Enhanced asphalt dynamic modulus prediction: A detailed analysis of artificial hummingbird algorithm-optimised boosted trees

This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt concrete.Using a substantial dataset from NCHRP Report-547,the model was trained and rigorously tested.Performance metrics,specifically RMSE,MAE,and R2,were employed to assess the model's predictive accuracy,robustness,and generalisability.When benchmarked against well-established models like support vector machines(SVM)and gaussian process regression(GPR),the AHA-boosted model demonstrated enhanced performance.It achieved R2 values of 0.997 in training and 0.974 in testing,using the traditional Witczak NCHRP 1-40D model inputs.Incorporating features such as test temperature,frequency,and asphalt content led to a 1.23%increase in the test R2,signifying an improvement in the model's accuracy.The study also explored feature importance and sensitivity through SHAP and permutation importance plots,highlighting binder complex modulus|G*|as a key predictor.Although the AHA-boosted model shows promise,a slight decrease in R2 from training to testing indicates a need for further validation.Overall,this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete,making it a valuable asset for pavement engineering.

Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.

A Novel Gradient Boosted Energy Optimization Model(GBEOM)for MANET

Mobile Ad Hoc Network(MANET)is an infrastructure-less network that is comprised of a set of nodes that move randomly.In MANET,the overall performance is improved through multipath multicast routing to achieve the quality of service(quality of service).In this,different nodes are involved in the information data collection and transmission to the destination nodes in the network.The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes.The route identification and routing are established based on the data broadcast by the network nodes.In transmitting the data packet,evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network.Furthermore,energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate.In this paper,a Gradient-Based Energy Optimization model(GBEOM)for the route in MANET is proposed to achieve an improved data delivery rate.Initially,the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing(WMC-SMLB)technique is utilized for obtaining energy efficiency and load balancing routing.The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET.Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation.Next,the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency.The proposed GBEOM exhibits∼4%improved performance in MANET routing.

基于Boosted Cascade算法的人脸检测和跟踪系统

将基于BoostedCascade的人脸检测算法运用到视频图像当中,并结合图像序列中的运动信息,提出并实现了一种实时的人脸检测跟踪系统。首先根据图像的运动信息提取出可能存在人脸的候选区域,然后在候选区域中用BoostedCascade算法进行检测。实验结果表明该系统能够实时地对于人脸进行检测跟踪,可以被应用在智能视频监控方面。

Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making

Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications.

Linear and Nonlinear Trading Models with Gradient Boosted Random Forests and Application to Singapore Stock Market

This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow three modes of trades, namely, buy, sell or stand by, and the stand-by case is important as it caters to the market conditions where a model does not produce a strong signal of buy or sell. Linear trading models are firstly developed with the scoring technique which weights higher on successful indicators, as well as with the Least Squares technique which tries to match the past perfect trades with its weights. The linear models are then made adaptive by using the forgetting factor to address market changes. Because stock markets could be highly nonlinear sometimes, the Random Forest is adopted as a nonlinear trading model, and improved with Gradient Boosting to form a new technique—Gradient Boosted Random Forest. All the models are trained and evaluated on nine stocks and one index, and statistical tests such as randomness, linear and nonlinear correlations are conducted on the data to check the statistical significance of the inputs and their relation with the output before a model is trained. Our empirical results show that the proposed trading methods are able to generate excess returns compared with the buy-and-hold strategy.

基于Bagged CART和Boosted CART的高光谱影像分类技术研究

本文针对高光谱影像数据光谱分辨率高,数据量大的特点,采用以CART决策树为弱分类器的Bagging和Boosting集成学习算法对该影像进行分类,通过实验分析比较,体现出了Bagged CART和Boosted CART算法用于分类时的有效性和准确性。

Improving Lives Through Skills Transfer Zimbabwe's agricultural sector boosted by China-assisted training

Dry streams filled with sand,and sun-baked soil and drought resistant mopane trees characterize vast expanse of land in the rural Chiredzi District,more than 600 km southeast of Zimbabwe’s capital Harare.Topless and barefooted children make a beeline waving at modern non-governmental organization vehicles which frequent the district.

Predicting plant disease epidemics using boosted regression trees

Plant epidemics are often associated with weather-related variables.It is difficult to identify weather-related predictors for models predicting plant epidemics.In the article by Shah et al.,to predict Fusarium head blight(FHB)epidemics of wheat,they explored a functional approach using scalar-on-function regression to model a binary outcome(FHB epidemic or non-epidemic)with respect to weather time series spanning 140 days relative to anthesis.The scalar-on-function models fit the data better than previously described logistic regression models.In this work,given the same dataset and models,we attempt to reproduce the article by Shah et al.using a different approach,boosted regression trees.After fitting,the classification accuracy and model statistics are surprisingly good.

Analogous comparison unravels heightened antiviral defense and boosted viral infection upon immunosuppression in bat organoids

Horseshoe bats host numerous SARS-related coronaviruses without overt disease signs.Bat intestinal organoids,a unique model of bat intestinal epithelium,allow direct comparison with human intestinal organoids.We sought to unravel the cellular mechanism(s)underlying bat tolerance of coronaviruses by comparing the innate immunity in bat and human organoids.We optimized the culture medium,which enabled a consecutive passage of bat intestinal organoids for over one year.Basal expression levels of IFNs and IFN-stimulated genes were higher in bat organoids than in their human counterparts.Notably,bat organoids mounted a more rapid,robust and prolonged antiviral defense than human organoids upon Poly(I:C)stimulation.TLR3 and RLR might be the conserved pathways mediating antiviral response in bat and human intestinal organoids.The susceptibility of bat organoids to a bat coronavirus CoV-HKU4,but resistance to EV-71,an enterovirus of exclusive human origin,indicated that bat organoids adequately recapitulated the authentic susceptibility of bats to certain viruses.Importantly,TLR3/RLR inhibition in bat organoids significantly boosted viral growth in the early phase after SARS-CoV-2 or CoV-HKU4 infection.Collectively,the higher basal expression of antiviral genes,especially more rapid and robust induction of innate immune response,empowered bat cells to curtail virus propagation in the early phase of infection.

基于LMMHD波浪能发电机特性的高效实时输出功率控制系统

针对目前液态金属磁流体(LMMHD)波浪能发电机输出功率控制系统响应慢、精度差、变换效率低、低压下不能可靠换向的问题,在LMMHD波浪能发电机输出特性分析的基础上,提出一种通过实时调节LMMHD发电机等效负载来控制发电机输出功率的控制策略,该控制策略快速准确且多模块并联时不存在并联均流问题。通过对考虑寄生参数影响的主电路效率的理论分析,得到了所提输出功率控制策略下主电路效率的变化规律,进而提出了多模块并联的方案,提高了系统效率,降低了单模块的设计难度。并提出了一种滚动时域判断和双模块独立驱动的换向驱动方法,在实现低压下可靠换向的同时避免了电流从体二极管流通,降低了导通损耗。仿真和样机实验结果验证了所提策略的有效性。

基于迁移学习的轨道交通特殊OD客流预测研究

客流预测一直是轨道交通运营公司关注的重点,由于受到运输能力的限制等因素影响,部分OD的实际客流数据与真实需求有偏差,出现异常或者样本缺失,从而造成总体样本量偏小,直接采用这些样本进行预测会明显影响预测精度,但通过还原样本值增加样本量难度太大。根据上述特点选择基于实例的迁移学习,先确定源域的对象和范围,从源域中选择合适的样本补充到总体样本中,共同组成最终的训练样本数据集,完成迁移学习。同时选择改进的Boost算法,通过误差调整样本权重,不断迭代,得到最终的预测模型。结果表明:基于实例的迁移学习结合改进Boost算法的预测精度要好于传统集成学习、ARIMA模型、多元回归模型,为轨道交通运营公司对特定OD的客流预测提供新的有益尝试。

基于新型CD单元的两相交错并联高增益Boost变换器

为减少基于电容-二极管(CD)升压单元的两相交错并联高增益Boost变换器的CD单元数量,提升变换器电压增益,提出一种最后两级CD单元电容并联充电、串联供电的新型两相交错Boost变换器拓扑结构,进一步发挥CD单元的升压能力。分析新型3CD、4CD两相交错并联Boost变换器的拓扑演化过程,提出新型NCD两相交错并联Boost变换器的拓扑演化规律。以新型4CD两相交错并联Boost变换器为例,分析变换器工作原理,以及电感、电容寄生电阻对变换器电压增益的影响。最后在StarSim硬件在环实验平台搭建1 kW的新型4CD单元交错并联Boost变换器,验证该文所提拓扑的正确性。

一种基于分数阶微积分的CCM Boost变换器准在线无源参数的数字孪生辨识方法

由于具有高性价比、准确性和数字化等优点,数字孪生已成为电力电子变换器故障趋势判断和预知维护的先进技术。针对当前电力电子变换器所建立的数字孪生模型尚未考虑实际电感、电容的分数阶特性的问题,基于分数阶微积分构建电力电子电路的预估-校正数字孪生模型,应用基于粒子群优化(particle swarm optimization,PSO)算法的孪生参数辨识方法对不同分数阶阶次下的电感值(L)和电容值(C)进行辨识,并计算出等效串联电阻。通过与现有方法对比,该方法不仅提高了实际电感和实际电容的辨识精度,还能辨识出不同阶次下与不同C下的分数阶参数。最后,搭建不同L和C及分数阶阶次的连续导通模式Boost变换器物理样机,并考虑不同工况条件与不同辨识次数等因素来进行实验验证。实验结果验证了所提模型与方法的有效性。

一种应用于两相交错Boost的耦合电感的优化设计

两相交错Boost变换器具有纹波电流小的优势,但其采用的交错并联技术增加了电感数量,进而增加了装置的体积和重量,不利于其功率密度的提升。耦合电感通过将多个磁性元件集成到一个磁芯实现磁路的部分共享,从而减小了磁元件的数量和重量。因此,设计了一种反向耦合电感,并将其应用于两相交错Boost变换器,实现了装置功率密度的提升。首先,对反向耦合电感的工作原理和损耗来源进行分析;然后,在此基础上设计了一种改进的“EE”型磁芯,一方面有效提高了磁芯利用率,另一方面降低了电感的体积与重量;最后,通过有限元仿真对所提优化设计方案进行验证,同时搭建了功率等级为2 kW的两相交错Boost变换器实验平台。仿真和实验结果均验证了所提优化设计方案的有效性。

分数阶Boost变换器的混沌控制研究

基于电容电感均为分数阶的事实,对分数阶连续导通模式Boost变换器的非线性动力学特性进行分析,提出了基于优化参数共振微扰法的分数阶Boost变换器混沌控制策略。首先,采用预估-校正算法建立了峰值电流控制分数阶Boost变换器的预估-校正模型,通过分岔图详细分析了电路参数对变换器非线性动力学特性的影响。然后,采用优化参数共振微扰法对变换器进行混沌控制,推导了系统的稳定判据,计算了扰动信号的最优幅值与相位。最后,在MATLAB/Simulink中进行仿真实验。研究表明,选择合理的扰动信号,能够有效抑制变换器的混沌现象,使变换器由混沌回归稳定状态。与参数共振微扰法相比,优化后的控制策略提高了系统的鲁棒性。仿真结果验证了所提策略的有效性。

Machine learning prediction of methane,ethane,and propane solubility in pure water and electrolyte solutions:Implications for stray gas migration modeling

Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers.The stray gas can dissolve in groundwater leading to chemical and biological reactions,which could negatively affect groundwater quality and contribute to atmospheric emissions.The knowledge oflight hydrocarbon solubility in the aqueous environment is essential for the numerical modelling offlow and transport in the subsurface.Herein,we compiled a database containing 2129experimental data of methane,ethane,and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure.Two machine learning algorithms,namely regression tree(RT)and boosted regression tree(BRT)tuned with a Bayesian optimization algorithm(BO)were employed to determine the solubility of gases.The predictions were compared with the experimental data as well as four well-established thermodynamic models.Our analysis shows that the BRT-BO is sufficiently accurate,and the predicted values agree well with those obtained from the thermodynamic models.The coefficient of determination(R2)between experimental and predicted values is 0.99 and the mean squared error(MSE)is 9.97×10^(-8).The leverage statistical approach further confirmed the validity of the model developed.

众数回归提升树模型构建及应用

众数回归模型估计的是在给定解释变量条件下响应变量的条件众数,而不是一般意义上的条件均值,因此可以揭示一般回归方法遗漏的重要结构。文章基于众数回归模型和提升回归树模型,提出了一个新的非参数众数回归模型:众数回归提升树(MRBT)模型。该模型一方面可以解决含有多元解释变量的非参数众数回归问题,另一方面采用Boosting技术解决了众数回归树模型预测性能差的问题。数值模拟和应用研究的结果表明:在任何分布中,MRBT模型显著优于线性众数回归和众数回归树模型;在数据呈对称分布时,MRBT模型与中位数回归提升树和均值回归提升树模型的表现相同;但在数据呈非对称分布或具有异常值时,MRBT模型显著优于中位数回归提升树和均值回归提升树模型。

基于能量模型的临界导通模式Boost变换器软开关方法

在变换器高频化发展的趋势中,功率器件的软开关实现对变换效率的影响更加突出。无辅助电路的临界导通工作模式下,Boost变换器主开关管在特定增益下无法实现软开关。为此,该文提出一种能量模型及相应的软开关实现方法。首先,建立死区前后储能元件能量变化的数学模型。然后,结合死区起止时刻的能量平衡方程,研究软开关无法实现的电路机理。在此基础上,考虑开关管输出电容非线性特征,提出软开关实现方法,避免了复杂谐振过程的时域精确建模,提高软开关实现的准确性。最后,搭建500W实验样机进行实验,结果表明,相较于对谐振过程建模的传统时域模型,所提方法将实际开通电压降低47%,使峰值变换效率提升0.4%,进而验证了其有效性。