为了用水生生物生态状况评价乌江的水质情况,在乌江干、支流布置了14个采样断面,以Novak等的百分比模式相似性指数(Percent Model Affinity,PMA)对水电建设期间的乌江下游水质状态进行了评价,并与种类丰富度、EPT%和物种多样性指数相关...为了用水生生物生态状况评价乌江的水质情况,在乌江干、支流布置了14个采样断面,以Novak等的百分比模式相似性指数(Percent Model Affinity,PMA)对水电建设期间的乌江下游水质状态进行了评价,并与种类丰富度、EPT%和物种多样性指数相关性分析结果进行了比较。结果表明,支流除郁江水质一般(Moderate)外,其余断面水质处于良好(Good)状态,干流除彭水和洪渡较好外,其余断面水质一般(Moderate)。总体而言,支流水质要好于干流,导致乌江水质恶化的根源主要并不是有机污染,其原因可能与水电工程的建设活动及频繁航运有关。分析表明,百分比模式相似性指数与种类丰富度、EPT%和物种多样性指数相关性均达到显著水平。展开更多
A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,wher...A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.展开更多
文摘为了用水生生物生态状况评价乌江的水质情况,在乌江干、支流布置了14个采样断面,以Novak等的百分比模式相似性指数(Percent Model Affinity,PMA)对水电建设期间的乌江下游水质状态进行了评价,并与种类丰富度、EPT%和物种多样性指数相关性分析结果进行了比较。结果表明,支流除郁江水质一般(Moderate)外,其余断面水质处于良好(Good)状态,干流除彭水和洪渡较好外,其余断面水质一般(Moderate)。总体而言,支流水质要好于干流,导致乌江水质恶化的根源主要并不是有机污染,其原因可能与水电工程的建设活动及频繁航运有关。分析表明,百分比模式相似性指数与种类丰富度、EPT%和物种多样性指数相关性均达到显著水平。
基金Projects(61273163,61325015,61304121)supported by the National Natural Science Foundation of China
文摘A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.
