Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R2 values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

基于BayesianOpt-XGBoost的煤电机组碳排放因子预测

以贝叶斯参数优化的XGBoost算法为基础,基于机组特征和煤炭特性建立BayesianOpt-XGBoost预测模型,其发电、供热碳排放因子预测的相关系数R^(2)分别为0.91和0.87,绝对误差百分比为2.51%和2.91%.进一步,通过特征标准化方法减少对煤炭特性的依赖,模型预测R2分别为0.79和0.77,绝对误差百分比为3.94%和2.75%,精度仍可得到保障.基于该模型分析全国各省区煤电机组碳排放因子并与公布数据进行比较,证明了该模型的有效性.对机组预测结果的分析表明对现存的低容量机组进行改造、对新建造电机组采用大容量高参数可以减少碳排放强度.

Bottom hole pressure prediction based on hybrid neural networks and Bayesian optimization

Many scholars have focused on applying machine learning models in bottom hole pressure (BHP) prediction. However, the complex and uncertain conditions in deep wells make it difficult to capture spatial and temporal correlations of measurement while drilling (MWD) data with traditional intelligent models. In this work, we develop a novel hybrid neural network, which integrates the Convolution Neural Network (CNN) and the Gate Recurrent Unit (GRU) for predicting BHP fluctuations more accurately. The CNN structure is used to analyze spatial local dependency patterns and the GRU structure is used to discover depth variation trends of MWD data. To further improve the prediction accuracy, we explore two types of GRU-based structure: skip-GRU and attention-GRU, which can capture more long-term potential periodic correlation in drilling data. Then, the different model structures tuned by the Bayesian optimization (BO) algorithm are compared and analyzed. Results indicate that the hybrid models can extract spatial-temporal information of data effectively and predict more accurately than random forests, extreme gradient boosting, back propagation neural network, CNN and GRU. The CNN-attention-GRU model with BO algorithm shows great superiority in prediction accuracy and robustness due to the hybrid network structure and attention mechanism, having the lowest mean absolute percentage error of 0.025%. This study provides a reference for solving the problem of extracting spatial and temporal characteristics and guidance for managed pressure drilling in complex formations.

基于贝叶斯优化XGBoost的石灰窑气预测

石灰窑是碳酸钙产业的关键生产设备,窑气中的CO_(2)是生产碳酸钙的原料,CO_(2)浓度直接影响碳酸钙产量,然而石灰窑气浓度依靠产品产出后采样化验得到,存在严重的滞后性,无法作为石灰窑在线工艺参数调整的依据。因此提出一种基于贝叶斯优化的eXtreme Gradient Boosting石灰窑气浓度预测模型BO-XGBoost,根据历史数据预测1 h后的窑气浓度,为生产工艺参数的调整提供依据。该方法首先对石灰窑传感器数据集中的缺失值、异常值进行剔除、插补,然后统一窑气浓度检测历史数据的时间尺度,构成石灰窑气监测数据集,在此基础上提出针对石灰窑气的BO-XGBoost模型。模型经训练后,采用实际生产数据进行测试,并与Light Gradient Boosting Machine(Light-GBM)模型、Category Boosting(Catboost)模型预测结果进行比较,结果表明,所提模型可以实现高维数据集的超参数快速优化,且预测模型有较好的精度,均方根误差(RMSE)达到0.70,平均绝对百分比误差(MAPE)达到2.03%。

基于BO-BiGRU-Attention短期电力负荷预测

电力系统的可靠供应对于工业、商业和居民的生活至关重要。为了满足电力需求并维持电力系统的稳定运行,提高短期电力负荷预测的准确性和可靠性尤为关键;针对负荷数据存在复杂的非线性特性,该文提出一种基于贝叶斯优化算法的双向门控循环单元和注意力机制(BO-BiGRU-Attention)的混合预测模型对短期电力负荷进行精准预测。首先,使用Min-Max Normalization方法对负荷数据进行归一化处理。其次,利用BiGRU网络捕获序列中的长期依赖关系和上下文信息,结合注意力机制,通过在输入序列的不同部分给予不同的权重,从而突出关键特征。最后,针对BiGRU-Attention模型的超参数难以选取最优解的问题,引入贝叶斯优化算法对BiGRU-Attention模型的超参数进行寻优,完成短期电力负荷的预测。采用印度北部某地区的电力负荷数据进行预测分析,仿真结果表明,BO-BiGRU-Attention网络表现优于其他模型,各误差评价指标最小,其中MAE、RMSE和MAPE分别为56.67,73.49和1.16%,预测精度达到了99.47%。

基于Bo-BiLSTM网络的IGBT老化失效预测方法

针对绝缘栅双极型晶体管(IGBT)受热应力冲击后对其进行老化失效预测精度不高的情况,提出一种基于贝叶斯优化(Bo)-双向长短期记忆(BiLSTM)网络的IGBT老化失效预测方法。首先分析IGBT模块老化失效原理,然后基于NASA老化实验数据集建立失效特征数据库,最后利用Matlab软件构造Bo-BiLSTM网络预测失效特征参数数据。选取常用回归预测性能评估指标将长短期记忆(LSTM)网络模型、BiLSTM网络模型与Bo-BiLSTM网络模型的预测结果进行对比分析。结果表明,Bo-BiLSTM网络的模型拟合精度更高,基于Bo-BiLSTM网络的IGBT老化失效预测方法具有较好的预测效果,能够应用于IGBT的失效预测。

基于MIC特征提取与BO-CatBoost的航空发动机RUL预测

针对航空发动机传感器监测的退化参数提取困难,易受噪声干扰及发动机剩余使用寿命预测精度不足等问题,利用最大信息系数、贝叶斯优化算法和类别特征梯度提升算法,提出了一种新的发动机剩余使用寿命预测模型。首先,为有效解决特征提取不足的问题,对采集的传感器历史监测特征进行最大信息系数相关性计算,提取出对发动机寿命运行周期影响较大的关键退化特征。其次,为解决剩余使用寿命预测中的梯度偏差及预测偏移问题,使用基于贝叶斯优化的类别特征梯度提升方法对航空发动机进行剩余使用寿命预测。最后,在美国航空航天局提供的商用模块化航空推进系统仿真数据集上进行实验,结果表明所提预测方法的性能较好,验证了该方法的有效性。

Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random forest(RF)ensemble learning methods for capturing the relationships between the USS and various basic soil parameters.Based on the soil data sets from TC304 database,a general approach is developed to predict the USS of soft clays using the two machine learning methods above,where five feature variables including the preconsolidation stress(PS),vertical effective stress(VES),liquid limit(LL),plastic limit(PL)and natural water content(W)are adopted.To reduce the dependence on the rule of thumb and inefficient brute-force search,the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF.The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation(CV).It is shown that XGBoost-based and RF-based methods outperform these approaches.Besides,the XGBoostbased model provides feature importance ranks,which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.

Dendritic Cell Algorithm with Bayesian Optimization Hyperband for Signal Fusion

The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA.The loss function of DCA is ambiguous due to its complexity.To reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter combination.However,these studies are either time-consuming or need to be revised in the case of non-convex functions.To overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function.This study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion process.The BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration.The experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.

一种基于改进的XGBoosting算法对婴幼儿奶粉中的脂肪含量的预测模型

婴儿奶粉成分配比中,脂肪有着重要地位。脂肪不仅是婴儿生长发育中的重要成分,同时也为婴儿的生长提供必需的能量,对于婴儿脑发育及神经髓鞘的形成具有重要意义。化学的婴儿奶粉脂肪含量检测如乙醚提取法,方法检测灵敏,但存在破坏样本和检测周期较长的缺点,因此寻求一种为婴儿奶粉成分的无损检测方法,高光谱成像技术提供了一种可能的途径。以内蒙古地区不同阶段的婴儿奶粉为研究对象,采用多元散射校正(MSC)、标准正态变换(SNV)、平滑滤波算法(Savitzky-Golay)、鲁斯特算法(Roust)等对高光谱数据进行预处理,再利用竞争性自适应重加权算法(CARS)算法从125个特征波长中筛除光谱数据中冗余的波长保留有效波长66个。对极值梯度提升算法(XGBoosting)算法进行了贝叶斯优化(BO),最终构建了基于BO-XGBoosting对婴儿奶粉脂肪含量的预测模型。结果显示,该模型预测效果优于传统的偏最小二乘回归(PLSR)和支持向量回归(SVR)模型,且优于集成算法中Bagging、GrdientBoosting算法。贝叶斯优化极值梯度提升算法BO-XGBoosting模型在测试集实验,得到的决定系数(R^(2))和均方根误差(RMSEP)分别为0.9537和0.5773,比XGBoosting算法的R^(2)和RMSEP分别提高2.91%和降低19.2%。该研究为奶粉中脂肪含量的预测提供了基于BO-XGboosting集成算法的快速无损检测的算法支持和理论依据。

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be diabetic.There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’lives.Due to its rapid development,deep learning(DL)was used to predict numerous diseases.However,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization.Therefore,the selection of hyper-parameters is critical in improving classification performance.This study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters optimization.Two issues have been investigated and solved during the experiment to enhance the results.The first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)technique.The second issue is the model’s poor performance,which has been solved using the Bayesian optimization algorithm.The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.

BN-GEPSO:Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

At present Bayesian Networks(BN)are being used widely for demonstrating uncertain knowledge in many disciplines,including biology,computer science,risk analysis,service quality analysis,and business.But they suffer from the problem that when the nodes and edges increase,the structure learning difficulty increases and algorithms become inefficient.To solve this problem,heuristic optimization algorithms are used,which tend to find a near-optimal answer rather than an exact one,with particle swarm optimization(PSO)being one of them.PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds(how they search for food).PSO is employed widely because it is easier to code,converges quickly,and can be parallelized easily.We use a recently proposed version of PSO called generalized particle swarm optimization(GEPSO)to learn bayesian network structure.We construct an initial directed acyclic graph(DAG)by using the max-min parent’s children(MMPC)algorithm and cross relative average entropy.ThisDAGis used to create a population for theGEPSO optimization procedure.Moreover,we propose a velocity update procedure to increase the efficiency of the algorithmic search process.Results of the experiments show that as the complexity of the dataset increases,our algorithm Bayesian network generalized particle swarm optimization(BN-GEPSO)outperforms the PSO algorithm in terms of the Bayesian information criterion(BIC)score.

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Breast cancer seriously affects many women.If breast cancer is detected at an early stage,it may be cured.This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage.It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models.Support Vector Machine,K-Nearest Neighbor,Naive Bayes,Ensemble Learning and Decision Tree approaches were used as machine learning algorithms.All experiments were tested on two different datasets,which are Wisconsin Breast Cancer Dataset(WBCD)and Mammographic Breast Cancer Dataset(MBCD).Experiments were implemented to obtain the best classification process.Relief,Least Absolute Shrinkage and Selection Operator(LASSO)and Sequential Forward Selection were used to determine the most relevant features,respectively.The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values.Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms.Among the various experiments,LASSO-BO-SVM showed the highest accuracy,precision,recall and F1-score for two datasets(97.95%,98.28%,98.28%,98.28%for MBCD and 98.95%,97.17%,100%,98.56%for MBCD),yielding outperforming results compared to recent studies.

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Sign language recognition can be treated as one of the efficient solu-tions for disabled people to communicate with others.It helps them to convey the required data by the use of sign language with no issues.The latest develop-ments in computer vision and image processing techniques can be accurately uti-lized for the sign recognition process by disabled people.American Sign Language(ASL)detection was challenging because of the enhancing intraclass similarity and higher complexity.This article develops a new Bayesian Optimiza-tion with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication(BODL-HGRSLC)for Disabled People.The BODL-HGRSLC technique aims to recognize the hand gestures for disabled people’s communica-tion.The presented BODL-HGRSLC technique integrates the concepts of compu-ter vision(CV)and DL models.In the presented BODL-HGRSLC technique,a deep convolutional neural network-based residual network(ResNet)model is applied for feature extraction.Besides,the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning process.At last,a bidir-ectional gated recurrent unit(BiGRU)model is exploited for the HGR procedure.A wide range of experiments was conducted to demonstrate the enhanced perfor-mance of the presented BODL-HGRSLC model.The comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

基于BO-XGBoost优化方法的砂岩厚度预测方法研究与应用

河道砂体预测在油气勘探中具有十分重要的作用,但在实际勘探开发中,复杂河道砂体地层埋深及河道本身结构的复杂性会使其井震关系较差,进而导致砂岩预测精度较低。针对该问题,笔者利用地震多属性信息,发挥密井网优势,利用BO-XGBoost砂岩厚度预测方法,在验证集占比为25%时取得最佳的预测效果,且预测效果要好于常规SVM和XGBoost方法。研究方法对同类复杂储层的砂岩厚度预测,有借鉴意义。

基于贝叶斯优化XGBoost的协作频谱感知算法

为了提高无线信道环境的频谱感知性能,提出一种基于贝叶斯优化XGBoost的协作频谱感知算法。在一个主用户(PU)和三个次用户(SU)的协作频谱感知场景下,提取信号的归一化能量特征,采用贝叶斯优化算法同时优化XGBoost模型的多个超参数,最后利用优化XGBoost算法实现待检测信号的分类。仿真结果表明,与传统频谱感知算法和KNN、GNB、SVM、MLP等机器学习算法相比,该算法在Rayl和AWGN信道环境检测准确率分别为88.4%和90.25%,可以有效提高不同信道环境下的协作频谱感知性能。

基于贝叶斯优化XGBoost的建筑施工事故类型预测

为探究建筑工程施工过程中不同风险特征与施工事故类型之间的关联性,提出1种融合特征选择算法和机器学习算法的建筑事故类型预测模型。基于619项国内建筑事故报告建立建筑施工风险特征体系,通过条件互信息最大化(CMIM)-Boruta方法筛选出26个关键风险特征,将其作为贝叶斯优化极限梯度提升(XGBoost)预测模型的输入变量并在测试集上评估该模型的预测精度。研究结果表明:XGBoost模型的预测性能优于其他机器学习模型;CMIM-Boruta方法和贝叶斯优化方法能够有效提升机器学习模型的预测性能;通过2个实际事故案例验证得到该模型具有一定实用性。研究结果对相关企业安全管理人员更准确地识别施工现场潜在危险、采取更具针对性的预防措施具有一定参考意义。

基于OOB-BO-LightGBM的风电机组故障诊断方法

随着风电机组大规模的并网运行,风电机组的故障诊断逐渐成为行业的研究热点。风电机组故障的及时发现与处理,能够保障机组的安全稳定运行,提升风电场经济效益。通过SCADA运行数据进行风电机组故障诊断是一种重要的故障诊断诊断方式,文章从故障诊断的特征提取及故障诊断模型构建角度出发,提出应用随机森林袋外估计(OOB)进行特征选择的特征提取方法和改进参数优化机器学习算法(BO-LightGBM)的风电机组故障诊断模型,提高基于数据驱动的风电机组故障预测的精度。通过风电场实际运行数据对所提故障诊断方法进行验证,结果证明该模型对于不同类型的故障均有92%以上的预测准确性,表明该模型对风电机组故障诊断具有较好的适用性。

基于参数优化VMD与XGBoost算法的玉米蛋白粉价格预测

玉米蛋白粉价格稳定对饲料工业可持续发展和国家粮食安全具有重要意义,但其价格序列具有非平稳、非线性特征,难以精确预测。试验旨在基于XGBoost算法,构建玉米蛋白粉价格预测模型。首先,利用鲸鱼算法(WOA)优化变模分解(VMD)的K值和惩罚参数,对原始价格序列进行自适应分解,降低数据噪声。其次,将Pearson特征筛选后的变量作为极限梯度提升树(XGBoost)模型的输入,进行训练和测试。最后,使用10折交叉验证和学习曲线检验模型性能,并结合SHAP模型分析关键影响因素的非线性效应。结果显示,上一期豆粕期货价格对本期玉米蛋白粉价格波动具有显著的正向影响。研究表明,贝叶斯算法(BO)优化的XGBoost模型具有较好的预测性能,优于基准模型。