Bagged and dispersed Pyrinuron (a self-formulated rodenticide) were tested for control of Clethrionomus rutilus and Clethrionomus rufocanus in larch plantation, Pingshan area, Heilongjiang Province from Oct. 1998 to A...Bagged and dispersed Pyrinuron (a self-formulated rodenticide) were tested for control of Clethrionomus rutilus and Clethrionomus rufocanus in larch plantation, Pingshan area, Heilongjiang Province from Oct. 1998 to Apr. 1999. The results showed that the bagged Pyrinuron has good result for control of the two rodent pests. The density of rodents was reduced by 90.5% after application of bagged Pyrinuron. This application method has characteristics of long residual period, bait not going mould and saving labour force.展开更多
通过合金相图热力学与扩散动力学计算,并将结果引入相场模型,采用试验和模拟相结合的方法研究了BAg30CuZnSn钎料焊缝的凝固行为。结果发现:B元素的加入使得焊缝枝晶发生了明显的柱状晶向等轴晶转变(columnar to equiaxed transition, C...通过合金相图热力学与扩散动力学计算,并将结果引入相场模型,采用试验和模拟相结合的方法研究了BAg30CuZnSn钎料焊缝的凝固行为。结果发现:B元素的加入使得焊缝枝晶发生了明显的柱状晶向等轴晶转变(columnar to equiaxed transition, CET)的现象;随着钎焊后冷却速率的增加,焊缝凝固过程中枝晶形核数量增加,枝晶尖端半径减小;当B含量较低时,枝晶尖端半径较大,形核数量较少;随着B含量的增加,枝晶尖端半径逐渐减小,形核数量增加;但当B质量分数增加至4%时,枝晶形貌基本不再发生变化。展开更多
Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detecti...Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.展开更多
风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成...风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成若干自举时间序列。对于每个时间序列,采用向量自回归(vector autoregression,VAR)模型进行预测。针对传统模型在风场数量较多时容易出现的过拟合问题,采用稀疏向量自回归模型,筛选最有效的回归系数,得到稀疏系数矩阵。每个时间序列训练的预测模型分别产生点预测结果,对于多重点预测结果,使用KDE方法产生概率密度的预测结果。在真实风电集群数据上,验证所提多场站概率预测方法的有效性,采用分位数得分评估概率预测精度。相关实验结果表明,该方法可以有效提高概率预测精度。展开更多
干旱半干旱地区急需高分辨率的土壤盐度图用于显示盐度空间分布的细微变化,指导盐渍化区域和潜在盐渍化区域制定土地资源管理政策和水资源管理政策,防止土壤进一步退化,保障农业经济可持续发展和粮食安全生产。基于PlanetScope影像,提...干旱半干旱地区急需高分辨率的土壤盐度图用于显示盐度空间分布的细微变化,指导盐渍化区域和潜在盐渍化区域制定土地资源管理政策和水资源管理政策,防止土壤进一步退化,保障农业经济可持续发展和粮食安全生产。基于PlanetScope影像,提取植被光谱指数和土壤盐度指数,共计21个变量,将其输入装袋回归(Bootstrap aggregating,Bagging)算法中,构建了土壤盐度预测模型Model-Ⅰ;使用最相关最小冗余(Max-relevance and min-redundancy,mRMR)方法筛选特征变量,将其输入Bagging中,构建了土壤盐度预测模型Model-Ⅱ,使用野外采样数据来辅助建模并进行验证。通过模型评价指标对Model-Ⅰ和Model-Ⅱ进行评估。结果表明:Model-Ⅱ的预测性能优于Model-Ⅰ(验证集决定系数为0.66,均方根误差为18.00 dS·m-1,四分位数的相对预测误差为3.21),mRMR有效降低了多维特征冗余问题。PlanetScope影像结合mRMR方法成功绘制了高分辨率土壤盐度图,提供了更详细的土壤盐度空间分布信息,研究结果对利用PlanetScope数据监测土壤盐渍化信息起推动作用。展开更多
This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine lear...This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.展开更多
文摘Bagged and dispersed Pyrinuron (a self-formulated rodenticide) were tested for control of Clethrionomus rutilus and Clethrionomus rufocanus in larch plantation, Pingshan area, Heilongjiang Province from Oct. 1998 to Apr. 1999. The results showed that the bagged Pyrinuron has good result for control of the two rodent pests. The density of rodents was reduced by 90.5% after application of bagged Pyrinuron. This application method has characteristics of long residual period, bait not going mould and saving labour force.
文摘通过合金相图热力学与扩散动力学计算,并将结果引入相场模型,采用试验和模拟相结合的方法研究了BAg30CuZnSn钎料焊缝的凝固行为。结果发现:B元素的加入使得焊缝枝晶发生了明显的柱状晶向等轴晶转变(columnar to equiaxed transition, CET)的现象;随着钎焊后冷却速率的增加,焊缝凝固过程中枝晶形核数量增加,枝晶尖端半径减小;当B含量较低时,枝晶尖端半径较大,形核数量较少;随着B含量的增加,枝晶尖端半径逐渐减小,形核数量增加;但当B质量分数增加至4%时,枝晶形貌基本不再发生变化。
文摘Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.
文摘风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成若干自举时间序列。对于每个时间序列,采用向量自回归(vector autoregression,VAR)模型进行预测。针对传统模型在风场数量较多时容易出现的过拟合问题,采用稀疏向量自回归模型,筛选最有效的回归系数,得到稀疏系数矩阵。每个时间序列训练的预测模型分别产生点预测结果,对于多重点预测结果,使用KDE方法产生概率密度的预测结果。在真实风电集群数据上,验证所提多场站概率预测方法的有效性,采用分位数得分评估概率预测精度。相关实验结果表明,该方法可以有效提高概率预测精度。
文摘干旱半干旱地区急需高分辨率的土壤盐度图用于显示盐度空间分布的细微变化,指导盐渍化区域和潜在盐渍化区域制定土地资源管理政策和水资源管理政策,防止土壤进一步退化,保障农业经济可持续发展和粮食安全生产。基于PlanetScope影像,提取植被光谱指数和土壤盐度指数,共计21个变量,将其输入装袋回归(Bootstrap aggregating,Bagging)算法中,构建了土壤盐度预测模型Model-Ⅰ;使用最相关最小冗余(Max-relevance and min-redundancy,mRMR)方法筛选特征变量,将其输入Bagging中,构建了土壤盐度预测模型Model-Ⅱ,使用野外采样数据来辅助建模并进行验证。通过模型评价指标对Model-Ⅰ和Model-Ⅱ进行评估。结果表明:Model-Ⅱ的预测性能优于Model-Ⅰ(验证集决定系数为0.66,均方根误差为18.00 dS·m-1,四分位数的相对预测误差为3.21),mRMR有效降低了多维特征冗余问题。PlanetScope影像结合mRMR方法成功绘制了高分辨率土壤盐度图,提供了更详细的土壤盐度空间分布信息,研究结果对利用PlanetScope数据监测土壤盐渍化信息起推动作用。
基金funded by Vietnam National Foundation for Science and Tech-nology Development(NAFOSTED)under Grant No.105.99-2019.309.
文摘This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.
