基于类内类间距离和AdaBoost-SCN的分类识别方法  

Classification and recognition method based on intra-class and inter-class distances and AdaBoost-SCN

作　　者：赵怀亮 杨润平 赵少华 苏润梅 陈林毓 尚秋峰[2] 姚国珍[2] ZHAO Huailiang;YANG Runping;ZHAO Shaohua;SU Runmei;CHEN Linyu;SHANG Qiufeng;YAO Guozhen(Inner Mongolia Electric Power(Group)Co.,Ltd.,Inner Mongolia Ultra High Voltage Power Supply Branch,Hohhot 010000,China;Department of Electronics and Communication Engineering,North China Electric Power University,Baoding Hebei 071003,China)

机构地区：[1]内蒙古电力(集团)有限责任公司内蒙古超高压供电分公司,呼和浩特010000 [2]华北电力大学电子与通信工程系,河北保定071003

出　　处：《光通信技术》2025年第2期29-33,共5页Optical Communication Technology

基　　金：内蒙古电力(集团)有限责任公司科技项目(2023-5-25)资助。

摘　　要：为提高输电线路覆冰监测的识别准确率和实时性,提出了一种基于类内类间距离与自适应增强随机配置网络(Ada Boost-SCN)的分类识别方法。首先,对相位敏感光时域反射仪相位信号进行类内距离和类间距离的联合评估,通过评分策略实现全特征向量的降维筛选,从而提取关键敏感特征;随后,采用Ada Boost-SCN算法进行覆冰等级分类,该算法以随机配置网络作为基分类器,通过迭代优化构建强分类模型。实验结果表明,该方法在测试集上的平均识别准确率达到94.7%,相比传统方法提高了2%~5%,计算用时从0.54 s降低至0.32 s。To improve the recognition accuracy and real-time performance of transmission line icing monitoring,this paper proposes a classification and recognition method based on intra-class and inter-class distances and adaptive boosting random configuration network(AdaBoost-SCN).First,the phase signals from phase-sensitive optical time-domain reflectometry are jointly evaluated using intra-class and inter-class distance metrics.A scoring strategy is then applied to reduce the dimensionality of the full feature vector,thereby extracting key sensitive features.Subsequently,the AdaBoost-SCN algorithm is employed for icing severity classification and recognition,where the random configuration network serves as the base classifier,and a strong classification model is constructed through iterative optimization.The experimental results demonstrate that the proposed method achieves an average recognition accuracy of 94.7%on the test set,outperforming traditional methods by 2%~5%,while reducing computation time from 0.54 s to 0.32 s.

关 键 词：相位敏感光时域反射仪 光纤传感 特征选择 

分 类 号：TN256[电子电信—物理电子学]