Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thic...Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.展开更多
针对体育教学过程中,单纯依靠教师人工观察识别学生的体育动作容易出现反馈不及时、主观评价等问题,提出一种基于融合姿态特征与动态时间规整算法(dynamic time warping,DTW)的体育动作识别方法.基于OpenPose的骨骼点特征输出,融合学生...针对体育教学过程中,单纯依靠教师人工观察识别学生的体育动作容易出现反馈不及时、主观评价等问题,提出一种基于融合姿态特征与动态时间规整算法(dynamic time warping,DTW)的体育动作识别方法.基于OpenPose的骨骼点特征输出,融合学生在体育动作中的重心、肢体角度、朝向等特征,并采用DTW进行规整和评价.实验结果显示,在训练数据集的400份动作样本中,OpenPose+DTW模型正确识别样本数为372,总识别率为93%.高于其他模型.同时广播体操教学的50个动作实验中,OpenPose+DTW模型的误判样本为5个,识别精度为90%.结果表明,基于融合姿态特征以及DTW的体育动作识别模型具备优秀的识别性能,能够满足在线体育教学的应用场景.展开更多
配电网环境复杂,配电网同步相量测量装置(distribution network synchronous phasor measurement unit, D-PMU)容易受到干扰而产生坏数据,进一步影响基于测量数据的应用效果。为了提高D-PMU数据质量,提出一种不依赖系统拓扑的基于密度...配电网环境复杂,配电网同步相量测量装置(distribution network synchronous phasor measurement unit, D-PMU)容易受到干扰而产生坏数据,进一步影响基于测量数据的应用效果。为了提高D-PMU数据质量,提出一种不依赖系统拓扑的基于密度的噪场应用空间聚类(density-based spatial clustering of applications with noise, DBSCAN)的配电网同步测量坏数据检测方法。首先利用基于密度的聚类算法DBSCAN进行异常数据检测。通过轮廓系数和邓恩指数对DBSCAN的聚类结果进行综合评价。利用麻雀搜索算法实现自适应参数调整,解决检测时需要预先处理训练、标记数据的问题。在此基础上,将时间序列聚类的K-Medoids算法和动态时间规整算法相结合,通过衡量不同时间序列之间的相似性,解决了D-PMU在电气联系较弱时对扰动数据与坏数据的区分问题,增强了数据处理的准确性与噪声环境下的稳健性。仿真和实际数据的测试结果表明,所提方法能有效区分真实扰动数据并准确识别D-PMU坏数据。展开更多
针对传统拓扑识别方法不适用于电压数据质量差、三相不平衡度低的台区的问题,提出了基于电流平衡和电压曲线动态时间规整(Dynamic Time Warping,DTW)距离的户变户相组合优化识别方法。基于二次优化的思想,利用电流平衡原理为组合优化提...针对传统拓扑识别方法不适用于电压数据质量差、三相不平衡度低的台区的问题,提出了基于电流平衡和电压曲线动态时间规整(Dynamic Time Warping,DTW)距离的户变户相组合优化识别方法。基于二次优化的思想,利用电流平衡原理为组合优化提供了一个次优的户变户相初始解集,利用DTW距离度量用户与台区变压器电压时间序列的相似性,构建了电流平衡和电压DTW距离的组合优化模型,采用自适应变异概率遗传算法求解,改善了寻优过程中易陷入局部最优、早熟的问题,提高了求解效率。仿真结果表明,与传统方法相比,所提方法可以有效提升三相不平衡度低和数据缺失台区的拓扑识别准确率。展开更多
Dynamic time warping (DTW) and dynamic spectral wafliing (DSW)techniques are introduced into learning vector quantization (LVQ) algorithm to con-struct a “dynamic” Bayes classifier for speech recognition. It can pre...Dynamic time warping (DTW) and dynamic spectral wafliing (DSW)techniques are introduced into learning vector quantization (LVQ) algorithm to con-struct a “dynamic” Bayes classifier for speech recognition. It can preduce highly dis-criminiative “dynamic” reference vectors to represent the temporal and spectral vari-abilities of speech. Recognition experiments on 19 Chinese consonants show that the“dynamic” classifier outperforms the original “static” classifier significantly.展开更多
基金Supported by the National Natural Science Foundation of China(42272110)CNPC-China University of Petroleum(Beijing)Strategic Cooperation Project(ZLZX2020-02).
文摘Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.
文摘针对体育教学过程中,单纯依靠教师人工观察识别学生的体育动作容易出现反馈不及时、主观评价等问题,提出一种基于融合姿态特征与动态时间规整算法(dynamic time warping,DTW)的体育动作识别方法.基于OpenPose的骨骼点特征输出,融合学生在体育动作中的重心、肢体角度、朝向等特征,并采用DTW进行规整和评价.实验结果显示,在训练数据集的400份动作样本中,OpenPose+DTW模型正确识别样本数为372,总识别率为93%.高于其他模型.同时广播体操教学的50个动作实验中,OpenPose+DTW模型的误判样本为5个,识别精度为90%.结果表明,基于融合姿态特征以及DTW的体育动作识别模型具备优秀的识别性能,能够满足在线体育教学的应用场景.
文摘配电网环境复杂,配电网同步相量测量装置(distribution network synchronous phasor measurement unit, D-PMU)容易受到干扰而产生坏数据,进一步影响基于测量数据的应用效果。为了提高D-PMU数据质量,提出一种不依赖系统拓扑的基于密度的噪场应用空间聚类(density-based spatial clustering of applications with noise, DBSCAN)的配电网同步测量坏数据检测方法。首先利用基于密度的聚类算法DBSCAN进行异常数据检测。通过轮廓系数和邓恩指数对DBSCAN的聚类结果进行综合评价。利用麻雀搜索算法实现自适应参数调整,解决检测时需要预先处理训练、标记数据的问题。在此基础上,将时间序列聚类的K-Medoids算法和动态时间规整算法相结合,通过衡量不同时间序列之间的相似性,解决了D-PMU在电气联系较弱时对扰动数据与坏数据的区分问题,增强了数据处理的准确性与噪声环境下的稳健性。仿真和实际数据的测试结果表明,所提方法能有效区分真实扰动数据并准确识别D-PMU坏数据。
文摘针对传统拓扑识别方法不适用于电压数据质量差、三相不平衡度低的台区的问题,提出了基于电流平衡和电压曲线动态时间规整(Dynamic Time Warping,DTW)距离的户变户相组合优化识别方法。基于二次优化的思想,利用电流平衡原理为组合优化提供了一个次优的户变户相初始解集,利用DTW距离度量用户与台区变压器电压时间序列的相似性,构建了电流平衡和电压DTW距离的组合优化模型,采用自适应变异概率遗传算法求解,改善了寻优过程中易陷入局部最优、早熟的问题,提高了求解效率。仿真结果表明,与传统方法相比,所提方法可以有效提升三相不平衡度低和数据缺失台区的拓扑识别准确率。
文摘Dynamic time warping (DTW) and dynamic spectral wafliing (DSW)techniques are introduced into learning vector quantization (LVQ) algorithm to con-struct a “dynamic” Bayes classifier for speech recognition. It can preduce highly dis-criminiative “dynamic” reference vectors to represent the temporal and spectral vari-abilities of speech. Recognition experiments on 19 Chinese consonants show that the“dynamic” classifier outperforms the original “static” classifier significantly.
