, and NH4+. This work provides a new idea for the development of novel double p-n junction MAO-CN-YMO heterojunction photocatalysts for antibiotic degradation and the prediction

关键词： C3N4 MgAl2O4 polyacrylamide gel method whale optimization algorithm YMnO3

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Study on a hybrid algorithm for accurate ripple detection in DC microgrids

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IET POWER ELECTRONICS 2023年第14期16卷 2409-2422页

作者： Zhang, Yongjun Chen, Mingli Deng, Wenyang Zhong, Kanghua South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

With the advantages of the low cost of transmission lines and high efficiency, the DC microgrid has become a rising star in the low-voltage network. However, multi-source and multi-transformation characteristics in DC microgrids will lead to the existence of various forms of ripple generation sources in the system. The existence of a large amount of ripple not only directly cause the reduction of power quality of DC microgrids, but also decreases the accuracy of electricity billing. It is vital to achieving accurate ripple detection for the reliable power supply of the DC microgrid. A DC-side ripple detection method, which combines the whale optimization algorithm (WOA), Variational Modal Decomposition (VMD), and Hilbert Transform (HT), is therefore investigated and proposed here. Firstly, the sample entropy (SampEn) is used as the fitness function, applied the whale optimization algorithm, is to determine the optimal decomposition scale and penalty factor, of the variational modal decomposition;then, the ripple-containing DC signal is decomposed into a series of eigenmodal components (imf) by the optimized variational modal decomposition algorithm. Finally, the Hilbert Transform is performed to obtain the amplitude and frequency of the ripple components. To prove the effectiveness, the proposed method is compared with the conventional Empirical Mode Decomposition (EMD) and the VMD algorithm, by testing simulated DC signals without and with noise. Results show that the proposed method has higher accuracy and noise robustness than the EMD and VMD algorithms.

关键词： DC microgrid Hilbert transform ripple variational modal decomposition whale optimization algorithm

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Short-Term Power Prediction by Using Least Square Support Vector Machine With Variational Mode Decomposition in a Photovoltaic System

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IEEE ACCESS 2023年 11卷 143486-143500页

作者： Cai, Yongxiang Hao, Shuqing Wen, Xiankui Li, Hongwei He, Xiaomeng Chen, Lu Ren, Jiakuan Guizhou Power Grid Co LTD Power Sci Res Inst Guiyang 550002 Guizhou Peoples R China Qingdao Topscomm Commun Co Ltd Qingdao 266109 Peoples R China

In order to improve the accuracy of short-term photovoltaic power prediction, a comprehensively artificial intelligent (AI) based algorithm has been proposed. Firstly, a fuzzy C-means (FCM) is used to cluster historical data into different groups according to meteorological variables;Secondly, a variational mode decomposition (VMD) is used to divide original signals into multi-frequency components as a series of intrinsic mode functions (IMF) while whale optimization algorithm (WOA) externally optimizes the parameters of VMD so as to stimulate the data processing. Least squares support vector machine (LSSVM) is adapted to build up the prediction framework with an assistance from improved sparrow search algorithm (ISSA) that aims to solve the key issues on local optimization over LSSVM training process. Under the proposed framework, practical power signals from Northwest China has been tested. The outcomes verify the effectiveness of this comprehensive algorithm. The indicator, mean absolute percentage error (MAPE), has decreased by 17.17%, 15.78% and 5.37% while R2 has increased by 0.17, 0.15 and 0.03 comparing with LSSVM, ISSA-LSSVM and VMD-LSSVM. Moreover, the study outcomes have also evidenced the superiority of ISSA over other fellow heuristic algorithms such as PSO, QPSO and SSA in both convergence speed and accuracy when an ISSA facilitates LSSVM for prediction.

关键词： Fuzzy C-means improved sparrow search algorithm least squares support vector machine short-term photovoltaic power prediction variational modal decomposition whale optimization algorithm

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Enhanced Facial Emotion Recognition by Optimal Descriptor Selection with Neural Network

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IETE JOURNAL OF RESEARCH 2023年第5期69卷 2595-2614页

作者： Ashok Kumar, P. M. Maddala, Jeevan Babu Martin Sagayam, K. KL Deemed Univ Dept Comp Sci Engn Vaddeswaram Andhra Pradesh India Vasireddy Venkatadri Inst Technol Dept Comp Sci Engn Guntur Andhra Pradesh India Karunya Inst Technol & Sci Dept Elect & Commun Engn Coimbatore Tamil Nadu India

Facial Emotion Recognition (FER) is the approach of detecting emotions of humans from facial expressions. Emotions are detected automatically by the human brain and hence, many computer aided techniques are implemented for recognizing emotions. Recognition of natural emotions is an exciting area with a broad range of real time applications like automated tutoring models, smart environments computer-human interaction, driver warning systems, and video and image retrieval. The major goal of this paper is to implement an intelligent model for FER. The proposed model involves few steps: (a) Face extraction (b) Image filtering, (c) Extraction of facial components, (d) Descriptor selection, and (d) Classification. Initially, the face is extracted from the input image by the Viola-Jones method, which is generally employed for the detection of object. Further, the noise of the image is filtered by the Gabor Filtering. Further, the facial components are extracted as features via the Affine-Scale-Invariant Feature Transform (ASIFT), which is the modified version of SIFT method. Since the length of the descriptors generated from ASIFT is high;the number of descriptors is reduced by the optimal descriptor selection approach with the aid of hybrid meta-heuristic algorithm termed as MV-WOA. The extracted descriptors are subjected to Neural Network (NN). As a modification to the existing machine learning algorithms, the number of hidden neurons in the NN is optimized by the proposed WOA+MVO algorithm. By conducting experiments, the results demonstrate that the developed system can perform better the recent traditional approaches for emotion recognition for classifying seven emotions like normal, smile, sad, surprise, anger, fear, and disgust.

关键词： Affine-scale-invariant feature transform Convolutional neural network Facial emotion recognition Multi-verse optimization Neural network Scale-invariant feature transform whale optimization algorithm

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Modeling the hardness properties of high-performance concrete via developed RBFNN coupling matheuristic algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 4801-4815页

作者： Zhao, Xiaofang Li, Faming Chen, Biao Li, Xiaofei Lu, Shasha Lanzhou Resources Environm Voc Tech Univ LanZhou Lanzhou 730000 Peoples R China Liaoning Tech Univ Fuxin Liaoning Peoples R China

Examining the properties of High-Performance Concrete (HPC) has been a big challenge due to the highly heterogeneous relationships and coherence among several constituents. The employment of silica fume and fly ash as ecofriendly components in mixtures benefits the concrete to improve its physical features. Although machine learning approaches are utilized broadly in many studies solitarily to estimate the mechanical features of concrete, causing to reduce accuracy and lift the cost and complexities of computational networks. Consequently, current research aims to develop a Radial Basis Function Neural Network (RBFNN) integrating with optimization algorithms in order to precisely model the mechanical characteristics of HPC mixtures including compressive strength (CS) and slump (SL). Feeding the dataset of HPC samples to hybrid models will result to reproduce the given CS and SL factors simultaneously. The results of the models showed that the maximum rate of correlation between estimated values and measured ones was obtained at 98.3% while the minimum rate of RMSE was calculated at 3.684mm (and MPa) in the testing phase. Employing such soft-oriented approaches has been benefiting us to reduce costs and increase the result accuracy.

关键词： High-performance concrete radial basis function neural network compressive strength slump whale optimization algorithm

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Monthly runoff time series interval prediction based on WOA-VMD-LSTM using non-parametric kernel density estimation

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EARTH SCIENCE INFORMATICS 2023年第3期16卷 2373页

作者： Wang, Wen-chuan Wang, Bo Chau, Kwok-wing Xu, Dong-mei North China Univ Water Resources & Elect Power Coll Water Resources Henan Key Lab Water Resources Conservat & Intens U Zhengzhou 450046 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom Kowloon Hong Kong Peoples R China

Logical development and effective use of water resources depend heavily on the practicability of runoff forecast. A monthly runoff interval prediction approach based on WOA-VMD-LSTM and non-parametric kernel density estimation is proposed in an effort to address the issue that conventional point prediction method cannot properly convey the uncertainty of prediction findings. Using variational modal decomposition (VMD) optimized by whale optimization algorithm (WOA), monthly runoff series is first divided into a number of relatively stable subsequences. Each subsequence is then predicted using long-term and short-term memory neural network (LSTM), and final point prediction results are obtained by superposition. Non-parametric kernel density estimation is then used to forecast monthly runoff interval and is compared with LSTM, EMD-LSTM, and VMD-LSTM models based on findings of point prediction. Findings indicate that this model's prediction accuracy is noticeably higher than that of other models utilized in this paper. A realistic fluctuation range for runoff interval prediction is also provided by non-parametric kernel density estimation, which can be a useful reference for decision-makers in water resources management.

关键词： whale optimization algorithm Variational mode decomposition Long-term and short-term memory neural network Non-parametric nuclear density estimation Runoff interval forecasting

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Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第28期30卷 71677-71688页

作者： Naderipour, Amirreza Abdul-Malek, Zulkurnain Davoodkhani, Iraj Faraji Kamyab, Hesam Ali, Roshafima Rasit Univ Teknol Malaysia Fac Engn Sch Elect Engn Inst High Voltage & High Current Johor Baharu 81310 Malaysia Islamic Azad Univ Dept Elect Engn Khalkhal Branch Khalkhal Iran Univ Teknol Malaysia Malaysia Japan Int Inst Technol Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia

Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations.

关键词： Renewable energy sources Microgrid Load-frequency control Self-tuning fuzzy control Fractional-order controller whale optimization algorithm

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