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Classification forecasting research of rock burst intensity based on the BO-XGBoost-Cloud model

EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-14页

作者： Yuan, Haiping Ji, Shuaijie Li, Hengzhe Zhu, Chuanqi Zou, Yangyao Ni, Bin Gu, Ziang Anhui Univ Sci & Technol State Key Lab Min Induced Response & Disaster Prev Huainan 232001 Peoples R China Hefei Univ Technol Coll Civil Engn Hefei 230009 Peoples R China

The rock burst belongs to a serious engineering geological disaster among the underground and deep resource exploitation engineering. The prediction of rock burst intensity has become an urgent problem to be addressed in underground engineering. In order to effectively predict the rock burst intensity grades, the four indicators were comprehensively selected in this paper to establish a rock burst evaluation indicator system. More specifically, the bayesian optimization algorithm was employed to optimize the XGBoost machine learning algorithm to determine the weights of various evaluation indicators. Afterwards, the cloud model theory was introduced into the grade prediction study of rock burst intensity, as well as ultimately establishing a comprehensive evaluation model of rock burst intensity based on the BO-XGBoost-Cloud model. Consequently, the feasibility and validity of the model were examined with the assistance of on-site measured data and rock burst discrimination results among the relevant literature, which indicated that: the BO- XGBoost-Cloud model exhibited the highest accuracy in predicting rock burst intensity, significantly surpassing the extension evaluation method and the entropy-cloud model. Therefore, the BO-XGBoost-Could model was the optimal model for predicting rock burst intensity, which provided a creative forecasting method for the classification prediction of rock burst intensity in underground engineering.

关键词： Rock burst Underground engineering XGBoost bayesian optimization algorithm Cloud model

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A bayesian optimization-based Evolutionary algorithm for Flexible Job Shop Scheduling

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Procedia Computer Science 2015年 61卷 521-526页

作者： Lu Sun Lin Lin Yan Wang Mitsuo Gen Hiroshi Kawakami Dalian University of Technology 116620 China Fuzzy Logic Systems Institute 820-0067 Japan Tokyo University of Science 113-8656 Japan Kyoto University 606-8306 Japan

Flexible Job-shop Scheduling Problem (fJSP) is a typical and important scheduling problem in Flexible Manufacturing System (FMS). The fJSP is an extended version of Job-shop Scheduling (JSP) that is NP hard problem. Due to it according with the real production system, we adopt a hybrid evolutionary computation algorithm to solve the fJSP problems. Among them, the bayesian optimization algorithm (BOA) is introduced to the characteristics of scheduling and uncertainty characteristics of the time in the fJSP. On this basis, we propose a distributed evolutionary algorithm and parameter adaptive mechanism. Finally, through experiments, we conclude that the proposed hybrid evolutionary algorithm based on BOA with grouping mechanism get better solution than original algorithm and improve robustness of algorithm. Meanwhile, the paper also have objective perspective, that is we can group the data different from each other, make the whole population into sub-populations, and then make the experiment separately on different and parallel machines in distributed environment, so that not only optimizes the best solution, but also enhance the efficiency and shortened the time.

关键词： bayesian optimization algorithm Flexible Job-shop Scheduling Problem Evolutionary algorithms

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Prediction of shear strength of RC deep beams using XGBoost regression with bayesian optimization

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2023年第14期27卷 4046-4066页

作者： Truong, Gia Toai Choi, Kyoung-Kyu Nguyen, Tri-Hai Kim, Chang-Soo Seoul Natl Univ Sci & Technol Sch Architecture Seoul South Korea Soongsil Univ Sch Architecture Seoul South Korea Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul South Korea Seoul Natl Univ Sci & Technol Sch Architecture Seoul 01811 South Korea

For the shear strength of deep beams, most of current design codes adopt the strut-and-tie model. However, since the shear resistance mechanism of deep beams is quite complicated and shear failure could be catastrophic with no prior warning, more reliable and robust shear strength prediction models are required. To this end, in the present study, using the extreme gradient boosting (XGBoost) and bayesian optimization (BO) algorithms, a BO-XGBoost hybrid model was developed and its hyperparameters were optimized based on a database of 320 test specimens. The prediction accuracy of the proposed BO-XGBoost hybrid model was compared with G-XGBoost (with hyperparameters tuned by grid search), R-XGBoost (with hyperparameters tuned by random search), D-XGBoost (with default hyperparameters), and existing design equations, and the comparison showed that the proposed model can predict most accurately the shear strength of deep beams without any significant and evident bias. The parametric study using the proposed model revealed that the effective depth, beam width, shear span-to-depth ratio, tension reinforcement ratio, and concrete compressive strength are critical to the shear strength of deep beams. For ease and convenience of the utilization of the proposed model, a stand-alone application program was also developed.

关键词： RC deep beam shear strength machine learning bayesian optimization algorithm extreme gradient boosting graphical user interface

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Uncertainty Analysis for EMC Simulation Based on bayesian optimization

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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2024年第2期67卷 587-597页

作者： Bai, Jinjun Hu, Bing Duffy, Alistair Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Peoples R China De Montfort Univ Sch Engn & Sustainable Dev Leicester LE1 9BH England

Nonintrusive uncertainty analysis methods are widely applied in the field of electromagnetic compatibility (EMC). When selecting deterministic EMC simulation sampling points as the training set, the existing methods are relatively mechanistic, which can seriously affect the computational efficiency of the uncertainty analysis process. In order to address this computational efficiency issue, this article proposes an EMC simulation uncertainty analysis method based on bayesian optimization algorithm. This method constructs a Gaussian process surrogate model through a more adaptive training set filtering strategy so as to achieve the goal of giving consideration to both computational efficiency and accuracy. Crosstalk prediction of parallel cables, electromagnetic interference of lightning electromagnetic pulse, and strong nonlinear uncertainty analysis are used to quantitatively verify that the proposed uncertainty analysis method is more efficient and accurate than the traditional non-intrusive method, especially when dealing with complex EMC simulation problems with strong nonlinearity.

关键词： Electromagnetic compatibility Uncertainty optimization Bayes methods Training Computational modeling Analytical models Accuracy Probabilistic logic Closed box bayesian optimization algorithm electromagnetic compatibility simulation nonintrusive uncertainty analysis methods

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Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on bayesian optimization

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Rao, Zhibo Wu, Jiahui Li, Guodong Wang, Haiyun Xinjiang Univ Engn Res Ctr Educ Minist Renewable Energy Generat & Grid Connec Urumqi 830049 Xinjiang Peoples R China Xinjiang Elect Power Co Ltd Elect Power Res Inst Urumqi 830049 Xinjiang Peoples R China

Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods.

关键词： Informer Energy storage power station Voltage abnormity prediction bayesian optimization algorithm

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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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A rolling bearing fault diagnosis technique based on recurrence quantification analysis and bayesian optimization SVM

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APPLIED SOFT COMPUTING 2024年 156卷

作者： Wang, Bing Qiu, Wentao Hu, Xiong Wang, Wei Shanghai Maritime Univ Shanghai 201306 Peoples R China

A rolling bearing fault diagnosis technique is proposed based on Recurrence Quantification Analysis (abbreviated as RQA) and bayesian optimized Support Vector Machine (abbreviated as RQA-Bayes-SVM). Firstly, analyzing the vibration signal with recurrence plot and the nonlinear feature parameters are extracted with RQA, constructing a feature matrix describing the fault mode and fault degree comprehensively. Finally, bayesian optimization algorithm is introduced for searching the best penalty factor C and kernel function parameter g of SVM and establishing an optimal Bayes-SVM model. Bearing datasets from CWRU is imported for diagnosis on fault mode and fault degree. The results show that the technique presents a good performance on fault mode diagnosis as well as fault degree distinction. Compared with common k-Nearest Neighbor (abbreviated as KNN) and Random Forest (abbreviated as RF) diagnosis models, Bayes-SVM has the best accuracy and stability, which indicates a potential value for engineering applications.

关键词： Rolling bearing RQA Fault diagnosis SVM bayesian optimization algorithm

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Short-term load forecasting based on CNN-BiLSTM with bayesian optimization and attention mechanism

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第17期35卷

作者： Shi, Huifeng Miao, Kai Ren, Xiaochen North China Elect Power Univ Dept Math & Phys Baoding 071003 Peoples R China

Accurate short-term load forecasting can ensure the reliable power supply of the power system and the regular operation of the grid economy. This article proposes a hybrid model based on convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) with bayesian optimization (BO) and attention mechanism (AM) for short-term load forecasting. CNN is used to capture the significant features of input data. BiLSTM is adept in time series forecasting and AM can reduce the computational complexity of model. BO can help to tune the hyperparameters automatically. The input features of models are recent load, time slot, date type, and meteorological factors. In order to eliminate the seasonality, the data set is divided into four subsets according to four seasons. The performance of the proposed model is compared with other models by MAE, RMSE, MAPE, and R2 score. The forecasting results represent that the proposed model is most suitable for short-term load forecasting among the contrast models and the meteorological factors have an impact on forecasting accuracy.

关键词： attention mechanism bayesian optimization algorithm bidirectional long short-term memory network convolutional neural network short-term load forecasting

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bayesian-Based Hyperparameter optimization of 1D-CNN for Structural Anomaly Detection

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SENSORS 2023年第11期23卷 5058-5058页

作者： Li, Xiaofei Guo, Hainan Xu, Langxing Xing, Zezheng Dalian Maritime Univ Coll Transportat Engn Dalian 116026 Peoples R China Univ Jinan Coll Informat Sci & Engn Jinan 250022 Peoples R China

With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural abnormalities, the model hyperparameters need to be adjusted according to different application scenarios, which is a complicated process. In this paper, a new strategy for building and optimizing 1D-CNN models is proposed that is suitable for diagnosing damage to different types of structure. This strategy involves optimizing hyperparameters with the bayesian algorithm and improving model recognition accuracy using data fusion technology. Under the condition of sparse sensor measurement points, the entire structure is monitored, and the high-precision diagnosis of structural damage is performed. This method improves the applicability of the model to different structure detection scenarios, and avoids the shortcomings of traditional hyperparameter adjustment methods based on experience and subjectivity. In preliminary research on the simply supported beam test case, the efficient and accurate identification of parameter changes in small local elements was achieved. Furthermore, publicly available structural datasets were utilized to verify the robustness of the method, and a high identification accuracy rate of 99.85% was achieved. Compared with other methods described in the literature, this strategy shows significant advantages in terms of sensor occupancy rate, computational cost, and identification accuracy.

关键词： structural anomaly detection 1-D convolutional neural network bayesian optimization algorithm decision-level fusion vibration signals

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Performance degradation prediction method of PEM fuel cells using bidirectional long short-term memory neural network based on bayesian optimization

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ENERGY 2023年 285卷

作者： Chen, Dongfang Wu, Wenlong Chang, Kuanyu Li, Yuehua Pei, Pucheng Xu, Xiaoming Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

Proton exchange membrane (PEM) fuel cell is the core equipment that can directly convert hydrogen energy into electricity. In the process of long-term operation, due to the aging of membrane electrode assembly and other components, the fuel cell performance gradually deteriorates. The voltage prediction of fuel cells is very important for performance and lifetime optimization. Long short-term memory neural network is one of the widely used prediction methods. Based on the prediction method of bidirectional long short-term memory neural network, the hyperparameters of the neural network model by bayesian optimization algorithm is optimized to improve the accuracy of fuel cell performance degradation prediction. When the sampling time interval is 25 min and the training set is 45 %, the root mean square error and the average absolute percentage error of the prediction results is reduced to 6.3 mV and 0.1245 %, respectively. Moreover, by analyzing the influence of different sampling time intervals and training set proportion on the prediction results, a data set that takes into accounts both efficiency and accuracy is obtained. The proposed method based on bayesian optimization can achieve more accurate performance degradation prediction.

关键词： PEM fuel cell Performance degradation prediction Long short -term memory neural network bayesian optimization algorithm Sampling time interval

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