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State of health estimation of lithium-ion batteries based on multiple health factors and BO-Seq2Seq model

COMPUTERS & ELECTRICAL ENGINEERING 2024年 116卷

作者： Wang, Qilin Xie, Song Guo, Wenqi Li, Guishu Lv, Pengfei Civil Aviat Flight Univ China Coll Civil Aviat Safety Engn Guanghan 618307 Sichuan Peoples R China Civil Aircraft Fire Sci & Safety Engn Key Lab Sich Guanghan 618307 Sichuan Peoples R China Sichuan Key Technol Engn Res Ctr All Elect Navigab Guanghan 618307 Sichuan Peoples R China

State of health (SoH) is a key indicator for evaluating the degradation level of lithium-ion batteries (LIBs), and its accurate prediction is crucial for the reliability and safety of battery management systems. This paper proposes an improved sequence-to-sequence (Seq2Seq) model for SoH estimation based on the selection of relevant multiple types of health factors (HFs). In order to better describe the SoH of LIBs, Pearson's correlation coefficient is employed to select HFs that is highly correlated with the SoH. The hyperparameter optimization method based on bayesian optimization (BO) is used to solve the challenge of hyperparameter selection in Seq2Seq models. This method effectively utilizes the strong global search ability of BO to obtain the global optimal solution. The proposed method is validated using a dataset provided by NASA. The results show that the BO-Seq2Seq model proposed in this work exhibits competitive accuracy in estimation performance, with an error of less than 1 %. Compared with methods that only consider a single HF, the method that considers multiple HFs in this work significantly improves the accuracy of estimation. The estimation performance indicators of the proposed BO-Seq2Seq model, such as the root mean square errors are all within 1 %, the mean absolute errors are all within 0.5 % and the mean square errors are all within 0.004 %, are significantly lower than other methods.

关键词： Lithium-ion battery bayesian optimization algorithm Sequence-to-sequence Multiple health factors State of health

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Efficient Amplification of Large-Signal Spoof Surface Plasmon Polaritons in a Subwavelength Size

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ADVANCED MATERIALS TECHNOLOGIES 2024年第5期9卷

作者： Liao, Zhen Wu, Shaofei Guo, Jia Liu, Leilei Cai, Jialin Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Natl & Local Joint Engn Lab RF Integrat & Microass Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Flexible Elect Future Technol Nanjing 210023 Peoples R China Hangzhou Dianzi Univ Key Lab RF Circuit & Syst Minist Educ Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Zhejiang Key Lab Large Scale Integrated Circuit De Hangzhou 310018 Peoples R China

The advancement of communication systems demands ever-smaller sizes, leading to the integration of more circuits in compact forms. A promising solution lies in Spoof Surface Plasmon Polaritons (SSPPs), which enable strong confinement, low interference, and a significant wave vector, thereby facilitating smaller sizes. However, the amplification of large plasmonic signals in a compact size remains a daunting scientific challenge for the further development of SSPPs communication systems. In this study, this challenge is tackled by selecting GaN High Electron Mobility Transistors (HEMTs) and load/source-pull technologies. To ensure efficient operation, impedance-matching networks are designed, resulting in a remarkably small amplifier structure. The approach involves utilizing the bayesian optimization (BO) algorithm to fine-tune the matching network, leading to exceptional amplification performance. The results obtained in the frequency range of 1-3 GHz are highly promising, with both simulated and measured results demonstrating an impressive output power exceeding 40 dBm, a power added efficiency (PAE) surpassing 60%, and a gain exceeding 10 dB. These remarkable achievements underscore the viability of amplifying large-signal SSPPs and outputting high-power SSPPs waves within a subwavelength size, opening exciting possibilities for SSPPs communication, sensing, and imaging systems. A novel design of a high-power SSPPs amplifier at 1-3GHz utilizing HEMT is presented. The optimized structure is simulated, fabricated, and measured, resulting in an impressive output power exceeding 40 dBm, a PAE surpassing 60%, and a gain exceeding 10 dB. These results dvalidate the proposed design for amplifying large-signal SSPPs and its practical realization for high-power SSPPs ***

关键词： bayesian optimization algorithm power amplification spoof surface plasmon polaritons

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Explainable highway performance degradation prediction model based on LSTM

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ADVANCED ENGINEERING INFORMATICS 2024年 61卷

作者： Sun, Xin Wang, Honglei Mei, Shilong Guizhou Univ Sch Management Guiyang 550025 Peoples R China Guizhou Minzu Univ Architectural Engn Coll Guiyang 550025 Peoples R China Key Lab Internet Collaborat Intelligent Mfg Guizho Guiyang 550025 Peoples R China Guizhou Univ Coll Architecture & Urban Planning Guiyang 550025 Peoples R China

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decisionmaking. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic l

关键词： Long Short-Term Memory (LSTM) Highway performance degradation bayesian optimization algorithm SHapley Additive exPlanations (SHAP) Explainable prediction model

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Multi-objective optimization of environmental tax for mitigating air pollution and greenhouse gas

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Journal of Management Science and Engineering 2022年第3期7卷 473-488页

作者： Sijing Li Ning Jia Zhenni Chen Huibin Du Zengkai Zhang Bomin Bian College of Management and Economics Tianjin UniversityTianjin300072China School of Economics and Finance Xi'an Jiaotong UniversityXi'an710061China Institute of Operations Research and Analytics National University of Singapore117602Singapore

Government macro-control through various policies is an important way to mitigate air pollution and greenhouse ***,environmental tax is used worldwide as an important ***,few studies have considered the interaction between carbon and environmental protection ***,different sectors differ in their energy structure,pollution emission intensity,and economic status,and previous studies rarely proposed differentiated environmental tax rates based at the sectoral level.A model framework combining the computable general equilibrium(CGE)model and bayesian optimization(BO)algorithm is proposed to maximize GDP,meet environmental planning objectives,and explore the optimal environmental taxation scheme to realize the multi-objective optimization of the economy and ***,this study compares the different impact mechanisms of environmental protection tax and carbon *** discusses the impacts of differentiated environmental tax rates in different sectors on the environment and *** example,the results show that the coordinated implementation of environmental protection and carbon tax policies and the sectoral differentiated environmental tax rates in China could better balance economic development and environmental ***,the optimal taxation scheme could mitigate air pollution and greenhouse gases,promote economic growth,and realize sustainable economic and environmental ***,the optimized taxation scheme positively affects the energy and industrial structures.

关键词： Computable general equilibrium bayesian optimization algorithm Environmental tax Multi-objective optimization Air pollution Greenhouse gas

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Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved evolutionary algorithm

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ENERGY EXPLORATION & EXPLOITATION 2021年第1期39卷 273-298页

作者： Liu, Tan Yuan, Qinyun Wang, Lina Wang, Yonggang Zhang, Nannan Shenyang Agr Univ Coll Informat & Elect Engn Shenyang Peoples R China Liaoning Engn Res Control Informat Technol Agr Shenyang Peoples R China China Jiliang Univ Coll Mech & Elect Engn Hangzhou Peoples R China

This paper establishes an error compensation multi-objective optimization model of oil-gas production process for optimizing these production indices, including overall oil production, overall water production and comprehensive energy consumption per ton of oil. In order to reduce the error between the model output and the actual value of comprehensive energy consumption per ton of oil, combining the mechanism model with least squares support vector machine (LS-SVM) error model optimized by bayesian optimization algorithm (BOA), a hybrid model is established to predict the comprehensive energy consumption, in which the mechanism model is used to describe the overall characteristics of oil-gas production process, and LS-SVM error model is established to compensate the mechanism model error. Then, in order to improve the performance of Pareto non-dominated solutions, an improved non-dominated sorting genetic algorithm-II with multi-strategy improvement (IMS-NSGA-II) is proposed to solve the error compensation multi-objective optimization model. Finally, the effectiveness and superiority of the the proposed optimization method are verified by the experiment results on some stand test problems and the optimization problem for the oil-gas production process in a block of an oil production operation area.

关键词： Oil-gas production process LS-SVM error model bayesian optimization algorithm error compensation NSGA-II with multi-strategy improvement (IMS-NSGA-II)

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EVOLUTIONARY optimization IN DYNAMIC ENVIRONMENTS: BRINGING THE STRENGTHS OF DYNAMIC bayesian NETWORKS INTO bayesian optimization algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第6期9卷 2485-2503页

作者： Kaedi, Marjan Ghasem-Aghaee, Nasser Ahn, Chang Wook Univ Isfahan Dept Informat Technol Engn Esfahan *** Iran Univ Isfahan Dept Comp Engn Esfahan *** Iran Sungkyunkwan Univ Dept Comp Engn 2066 Seobu Ro Suwon 440746 South Korea

In this paper, a new evolutionary algorithm termed DBN-MBOA (Memory based BOA with Dynamic bayesian Networks) is proposed for the dynamic optimization. In DBN-MBOA, the knowledge obtained from previously solved problems is encoded in some structures called network translators. The network translators defined on non-stationary Dynamic bayesian Networks (nsDBNs) describe the correlation between conditional dependencies of candidate solution variables before and after environmental changes. The network translators constructed for the changes are stored in memory. When any change occurs in the environment, a relevant network translator is retrieved from the memory and is used for modifying the dependencies of the current bayesian network. In the retrieve stage, unlike existing memory-based methods, the relevant network translator is selected based on the characteristic of the change itself, not that of the new environmental state. Experimental results show that DBN-MBOA achieves better performance in random environments as well as cyclic environments.

关键词： bayesian optimization algorithm Dynamic bayesian network Dynamic optimization Environmental change Memory-based method Network translator

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HTS-SLIM design based on bayesian multi-level, multi-objective optimization and Gaussian process models

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PHYSICA C-SUPERCONDUCTIVITY AND ITS APPLICATIONS 2021年 591卷 1353970-1353970页

作者： Ahmadpour, Ali Dejamkhooy, Abdolmajid Shayeghi, Hossein Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran Univ Mohaghegh Ardabili Energy Management Res Ctr Ardebil Iran

The performance of the Linear Induction Motor (LIM) is based on the linear force in the moving direction. However, due to the length limitation of LIMs, the end-effects as the barrier force decrease their performance. On the other hand, the primary width is lower than the secondary that causes the non-homolographic distribution of the air gap flux density. These effects result in the reduction of output thrust. In recent decades, the windings are made of High-Temperature Superconducting (HTS) tapes to reduce their power losses and total weight. However, due to their magnetic losses, the HTS-LIMs generally are low efficiency and need an optimal design. In this paper, an optimal design of an HTS Single-sided LIM (SLIM) is conducted by analyzing the proposed equivalent circuit parameters. Also, the output variables of the HTS-SLIM are calculated using the proposed analysis, which is different from the conventional SLIMs. The bayesian optimization algorithm (BOA) is used to optimize the objective functions, which consists of the efficiency, power factor, braking force due to end effects, and AC losses. Moreover, various models of the Gaussian Process (GP) are employed for modeling the objective functions at each step of the BOA process. Since the problem construction is multi-objective, the optimization algorithm is modified as the Multi-Objective BOA (MO-BOA) to optimize the mentioned objectives simultaneously. The problem is also considered as a multi-level problem owing to the various levels based on the sensitive analysis of HTS-SLIM parameters. For increasing the results accuracy, different effects of SLIM such as end-, skin-, and edge effects are studied in the analytical model. The results of different models of GP and other methods are compared to show the robustness of the proposed method. Also, a 3D model of the motor is built and analyzed with the Finite Element Method (FEM) to verify the results of the analytical approach. The results for both FEM and analyt

关键词： Linear induction motors High-temperature superconducting Multi-objective optimization bayesian optimization algorithm Gaussian proces Multi-level optimization

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AN IMPROVED REAL-CODED bayesian optimization algorithm FOR CONTINUOUS GLOBAL optimization

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第6期9卷 2505-2519页

作者： Moradabadi, Behnaz Beigy, Hamid Ahn, Chang Wook Sharif Univ Technol Dept Comp Engn Tehran Iran Sungkyunkwan Univ Dept Comp Engn 2066 Seobu Ro Suwon 440746 South Korea

bayesian optimization algorithm (BOA) utilizes a bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed bayesian network. This paper proposes an improved real-coded BOA (IrBOA) for continuous global optimization. In order to create a set of bayesian networks, the candidate solutions are partitioned by an adaptive clustering method. Each bayesian network has its own structure and parameters, and the next generation is produced from this set of networks. The adaptive clustering method automatically determines the correct number of clusters so that the probabilistic building-block crossover (PBBC) is effectively preserved. This leads to a better search when the diversity of population is high at the beginning of search. Moreover, it tunes the solutions by automatically decreasing the number of clusters as the diversity of population decreases during the search process. The experimental results demonstrate that the proposed algorithm achieves better performance on well-known benchmark functions in the continuous global optimization.

关键词： Evolutionary algorithms bayesian optimization algorithm Adaptive clustering method Probabilistic building-block crossover Continuous global optimization

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An efficient classification framework for breast cancer using hyper parameter tuned Random Decision Forest Classifier and bayesian optimization

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102682-102682页

作者： Kumar, Pratheep P. Bai, Mary Amala, V Nair, Geetha G. Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumaracoil Tamil Nadu India Noorul Islam Ctr Higher Educ Dept Informat Technol Kumaracoil Tamil Nadu India Women & Children Hosp Thampanoor Kerala India

Decision tree algorithm is one of the algorithm which is easily understandable and interpretable algorithm used in both training and application purpose during breast cancer prognosis. To address this problem, Random Decision Forests are proposed. In this manuscript, the breast cancer classification can be determined by combining the advantages of Feature Weight and Hyper Parameter Tuned Random Decision Forest classifier. Here the Kernel Neutrosophic C-Means Clustering is used as Feature Weight, which allocates greater weights to applicable features and smaller weights to less applicable features. Then Random Decision Forest classifier model are optimized with the help of the bayesian optimization algorithm to obtain optimal hyper tuning parameters. By this, the accurate classification of breast cancer is successfully achieved. Then the efficiency of the proposed system is executed in python. The performance analysis are executed in Wisconsin prognostic Breast Cancer (WPBC) dataset, 70 % training and remaining 30 % testing is compared with the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of proposed feature weight and Random Decision Forest Classifier with bayesian optimization (FW + BOA-RDF) in Breast Cancer Wisconsin (Prognosis) Data Set is 6.66 %, 12.659 % and 37.618 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively. The performance analysis in Wisconsin prognostic Breast Cancer (WPBC) dataset, 75 % training and the remaining 25 % testing is compared at Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of FW + BOA-RDF in Breast Cancer Wisconsin (Prognosis) Data Set is 3.7146 %, 5.27398 % and 4.4413 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively.

关键词： Breast cancer Feature weighting Random Decision Forest Classifier Kernel neutrosophic C-Means clustering bayesian optimization algorithm

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Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning algorithms to Enhance Electric Power Supply Management

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ENERGIES 2023年第4期16卷 2035-2035页

作者： Almuhaini, Salma Hamad Sultana, Nahid Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci Dammam 34212 Saudi Arabia

This study aims to develop statistical and machine learning methodologies for forecasting yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) determining significant features that have a considerable influence on electricity consumption, (ii) utilizing a bayesian optimization algorithm (BOA) to enhance the model's hyperparameters, (iii) hybridizing the BOA with the machine learning algorithms, viz., support vector regression (SVR) and nonlinear autoregressive networks with exogenous inputs (NARX), for modeling individually the long-term electricity consumption, (iv) comparing their performances with the widely used classical time-series algorithm autoregressive integrated moving average with exogenous inputs (ARIMAX) with regard to the accuracy, computational efficiency, and generalizability, and (v) forecasting future yearly electricity consumption and validation. The population, gross domestic product (GDP), imports, and refined oil products were observed to be significant with the total yearly electricity consumption in Saudi Arabia. The coefficient of determination R2 values for all the developed models are > 0.98, indicating an excellent fit of the models with historical data. However, among all three proposed models, the BOA-NARX has the best performance, improving the forecasting accuracy (root mean square error (RMSE)) by 71% and 80% compared to the ARIMAX and BOA-SVR models, respectively. The overall results of this study confirm the higher accuracy and reliability of the proposed methods in total electricity consumption forecasting that can be used by power system operators to more accurately forecast electricity consumption to ensure the sustainability of electric energy. This study can also provide significant guidance and helpful insights for researchers to enhance their understanding of crucial research, emerging trends, and new developments in future energy studies.

关键词： electricity consumption long-term forecast ARIMAX bayesian optimization algorithm SVR NARX

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