极端冰灾天气下线路覆冰闪络跳闸是激发大电网脆弱性、导致大面积停电的重要因素。覆冰闪络跳闸等现象的风险评估是建立冰灾防御体系的基础。基于电网层面,进行了冰灾天气下线路覆冰闪络跳闸的风险状态识别与风险建模。根据覆冰闪络跳...极端冰灾天气下线路覆冰闪络跳闸是激发大电网脆弱性、导致大面积停电的重要因素。覆冰闪络跳闸等现象的风险评估是建立冰灾防御体系的基础。基于电网层面,进行了冰灾天气下线路覆冰闪络跳闸的风险状态识别与风险建模。根据覆冰闪络跳闸特性分析,界定了输电网绝缘系统的脆弱点,即覆冰期绝缘系统冰凌桥接、融冰期冰凌断流等临界点。进一步建立了覆冰闪络状态的划分原则与风险等级,并对预测冰况进行了模糊模式识别和风险评级,为运行人员提供了动态风险信息。针对数据的小样本、多输入等特点,采用统计学习理论结构风险最小化方法,构建了最小二乘支持向量机(least squares support vector machine,LSSVM)冰闪跳闸风险评估模型,依据贝叶斯(Bayesian)证据推理优化模型参数。通过与误差反向传播人工神经网络(artificial neural networkwith error back propagation,BP-ANN)算法对比,验证了该模型的有效性。最后通过脆弱性指标分析了网架结构破坏的严重性与电网绝缘系统的脆弱性。展开更多
In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learni...In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learning （AL） is adopted for speech recognition, where only the most informative training samples are selected for manual annotation. In this paper, we propose a novel active learning method for Chinese acoustic modeling, the methods for initial training set selection based on Kullback-Leibler Divergence （KLD） and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system, respectively. Our experiments show that our proposed method can achieve satisfying performances.展开更多
文摘极端冰灾天气下线路覆冰闪络跳闸是激发大电网脆弱性、导致大面积停电的重要因素。覆冰闪络跳闸等现象的风险评估是建立冰灾防御体系的基础。基于电网层面,进行了冰灾天气下线路覆冰闪络跳闸的风险状态识别与风险建模。根据覆冰闪络跳闸特性分析,界定了输电网绝缘系统的脆弱点,即覆冰期绝缘系统冰凌桥接、融冰期冰凌断流等临界点。进一步建立了覆冰闪络状态的划分原则与风险等级,并对预测冰况进行了模糊模式识别和风险评级,为运行人员提供了动态风险信息。针对数据的小样本、多输入等特点,采用统计学习理论结构风险最小化方法,构建了最小二乘支持向量机(least squares support vector machine,LSSVM)冰闪跳闸风险评估模型,依据贝叶斯(Bayesian)证据推理优化模型参数。通过与误差反向传播人工神经网络(artificial neural networkwith error back propagation,BP-ANN)算法对比,验证了该模型的有效性。最后通过脆弱性指标分析了网架结构破坏的严重性与电网绝缘系统的脆弱性。
基金Acknowledgements This study is supported by the National Natural Science Foundation of China （60705019）, the National High-Tech Research and Development Plan of China （ 2006AA010102 and 2007AA01Z417）, the NOKIA project, and the 111 Project of China under Grant No. 1308004.
文摘In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learning （AL） is adopted for speech recognition, where only the most informative training samples are selected for manual annotation. In this paper, we propose a novel active learning method for Chinese acoustic modeling, the methods for initial training set selection based on Kullback-Leibler Divergence （KLD） and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system, respectively. Our experiments show that our proposed method can achieve satisfying performances.
