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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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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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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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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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Recent Studies on Chicken Swarma* optimization algorithm: a review (2014-2018)

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第3期53卷 1737-1765页

作者： Deb, Sanchari Gao, Xiao-Zhi Tammi, Kari Kalita, Karuna Mahanta, Pinakeswar Indian Inst Technol Ctr Energy Gauhati India Univ Eastern Finland Sch Comp Kuopio Finland Aalto Univ Dept Mech Engn Espoo Finland Indian Inst Technol Dept Mech Engn Gauhati India

Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-baseda* optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarma* optimization algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.

关键词： Chicken Swarma* optimization algorithm Nature inspired intelligence optimization algorithm Applications Review

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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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Jaya* optimization algorithm for transient response and stability enhancement of a fractional-order PID based automatic voltage regulator system

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ALEXANDRIA ENGINEERING JOURNAL 2020年第4期59卷 2429-2440页

作者： Jumani, Touqeer Ahmed Mustafa, Mohd Wazir Hussain, Zohaib Rasid, Madihah Md Saeed, Muhammad Salman Memon, Mehran M. Khan, Ilyas Nisar, Kottakkaran Sooppy Univ Teknol Malaysia Sch Elect Engn Skudai Johor Bahru Malaysia Mehran Univ Engn & Technol Dept Elect Engn SZAB Campus Khairpur Mirs Pakistan Mehran Univ Engn & Technol Dept Elect Engn Jamshoro Pakistan Multan Elect Power Co Multan Pakistan Ton Duc Thang Univ Fac Math & Stat Ho Chi Minh City 72915 Vietnam Prince Sattam bin Abdulaziz Univ Coll Arts & Sci Dept Math Wadi Aldawaser 11991 Saudi Arabia

Considering the higher flexibility in tuning process and finer control action of the fractional-order proportional integral derivative (FOPID) controller over the conventional propor-tional integral derivative (PID) controller, this paper explores its application in the automatic volt-age regulator system. FOPID contains five tuning parameters as compared to three in the conventional PID controller. The additional tuning knobs in FOPID provide increased control flex-ibility and precise control action, however, their inclusion makes the tuning process more complex and tedious. Thus, the intelligence of an artificial intelligence (AI) technique called jaya optimiza-tion algorithm (JOA) is utilized in order to obtain an optimal combination of FOPID gains which further led to the optimal transient response and improved stability of the considered AVR system. To validate the performance superiority of the proposed approach its corresponding system's dynamic response is compared with that of the other well-known AI-based approaches explored in recent literature. Furthermore, the stability study of the proposed AVR system is carried out by evaluating its pole/zero and bode maps. Finally, the robustness of the proposed optimized AVR system against the system's parameter variation is evaluated by varying the time constants of all the four components of AVR (generator, exciter, amplifier and sensor) from-50% to +50% independently. The proposed algorithm based FOPID tuning technique provides 59.82%, 56.09%, 14.94%, 34.24%, 35.70%, 21.64%, 12.0%, 41.33%, 14.84% and 15.17% reduced over-shoot than that of differential evolution (DE), particle swarm optimization (PSO), Artificial Bee Colony (ABC), Bibliography Based optimization (BBO), Grasshoppera* optimization algorithm (GOA), Pattern Search algorithm (PSA), Improved Kidney Inspired algorithm (IKA), Whalea* optimization algorithm (WOA), Salp Swarm algorithm (SSA) and Local Unimodal Sampling (LUS) algorithm respectively, thus validat

关键词： optimization algorithm Transient response FOPID AVR JOA

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Modeling of an Air-Core Compulsator and the Pulse Shaping Usinga* optimization algorithm

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IEEE TRANSACTIONS ON PLASMA SCIENCE 2019年第1期47卷 611-618页

作者： Tao, Xuefeng Liu, Kun Wang, Haoze Space Engn Univ Dept Grad Management Beijing 101400 Peoples R China Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen 518101 Peoples R China

Compensated pulsed alternator (compulsator) plays a significant role in the field of pulsed power supply. Different kinds of compulsators have been used to drive high-energy weapons. In this paper, the mathematical model of a two-phase four-pole air-core compulsator is established, taking the current coupling, the change of rotor speed and changing load characteristics into account. Both the self-excitation process and the discharge process are modeled. Simulation results indicate that our model has high accuracy in comparison with the results of the co-simulation method using finite-element method and circuit principle. Moreover, the usage of the mathematical model can improve the simulation efficiency and flexibility. The current pulse requirements of electromagnetic rail gun (EMRG), flash lamp, and electro-thermal-chemical gun (ETCG) are analyzed, respectively, and the pulse shape optimization problem is studied based on the intelligenta* optimization algorithm. For EMRG and flash lamp, the term "acceleration ratio" is introduced to identify whether a pulse is flat or spiked. For ETCG, the conception of "shape variance" is proposed to evaluate the fitness of its pulse shape. With the help of suitable objection function and intelligenta* optimization algorithm, the optimal discharge pulse for specific load can be obtained easily.

关键词： Air-core compensated pulsed alternator (compulsator or CPA) mathematical model optimization algorithm pulse shaping techniques

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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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An Effective Method for Segmentation of MR Brain Images Using the Ant Colonya* optimization algorithm

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JOURNAL OF DIGITAL IMAGING 2013年第6期26卷 1116-1123页

作者： Taherdangkoo, Mohammad Bagheri, Mohammad Hadi Yazdi, Mehran Andriole, Katherine P. Taba Med Imaging Ctr Shiraz Iran Harvard Univ Sch Med Ctr Evidence Based Imaging Dept RadiolBrigham & Womens Hosp Brookline MA 02446 USA Shiraz Univ Fac Elect & Comp Engn Dept Commun & Elect Shiraz Iran Shiraz Univ Med Sci Med Imaging Res Ctr Shiraz Iran

Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations;however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area.

关键词： Image processing Segmentation optimization algorithm Ant colony optimization

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