In this paper,we make use of the boosting method to introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called adapted Boosted Mixture Learning (BML). The method possesses the ability to rectify the ...In this paper,we make use of the boosting method to introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called adapted Boosted Mixture Learning (BML). The method possesses the ability to rectify the existing problems in other conventional techniques for estimating the GMM parameters, due in part to a new mixing-up strategy to increase the number of Gaussian components. The discriminative splitting idea is employed for Gaussian mixture densities followed by learning via the introduced method. Then, the GMM classifier was applied to distinguish between healthy infants and those that present a selected set of medical conditions. Each group includes both full-term and premature infants. Cry-pattern for each pathological condition is created by using the adapted BML method and 13-dimensional Mel-Frequency Cepstral Coefficients (MFCCs) feature vector. The test results demonstrate that the introduced method for training GMMs has a better performance than the traditional method based upon random splitting and EM-based re-estimation as a reference system in multi-pathological classification task.展开更多
以风电为代表的可再生能源发电系统在提供清洁能源的同时,其高比例并网也同时增加了电力系统运行的不确定性。作为指导电力市场交易和评估电网安全可靠性的重要测度,可用输电能力(available transfer capability,ATC)的计算需要合理计...以风电为代表的可再生能源发电系统在提供清洁能源的同时,其高比例并网也同时增加了电力系统运行的不确定性。作为指导电力市场交易和评估电网安全可靠性的重要测度,可用输电能力(available transfer capability,ATC)的计算需要合理计及风电功率不确定性的影响。为此,该文从控制由风电功率波动所引起的系统潮流越限风险的角度,提出一种基于机会约束规划的含风电场电力系统ATC计算方法。借助高斯混合模型和线性化潮流方程,将机会约束规划问题转换为等价的线性优化问题求解,最终获得兼顾电力系统运行安全性和经济性的ATC指标。通过2个测试系统的算例分析,验证了所提计算方法的可行性和有效性。展开更多
文摘In this paper,we make use of the boosting method to introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called adapted Boosted Mixture Learning (BML). The method possesses the ability to rectify the existing problems in other conventional techniques for estimating the GMM parameters, due in part to a new mixing-up strategy to increase the number of Gaussian components. The discriminative splitting idea is employed for Gaussian mixture densities followed by learning via the introduced method. Then, the GMM classifier was applied to distinguish between healthy infants and those that present a selected set of medical conditions. Each group includes both full-term and premature infants. Cry-pattern for each pathological condition is created by using the adapted BML method and 13-dimensional Mel-Frequency Cepstral Coefficients (MFCCs) feature vector. The test results demonstrate that the introduced method for training GMMs has a better performance than the traditional method based upon random splitting and EM-based re-estimation as a reference system in multi-pathological classification task.
文摘以风电为代表的可再生能源发电系统在提供清洁能源的同时,其高比例并网也同时增加了电力系统运行的不确定性。作为指导电力市场交易和评估电网安全可靠性的重要测度,可用输电能力(available transfer capability,ATC)的计算需要合理计及风电功率不确定性的影响。为此,该文从控制由风电功率波动所引起的系统潮流越限风险的角度,提出一种基于机会约束规划的含风电场电力系统ATC计算方法。借助高斯混合模型和线性化潮流方程,将机会约束规划问题转换为等价的线性优化问题求解,最终获得兼顾电力系统运行安全性和经济性的ATC指标。通过2个测试系统的算例分析,验证了所提计算方法的可行性和有效性。
