低压配电网拓扑缺乏维护,准确度低,难以支撑各种高级应用的实现。为此,文章提出一种基于Levenshtein距离和时间切面的拓扑识别算法。首先分析配电网拓扑架构,提出使用电流事件特征进行拓扑识别研究。其次描述了电流事件的提取方法,使用...低压配电网拓扑缺乏维护,准确度低,难以支撑各种高级应用的实现。为此,文章提出一种基于Levenshtein距离和时间切面的拓扑识别算法。首先分析配电网拓扑架构,提出使用电流事件特征进行拓扑识别研究。其次描述了电流事件的提取方法,使用改进滑动窗的累积和(cumulative sum control chart,CUSUM)事件检测算法进行电流事件提取。接着描述了采用Levenshtein距离算法和时间切面方法对箱变电流序列和用户表箱采集设备电流序列进行比对,提出使用序列匹配度描述台区户变拓扑的相关程度。最后基于现场台区数据进行计算分析,验证了该方法的可行性和准确性。展开更多
G-protein coupled receptors （GPCRs） are a class of seven-helix transmembrane proteins that have been used in bioinformatics as the targets to facilitate drug discovery for human diseases. Although thousands of GPCR ...G-protein coupled receptors （GPCRs） are a class of seven-helix transmembrane proteins that have been used in bioinformatics as the targets to facilitate drug discovery for human diseases. Although thousands of GPCR sequences have been collected, the ligand specificity of many GPCRs is still unknown and only one crystal structure of the rhodopsin-like family has been solved. Therefore, identifying GPCR types only from sequence data has become an important research issue. In this study, a novel technique for identifying GPCR types based on the weighted Levenshtein distance between two receptor sequences and the nearest neighbor method （NNM） is introduced, which can deal with receptor sequences with different lengths directly. In our experiments for classifying four classes （acetylcholine, adrenoceptor, dopamine, and serotonin） of the rhodopsin-like family of GPCRs, the error rates from the leave-one-out procedure and the leave-half-out procedure were 0.62% and 1.24%, respectively. These results are prior to those of the covariant discriminant algorithm, the support vector machine method, and the NNM with Euclidean distance.展开更多
文摘低压配电网拓扑缺乏维护,准确度低,难以支撑各种高级应用的实现。为此,文章提出一种基于Levenshtein距离和时间切面的拓扑识别算法。首先分析配电网拓扑架构,提出使用电流事件特征进行拓扑识别研究。其次描述了电流事件的提取方法,使用改进滑动窗的累积和(cumulative sum control chart,CUSUM)事件检测算法进行电流事件提取。接着描述了采用Levenshtein距离算法和时间切面方法对箱变电流序列和用户表箱采集设备电流序列进行比对,提出使用序列匹配度描述台区户变拓扑的相关程度。最后基于现场台区数据进行计算分析,验证了该方法的可行性和准确性。
基金supported by the Natural Science Foundation of Jiangsu Province(No.BK2004142)partly by the National Natural Science Foundation of China(No.60275007).
文摘G-protein coupled receptors （GPCRs） are a class of seven-helix transmembrane proteins that have been used in bioinformatics as the targets to facilitate drug discovery for human diseases. Although thousands of GPCR sequences have been collected, the ligand specificity of many GPCRs is still unknown and only one crystal structure of the rhodopsin-like family has been solved. Therefore, identifying GPCR types only from sequence data has become an important research issue. In this study, a novel technique for identifying GPCR types based on the weighted Levenshtein distance between two receptor sequences and the nearest neighbor method （NNM） is introduced, which can deal with receptor sequences with different lengths directly. In our experiments for classifying four classes （acetylcholine, adrenoceptor, dopamine, and serotonin） of the rhodopsin-like family of GPCRs, the error rates from the leave-one-out procedure and the leave-half-out procedure were 0.62% and 1.24%, respectively. These results are prior to those of the covariant discriminant algorithm, the support vector machine method, and the NNM with Euclidean distance.
