电力系统复杂应用软件的开发必能采用先进的软件工程学方法。统一建模语言 ( U nified Modeling L anguage,U ML )是近年来软件工程领域内取得的最重要成果之一。文章介绍了 UML 的产生背景及特点 ,然后详细讨论了 U ML在智能继电保护...电力系统复杂应用软件的开发必能采用先进的软件工程学方法。统一建模语言 ( U nified Modeling L anguage,U ML )是近年来软件工程领域内取得的最重要成果之一。文章介绍了 UML 的产生背景及特点 ,然后详细讨论了 U ML在智能继电保护整定计算及管理系统 ( ICAPE)开发中的应用。结果表明 ,统一建模语言 ( UML )具有标准性、系统性、可视化、自动化等优点 ,在电力系统复杂软件的开发中 ,将其合理地应用于软件开发的各个阶段 ,有助于提高软件开发效率及软件质量。因此 ,UML展开更多
The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in featu...The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in feature space and reverberation is only treated as interference. In this paper, reverberation is considered as a kind of signal with steady characteristic, and the clustering of reverberation in frequency discrete wavelet transform （FDWT） feature space is studied. In order to extract the identifying information of echo signals, feature compression and cluster analysis are adopted in this paper, and the criterion of separability between object echoes and reverberation is given. The experimental data processing results show that reverberation has steady pattern in FDWT feature space which differs from that of object echoes. It is proven that there is separability between reverberation and object echoes.展开更多
基金Supported by the National Natural Science Foundation of China, under Grant No.51279033.
文摘The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in feature space and reverberation is only treated as interference. In this paper, reverberation is considered as a kind of signal with steady characteristic, and the clustering of reverberation in frequency discrete wavelet transform （FDWT） feature space is studied. In order to extract the identifying information of echo signals, feature compression and cluster analysis are adopted in this paper, and the criterion of separability between object echoes and reverberation is given. The experimental data processing results show that reverberation has steady pattern in FDWT feature space which differs from that of object echoes. It is proven that there is separability between reverberation and object echoes.
