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Optimal Design of Magnetorheological Damper Based on Tuning Bouc-Wen Model Parameters Using Hybrid algorithms

KSCE JOURNAL OF CIVIL ENGINEERING 2020年第3期24卷 867-878页

作者： Azar, Bahman Farahmand Veladi, Hedayat Talatahari, Siamak Raeesi, Farzad Univ Tabriz Dept Civil Engn Tabriz Iran Near East Univ Dept Engn TR-99138 Nicosia Turkey

This paper presents a useful approach to optimally design magnetorheological (MR) dampers used in structural buildings. To fulfill this aim, damper parameters are regarded as the design variables whose values can be obtained through an optimization process. To improve the quality of searching for the optimum parameters of MR dampers, charged system search (CSS) and grey wolf (GW) algorithms, two of the most widely utilized meta-heuristic algorithms, are used together, and hybrid CSS-GW is presented. To show the authenticity and robustness of the new algorithm in solving optimization problems, some benchmark test functions are tested, at first. Then, an eleven-story benchmark building equipped with 3 MR dampers is considered to get the optimum design of the MR damper using the hybrid CSS-GW. Results show that the developed hybrid algorithm can successfully figure out the optimum parameters of the MR dampers.

关键词： Optimum design Magnetorheological damper Optimum parameters grey wolf algorithm Charged system search algorithm Hybrid algorithms

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A novel hybridized feature selection strategy for the effective prediction of glaucoma in retinal fundus images

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第15期83卷 46087页

作者： Singh, Law Kumar Khanna, Munish Thawkar, Shankar Singh, Rekha GLA Univ Dept Comp Engn & Applicat Mathura 281406 India Hindustan Coll Sci & Technol Dept Comp Sci & Engn Mathura 281122 India Hindustan Coll Sci & Technol Dept Informat Technol Mathura 281122 India Uttar Pradesh Rajarshi Tandon Open Univ Dept Phys Prayagraj 211021 Uttar Pradesh India

Feature selection (FS) is crucial to transforming high-dimensional data into low-dimensional data. The FS approach selects influential traits and ignores the rest. This approach improves machine learning (ML) classifiers by reducing computational complexity and solution time. This empirical study presents a novel and effective methodology that uses two contemporary state-of-the-art soft-computing algorithms, the grey wolf Optimizer (GWO) and the Whale Optimization algorithm (WOA). We have also created the hybrid version (hGWWO) of these two approaches as our novel, innovative scientific contribution. The baseline algorithms above have been used previously for feature selection across different domains. According to our understanding, these three algorithms are being used for the first time in glaucoma identification, particularly on the publicly available benchmark dataset, ORIGA. The rising global prevalence of glaucoma prompted this proposed methodology's focus on the illness. This illness is second only to cataracts in causing visual loss. Medical imaging professionals are examining retinal scans to diagnose glaucoma. Manual eye screening and retinal fundus imaging for confirmation of this infection require skilled ophthalmologists. The screening analysis method is time-consuming, requires experienced staff, and is subject to observational differences. In order to overcome these issues and to support the medical fraternity, an artificial intelligence-supported computer-aided clinical decision support system (CA-CDSS) is implemented in the present endeavor for confirmation of this disease from retinal fundus images. Nature-inspired computing strategies for feature selection and ML models for classification are employed to classify fundus retinal images under investigation. From the ORIGA dataset, sixty-five features were retrieved. A subset of most influential features is selected from the original dataset using three soft-computing-based FS methods. ML classifier

关键词： Glaucoma prediction Retinal fundus images grey wolf algorithm Whale optimization algorithm Hybrid approach

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Nature Inspired algorithm-Based Improved Variants of DV-Hop algorithm for Randomly Deployed 2D and 3D Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2018年第1期101卷 567-582页

作者： Kaur, Amanpreet Kumar, Padam Gupta, Govind P. Jaypee Inst Informat Technol Dept Comp Sci & Engn Noida India Natl Inst Technol Dept Informat Technol Raipur Madhya Pradesh India

Localization is a significant challenge in the area of wireless sensor networks (WSNs). Distance Vector Hop (DV-Hop) algorithm is most preferable algorithm due to its low cost, distributed nature, and its feasibility for all kinds of sensor networks, but it suffers from high localization error. In order to reduce the problem of high localization error for 2-dimensional and 3-dimensional WSNs, two Nature Inspired algorithm based improved variants have been proposed. The first one uses grey-wolf optimization (GWO-DV-Hop) to identify a better estimate of average distance per hop and second one, a weighted grey-wolf optimization (Weighted GWO-DV-Hop), finds average distance per hop as computed by each beacon node using grey wolf algorithm and then, a weighted approach is applied by each node to get weighted average distance per hop (weights based on distance from each beacon) so as to consider impact of all types of beacons. The results prove the superiority of proposed algorithms over traditional DV-Hop in terms of localization error.

关键词： Wireless sensor networks Localization grey wolf algorithm Average distance per hop Localization error

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Adaptive tacholess order tracking method based on generalized linear chirplet transform and its application for bearing fault diagnosis

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ISA TRANSACTIONS 2022年 127卷 324-341页

作者： Duan, Rongkai Liao, Yuhe Yang, Lei Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & Rotor Bearing Xian 710049 Peoples R China Xi An Jiao Tong Univ Shannxi Key Lab Mech Prod Qual Assurance & Diagnos Xian 710049 Peoples R China

Bearing plays an important role in industrial equipment and it may operate under varying conditions. When the speed of shaft changes, whether monotonous or non-monotonous speed, common diagnostic approaches cannot effectively extract fault features. But encoders and tachometers are not always available. Therefore, tacholess order tracking methods which can directly extract the instantaneous rotating frequency (IRF) from vibration signal are very useful in bearing fault diagnosis under varying speed. Among these methods, the generalized linear chirplet transform (GLCT) can produce time- frequency representation without constructing any mathematical model, but there are two parameters must be set in advance. The parameters have great influence on the analysis result. To reduce the dependence on the prior knowledge of presetting the parameters in varying conditions, two different improved GLCT methods are proposed in this paper. To do with the situation where the trend of speed changes is monotonous, the scale-space is introduced to lift GLCT which can adaptively set a vital parameter, and the other parameter is set to default value. When faced with non-monotonous speed, the second method is proposed which the grey wolf optimizer (GWO) and Gini index are introduced to search the optimal parameters of GLCT without any prior knowledge. With the help of the proposed methods, the IRF can be extracted directly from vibration signal. Then, the raw signal can be resampled based on the IRF to eliminate the influences of speed. The morphological filtering is adopted to remove the noise and extract the fault characteristics order (FCO). Another two typical time-frequency analysis methods are used for comparisons. Three different signals are used for analysis to demonstrate the superiority of the proposed methods. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Generalized linear chirplet transform grey wolf algorithm Gini index Scale-space

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Interpretation of seizure dynamics using fuzzy-based neural computational modelling

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SOFT COMPUTING 2023年 1-15页

作者： Prathaban, Banu Priya Rajendran, Subash Ganeshkumar, N. Gayatri, M. Chembian, W. T. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept ECE Chennai India SRM Inst Sci & Technol Sch Comp Dept Comp Technol Chennai India Gandhi Inst Technol & Management GITAM Sch Technol Dept CSE Bengaluru India Vel Tech High Tech Dr Rangarajan Dr Sakunthala Eng Dept CSE Chennai India

The epileptic seizures result due to the nonlinear dynamic asynchronization of the brain waves. The temporal dynamics of the brain waves are usually depicted from long-term EEG signals. Prediction of seizures craves for the capacity to reliably recognize the distinct preictal state comprehended from the remaining seizure states. The differentiation between the preictal and interictal states is the key challenge in such a prediction problem, which intently requires the least number of false alarms. Computational models trace the seizure dynamics and descend the segment changeover of the seizure states from interictal toward preictal, preictal toward ictal and ictal toward postictal. In this work, a model driven-computational approach for interpreting the nonlinear temporal dynamics of the four different seizure states is developed. An Augmented Wendling Model for generating type 1 to 10 seizure-like EEG waveforms is established. Tracking and prediction of artificially generated seizure-like EEG waveforms are done via an Optimized Model Driven approach using grey wolf optimizer. Then, the seizure-like EEG waveforms are controlled, using an fuzzy-based neural computational modelling, comprising of Optimized Forward Back Propagation-based Closed Loop Controller (OFBP-CLC) is implemented. Ordered Fuzzy Number System (OFNS) is applied on to the neurons of the OFBP-CLC approach to reduce the complexity of the learning approach. The pattern driven method in concurrence with a computational model is established. Epileptic seizure states are modelled, tracked, predicted and controlled using an arithmetic ordered fuzzy number system.

关键词： Augmented Wendling model Epileptic seizures Fuzzy-based neural computational modelling grey wolf algorithm Optimized model driven approach Ordered fuzzy number system

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Optimal Interval Type-2 Fuzzy Logic Control Based Closed-Loop Regulation of Mean Arterial Blood Pressure Using the Controlled Drug Administration

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IEEE SENSORS JOURNAL 2022年第7期22卷 7195-7207页

作者： Sharma, Richa Kumar, Anupam IIT Delhi Ctr Biomed Engn New Delhi 110016 India IIIT Kota Elect & Commun Engn Dept Jaipur 302017 Rajasthan India

In pandemic times, the remote and automatic control/observation of physiological variables of the patients is the prime necessity for healthcare technologies. The closed-loop regulation of mean arterial pressure is essential for critically ill patients or post-surgery recovery patients. The physiological variables have serious issues such as parameter variations and uncertainties. In this paper, an interval type-2 fuzzy logic controller (IT2FLC) with footprints of uncertainty in the membership functions is presented in a two-layered design (TL-IT2FLC) that can efficiently deal with uncertainties and parameter variations. In this design, a pre-compensator IT2FLC is used, which deals with the uncertainties and parameter variations before the primary IT2FLC approach. The robustness analysis of the proposed method is investigated for external noise, and results are compared with traditional PID and T1-FLC techniques.

关键词： Drugs Uncertainty Regulation Blood pressure Sensors Fuzzy logic Physiology Mean arterial pressure controlled drug infusion fuzzy logic control interval type-2 fuzzy logic control grey wolf algorithm

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SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2024年第7期238卷 1251-1260页

作者： Wu, Zhongqiang Hu, Xiaoyu Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov 438 West Hebei St Qinhuangdao 066004 Hebei Peoples R China

This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than +/- 0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation.

关键词： Battery SOC estimation particle filter grey wolf algorithm parameter identification

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Wireless Sensor Network (WSN) Model Targeting Energy Efficient Wireless Sensor Networks Node Coverage

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IEEE ACCESS 2024年 12卷 27596-27610页

作者： Jia, Runliang Zhang, Haiyu Shanxi Finance & Taxat Coll Informat Technol Inst Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Finance & Econ Taiyuan 030024 Peoples R China

With the rise of the Internet of Things, the application fields of wireless sensor networks (WSN) continue to expand. From agriculture to urban infrastructure monitoring, application requirements in various fields are increasing. The research focuses on designing and improving energy-efficient coverage methods for wireless sensor network nodes, with the goal of improving energy efficiency and data transmission reliability. Through detailed research and analysis of hierarchical and flat routing protocols, the article explores how to ensure that each monitoring point is covered by at least one sensor node by designing an energy-saving sensor network node coverage model. At the same time, the study explores an energy-efficient coverage method based on the improved gray wolf algorithm, aiming to optimize the deployment of sensor nodes and enhance the effectiveness of node coverage. Research results show that the algorithm performs significantly in network coverage optimization and achieves 100% coverage of monitoring target points. Under the 30-dimensional condition, the improved gray wolf algorithm shows excellent average performance and the smallest standard deviation. When the number of nodes is 40, compared with other algorithms, the improved gray wolf algorithm improves the coverage rate by 5.08% and achieves 100% coverage performance in a more energy-saving manner. Research on the exploration of energy-saving wireless sensor network models will help to better meet the needs of future intelligent monitoring and control, improve resource utilization efficiency, reduce maintenance costs, and promote the sustainable development of wireless sensor networks.

关键词： Wireless sensor network node coverage grey wolf algorithm routing protocol monitoring area

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Error Measurement of LCC Resonant Converter for X-Ray

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RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING 2020年第4期56卷 375-385页

作者： Bhuvaneswari, C. Babu, R. S. R. Sathyabama Inst Sci & Technol Chennai Tamil Nadu India

X-rays are devices immensely used for diagnosis and treatment in medicinal field. X-rays are very popular as they are used in non-destructive testing. An X-ray device uses a high voltage power supply which can be produced by DC-DC converters. Resonant converters are prominent among the dc-dc converters for X-ray application as the switching is done at zero current and zero voltage. This paper presents a LCC resonant converter based PID controller tuned with optimization algorithms for non-linear system. Differential evolution optimization (DEO), grey wolf optimization (GWO) and grasshopper optimization algorithms (GOA) are used to identify the local minima in time domain system. The objective functions considered are minimization of integral absolute error (IAE), integral square error (ISE) and integral time absolute error (ITAE). The convergence curve is also plotted for the different optimization algorithms using MATLAB Simulink to study the convergence time.

关键词： optimization differential evolution algorithm grey wolf algorithm grasshopper algorithm PID controller integral absolute error integral square error integral time absolute error

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Assessment of Unknown Parameters in Photovoltaic Cells Utilizing Multi- Strategy Fusion Slime Mould algorithm

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IEEE ACCESS 2024年 12卷 72504-72520页

作者： Chen, Chi Chen, Xian Lin, Aiju Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325000 Peoples R China Wenzhou Polytech Wenzhou 325035 Zhejiang Peoples R China Wenzhou Univ Coll Int Educ Wenzhou 325035 Zhejiang Peoples R China

The study presents the Multi-Strategy Learning Fusion Slime Mould algorithm (MFSMA), a novel method designed to optimize parameters in photovoltaic systems, thereby increasing the efficiency of solar energy conversion. MFSMA employs an advanced technique that incorporates elite opposition-based learning to quickly identify optimal solutions while preserving diversity within the population. Additionally, it integrates a ranking mechanism from the grey wolf algorithm, which categorizes individuals according to their fitness levels, ensuring a balanced approach between exploratory diversity and exploitative precision during the optimization journey. Through rigorous testing on various benchmark functions, MFSMA has demonstrated its exceptional ability to outperform existing algorithms widely used in the sector. The algorithm's effectiveness is further validated through its application in determining the parameters of single, double, and triple-junction photovoltaic modules. Moreover, the durability and effectiveness of the MFSMA algorithm have been rigorously evaluated using data from manufacturers' datasheets across different temperature and irradiance conditions. Statistical analysis supports the conclusion that MFSMA offers superior accuracy and dependability in estimating vital parameters for photovoltaic modules, making it an invaluable tool for overcoming the challenges of parameter identification in solar energy technologies.

关键词： Photovoltaic systems Solar power generation Optimization methods Solar energy Batteries Resistance Semiconductor diodes Parameter estimation Photovoltaic models parameter extraction slime mould algorithm multi-strategy learning parameter optimization grey wolf algorithm elite opposition-based learning

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