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Front-End Parameter Identification Method Based on Adam-Wa* optimization algorithm for Underwater Wireless Power Transfer System

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2025年第4期40卷 6307-6318页

作者： Luo, Bo Wu, Huan Wang, Mengyao Wang, Fangrui Bai, Longlei Jiang, Chaoqiang You, Jiang Harbin Engn Univ Yantai Res Inst Yantai 264000 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Monitoring coupler parameters in underwater wireless power transfer (UWPT) systems is crucial for improving the system transmission characteristics. Due to the eddy current effect, the equivalent circuits of the magnetic coupler are more complex and contain more parameters than those in air-based circuits. Traditional parameter identification methods, which rely on solving circuit matrix equations, often struggle with the complex UWPT systems or require significant computational resources to solve high-order multivariate equations. This article proposes a coupler parameter identification method based on the adaptive moment estimation with a weighted adjustment (Adam-W)a* optimization algorithm to address the multiparameter identification problems in the UWPT system. This method transforms the problem of solving high-order matrix equations into an optimization problem to address multiparameter identification problem caused by seawater eddy effect, which is challenging for traditional methods. In addition, it facilitates online monitoring of the characteristic parameters of the system's coupler by detecting the system's front-end input current and voltage without wireless communication and additional sensor modules, and exhibits better reliability in underwater environments. The experimental results show that the proposed Adam-W algorithm achieves a high parameter identification accuracy within 1 s and 94 iterations with an average error of 2.78%. Comparably, an average error of 56.74% is achieved by gradient descent and 41.49% by adaptive moment estimation, and 37.42% by particle swarm optimization in 150 iterations. The proposed Adam-W algorithm achieves higher accuracy in parameter identification of the UWPT system within a shorter time and demonstrates better applicability in seawater environments.

关键词： Coils Mathematical models Eddy currents Integrated circuit modeling Circuits optimization Parameter estimation Couplings Couplers Inductance Adaptive moment estimation (Adam) coupler parameter identification gradient descent (GD) inductive power transfer (IPT) optimization algorithm underwater wireless power transfer (UWPT)

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Dynamic fitness-distance balance-based artificial rabbitsa* optimization algorithm to solve optimal power flow problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 240卷

作者： Bakir, Huseyin Dogus Univ Vocat Sch Dept Elect & Automat TR-34775 Istanbul Turkiye

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (dFDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the dFDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (dFDBARO) algorithm. Moreover, the practicability of the proposed dFDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed dFDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the dFDBARO algorithm is available at https://***/matlabcentral/filee xchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm.

关键词： A novel enhanced artificial rabbits optimization algorithm Metaheuristic algorithm design Roulette and dynamic fitness -distance balance selection methods Optimal power flow Renewable energy sources

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Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawka* optimization algorithm and stacking based machine learning

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APPLIED WATER SCIENCE 2024年第4期14卷 1-23页

作者： Costache, Romulus Pal, Subodh Chandra Pande, Chaitanya B. Islam, Abu Reza Md. Towfiqul Alshehri, Fahad Abdo, Hazem Ghassan Transilvania Univ Brasov Dept Civil Engn 5 Turnului Str Brasov 500152 Romania Danube Delta Natl Inst Res & Dev 165 Babadag St Tulcea 820112 Romania Natl Inst Hydrol & Water Management Bucuresti Ploiesti Rd 97E Bucharest 013686 Romania Univ Bucharest Res Inst 90-92 Sos Panduri Bucharest 050107 Romania Univ Burdwan Dept Geog Bardhaman 713104 West Bengal India Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar 64001 Iraq Univ Tenaga Nas Inst Energy Infrastructure Kajang 43000 Malaysia King Saud Univ Coll Sci Geol & Geophys Dept Abdullah Alrushaid Chair Earth Sci Remote Sensing Riyadh Saudi Arabia Begum Rokeya Univ Dept Disaster Management Rangpur 5400 Bangladesh Daffodil Int Univ Dept Dev Studies Dhaka 1216 Bangladesh Tartous Univ Fac Arts & Humanities Geog Dept Tartous Syria

Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buzau river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance approximate to 20%), distance from river (importance approximate to 17.5%), land use (importance approximate to 12%) and TPI (importance approximate to 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35-40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924).

关键词： Artificial intelligence optimization algorithm Flood potential Buzau river basin Romania

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Estimation of suspended sediment load utilizing a super-optimized deep learning approach informed by the red foxa* optimization algorithm

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EARTH SCIENCE INFORMATICS 2025年第3期18卷 1-17页

作者： Malekpour, Mohammad Mahdi Ahmadi, Mohammad Mehdi Gugliotta, Marcello Tabari, Mahmoud Mohammad Rezapour Qaderi, Kourosh Shahid Bahonar Univ Kerman Fac Agr Dept Water Engn Kerman Iran Martin Luther Univ Halle Wittenberg Inst Geosci & Geog Halle An Der Saale Germany Univ Mazandaran Fac Engn & Technol Dept Civil Engn Babolsar Mazandaran Iran

The suspended sediment load transported by rivers can be estimated using various methodologies, including those based on artificial intelligence. In this study, we employed the Long Short-Term Memory (LSTM) model to estimate the suspended sediment concentration in the Mississippi River, United States of America. The input variables for the LSTM model included river discharge, water depth, suspended sediment load, and flow velocity. To enhance the model's performance, the input data and initial parameters were optimized using the Red Fox optimization (RFO) algorithm, resulting in a super-optimized LSTM model (SLSTM-RFO) developed through a two-phase optimization process. Additionally, sediment load estimations were conducted using alternative models, specifically the Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) models. The performance of these models was assessed using five performance indicators, the correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), Nash and Sutcliffe efficiency (NS), and RMSE observations standard deviation ratio (RSR), demonstrating that the SLSTM-RFO model significantly outperformed the other models. Specifically, the SLSTM-RFO yielded improved estimation results, achieving reductions in error (RMSE) of 73.30%, 81.50%, and 82.56% compared to the LSTM, ANN, and GRNN models, respectively.

关键词： Artificial neural network Long short-term memory (LSTM) optimization algorithm Mississippi River Sedimentation concentration

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Dual-function waveforma* optimization algorithm of joint transmitter and receiver via W-ADPM

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SIGNAL PROCESSING 2025年 235卷

作者： Ji, Wenshuai Lin, Hangyu Liu, Tao Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518107 Peoples R China

This paper proposes a novel algorithm for joint transmitter and receiver dual-function radar communication (DFRC) systems based on orthogonal frequency division multiplexing (OFDM) waveforms. The algorithm achieves a better detection performance while containing communication bit error rate (BER) performance, modulating communication information into the phase of the OFDM waveform using M-phase-shift keying (MPSK) schemes such as Binary-PSK (BPSK) and Qinary-PSK (QPSK). Subsequently, the waveform minimizes the weighted peak sidelobe level (WPSL) of the transmit waveform and the receive mismatch filter while ensuring the bit error rate (BER) condition to reduce sidelobes. Additionally, constraints are placed on constant amplitude, BER, mainlobe energy, and signal-to-noise ratio (SNR) loss. This paper employs a Weight Alternating Direction Method of Penalty (W-ADPM) network-based approach to simultaneously optimize the transmit waveform and receive mismatched filters to address these issues, achieving the desired effect. The simulation experiments demonstrate that the proposed algorithm has better convergence performance for the DFRC OFDM waveform compared to the Alternating Direction Method of Multipliers (ADMM) algorithm. Besides, the simulation experiments show that, compared to traditional matched filters, the jointly transmitted and received mismatched filters proposed in this paper provide better ISL cross-correlation performance while ensuring the BER.

关键词： Dual-function radar communication (DFRC) Orthogonal frequency-division multiplexing (OFDM) Weight peak sidelobe level (WPSL) Alternating direction method of penalty (ADPM) optimization algorithm

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Predicting density log from well log using machine learning techniques and heuristica* optimization algorithm:A comparative study

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Petroleum Research 2024年第2期9卷 176-192页

作者： Mehdi Rahmati Ghasem Zargar Abbas Ayatizadeh Tanha National Iranian Drilling Company Well Logging DepartmentAhwazIran Faculty Member of Petroleum University of Technology Iran Department of Petroleum Engineering Tarbiat Modares UniversityTehranIran

In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve *** recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect *** study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon *** is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant *** tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanceda* optimization algorithms,including Particle Swarm optimization(PSO),Imperialist Competitive algorithms(ICA),and Genetic algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log ***,our results highlight that the Genetic algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of *** comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.

关键词： Density log Machine learning approaches Artificial neural networks(ANN) Adaptive neuro-fuzzy inference system(ANFIS) optimization algorithm

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Enhancing neurological disease diagnostics: fusion of deep transfer learning witha* optimization algorithm for acute brain stroke prediction using facial images

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SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Alrowais, Fadwa Alqahtani, Mohammed Khan, Jahangir Ben Miled, Achraf Albalawneh, Da'ad Alkharashi, Abdulwhab Al Zanin, Samah Marzouk, Radwa Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Univ Bisha Coll Comp & Informat Technol Dept Informat Syst & Cyber Secur Bisha 61922 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 73213 Saudi Arabia Univ Tabuk Univ Coll Umluj Dept Comp Sci Tabuk Saudi Arabia Saudi Elect Univ Coll Comp & Informat Dept Comp Sci Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Kharj Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascular accident. It contains a nervous illness, which can result from haemorrhage or ischemia of the brain veins, and regular mains to assorted motor and cognitive damages that cooperate with functionality. Screening for stroke comprises physical examination, history taking, and valuation of risk features like age or certain cardiovascular illnesses. Symptoms and signs of stroke include facial weakness. Even though computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis techniques, artificial intelligence (AI) systems have been constructed based on these methods, which deliver fast detection. AI is gaining high attention and is being combined into numerous areas with medicine to enhance the accuracy of analysis and the quality of patient care. This paper proposes an enhancing neurological disease diagnostics fusion of transfer learning for acute brain stroke prediction using facial images (ENDDFTL-ABSPFI) method. The proposed ENDDFTL-ABSPFI method aims to enhance brain stroke detection and classification models using facial imaging. Initially, the image pre-processing stage applies the fuzzy-based median filter (FMF) model to eliminate the noise in input image data. Furthermore, fusion models such as Inception-V3 and EfficientNet-B0 perform the feature extraction. Moreover, the hybrid of convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) model is employed for the brain stroke classification process. Finally, the multi-objective sailfish optimization (MOSFO)-based hyperparameter selection process is carried out to optimize the classification outcomes of the CNN-BiLSTM model. The simulation validation of the ENDDFTL-ABSPFI technique is investigated under the Kaggle dataset concerning various measures. The comparative evaluation of the ENDDFTL-ABSPFI technique portra

关键词： Fusion of transfer learning Acute brain stroke prediction Facial images optimization algorithm Image pre-processing

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Complex ontology alignment for autonomous systems via the Compact Co-Evolutionary Brain Storma* optimization algorithm

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ISA TRANSACTIONS 2023年 132卷 190-198页

作者： Xue, Xingsi Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China

Autonomous Systems (ASs) that work in the open, dynamic environment are required to share their data entities and semantics to implement the co-operations. Typically, AS's data schemas and semantics are described via ontology. Since ASs need to maintain their autonomy and conceptual specificity, their ontologies might define one concept with different terms or in different contexts, which yields the heterogeneity issue and hampers their co-operations. An effective solution is to establish a set of data entity's correspondences through the Ontology Alignment (OA). Sine the simple correspondence of one-to-one style lacks expressiveness and cannot completely cover different types of heterogeneity, ASs' co-operations require using the complex correspondence of one-to-many or many-to-may style. Inspired by the success of applying the Brain Storma* optimization algorithm (BSO) to solve diverse complex optimization problems, this work proposes a Compact Co-Evolutionary BSO (CCBSO) to face the challenge of aligning AS ontologies. In particular, the AS ontology aligning problem is formally defined, a hybrid confidence measure for distinguishing the simple and complex correspondences is proposed, and a problem-specific CCBSO is presented. The experiment tests CCBSO's performance on different AS ontology aligning tasks, which consist of two simple ontology aligning tasks and one complex ontology aligning track. The experimental results show that CCBSO outperforms the state-of-the-art ontology aligning techniques on all simple and complex ontology aligning tasks.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Autonomous system Complex ontology alignment Compact Co-Evolutionary Brain Storm optimization algorithm Hybrid confidence measure

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Flight Conflict Resolution Simulation Study Based on the Improved Fruit Flya* optimization algorithm

IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS

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IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS 2024年第3期5卷 200-209页

作者： Sun, Yulong Ding, Guoshen Zhao, Yandong Zhang, Renchi Wang, Wenjun North Automat Control Technol Inst Software Dept Taiyuan 030006 Peoples R China

Due to the increasingly widespread application of unmanned aerial vehicle (UAV), the study of flight conflict resolution can effectively avoid the collision of different UAVs. First, describe flight conflict resolution as an optimization problem. Second, the improved fruit flya* optimization algorithm (IFOA) is proposed. The smell concentration judgment is equal to the coordinate instead of the reciprocal of the distance in order to make the variable accessible to be negative and occur with equal probability in the defined domain. Next, introduce the limited number of searches of the Artificial Bee Colony algorithm to avoid falling into the local optimum. Meanwhile, generate a direction and distance of the fruit fly individual through roulette. Finally, the effectiveness of the algorithm is demonstrated by computational experiments on 18 benchmark functions and the simulation of the flight conflict resolution of two and four UAVs. The results show that compared with the standard fruit flya* optimization algorithm, the IFOA has superior global convergence ability and effectively reduces the delay distance, which has important potential in flight conflict resolution.

关键词： optimization Autonomous aerial vehicles Statistics Sociology Standards Safety Military aircraft Flight conflict resolution fruit flya* optimization algorithm (FOA) optimization algorithm path planning unmanned aerial vehicle (UAV)

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A novela* optimization algorithm for assemblable region boundary points search in high-dimensional posture space of parts assembly

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第9-10期128卷 4615-4633页

作者： Li, Yue Qie, Yifan Huang, Zhicheng Qiao, Lihong Anwer, Nabil Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Univ Paris Saclay ENS Paris Saclay LURPA F-91190 Gif Sur Yvette France

Surface deviations caused by manufacturing errors have an essential influence on the assemblability of mechanical parts. The concept of assemblable region in the posture space has been introduced as an effective tool to express the posture feasible adjustment range of non-ideal parts in the assembly process. It can reflect the complexity of posture control through ergonomic principles and thus indicates the difficulty and feasibility of assembly. The assemblability considering surface deviations can be evaluated based on the geometric and topological (G & T) properties of the assemblable region. However, one necessary precondition for realizing the analysis of G & T properties is to obtain the boundary of assemblable region in high-dimensional posture space while guaranteeing the computational efficiency. In this paper, a novela* optimization algorithm is proposed for the boundary calculation of the assemblable region. The high-dimensional irregular boundary calculation problem is firstly transformed into a boundary points search problem. Then, a new heuristic boundary points diffusion search (BDS) algorithm is developed. Two main parts are designed within the proposed algorithm, including the diffusion search rules as well as the collaborative search process. By optimizing the location and quantity of points in searching group, the algorithm can efficiently achieve the boundary points search goal and realize the boundary calculation of assemblable region. Finally, two typical cases are carried out to verify the feasibility of the proposed algorithm. The comparison with an improved genetic algorithm in application also highlights the efficiency and stability of the proposed algorithm.

关键词： Assemblability evaluation Posture space Assemblable region Boundary points search optimization algorithm

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