为解决松散煤体热物性参数的测试周期长与实验误差大等问题,构建测试装置实验平台,结合交叉热线法和平行热线法,对松散煤体热物性参数进行准确测量与计算,对1~2 mm,0.5~0.6 mm和0.2~0.3 mm 3种不同粒径煤样在不同水分含量下的热物性参...为解决松散煤体热物性参数的测试周期长与实验误差大等问题,构建测试装置实验平台,结合交叉热线法和平行热线法,对松散煤体热物性参数进行准确测量与计算,对1~2 mm,0.5~0.6 mm和0.2~0.3 mm 3种不同粒径煤样在不同水分含量下的热物性参数的变化规律进行研究,利用Fluent数值模拟软件对松散煤体温度场进行模拟研究,并对比模拟结果与实验结果的差异性。结果表明:在所测粒径范围里,同等水分含量下的松散煤体粒径越大,导热系数越小,热扩散率与比热容越大;松散煤体的导热系数随水分含量的增加而增加,但增加趋势渐缓;松散煤体的热扩散率随着水分含量的增加而增大,当水分含量达到11.73~13.88%后热扩散率开始逐渐下降,而比热容随着水分含量的增加逐渐增大。展开更多
Customer is a determinant factor that decides whether a security company will be alive. As a result, the competition for customers is more and more intense between security companies. In order to avoid profit decrease...Customer is a determinant factor that decides whether a security company will be alive. As a result, the competition for customers is more and more intense between security companies. In order to avoid profit decrease caused by churn, security companies must find those customers who have the loss risk and make measures to maintain loyal customers. Now it is the question that how to find and analyze those customers. In this paper, a two-step classification method about churn Analysis is proposed and the problem of churn in security is analyzed.展开更多
We address the problem of metric learning for multi-view data. Many metric learning algorithms have been proposed, most of them focus just on single view circumstances, and only a few deal with multi-view data. In thi...We address the problem of metric learning for multi-view data. Many metric learning algorithms have been proposed, most of them focus just on single view circumstances, and only a few deal with multi-view data. In this paper, motivated by the co-training framework, we propose an algorithm-independent framework, named co-metric, to learn Mahalanobis metrics in multi-view settings. In its implementation, an off-the-shelf single-view metric learning algorithm is used to learn metrics in individual views of a few labeled examples. Then the most confidently-labeled examples chosen from the unlabeled set are used to guide the metric learning in the next loop. This procedure is repeated until some stop criteria are met. The framework can accommodate most existing metric learning algorithms whether types-of- side-information or example-labels are used. In addition it can naturally deal with semi-supervised circumstances under more than two views. Our comparative experiments demon- strate its competiveness and effectiveness.展开更多
文摘为解决松散煤体热物性参数的测试周期长与实验误差大等问题,构建测试装置实验平台,结合交叉热线法和平行热线法,对松散煤体热物性参数进行准确测量与计算,对1~2 mm,0.5~0.6 mm和0.2~0.3 mm 3种不同粒径煤样在不同水分含量下的热物性参数的变化规律进行研究,利用Fluent数值模拟软件对松散煤体温度场进行模拟研究,并对比模拟结果与实验结果的差异性。结果表明:在所测粒径范围里,同等水分含量下的松散煤体粒径越大,导热系数越小,热扩散率与比热容越大;松散煤体的导热系数随水分含量的增加而增加,但增加趋势渐缓;松散煤体的热扩散率随着水分含量的增加而增大,当水分含量达到11.73~13.88%后热扩散率开始逐渐下降,而比热容随着水分含量的增加逐渐增大。
文摘Customer is a determinant factor that decides whether a security company will be alive. As a result, the competition for customers is more and more intense between security companies. In order to avoid profit decrease caused by churn, security companies must find those customers who have the loss risk and make measures to maintain loyal customers. Now it is the question that how to find and analyze those customers. In this paper, a two-step classification method about churn Analysis is proposed and the problem of churn in security is analyzed.
基金We would like to thank the National Natural Science Foundations of China （NSFC） （Grant Nos. 61035003 and 61170151） for support.
文摘We address the problem of metric learning for multi-view data. Many metric learning algorithms have been proposed, most of them focus just on single view circumstances, and only a few deal with multi-view data. In this paper, motivated by the co-training framework, we propose an algorithm-independent framework, named co-metric, to learn Mahalanobis metrics in multi-view settings. In its implementation, an off-the-shelf single-view metric learning algorithm is used to learn metrics in individual views of a few labeled examples. Then the most confidently-labeled examples chosen from the unlabeled set are used to guide the metric learning in the next loop. This procedure is repeated until some stop criteria are met. The framework can accommodate most existing metric learning algorithms whether types-of- side-information or example-labels are used. In addition it can naturally deal with semi-supervised circumstances under more than two views. Our comparative experiments demon- strate its competiveness and effectiveness.
