This paper first illustrates S.D. Krashen's language acquisition theory, especially two of his five hypotheses as well as the guidance of these theories towards the teaching of business English reading for student...This paper first illustrates S.D. Krashen's language acquisition theory, especially two of his five hypotheses as well as the guidance of these theories towards the teaching of business English reading for students majored in business English.展开更多
The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, d...The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, drought entropy was used to determine the weights of the three critical indices. Conventional simulation results regarding the risk load of water security during drought periods were often regarded as precise. However, neither the simulation process nor the DRI gives any consideration to uncertainties in drought events. Therefore, the Dempster-Shafer （D-S） evidence theory and the evidential reasoning algorithm were introduced, and the DRI values were calculated with consideration of uncertainties of the three indices. The drought entropy and evidential reasoning algorithm were used in a case study of the Haihe River Basin to assess water security risks during drought periods. The results of the new DRI values in two scenarios were compared and analyzed. It is shown that the values of the DRI in the D-S evidence algorithm increase slightly from the original results of Zhang et al. （2005）, and the results of risk assessment of water security during drought periods are reasonable according to the situation in the study area. This study can serve as a reference for further practical application and planning in the Haihe River Basin, and other relevant or similar studies.展开更多
弹道中段目标为一个目标群,包括弹头、诱饵、碎片等,并且由于距离传感器较远,红外成像为点目标,可用信息较少,因此单一的红外传感器往往难以满足识别要求,需要融合多个传感器进行识别。针对红外多传感器的融合识别问题,本文提出了基于...弹道中段目标为一个目标群,包括弹头、诱饵、碎片等,并且由于距离传感器较远,红外成像为点目标,可用信息较少,因此单一的红外传感器往往难以满足识别要求,需要融合多个传感器进行识别。针对红外多传感器的融合识别问题,本文提出了基于增量支持向量机和D-S(increment support vector machine-Dempster-Shafer,ISVM-DS)证据理论的融合识别方法。首先,训练多个波段传感器红外特征的支持向量数据描述(support vector data description,SVDD)模型,生成壳向量并训练其ISVM模型;接着,采用ISVM模型的后验概率生成基本概率赋值(basic probability assignment,BPA);最后,利用D-S证据理论对多个证据的BPA进行融合,输出分类结果。实验结果表明,该方法能有效提高目标识别的准确性。展开更多
Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operatio...Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.展开更多
文摘This paper first illustrates S.D. Krashen's language acquisition theory, especially two of his five hypotheses as well as the guidance of these theories towards the teaching of business English reading for students majored in business English.
基金supported by the National Natural Science Foundation of China(Grants No.51190094,50909073,and 51179130)the Hubei Province Natural Science Foundation(Grant No.2010CDB08401)
文摘The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, drought entropy was used to determine the weights of the three critical indices. Conventional simulation results regarding the risk load of water security during drought periods were often regarded as precise. However, neither the simulation process nor the DRI gives any consideration to uncertainties in drought events. Therefore, the Dempster-Shafer （D-S） evidence theory and the evidential reasoning algorithm were introduced, and the DRI values were calculated with consideration of uncertainties of the three indices. The drought entropy and evidential reasoning algorithm were used in a case study of the Haihe River Basin to assess water security risks during drought periods. The results of the new DRI values in two scenarios were compared and analyzed. It is shown that the values of the DRI in the D-S evidence algorithm increase slightly from the original results of Zhang et al. （2005）, and the results of risk assessment of water security during drought periods are reasonable according to the situation in the study area. This study can serve as a reference for further practical application and planning in the Haihe River Basin, and other relevant or similar studies.
文摘弹道中段目标为一个目标群,包括弹头、诱饵、碎片等,并且由于距离传感器较远,红外成像为点目标,可用信息较少,因此单一的红外传感器往往难以满足识别要求,需要融合多个传感器进行识别。针对红外多传感器的融合识别问题,本文提出了基于增量支持向量机和D-S(increment support vector machine-Dempster-Shafer,ISVM-DS)证据理论的融合识别方法。首先,训练多个波段传感器红外特征的支持向量数据描述(support vector data description,SVDD)模型,生成壳向量并训练其ISVM模型;接着,采用ISVM模型的后验概率生成基本概率赋值(basic probability assignment,BPA);最后,利用D-S证据理论对多个证据的BPA进行融合,输出分类结果。实验结果表明,该方法能有效提高目标识别的准确性。
文摘Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.
