基本释义:

化能有机营养菌

网络释义

1)chemoorganotroph,化能有机营养菌

2)activation energy,活化能Ea

3)apparent activation energy,活化能

4)nitrification ability,硝化能力

5)activation energy,活化能

6)generalization performance,泛化能力

用法和例句

Being concerned on both the constant rate period and falling rate period(s) of dynamic process for the drying particulate and thin layer materials, a approach of warp apparent activation energy was put forward and used for predicting the dynamic characteristics of various stuffs dried in the industrial drying.

针对小质量物料干燥动态过程恒速率干燥阶段和降速率干燥阶段,提出了用偏差活化能的方法来避免寻找该两个干燥阶段临界湿分点的困难,并从物理化学的概念推导出了理论动态数学模型,建立了物料干燥动态特性实验台,以实验数据验证模拟结果,效果较好。

On the basis of DSC curves of B/KNO_3 igniter powder,the apparent activation energy E of thermal decomposition is calculated with Kissinger′s methods and the probable mechanism function is available from master plot method.

利用差示扫描量热法得到B/KNO3的热分解曲线,用K iss inger方程估算了B/KNO3的表观活化能,用标准作图法得到最可几热分解机理函数,再用积分法求得不同升温速率下热分解的动力学参数活化能E和指前因子A。

The apparent activation energy by the initial rate method was 94kJ/mol, indicating that the process was controlled by a chemical reaction.

由初速率方法求出反应活化能为94kJ/mol,表明水热反萃过程为化学反应控制。

The nitrification ability of different layers in a subsurface constructed wetland treating sewage was measured .

研究了潜流式人工湿地内部不同填料层和沿水流方向硝化能力的变化 。

Study on spontaneous combustion period of coal based on activation energy index;

基于活化能指标的煤的自然发火期研究

Comparison of different methods for analyzing the activation energy of oil shale combustion;

油页岩燃烧反应活化能不同求解方法的比较

Study on the spontaneous combustion tendency of coal based on activation energy index;

基于活化能指标煤的自燃倾向性研究

This paper presents an ensemble of support vector regression(ESVR) which has better generalization performance than other intelligent approaches.

泛化能力是智能方法用于参数预测的最重要的问题之一,提出了支持向量回归集成方法。

The optimized BP artificial neural network model has advantageous properties such as more rapid calculation,high generalization performance and high accuracy.

经优化后的BP人工神经网络运算速度快、泛化能力强、预测精度高。

This paper introduces the concepts and methods of the support vector machine(SVM),and points out that merging the invariance into the support vector machine by using VSV technique can improve the generalization performance of the SVM.

介绍了支持向量机(Support Vector Machine,SVM)的概念和方法,指出通过采用VSV(Virtual SV)方法将不变性常识(Invariance)融合于支持向量机,可提高模型的泛化能力。