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IoT-based systemic lupus erythematosus prediction model using hybrid genetic algorithm integrated with ANN

ETRI JOURNAL 2023年第4期45卷 594-602页

作者： Prabhu, Edison K. Surendran, D. Nehru Inst Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Karpagam Coll Engn Dept Informat Technol Coimbatore India

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

关键词： artificial neural network atom search optimization internet of things marine predators algorithm systemic lupus erythematosus

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A DC-DC Converter Topology for Optimal Utilization of Hybrid Renewable Energy Resources Using Hybrid Technique

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IETE JOURNAL OF RESEARCH 2023年第10期69卷 7357-7373页

作者： Selvabharathi, P. Kannan, V. Kamatchi Bannari Amman Inst Technol Dept Elect & Elect Engn Sathyamangalam Tamil Nadu India Annasaheb Dange Coll Engn & Technol Dept Elect Engn Ashta Maharashtra India

This article proposes a DC-DC converter for the optimal use of hybrid renewable energy sources (HRES) with the hybrid control approach using the proposed technology. HRES-like photovoltaic (PV), wind turbine (WT), and battery energy storage technology are examined (BESS). The proposed hybrid control technique is a hybrid wrapper of the marine predators algorithm (MPA) and recalling-enhanced recurrent neural network (RERNN). The biological interaction of MPA is improved using the crossover (C) and mutation (M) method, which is later referred to as the combined CM-MPA (C2MPA) technique. The proposed system is called as C2MPA-RERNN technique. The C2MPA approach is used in the proposed method to evaluate the exact control signals system and to build up a control signals database for offline use according to power variation of source and load sides. The collected dataset is utilized to run the RERNN technique online, allowing the control method to run in less time. Based on the error value of active and reactive power, the C2MPA-RERNN determines optimal gain parameters of a proportional integral (PI) controller. The real and reactive power values are input algorithms. The system error is diminished, and optimal power flow management is connected to tune the PI parameters. The proposed system is tested using the MATLAB/Simulink platform. The gain parameters like K-p and K-i of the proposed system refer 2.5 and 150, respectively.

关键词： DC-DC converter hybrid renewable energy resources marine predators algorithm recalling-enhanced recurrent neural network

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Constrained economic dispatch following generation outage for hot spinning reserve allocation using hybrid grey wolf optimizer

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ALEXANDRIA ENGINEERING JOURNAL 2023年 62卷 169-180页

作者： Hoballah, Ayman Azmy, Ahmed M. Tanta Univ Fac Engn Elect Power & Machines Engn Dept Tanta 31521 Egypt Taif Univ Coll Engn Elect Engn Dept Taif 21974 Saudi Arabia

The paper presents a methodology for hot spinning reserve allocation (HSRA) following expected disconnection of big generators during peak demand. The proposed method includes generation rescheduling, optimal reactive power allocation, and load curtailment to prevent system cascaded outages. Hybrid grey wolf optimization (HGWO) and marine predators algorithm (MPA) are implemented to solve the economic HSRA problem with total costs minimization. The conversion process of HGWO is enhanced utilizing two internal loops based on particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The effectiveness of the optimization algorithms is conducted on a 66-bus three-area test system for cost minimization following the outage of the biggest generator in each area. The optimal solution maximizes the social benefits of participating consumers, keeping other consumers' reliability at minimum total cost. The percentages of load curtailment in the three cases are 6.9%, 8% and 6.7% of system load, respectively, where there was no solution for the optimal power flow without shedding some loads to satisfy the network constraints. HGWO and MPA are used to determine the optimal solution. The optimal solution depends on the energy price of load curtailment, system configuration as well as the cost coefficients of generators. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Hot spinning reserve allocation Economic dispatch Hybrid grey wolf optimization marine predators algorithm

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Enhanced feature selection technique using slime mould algorithm: a case study on chemical data

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NEURAL COMPUTING & APPLICATIONS 2023年第4期35卷 3307-3324页

作者： Ewees, Ahmed A. Al-qaness, Mohammed A. A. Abualigah, Laith Algamal, Zakariya Yahya Oliva, Diego Yousri, Dalia Abd Elaziz, Mohamed Univ Bisha Coll Comp & Informat Technol Dept Informat Syst Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat 34517 Egypt Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Zhejiang Peoples R China Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Univ Mosul Dept Stat & Informat Mosul Iraq Univ Warith Al Anbiyaa Coll Engn Karbala Iraq Univ Guadalajara CUCEI Depto Ciencias Computac Ave Revoluc 1500 Guadalajara Jal Mexico Fayoum Univ Dept Elect Engn Fac Engn Al Fayyum Egypt Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Galala Univ Fac Comp Sci & Engn Suez Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon

Feature selection techniques are considered one of the most important preprocessing steps, which has the most significant influence on the performance of data analysis and decision making. These FS techniques aim to achieve several objectives (such as reducing classification error and minimizing the number of features) at the same time to increase the classification rate. FS based on Metaheuristic (MH) is considered one of the most promising techniques to improve the classification process. This paper presents a modified method of the Slime mould algorithm depending on the marine predators algorithm (MPA) operators as a local search strategy, which leads to increasing the convergence rate of the developed method, named SMAMPA and avoiding the attraction to local optima. The efficiency of SMAMPA is evaluated using twenty datasets and compared its results with the state-of-the-art FS methods. In addition, the applicability of SMAMPA to work with real-world problems is evaluated by using it as a quantitative structure-activity relationship (QSAR) model. The obtained results show the high ability of the developed SMAMPA method to reduce the dimension of the tested datasets by increasing the prediction rate. In addition, it provides results better than other FS techniques in terms of performance metrics.

关键词： Slime mould algorithm marine predators algorithm Optimization feature selection Quantitative structure-activity relationship (QSAR)

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RETRACTED: An efficient hybrid MPA-MOA control approach based on DC microgrid connected constant power loads (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 691-712页

作者： Karthika, J. Rajkumar, M. Vishnupriyan, J. Sri Krishna Coll Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Sri Krishna Coll Engn & Technol Dept Informat Technol Coimbatore Tamil Nadu India Chennai Inst Technol Ctr Energy Res Chennai Tamil Nadu India

The increased use of DC microgrid for complex application leads to the need for advanced control design for stable operation of the system. Loads connected to a DC microgrid are controlled by power electronic devices and exhibit constant power load (CPL) behavior, which is a serious challenge for stability as it enhances nonlinearity and reduces effective damping. This manuscript proposes an effective hybrid approach based on DC micro grid (MG) connected constant power loads. The proposed control approach is the consolidation of marine predators algorithm (MPA) and mayfly optimization algorithm (MOA), hence it is named as hybrid MPA-MOA approach. The DC microgrid system contains the sources, like two photovoltaic (PV), two wind turbine (WT), grid, battery. The major objective of the proposed approach is to find the problems while interfacing the sources of the microgrid and increase the security of the system. The proposed approach contains two controllers, they are primary and secondary. The primary controller is based on droop controller that shares the current and limits the oscillations because of the constant power loads (CPL). The secondary controller is used to regulate the voltage of the system from a single area. The secondary control is executed using the proposed MPA-MOA method. The proposed method is executed on MATLAB/Simulink platform;its performance is analyzed with the existing methods. The THD (%), efficiency (%) and Eigen value of the proposed technique achieves 1.4%, 92% and -9.3541 +/- j2.4209.

关键词： Microgrid primary controller secondary controller stability marine predators algorithm mayfly optimization algorithm

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Inverse problem for dynamic structural health monitoring based on slime mould algorithm

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 3期38卷 2205-2228页

作者： Tiachacht, Samir Khatir, Samir Cuong Le Thanh Rao, Ravipudi Venkata Mirjalili, Seyedali Wahab, Magd Abdel Mouloud Mammeri Univ Tizi Ouzou Lab Mech Struct & Energet LMSE BP 17 RP Tizi Ouzou 15000 Algeria Univ Ghent Fac Engn & Architecture Soete Lab Technol Pk Zwijnaarde 903 B-9052 Zwijnaarde Belgium Ho Chi Minh City Open Univ Fac Civil Engn Ho Chi Minh City Vietnam Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat Gujarat India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Ton Duc Thang Univ Div Computat Mech Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

In this paper, damage detection, localization and quantification are performed using modal strain energy change ratio (MSEcr) as damage indicator combined with a new optimization technique, namely slime mould algorithm (SMA) developed in 2020. The SMA algorithm is employed to assess structural damage and monitor structural health. Two structures, including a laboratory beam and a bar planar truss are considered to study the effectiveness of the proposed approach. Another recent algorithm called marine predators algorithm (MPA) is also used for comparison purposes with SMA. The MSEcr is utilized in the first stage to predict the location of the damaged elements. Single and multiple damages cases are analysed based on different number of modes to study the sensitivity of the proposed indicator to the total number of modes considered in the analysis. Next, this indicator is used as an objective function in a second stage to solve the inverse problem using SMA and MPA for damage quantification of the elements identified in the first stage. Experimental validation is conducted using a 3D frame structure with four stories that have damaged components. It is demonstrated that the proposed approach, using MSEcr and SMA, provides superior results for the considered structures. The effectiveness of this technique is tested by introducing a white Gaussian noise with different levels, namely 2% and 4%. The results show that the provided approach can predict the location and level of damage with high accuracy after introducing the noise.

关键词： Modal strain energy Damage indicator Slime mould algorithm marine predators algorithm Inverse problem Modal analysis Damage assessment

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Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第33期29卷 49684-49699页

作者： Sun, Wei Wang, Xiaoxuan Tan, Bin North China Elect Power Univ Econ & Management Dept Baoding 071000 Hebei Peoples R China

Accurate wind speed forecasting (WSF) not only ensures stable power system operation but also contributes to enhancing the competitiveness of wind power companies in the market. In this paper, a hybrid prediction model based on secondary decomposition algorithm (SDA) is proposed for WSF. First, wavelet transform (WT) is used to decompose the wind speed sequence into approximate and detailed components. Second, the obtained detailed components are further decomposed by symplectic geometry mode decomposition (SGMD). Then, the marine predators algorithm-optimized back-propagation neural network (BPNN) is used to predict the new subsequences. The case study was implemented on 4 datasets. The experimental results show that, first, the proposed hybrid model has the highest prediction accuracy and the best robustness among all the compared models in 1-4-step prediction. Second, the proposed hybrid decomposition strategy has significant utility in reducing the difficulty of WSF. After adding SDA, the average improvement levels of MAPE in 1-4-step prediction were 85.64%, 84.93%, 81.08% and 80.67%, respectively. Third, the re-decomposition of the details obtained by WT can improve the prediction accuracy. After the re-decomposition of the details obtained by WT, the proposed WT-SGMD-MPA-BP model leads to the average improvement percentages of 44.44%, 61.69%, 50.56% and 49.28% in RMSE compared with WT-MPA-BP model in various horizons. The proposed model provides valuable reference for WSF. In future work, the performance of the model for other nonlinear sequences is worth exploring.

关键词： Wind speed forecasting Secondary decomposition Symplectic geometry mode decomposition marine predators algorithm Back-propagation neural network

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Optimal parameter identification of triple diode model for solar photovoltaic panel and cells

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ENERGY REPORTS 2022年 8卷 1179-1188页

作者： Rezk, Hegazy Abdelkareem, Mohammad Ali Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya Egypt Univ Sharjah Ctr Adv Mat Res POB 27272 Sharjah U Arab Emirates Minia Univ Chem Engn Dept Elminia 61516 Egypt Univ Sharjah Dept Sustainable & Renewable Energy Engn POB 27272 Sharjah U Arab Emirates

The correct parameter determination of the photovoltaic module and the solar cell is considered an important phase to deliver a reliable simulation for the PV system characteristics. The triple diode model (TDM) has been examined to model the PVM 752 GaAs thin-film PV solar cell (SC), STM6 PV module, and RTC SC. A set of the measured I -V data at various levels of the weather conditions has been considered for the studied PV solar cell/modules. A new marine predators algorithm (MPA) is used to identify the TDM parameters based on measured datasets. Throughout the optimization procedure, the nine undetermined parameters of TDM are employed as decision variables but the fitness function requires to be least is the RMSE between the experimental and calculated datasets. The achieved results by MPA are compared with different algorithms. The comparison confirmed the superiority of MPA. (c) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Modelling marine predators algorithm Photovoltaic Solar cells

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Fractional-Order Ultra-Local Model-Based Optimal Model-Free Control for 7-DOF iReHave Upper-Limb Exoskeleton

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第8期69卷 3510-3514页

作者： He, Dingxin Wang, Haoping Tian, Yang Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

This brief proposes a fractional-order ultra-local model-based optimal model-free control (FO-OMFC) for trajectory tracking of 7-DOF iReHave upper-limb exoskeleton. This scheme is composed of fractional-order model-free controller (FO-MFC) and marine predators algorithm (MPA)-based parameter optimizer. Different from the existing model-free control, FO-MFC is designed based on a novel fractional-order ultra-local model. Then, the FO-MFC incorporates fractional-order time-delay estimation (FOTDE) as well as fractional-order error convergence controller (FOECC). The referred FOTDE is used to eliminate the lumped disturbance of the controlled exoskeleton. And the FOECC guarantees the convergence of the exoskeleton's trajectory tracking error. After that, the stability of closed-loop system is demonstrated by using Lyapunov theorem. Moreover, to obtain better control performance, the MPA is employed to search for optimal parameters of the FO-MFC. Finally, the co-simulation of iReHave exoskeleton based on SolidWorks and MATLAB is realized, and the results compared with other controllers and results under measurement noise are given to show the effectiveness and superiority of the proposed strategy.

关键词： Model-free control fractional-order ultra-local model robust control marine predators algorithm

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Design of a Wide-Area Damping Controller to Tolerate Multiple Permanent Communication Failures Using Bio-Inspired algorithms 15

Design of a Wide-Area Damping Controller to Tolerate Multipl...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： Bento, Murilo E.C. Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350314182

Inter-area oscillation modes resulting from the interconnection of large electrical systems cause operation problems and even blackouts if minimum damping rate requirements are not met. Recent and promising research shows that Wide-Area Damping Controllers (WADC) are capable of improving the damping rates of inter-area modes because they use data from remote locations of the system. However, the operation of the WADC can be compromised if multiple communication failures occur on the WADC communication channels because this contingency causes changes in control actions. This work proposes a procedure for the desired design of a WADC that meets the robustness requirements to multiple communication failures that may occur. Some bio-inspired algorithms were used, evaluated and discussed in the proposed procedure. The effectiveness of the method was evaluated and discussed on the IEEE 68-bus test system. The results show that it is possible to design a WADC up to a limit of possible permanent communication losses. Furthermore, different bio-inspired algorithms present different performances in solving the optimization problem. ©2023 IEEE.

关键词： Bat algorithm Cuckoo Search algorithm marine predators algorithm Small-Signal Stability Synchronized Phasor Measurement System Wide-Area Damping Control

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