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Prediction of the height of water-conducting fracture zone using the FA-ALO-SVR model

EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-24页

作者： Bi, Yaoshan Shen, Shuhao Wu, Jiwen Li, Dong Huainan Normal Univ Sch Mech & Elect Engn Huainan 232001 Peoples R China Key Lab Mine Geol Disaster Prevent Anhui Prov Huainan 232001 Peoples R China Suzhou Univ Sch Informat & Engn Suzhou 234000 Peoples R China Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Peoples R China

Accurately predicting the height of the water-conducting fracture zone (WCFZ) on the coal seam roof is of great significance for ensuring the safe and efficient mining of coal seams at the mining face. To enhance the prediction accuracy of the WCFZ height, a multi-factor comprehensive analysis based on measured data from several mines in the Huainan Mining Area and Huaibei Mining Area has been conducted. A prediction index system for the height of WCFZ was established, incorporating factors such as coal seam roof type, mining method, mining depth, coal seam inclination, mining thickness, length of the working face slope, and the presence of faults in the working face. The primary component of their prediction model is the Support Vector Regression (SVR) model. Factor analysis (FA) was utilized to optimize the original data structure, and the ant lion optimization (ALO) algorithm was used to optimize the penalty factor "C" and the kernel function parameter "g" of the SVR model. Consequently, a prediction model for the WCFZ height was established based on FA-ALO-SVR. Subsequently, the predictive performance of the model was tested using new samples, and a comprehensive evaluation was conducted from three perspectives: prediction accuracy, prediction ability, and generalization ability. Five indicators, namely, mean absolute error (MAE), root mean square error (RMSE), mean relative error (MRE), Willmott's index of agreement (WIA), and Theil inequality coefficient (TIC), were used for the comparison with the traditional SVR model, the FA-SVR model, the ALO-SVR model and gray wolf optimization (GWO)-SVR model. The results indicated that the model achieved the lowest values for MAE, RMSE, MRE, and TIC, as well as the highest WIA value. Specifically, these values were 3.7857, 4.5393, 7.9066%, 0.1099, and 0.9370 respectively. This performance demonstrates a strong predictive capability of the model. Finally, the model was utilized to predict the height of WCFZ at the 3220 w

关键词： Height of water-conducting fracture zone Factor analysis ant lion optimization algorithm SVR model Multifactor analysis

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Advanced methodology for maximum torque point tracking of hybrid excitation PMSM for EVs

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Scientific Reports 2025年第1期15卷 1-23页

作者： Elymany, Mahmoud M. Elsonbaty, Nadia A. FLah, Aymen Prokop, Lukas Kraiem, Habib Enany, Mohamed A. Shaier, Ahmed A. Electrical Power and Machines Department Faculty of Engineering Zagazig University Zagazig 44519 Egypt Processes Energy Environment and Electrical Systems National Engineering School of Gabès University of Gabès Gabès Tunisia Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Applied Science Research Center Applied Science Private University Amman 11931 Jordan ENET Centre CEET VSB-Technical University of Ostrava Ostrava 708 00 Czech Republic Center for Scientific Research and Entrepreneurship College of Engineering Northern Border University 73213 Arar Saudi Arabia

This manuscript presents an innovative control strategy for the Hybrid Excitation Permanent Magnet Synchronous Motor (HEPMSM) designed for electric vehicle (EV) applications. The strategy combines Maximum Torque Point Tracking (MTPT) and Maximum Torque Per Ampere (MTPA) techniques to track the ideal torque-speed profile, ensuring maximum torque at low speeds for starting and climbing, and high power at higher speeds for cruising. A novel unidirectional excitation current method is proposed to replace traditional bidirectional field current control, eliminating the risk of permanent magnet demagnetization, reducing copper losses, and increasing efficiency. This approach extends the constant power (CP) region by a 4.2:1 ratio. The manuscript also introduces a detailed mathematical model, considering both iron core losses and their impact on the EV profile. Additionally, the Multi-Objective ant lion Optimizer (MOALO) algorithm is used in two stages: first to optimize the hybridization ratio (HR) and base speed (N_b), and second to analyze the effect of varying the hybridization ratio while maintaining constrained output power. The proposed strategy is validated through MATLAB simulations, demonstrating its effectiveness in achieving high acceleration, efficiency, and reliability for EV applications. © The Author(s) 2025.

关键词： ant lion optimization algorithm Electric vehicle Hybrid excitation permanent magnet synchronous motor Hybridization ratio

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A HybridOpt approach for early Alzheimer's Disease diagnostics with ant lion Optimizer (ALO)

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ALEXANDRIA ENGINEERING JOURNAL 2024年 109卷 112-125页

作者： Sasithradevi, A. Baskar, Chanthini Shoba, S. Vellore Inst Technol Ctr Adv Data Sci CADS Chennai 600127 Tamil Nadu India Vellore Inst Technol Sch Elect Engn Chennai 600127 Tamil Nadu India

Alzheimer disease is a neurological disorder that affects the elderly, caused by abnormal protein buildup in the brain. It leads to difficulties such as financial mismanagement, disorientation, behavioral changes, and repetitive speech. The existing methods use traditional features to detect the early signs of AD with low detection accuracy. The potential features have to be identified that represent best the patterns associated with alzheimers. Feature selection using ant lion optimization resolves the issue by using complementary information from hybrid features. The proposed HybridOpt pipeling for AD diagnosis combines the high level and low level features for early stage detection The objective of this work is to select efficient features from different deep networks, such as AlexNet, Googlenet, VGG16, ResNet, Efficient, DenseNet, and traditional texture features. ant lion optimization is used to select the best feature among the deep network and traditional texture feature groups. Extensive experimentation on two highly challenging datasets called the Alzheimer's disease neuroimage dataset and KAGGLE reveals that the proposed HybridOpt pipeline achieves an accuracy of 99% and 98.1% respectively.

关键词： Texture features Deep features Hybrid learning Alzheimer disease ant lion optimization algorithm

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Design of a optimal robust adaptive neural network-based fractional-order PID controller for H-bridge single-phase inverter

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Kashfi, Rasoul Balochian, Saeed Alishahi, Mohammad Islamic Azad Univ Dept Elect Engn Gonabad Branch Gonabad Iran Islamic Azad Univ Dept Elect Engn Mashhad Branch Mashhad Iran Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Iran

Adaptive Neural Network- based Fractional-order PID Control (NN-FO-PID) approach is designed for H-bridge inverter. This inverter has an LC filter to decrease the level of Total Harmonic Distortion (THD) that can affect the efficiency of the system. In addition, the reduction of THD and stability insurance of the filter are challenging performances. To fulfill this function, a fractional order proportional integrated derivative (FO-PID) controller is developed for the inverter. A few merits of the Fractional-Order notion as a useful technique include reduced sensitivity to noise and parametric fluctuation;however, for a wider range of disturbances, such as noise, this approach shows an unsuitable practical application based on its fixed gain values. Moreover, having parameters uncertainties including parametric variations, load uncertainty, supply voltage variation uplifts this challenging condition severely, and the parameters need to be adjusted once more for more dependable operations. As a result, the control parameters must be optimized once more to provide ideal operations. Here, an adaptive mechanism is proposed based on neural network structure to optimize the gains of the FO-PID controller for better performances. In real applications, this approach has some benefits, since it uses the Black-box technique, which does not necessitate a precise mathematical model of the system, resulting in a reduced computational burden, simple implementation, and reduced dependence on the model's states. Even under extremely difficult circumstances, the artificial neural network structure effectively optimizes the FO-PID gains in real-time. This benefit can reduce the amount of THD-level for the inverter, properly. Additionally, to have a proper response in the first step condition, and trying to reduce the level of dangerous conditions, based on benefits of the ant lion optimization method, it is considered to select the initial values of the FO-PID controller gains. Here

关键词： H -bridge inverter Fractional-order PID controller Neural Network structure ant lion optimization algorithm Disturbance

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ant Cat Swarm optimization-Enabled Deep Recurrent Neural Network for Big Data Classification Based on Map Reduce Framework

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COMPUTER JOURNAL 2022年第12期65卷 3167-3180页

作者： Narayana, Satyala Chandanapalli, Suresh Babu Rao, Mekala Srinivasa Srinivas, Kalyanapu Seshadri Rao Gudlavalleru Engn Coll Dept Comp Sci & Engn Gudlavalleru 521356 Andhra Pradesh India Lakireddy Bali Reddy Coll Engn Dept Informat Technol Mylavaram 521230 Andhra Pradesh India

The amount of data generated is increasing day by day due to the development in remote sensors, and thus it needs concern to increase the accuracy in the classification of the big data. Many classification methods are in practice;however, they limit due to many reasons like its nature for data loss, time complexity, efficiency and accuracy. This paper proposes an effective and optimal data classification approach using the proposed ant Cat Swarm optimization-enabled Deep Recurrent Neural Network (ACSO-enabled Deep RNN) by Map Reduce framework, which is the incorporation of ant lion optimization approach and the Cat Swarm optimization technique. To process feature selection and big data classification, Map Reduce framework is used. The feature selection is performed using Pearson correlation-based Black hole entropy fuzzy clustering. The classification in reducer part is performed using Deep RNN that is trained using a developed ACSO scheme. It classifies the big data based on the reduced dimension features to produce a satisfactory result. The proposed ACSO-based Deep RNN showed improved results with maximal specificity of 0.884, highest accuracy of 0.893, maximal sensitivity of 0.900 and the maximum threat score of 0.827 based on the Cleveland dataset.

关键词： Map Reduce framework big data classification deep recurrent neural network Pearson correlation-based Black hole entropy fuzzy clustering ant lion optimization algorithm

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ant lion algorithm for Optimized Controller Gains for Power Quality Enrichment of Off-grid Wind Power Harnessing Units

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Chinese Journal of Electrical Engineering 2020年第3期6卷 85-97页

作者： Kodakkal Amritha Veramalla Rajagopal Kuthuri Narasimha Raju Sabha Raj Arya Department of Electrical and Electronics Engineering BVRIT HYDERABAD College of Engineering for WomenTelangana 500090India Department of Electrical and Electronics Engineering Kakatiya Institute of Technology and ScienceTelangana 506015India Department of Electrical and Electronics Engineering Koneru Lakshmaiah Education FoundationAndhra Pradesh 522502India Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology SuratGujarat 395007India

The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind *** admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the *** wind power harnessing unit comprising of a squirrel cage induction generator,a star-delta transformer,a battery storage system and the control unit are modeled using Matlab/Simulink *** isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral *** response of the system during the dynamic loading depends on the best possible compensator proportional-integral(PI)*** antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence,high efficiency and fast calculating *** is therefore used to extract the optimal values of frequency and voltage PI *** simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set *** algorithm is proven to have a quick response,maintain the required frequency,suppress the current harmonics,regulate voltage,help in balancing the load and compensating for the neutral current.

关键词： Wind power harnessing unit induction generator admittance based control algorithm ant lion optimization algorithm voltage and frequency control battery energy storage system

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Hybrid wind speed prediction model based on recurrent long short-term memory neural network and support vector machine models

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SOFT COMPUTING 2020年第7期24卷 5345-5355页

作者： Vinothkumar, T. Deeba, K. RVS Coll Engn & Technol Dept Elect & Elect Engn Coimbatore 641402 Tamil Nadu India Kalaignar Karunanidhi Inst Technol Dept Comp Sci & Engn Coimbatore 641402 Tamil Nadu India

Renewable energy has gained its significance in the recent years due to the increasing power demand and the requirement in various distribution and utilization sectors. To meet the energy demand, renewable energy resources which include wind and solar have attained significant attractiveness and remarkable expansions are carried out all over the world to enhance the power generation using wind and solar energy. This research paper focuses on predicting the wind speed so that it results in forecasting the possible wind power that can be generated from the wind resources which facilitates to meet the growing energy demand. In this work, a recurrent neural network model called as long short-term memory network model and variants of support vector machine models are used to predict the wind speed for the considered locations where the windmill has been installed. Both these models are tuned for the weight parameters and kernel variational parameters using the proposed hybrid particle swarm optimization algorithm and ant lion optimization algorithm. Experimental simulation results attained prove the validity of the proposed work compared with the methods developed in the early literature.

关键词： LSTM network SVM model Particle swarm optimization ant lion optimization algorithm Wind speed Prediction accuracy

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optimization Methods for Tunning of SMC Gains for Manipulator Control: A Comparative Study 1

Optimization Methods for Tunning of SMC Gains for Manipulato...

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1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Kumar, Jitendra Mishra, Puneet Kumar, Vineet Rana, K. P. S. Netaji Subhas Inst Technol New Delhi 110078 India

ISBN: (纸本)9781467385879

Cuckoo search algorithm (CSA) and ant lion optimization (ALO) algorithm have been explored in this work to optimize the gains of Fractional Order Fuzzy Sliding Mode Proportional Derivative Controller (FOFSMCPD). The controller has been applied to control a highly nonlinear and coupled multi-input multi-output system, i. e. a two link planar rigid robotic manipulator. In present case, the optimization problem is in the form of minimization of weighted summation of integral of absolute error and chattering, for the efficient control of robotic manipulator. On the basis of conducted investigations, it is inferred that FOFSMCPD, tuned by CSA for trajectory tracking shows much better performance than the onetuned by ALO algorithm, for set point tracking, disturbance rejection and noise suppression.

关键词： Sliding Mode Control Fractional Order Operators Fuzzy Logic Cuckoo Search algorithm ant lion optimization algorithm

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Inverse modeling of thermal decomposition of flame-retardant flexible PVC with model-free coupled with model-fitting method

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THERMOCHIMICA ACTA 2021年 700卷 178935-178935页

作者： Wang, Junxiang Zhao, Xuan Yu, Qiang Zhang, Shuo Changan Univ Sch Automobile Xian Peoples R China

The thermal decomposition model of flame-retardant flexible PVC is essential for relevant waste management and predicting its fire behavior. In this work, thermal decomposition kinetics of this material was investigated via thermogravimetric analysis in non-isothermal conditions. The kinetic analysis was performed using model-free (including Kissinger, Friedman, and advanced Vyazovkin methods) coupled with model-fitting method (ant lion optimization algorithm, ALO). The kinetic parameters obtained from model-free method provide guides for initial guess and search range for ALO. Two different decomposition models are developed and the validations show that second two-step parallel model could better predict experimental data and explain the decomposition process. The performance of ALO is compared with Shuffled Complex (SCE) Evolution algorithm. The results show that the ALO shows better performance because all solutions from SCE are trapped in local minima. In this way, the ALO could be used as an alternate optimization tool for inverse modeling.

关键词： Flame-retardant flexible PVC Thermal decomposition kinetics modeling Model-free method Model-fitting method ant lion optimization algorithm Shuffled complex evolution algorithm

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optimization Methods for Tunning of SMC Gains for Manipulator Control: A Comparative Study

Optimization Methods for Tunning of SMC Gains for Manipulato...

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International Conference on Power Electronics, Intelligent Control and Energy Systems

作者： Jitendra Kumar Puneet Mishra Vineet Kumar K.P.S. Rana Netaji Subhas Institute of Technology

ISBN: (纸本)9781467385886

Cuckoo search algorithm (CSA) and ant lion optimization (ALO) algorithm have been explored in this work to optimize the gains of Fractional Order Fuzzy Sliding Mode Proportional Derivative Controller (FOFSMCPD). The controller has been applied to control a highly nonlinear and coupled multi-input multi-output system, i.e. a two link planar rigid robotic manipulator. In present case, the optimization problem is in the form of minimization of weighted summation of integral of absolute error and chattering, for the efficient control of robotic manipulator. On the basis of conducted investigations, it is inferred that FOFSMCPD, tuned by CSA for trajectory tracking shows much better performance than the onetuned by ALO algorithm, for set point tracking, disturbance rejection and noise suppression.

关键词： Sliding Mode Control Fractional Order Operators Fuzzy Logic Cuckoo Search algorithm ant lion optimization algorithm

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