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Real-time health monitoring in WBANs using hybrid Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP)

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2023年 168卷

作者： Aryai, Pouya Khademzadeh, Ahmad Jassbi, Somayyeh Jafarali Hosseinzadeh, Mehdi Hashemzadeh, Omid Shokouhifar, Mohammad Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran *** Iran ITRC ICT Res Inst Tehran *** Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Human Dev Comp Sci Sulaymaniyah 07786 Iraq Islamic Azad Univ Dept Business Management Sci & Res Branch Tehran *** Iran Shahid Beheshti Univ Dept Elect & Comp Engn Tehran *** Iran

Wireless body area networks (WBANs) are helpful for remote health monitoring, especially during the COVID-19 pandemic. Due to the limited batteries of bio-sensors, energy-efficient routing is vital to achieve load-balancing and prolong the network's lifetime. Although many routing techniques have been presented for WBANs, they were designed for an application, and their performance may be degraded in other applications. In this paper, an ensemble Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP) is introduced as an adaptive real-time remote health monitoring in WBANs. The motivation behind this technique is to utilize the superior route optimization solutions offered by metaheuristics and to integrate them with the real-time routing capability of machine learning. The proposed method involves two phases: offline model tuning and online routing. During the offline pre-processing step, a metaheuristic algorithm based on the whale optimization algorithm (WOA) is used to optimize routes across various WBAN configurations. By applying WOA for multiple WBANs, a comprehensive dataset is generated. This dataset is then used to train and test a machine learning regressor that is based on support vector regression (SVR). Next, the optimized MDML-RP model is applied as an adaptive real-time protocol, which can efficiently respond to just-in-time requests in new, previously unseen WBANs. Simu-lation results in various WBANs demonstrate the superiority of the MDML-RP model in terms of application-specific performance measures when compared with the existing heuristic, metaheuristic, and machine learning protocols. The findings indicate that the proposed MDML-RP model achieves noteworthy improvement rates across various performance metrics when compared to the existing techniques, with an average improvement of 42.3% for the network lifetime, 15.4% for reliability, 31.3% for path loss, and 31.7% for hot-spot temperature.

关键词： Wireless body area networks Health monitoring Adaptive real-time routing Machine learning Support vector regression whale optimization algorithm

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Battery SOH estimation and RUL prediction framework based on variable forgetting factor online sequential extreme learning machine and particle filter

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JOURNAL OF ENERGY STORAGE 2023年 65卷

作者： Duan, Wenxian Song, Shixin Xiao, Feng Chen, Yuan Peng, Silun Song, Chuanxue Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130022 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China

Battery life prediction is of great practical significance to ensure the safety and reliability of equipment. This paper proposes a new framework to realize battery state of health (SOH) estimation and remaining useful life (RUL) prediction. The variable forgetting factor online sequential extreme learning machine (VFOS-ELM) is used to estimate battery SOH, and particle filter (PF) algorithm used to predict battery RUL. To improve the estimation accuracy, a new nonlinear decline method, adaptive weight and Gaussian variation are used to improve the standard whale optimization algorithm (WOA) algorithm. And the improved IWOA algorithm is used for parameter optimization of the VFOS-ELM and PF algorithm. The extremely randomized trees (ERT) algorithm is used to obtain the features with high correlation with the available capacity to reduce the complexity of the model and improve the estimation accuracy. Compared with other methods, the proposed IWOA-VFOS-ELM algorithm has higher estimation performance and noise anti-interference ability. The MAE of APR-3 and APR -4 for SOH estimation are both within 0.12 %, RMSE are within 0.15 %, and IA are both higher than 99.9 %. Compared with PF algorithm, the RUL prediction accuracy obtained by IWOA-PF algorithm is improved by 7.143 %, 6.445 % and 15.094, respectively. In summary, the IWOA-PF algorithm proposed in this paper can be used to predict the battery RUL, and the prediction performance is better than the PF algorithm.

关键词： State of health Remaining useful life whale optimization algorithm Extremely randomized trees Online sequential extreme learning machine

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Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification

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ENERGY 2023年第1期275卷

作者： Yu, Min Niu, Dongxiao Wang, Keke Du, Ruoyun Yu, Xiaoyu Sun, Lijie Wang, Feiran North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China Zhengzhou Univ Sch Management Zhengzhou 450001 Peoples R China Hebei GEO Univ Sch Urban Geol & Engn Shijiazhuang 050031 Peoples R China

A reliable short-term forecast of photovoltaic power (PVPF) is essential to maintaining stable power systems and optimizing power grid dispatch. A hybrid prediction framework of PVPF considering similar day screening, signal decomposition technique, and hybrid deep learning is proposed to realize accurate point-interval prediction. First, a double similar day screening model is constructed to divided weather into the three types, which improves the quality of the training set. Second, a double-layer signal decomposition model based on improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD)-Variational mode decomposition(VMD) is proposed to preprocess the data, eliminate the noise data. Third, whale optimization algorithm(WOA) is introduced to search the best hyper parameters of BiLSTM, and the attention mechanism(AM) is adopted to focus on the influence of key information. WOA-BiLSTM-AM is constructed to predict intrinsic mode function(IMF). Fourth, Kernel density estimation (KDE) is applied to estimate the PV prediction interval at different confidence levels. Finally, compared with the benchmark models, the prediction errors of cloudy, sunny and rainy days are reduced by 56.09%, 80.10% and 71.98%, respectively. In addition, according to the coverage width criterion, KDE (Gaussian) is superior to other interval prediction models and can achieve more reliable prediction interval.

关键词： Attention mechanism Interval prediction Photovoltaic prediction Secondary decomposition whale optimization algorithm

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Inadequate load output diagnosis of ultra-supercritical thermal power units based on MIWOA multi-label random forest

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APPLIED THERMAL ENGINEERING 2023年 227卷

作者： Tang, Mingzhu Liang, Zixin Ji, Dongxu Yi, Jiabiao Peng, Zhonghui Huang, Yujie Wang, Jiachen Chen, Donglin Changsha Univ Sci & Technol Sch Energy & Power Engn Changsha 410114 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518000 Peoples R China

Aiming at the problem of considerable economic losses caused by the large fluctuation range of response load and Automatic Generation Control (AGC) commanded load of ultra-supercritical thermal power units, an inadequate load output diagnosis model of multi-label random forest with multi-improved whale optimization algorithm (MIWOA-MLRF) is proposed. Thermal power units are a kind of high-dimensional, nonlinear, and complex industrial complex, which brings difficulty for conventional mechanistic models in comprehensively analyzing the inadequate output causes of the units. With the help of extensive data analysis and artificial in-telligence algorithms, a multi-label random forest (MLRF) for inadequate output cause analysis is constructed. To improve the accuracy of WOA for MLRF classification, a good point set strategy is used to optimize the initial population distribution of WOA, and an improved convergence factor is used to control the speed of WOA search. Three Gaussian simulation datasets with a different number of features and labels and three sets of thermal power operation data with varying periods are utilized. The test results show that the missing alarm rates (MAR) of MIWOA-MLRF are 0.301, 0.621, and 0.802 under three sets of Gaussian data tests, which are the smallest values among all analytical models. Under three sets of thermal power unit operation data, the average values of MIWOA-MLRF's MAR are 2.2%, 0.2%, and 1.5%, respectively, which have lower missing alarm rates compared with other algorithms. Furthermore, in the false alarm rate (FAR) comparison, the FAR of MIWOA-MLRF de-creases significantly compared to MLRF. In the optimization time comparison, the optimization time required for MIWOA-MLRF is about 1000 s, which saves 33% on average compared to the optimization time of other algo-rithms. The result indicates that MIWOA-MLRF can guarantee the same better real-time performance with reduced FAR and MAR of inadequate output cause classification.

关键词： Thermal power units Inadequate output Multi-label classification whale optimization algorithm Random forest

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A new population initialization of metaheuristic algorithms based on hybrid fuzzy rough set for high-dimensional gene data feature selection

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 166卷 107538-107538页

作者： Guo, Xuanming Hu, Jiao Yu, Helong Wang, Mingjing Yang, Bo Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325000 Peoples R China

In the realm of modern medicine and biology, vast amounts of genetic data with high complexity are available. However, dealing with such high-dimensional data poses challenges due to increased processing complexity and size. Identifying critical genes to reduce data dimensionality is essential. The filter-wrapper hybrid method is a commonly used approach in feature selection. Most of these methods employ filters such as MRMR and ReliefF, but the performance of these simple filters is limited. Rough set methods, on the other hand, are a type of filter method that outperforms traditional filters. Simultaneously, many studies have pointed out the crucial importance of good initialization strategies for the performance of the metaheuristic algorithm (a type of wrapper-based method). Combining these two points, this paper proposes a novel filter-wrapper hybrid method for high-dimensional feature selection. To be specific, we utilize the variant of bWOA (binary whale optimization algorithm) based on Hybrid Fuzzy Rough Set to perform attribute reduction, and the reduced attributes are used as prior knowledge to initialize the population. We then employ metaheuristics for further feature selection based on this initialized population. We conducted experiments using five different algorithms on 14 UCI datasets. The experiment results show that after applying the initialization method proposed in this article, the performance of five enhanced algorithms, has shown significant improvement. Particularly, the improved bMFO using our initialization method: fuzzy_bMFO outperformed six currently advanced algorithms, indicating that our initialization method for metaheuristic algorithms is suitable for high-dimensional feature selection tasks.

关键词： Population initialization Hybrid fuzzy rough set whale optimization algorithm Gene data feature selection Multiclass classification

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Hybrid optimal design method for magnetic field coils under magnetic shielding boundary

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SENSORS AND ACTUATORS A-PHYSICAL 2023年第1期359卷

作者： Wu, Zhihong Pang, Haoying Wang, Zhuo Fan, Wenfeng Liu, Feng Pei, Hongyu Quan, Wei Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Ultraweak Magnet Field Measurement Technol Beijing 100191 Peoples R China Hangzhou Innovat Inst Beihang Univ Zhejiang Prov Key Lab Ultraweak Magnet Field Space & Appl Technol Hangzhou 310000 Peoples R China

A novel strategy for designing uniform magnetic field and gradient magnetic field coils considering the boundary effect of magnetic shielding is proposed in this study. This strategy uses intelligent optimization algorithm to skillfully transform the complex coil design problem into the spatial optimization problem of individual population. The mirror method is combined with Green's function to accurately calculate the magnetic field, and the target field method (TFM) is combined with the whale optimization algorithm to optimize the stream functions using adaptive ridge regression. The minimum fitness function is defined as the real-time magnetic field relative error, and the optimal ridge coefficient is continuously obtained by determining the current density coefficient. The proposed method significantly reduces the magnetic field deviation in the target area compared to the conventional TFM, with reductions ranging from 12.2% to 0.11% (�� coil), 0.3% to 0.02% (�� coil), 20% to 10% (d ��/d �� coil) and 2.9% to 2.4% (d ��/d �� coil). The strategy has been effectively validated by analytical simulations and experimental results, and is expected to be applied to the design of uniform field coils and gradient coils for ultra-high precision atomic sensors.

关键词： Uniform field and field gradient coils Target field method Magnetic shielding whale optimization algorithm

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One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods

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ENERGIES 2023年第6期16卷 2688页

作者： Huang, Chao-Ming Chen, Shin-Ju Yang, Sung-Pei Chen, Hsin-Jen Kun Shan Univ Dept Elect Engn Tainan 710 Taiwan Kun Shan Univ Dept Green Energy Technol Res Ctr Tainan 710 Taiwan

This paper proposes an optimal ensemble method for one-day-ahead hourly wind power forecasting. The ensemble forecasting method is the most common method of meteorological forecasting. Several different forecasting models are combined to increase forecasting accuracy. The proposed optimal ensemble method has three stages. The first stage uses the k-means method to classify wind power generation data into five distinct categories. In the second stage, five single prediction models, including a K-nearest neighbors (KNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, a support vector regression (SVR) model, and a random forest regression (RFR) model, are used to determine five categories of wind power data to generate a preliminary forecast. The final stage uses an optimal ensemble forecasting method for one-day-ahead hourly forecasting. This stage uses swarm-based intelligence (SBI) algorithms, including the particle swarm optimization (PSO), the salp swarm algorithm (SSA) and the whale optimization algorithm (WOA) to optimize the weight distribution for each single model. The final predicted value is the weighted sum of the integral for each individual model. The proposed method is applied to a 3.6 MW wind power generation system that is located in Changhua, Taiwan. The results show that the proposed optimal ensemble model gives more accurate forecasts than the single prediction models. When comparing to the other ensemble methods such as the least absolute shrinkage and selection operator (LASSO) and ridge regression methods, the proposed SBI algorithm also allows more accurate prediction.

关键词： wind power forecasting ensemble method particle swarm optimization salp swarm algorithm whale optimization algorithm

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Design of uniform field coils coupled with a magnetic shield cylinder?

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2023年第1期583卷

作者： Huang, Binyue Cao, Qian Chen, Zhengyu Wang, Yuhao Yue, Ziqian Zhai, Yueyang Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Shen Yuan Honors Coll Beijing 100191 Peoples R China

Active magnetic field coils and magnetic shields are widely employed to achieve uniform and low magnetic fields, and the coil generating magnetic field would be influenced as a result of the coupling with the shields. It turns out to increase the magnetic field strength and shrink the uniform area, which is detrimental to atomic devices. An analytical solution that takes into account the coupling effect of shields and coils is established by using the magnetic vector potential. Analyses for three different configurations are first performed, including mu-metals, 2K-MnZn ferrites and 10K-MnZn ferrites. Analytical calculations reveal that the optimal radii of the standard Lee-Whiting coils with high uniformity are all around 0.81 of shield radii. Additionally, adopting the whale optimization algorithm (WOA), a modified coil with improved uniformity and comparable size is proposed. Experimental results show that a better uniformity of 2.68e-5 and a 34% longer uniform area is obtained when compared with the standard coil. The accuracy of the internal magnetic field distributions is compared and tested by simulations and experiments. Situations utilizing shields and coils will be facilitated by this effective application of WOA in uniform field coil design.

关键词： Uniform magnetic field coil Magnetic shield Shield-coil coupling whale optimization algorithm Atomic magnetometer

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Prediction of Rock Tensile Fracture Toughness: Hybrid ANN-WOA Model Approach

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RUDARSKO-GEOLOSKO-NAFTNI ZBORNIK 2024年第3期39卷 1-12页

作者： Dolatshahi, Alireza Molladavoodi, Hamed Amirkabir Univ Technol Dept Min Engn Tehran Iran Amirkabir Univ Technol Dept Min Engn Tehran Iran

Various techniques are used in rock engineering to evaluate tensile fracture toughness, which is a critical parameter in assessing and designing stable rock structures. These methods typically involve laboratory investigations and statistical analysis. Nevertheless, artificial neural networks can also establish correlations among different data sets. Artificial intelligence approaches are becoming increasingly essential in all engineering fields, including the ones that study rock fracture mechanics. In this work, an artificial neural network with a hidden layer and eight neurons as well as a hybrid artificial neural network with a whale optimization algorithm were utilized to determine the tensile fracture toughness of rocks. In order to develop accurate models, this study has carefully selected four fundamental parameters to serve as inputs. These parameters include radius, thickness, crack length, and mean tensile strength of specimens. Also, 113 rock datasets were collected for models. The results show that utilization of the optimization algorithm enhances the precision in estimating the tensile fracture toughness of rocks. The R2 improved to 0.93 when the whale optimization algorithm was used. On the other hand, the correlation factor reached 0.81 when the whale optimization algorithm was not implemented.

关键词： fracture toughness artificial neural networks whale optimization algorithm size effect tensile strength

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whale optimization Controller for Load Frequency Control of a Two-Area Multi-source Deregulated Power System

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第1期22卷 122-137页

作者： Kumar, Rajiv Sharma, V. K. Bhagwant Univ Elect Engn Dept Ajmer 305004 Rajasthan India

This article discusses a whale optimization (WO) controller for design analysis, stability management, application, and performance analysis of a deregulated two-area multi-source energy system. Within the power grid, the Automatic Generation Control (AGC) maintains the power balance and strengthens the sudden frequency disturbance of the grid. Various researchers are targeted many kinds of fast-acting energy-storing devices to dampen space frequency oscillations and diversion of the tie line because of unexpected load variation. To regulate the power flow between multi-areas, load frequency control (LFC) is used by keeping frequency constant. But, to design a controller for minimizing the issues of LFC remains a challenge within the deregulated atmosphere. The main objective of the proposed methodology is to design a controller for minimizing the issues in LFC within the deregulated atmosphere. A two-area deregulated hydrothermal power system is employed to analyze many LFC problems in the deregulated power grid. During this modeling, the Thyristor-Controlled Phase Shifters (TCPS) and Capacitive Energy Storage unit (CES) are placed in the tie line and its control space. To mitigate the various issues of LFC in a two-area deregulated hydrothermal power system, the CES-TCPS combination is placed between the two control areas and the transient performance of constant is evaluated. The WO controller will increase the system power size to focus on the better performance of low-frequency management tasks. In addition, the effectiveness of the WO algorithm in tuning the proportional integral controller is also compared in a different contract (unilateral, bilateral and contract violation cases) scenarios of the proposed deregulated power system. Finally, the simulation is carried out in MATLAB/Simulink platform and the results obtained for the WO controller is compared with the normal and fuzzy PI controllers.

关键词： Automatic generation control Capacitive energy storage Deregulation Load frequency control Multi-source power system Sine-cosine algorithm whale optimization algorithm

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