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Multikernel correntropy based robust least squares one-class support vector machine

NEUROCOMPUTING 2023年第1期545卷

作者： Zheng, Yunfei Wang, Shiyuan Chen, Badong Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

Least-squares one-class support vector machine (LS-OCSVM) is one of the most popular methods to perform one-class classification tasks, in which only the data of a specific class are available to train the classification model. However, the learning performance of LS-OCSVM heavily relies on the effectiveness of a squared loss function, which is sensitive to outliers, resulting in the poor robustness of LS-OCSVM to deal with contaminated data. In this paper, the original optimization problem of LS-OCSVM is therefore reformulated with a recently proposed robust similarity measure, called multikernel correntropy, generating a multikernel correntropy based LS-OCSVM (MKCLS-OCSVM). To find the solution to the new optimization problem effectively, a dynamic optimization algorithm developed with the popular half-quadratic optimization technique is adopted to perform the optimization process. Meanwhile, the convergence and computational complexity of the developed optimization algorithm are analyzed from theoretical perspectives. To further facilitate the implementation of MKCLS-OCSVM, an operationally simple search strategy, inspired by the hunting behavior of humpback whales, is designed for parameters selection. Experimental results on various one-class classification tasks are reported to demonstrate the performance superiority of the proposed MKCLS-OCSVM in comparison with LS-OCSVM and other robust LSOCSVM variants.

关键词： Least -squares one -class support vector machine One -class classification Multikernel correntropy Half -quadratic optimization technique whale optimization algorithm

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Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index

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APPLIED ENERGY 2023年第1期339卷

作者： Kumar, Abhishek Deng, Yan He, Xiangning Singh, Arvind R. Kumar, Praveen Bansal, R. C. Bettayeb, M. Ghenai, C. Naidoo, R. M. Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa Indian Inst Technol IIT Guwahati Dept Elect & Elect Engn Gauhati India Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah Saudi Arabia Univ Sharjah Sustainable & Renewable Energy Engn Dept Sharjah U Arab Emirates

This research work presents the tri-level optimization framework for the optimal scheduling of grid-connected and autonomous microgrids to diminish power losses and maximize loadability. Since the network's voltage profile depends on the loading level, the flexible load shaping-based demand-side management strategy is incorporated to investigate its impact on microgrid loadability. With the consideration of uncertain parameters related to renewable power generation, load demand, and power loss, voltage limit constraints, the resultant problem is formulated as a stochastic mixed-integer non-linear problem to enhance microgrid loadability and optimize daily operating costs. The interdependency of demand side management program and microgrid loadability is investigated. The seasonal load profiles covering the weekend and weekday loads in winter, summer, and spring/fall seasons are examined in this research work. The enhanced versions of the distribution networks IEEE-33 and IEEE-69 based microgrid test systems are chosen to evaluate the proposed framework in both off-grid and autonomous modes of operation. Simultaneously, the overall customer satisfaction index is evaluated and improved according to the seasonal load profiles winter weekday, winter-weekend, summer -weekday, summer-weekend, spring-weekday, and spring-weekend by 8.68%, 7.97%, 16.7%, 19.62%, 17.14%, 20.50% respectively. The recently reported whale optimization algorithm is adopted to solve the proposed optimization problem, and the obtained simulation results are validated by comparing them with popular metaheuristic algorithms. The computational burden on the utility is reduced for optimal scheduling of grid -integrated microgrid to extract maximum power by maintaining network voltage profile.

关键词： Energy Management Loadability whale optimization algorithm Microgrid Operation and Planning

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The Identification of ECG Signals Using Wavelet Transform and WOA-PNN

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SENSORS 2022年第12期22卷 4343-4343页

作者： Li, Ning He, Fuxing Ma, Wentao Wang, Ruotong Jiang, Lin Zhang, Xiaoping Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Minist Educ Key Lab Control Power Transmiss & Convers SJTU Shanghai 200240 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England Univ Birmingham Dept Elect Elect & Syst Engn Sch Engn Birmingham B15 2TT W Midlands England

Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.

关键词： electrocardiogram signal identification wavelet transform probabilistic neural network mean impact value whale optimization algorithm

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Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method

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APPLIED ENERGY 2021年 299卷 117291-117291页

作者： Gu, Bo Shen, Huiqiang Lei, Xiaohui Hu, Hao Liu, Xinyu North China Univ Water Resources & Elect Power Sch Elect Power Zhengzhou 450011 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 00038 Peoples R China Hebei Univ Engn Hebei Key Lab Intelligent Water Conservancy Handan 056038 Peoples R China

The primary means to promote grid-connected photovoltaic power generation is through accurately forecasting the power output from photovoltaic power stations. This paper proposes a method for day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis using fuzzy c-means (FCM), whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and non-parametric kernel density estimation (NPKDE). The FCM clustering algorithm was used to cluster historical data on numerical weather prediction and photovoltaic power stations, whereby daily data sharing similar meteorological information were clustered into one class. The rapid convergence speed and high convergence accuracy of the WOA were used to optimize the penalty factor and kernel function width of the LSSVM model;this was done to improve the calculation speed and forecasting accuracy of the LSSVM model. The WOA-LSSVM forecasting model was trained using the clustered numerical weather prediction and historical data of a photovoltaic power station. This was subsequently utilized to forecast day-ahead photovoltaic power. The NPKDE method was used to accurately calculate the probability density distribution of forecasting error and the confidence interval of the day-ahead PPF. The root mean square error (RMSE) values of the forecasting power of the WOA-LSSVM, PSO-LSSVM, LSSVM, LSTM and PSO-BP models are 2.55%, 3.00%, 5.60%, 6.03% and 3.18%, respectively, and the calculation results show that the forecasting accuracy of the WOA-LSSVM was higher relative to other models including PSO-LSSVM, LSSVM, LSTM and PSO-BP. Moreover, the NPKDE method was able to accurately describe the probability density distribution of the forecasting error.

关键词： Fuzzy c-means clustering whale optimization algorithm Least squares support vector machine Photovoltaic power forecasting

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Electricity Demand Forecasting With a Modified Extreme-Learning Machine algorithm

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Chen, Chen Ou, Chuangang Liu, Mingxiang Zhao, Jingtao NARI Technol Co Ltd Nanjing Peoples R China

To operate the power grid safely and reduce the cost of power production, power-load forecasting has become an urgent issue to be addressed. Although many power load forecasting models have been proposed, most still suffer from poor model training, limitations sensitive to outliers, and overfitting of load forecasts. The limitations of current load-forecasting methods may lead to the generation of additional operating costs for the power system, and even damage the distribution and network security of the related systems. To address this issue, a new load prediction model with mixed loss functions was proposed. The model is based on Pinball-Huber's extreme-learning machine and whale optimization algorithm. In specific, the Pinball-Huber loss, which is insensitive to outliers and largely prevents overfitting, was proposed as the objective function for extreme-learning machine (ELM) training. Based on the Pinball-Huber ELM, the whale optimization algorithm was added to improve it. At last, the effect of the proposed hybrid loss function prediction model was verified using two real power-load datasets (Nanjing and Taixing). Experimental results confirmed that the proposed hybrid loss function load prediction model can achieve satisfactory improvements on both datasets.

关键词： outliers whale optimization algorithm load forecasting Pinball-Huber regression extreme-learning machine

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A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism

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ENERGY 2023年第1期269卷

作者： Yu, Min Niu, Dongxiao Gao, Tian Wang, Keke Sun, Lijie Li, Mingyu Xu, Xiaomin 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 Engn Zhengzhou 450001 Peoples R China

With resource shortages and global warming becoming increasingly serious, it is urgent to accelerate the tran-sition to green and low-carbon energy. Wind power, as a kind of green, low-carbon, zero-cost renewable energy, has undergone rapid development. Aiming to address the problem of strong randomness and strong temporal correlations in wind power prediction (WPP), a new framework for WPP based on RF-WOA-VMD and BiGRU optimized by an attention mechanism is proposed. Firstly, the random forest algorithm (RF) is adopted to screen the influencing factors of wind power, effectively reducing the data redundancy and improving the prediction efficiency. Secondly, the variational modal decomposition (VMD) algorithm optimized by the whale algorithm (WOA) for WPP is adopted, which uses the WOA to adaptively determine the optimal parameters [K, alpha] in VMD, adaptively decompose raw wind power series, and reduce data noise. Furthermore, the BiGRU algorithm opti-mized by the attention mechanism is proposed for WPP. The attention mechanism is introduced to assign different weights to the hidden states of BiGRU to emphasize the impact of key information. Ultimately, the experimental result illustrated that the proposed model further enhances the prediction accuracy. According to data set 1, MAPE is reduced by 86.81% compared with BiGRU.

关键词： Wind power Random forest Variational modal decomposition whale optimization algorithm Attention mechanism Bidirectional gated recurrent unit

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Optimal Decomposition and Reconstruction of Discrete Wavelet Transformation for Short-Term Load Forecasting

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ENERGIES 2019年第24期12卷 4654-4654页

作者： Aprillia, Happy Yang, Hong-Tzer Huang, Chao-Ming Natl Cheng Kung Univ Dept Elect Engn Tainan 701 Taiwan Kun Shan Univ Dept Elect Engn Tainan 710 Taiwan

To achieve high accuracy in prediction, a load forecasting algorithm must model various consumer behaviors in response to weather conditions or special events. Different triggers will have various effects on different customers and lead to difficulties in constructing an adequate prediction model due to non-stationary and uncertain characteristics in load variations. This paper proposes an open-ended model of short-term load forecasting (STLF) which has general prediction ability to capture the non-linear relationship between the load demand and the exogenous inputs. The prediction method uses the whale optimization algorithm, discrete wavelet transform, and multiple linear regression model (WOA-DWT-MLR model) to predict both system load and aggregated load of power consumers. WOA is used to optimize the best combination of detail and approximation signals from DWT to construct an optimal MLR model. The proposed model is validated with both the system-side data set and the end-user data set for Independent System Operator-New England (ISO-NE) and smart meter load data, respectively, based on Mean Absolute Percentage Error (MAPE) criterion. The results demonstrate that the proposed method achieves lower prediction error than existing methods and can have consistent prediction of non-stationary load conditions that exist in both test systems. The proposed method is, thus, beneficial to use in the energy management system.

关键词： short-term load forecasting day-ahead load prediction multiple linear regression discrete wavelet transforms Daubechies wavelet whale optimization algorithm

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Characteristics and optimation of heat pipe radiator for space nuclear propulsion spacecraft

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PROGRESS IN NUCLEAR ENERGY 2022年 150卷

作者： Li, Zengen Zhang, Haochun Huang, Ziliang Zhang, Dong Wang, Heming Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China

With the maturity of nuclear reactor miniaturization technology and the increasing demand of human space exploration, nuclear powered spacecraft is expected to become a reality. With the continuous exploration of the deep universe by mankind, the capacity of the heat dissipation of the space radiators is required continuously be improved, which leads to the increasing weight and economical decline of the spacecraft. The heat pipe radiator has advantage that other radiators cannot compare with. In this paper, the finite difference method and the iterative method are used to study the influence of fin width, inlet temperature, length of the heat pipe condensing section, and coolant mass flow rate on the weight and the heat dissipation area of the heat pipe radiator. Taking the weight and the heat dissipation area of the radiator as the optimization goals, the whale optimization algorithm is used to optimize the design parameters of the heat pipe radiator with a given heat dissipation. ANSYS fluent is used to simulate and analyze the heat pipe-fin unit with the best design parameters.

关键词： Space nuclear power Space nuclear propulsion Heat pipe radiator whale optimization algorithm Simulation analysis

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Interface Reliability Modeling of Coaxial Through Silicon Via Based on WOA-BP Neural Network

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JOURNAL OF ELECTRONIC PACKAGING 2024年第3期146卷 031003-031003页

作者： Zhang, Liwen Yang, Chen Yang, He Wang, Jinchan Zhang, Jincan Henan Univ Sci & Technol Coll Informat Engn 263 Kaiyuan Ave Luoyang 471023 Henan Province Peoples R China

Due to the complex structure and thermal mismatch of coaxial through silicon via (TSV), cracks easily occur under thermal load, leading to interface delamination or spalling failure. The reliability issue of coaxial TSV is important for its application in three-dimensional packaging, so it is of great significance to predict the crack trend and evaluate the reliability of coaxial TSV. In this paper, an algorithm model with the combination of whale optimization algorithm (WOA) and back propagation (BP) neural network for the reliability prediction of coaxial TSV is proposed. Based on finite element method (FEM), the training and validation datasets of the energy release rates (ERR) of the crack at the critical interface are calculated to construct the deep learning neural network. Six key structure parameters affecting the reliability of coaxial TSV are selected as the input values of the BP neural network. The maximum relative error of whale optimization algorithm optimized back propagation (WOA-BP) neural network model is 0.88%, which is better than the prediction results of the traditional BP and genetic algorithm (GA) optimized BP models. The WOA-BP neural network model was also compared with BP and GA-BP neural network models with four error metric models. It is verified that WOA-BP neural network model has the best prediction performance. The proposed model can be used to achieve improved prediction accuracy for the interface reliability of coaxial TSV under complex structural conditions since it has higher accuracy and stronger robustness. Graphical Abstract Figure The Schematic Diagram of Coaxial TSV Structure

关键词： interface reliability coaxial through silicon via whale optimization algorithm back propagation neural network finite element method

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Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年第0期102卷 101766-000页

作者： Xu Xiaoxiao Bin, Luo Ramkumar, S. Saravanan, S. Balaji, M. Sundar Prakash Dhanasekaran, S. Thimmiaraja, J. Wuhan Univ Technol Sch Entrepreneurship Wuhan 430070 Hubei Peoples R China Wuhan Business Univ Sch Foreign Languages Wuhan 430056 Peoples R China Kalasalingam Acad Res & Educ Sch Comp Krishnankoil India CMR Inst Technol Dept Informat Sci & Engn Bangalore Karnataka India Dept ECE RVS Tech Campus Coimbatore Tamil Nadu India

Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.

关键词： Locked in state Continuous Wavelet Transform Spinal Cord Injury Information Transfer Rate Brain Computer Interface whale optimization algorithm Electroencephalogram

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