针对黄土高原坡地土壤旋耕部件互作机理研究以及坡地专用旋耕机具设计缺乏准确可靠离散元仿真参数的问题,以典型坡地粘壤土(含水率13.4%±1%)为研究对象,选取EDEM中Hertz-Mindlin with JKR Cohesion接触模型,对相关仿真参数进行标...针对黄土高原坡地土壤旋耕部件互作机理研究以及坡地专用旋耕机具设计缺乏准确可靠离散元仿真参数的问题,以典型坡地粘壤土(含水率13.4%±1%)为研究对象,选取EDEM中Hertz-Mindlin with JKR Cohesion接触模型,对相关仿真参数进行标定。首先,对土壤颗粒间接触参数进行了标定,以土壤颗粒的仿真堆积角为响应值,基于Design-Expert软件中Box-Behnken的方法,确定了土壤堆积角的回归模型;通过模型寻优得到了恢复系数、静摩擦因数、滚动摩擦因数及表面能参数分别为0.15、0.33、0.05和9.04 J/m^(2),此时土壤堆积角仿真值为41.59°,与实测值相对误差为3.8%。其次,对土壤与旋耕刀材料65Mn钢的接触参数进行了标定:通过静摩擦、斜板及碰撞等试验得到了土壤与65Mn钢之间静摩擦因数、滚动摩擦因数和恢复系数的范围,进一步以土壤在65Mn钢板上的静滑动摩擦角为响应值,基于Box-Behnken的方法得到了土壤静滑动摩擦角的回归模型;对该模型寻优得到了土壤颗粒与65Mn钢间的静摩擦因数、滚动摩擦因数及恢复系数分别为0.50、0.06和0.18,此时静滑动摩擦角仿真值为24.0°,与实测值相对误差为1.7%。最后,通过坡地旋耕试验验证模型参数的有效性:土壤颗粒水平、侧向位移实测值和仿真值最大相对误差分别为4.3%和5.1%。结果表明标定的参数准确可靠。展开更多
A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Th...A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.展开更多
β-SiAION hollow spheres were prepared by carbothermal reduction method, using coal fly ash （ 〈 15 μm, 43 - 77 μm, and 〉 100 μm） and active carbon in Some proportion （20% less than theoretical addition, theore...β-SiAION hollow spheres were prepared by carbothermal reduction method, using coal fly ash （ 〈 15 μm, 43 - 77 μm, and 〉 100 μm） and active carbon in Some proportion （20% less than theoretical addition, theoretical addition and 10% excess theoretical addition） as starting materials, putting into alumina crucible in high temperature nitriding furnace after well mixed, and holding at 1 300 ℃, 1 350 ℃, 1 400 ℃, 1 450 ℃, and 1 500 ℃ for 6 h. Effects of temperature, particle size of the microsphere . and addition of active carbon on the phase composition and microstructure of the nitridized products were studied by means of XRD and SEM. The results show that the nitridation reaction starts at 1 300 ℃ ; excess active carbon is necessary to form β-SiAION hollow spheres, and particle size is the important parameter to form the hollow spheres nitridized products β-SiAION; at 1 500 ℃, when the active carbon is 10% in excess, the β-SiAlON hollow spheres, which were prepared using coal fly ash with particle size 〉 100 μm, are featured with rough surface, high hollowness and low density.展开更多
文摘针对黄土高原坡地土壤旋耕部件互作机理研究以及坡地专用旋耕机具设计缺乏准确可靠离散元仿真参数的问题,以典型坡地粘壤土(含水率13.4%±1%)为研究对象,选取EDEM中Hertz-Mindlin with JKR Cohesion接触模型,对相关仿真参数进行标定。首先,对土壤颗粒间接触参数进行了标定,以土壤颗粒的仿真堆积角为响应值,基于Design-Expert软件中Box-Behnken的方法,确定了土壤堆积角的回归模型;通过模型寻优得到了恢复系数、静摩擦因数、滚动摩擦因数及表面能参数分别为0.15、0.33、0.05和9.04 J/m^(2),此时土壤堆积角仿真值为41.59°,与实测值相对误差为3.8%。其次,对土壤与旋耕刀材料65Mn钢的接触参数进行了标定:通过静摩擦、斜板及碰撞等试验得到了土壤与65Mn钢之间静摩擦因数、滚动摩擦因数和恢复系数的范围,进一步以土壤在65Mn钢板上的静滑动摩擦角为响应值,基于Box-Behnken的方法得到了土壤静滑动摩擦角的回归模型;对该模型寻优得到了土壤颗粒与65Mn钢间的静摩擦因数、滚动摩擦因数及恢复系数分别为0.50、0.06和0.18,此时静滑动摩擦角仿真值为24.0°,与实测值相对误差为1.7%。最后,通过坡地旋耕试验验证模型参数的有效性:土壤颗粒水平、侧向位移实测值和仿真值最大相对误差分别为4.3%和5.1%。结果表明标定的参数准确可靠。
基金the National Natural Science Foundation of China (No.60472072)the Specialized Research Foundation for the Doctoral Program of Higher Educa-tion of China (No.20040699034).
文摘A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.
文摘β-SiAION hollow spheres were prepared by carbothermal reduction method, using coal fly ash （ 〈 15 μm, 43 - 77 μm, and 〉 100 μm） and active carbon in Some proportion （20% less than theoretical addition, theoretical addition and 10% excess theoretical addition） as starting materials, putting into alumina crucible in high temperature nitriding furnace after well mixed, and holding at 1 300 ℃, 1 350 ℃, 1 400 ℃, 1 450 ℃, and 1 500 ℃ for 6 h. Effects of temperature, particle size of the microsphere . and addition of active carbon on the phase composition and microstructure of the nitridized products were studied by means of XRD and SEM. The results show that the nitridation reaction starts at 1 300 ℃ ; excess active carbon is necessary to form β-SiAION hollow spheres, and particle size is the important parameter to form the hollow spheres nitridized products β-SiAION; at 1 500 ℃, when the active carbon is 10% in excess, the β-SiAlON hollow spheres, which were prepared using coal fly ash with particle size 〉 100 μm, are featured with rough surface, high hollowness and low density.
