Purpose–The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system.Through voiceprint technology,the sounds emitted by the transformer can be monitored ...Purpose–The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system.Through voiceprint technology,the sounds emitted by the transformer can be monitored in real-time,thereby achieving real-time monitoring of the transformer’s operational status.However,the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer,severely impacting the accuracy and reliability of voiceprint identification.Therefore,effective preprocessing steps are required to identify and separate the sound signals of transformer operation,which is a prerequisite for subsequent analysis.Design/methodology/approach–This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique(REPET)algorithm to separate and denoise the transformer operation sound signals.By analyzing the Short-Time Fourier Transform(STFT)amplitude spectrum,the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold,effectively distinguishing and extracting stable background signals from transient foreground events.The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period,then constructs a repeating segment model.Through comparison with the amplitude spectrum of the original signal,repeating patterns are extracted and a soft time-frequency mask is generated.Findings–After adaptive thresholding processing,the target signal is separated.Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.Originality/value–A REPET method with adaptive threshold is proposed,which adopts the dynamic threshold adjustment mechanism,adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal.It also lays the foundation for transformer fault detection based on acoustic fingerprinting.展开更多
We provide a Landau theory of the coupled charge density wave and spin density wave order parameters to investigate the possibility of coexistence or competition between the two ordering processes occurring in the two...We provide a Landau theory of the coupled charge density wave and spin density wave order parameters to investigate the possibility of coexistence or competition between the two ordering processes occurring in the two-dimensional doped antiferromagnet.We find that the two ordering processes can coexist only in a certain region of coupling parameters,andthe one with lower transition temperature can only coexist together with the other one with higher transition temperature and can not be stabilized separately.Phase diagrams in different situations are given.展开更多
基于1982-2017年NCEP_CFSv2(NCEP Climate Forecast System version 2)模式预测资料对黑龙江省夏季降水进行降尺度预测。通过分析黑龙江省夏季降水与同期环流因子的关系、模式对关键区环流因子的预测,选取模式模拟与再分析资料相关较好...基于1982-2017年NCEP_CFSv2(NCEP Climate Forecast System version 2)模式预测资料对黑龙江省夏季降水进行降尺度预测。通过分析黑龙江省夏季降水与同期环流因子的关系、模式对关键区环流因子的预测,选取模式模拟与再分析资料相关较好、黑龙江降水实况与再分析资料关系较好的环流因子作为预测因子,结合最优子集回归法筛选因子,建立降尺度预测模型,最后采用交叉检验法进行预测效果检验和独立样本预测。结果表明:模式降尺度预测与实况的距平符号-致率为69%,6 a独立样本预测中有5 a预测正确,优于目前的业务预测效果。进-步研究发现,在模式能够准确预测环流因子的情况下,模式降尺度可以较好地预测黑龙江省夏季降水的趋势。此外,模式降尺度在拉尼娜年预测效果较好。展开更多
基金the China Academy of Railway Sciences Corporation Limited(2023YJ257).
文摘Purpose–The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system.Through voiceprint technology,the sounds emitted by the transformer can be monitored in real-time,thereby achieving real-time monitoring of the transformer’s operational status.However,the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer,severely impacting the accuracy and reliability of voiceprint identification.Therefore,effective preprocessing steps are required to identify and separate the sound signals of transformer operation,which is a prerequisite for subsequent analysis.Design/methodology/approach–This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique(REPET)algorithm to separate and denoise the transformer operation sound signals.By analyzing the Short-Time Fourier Transform(STFT)amplitude spectrum,the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold,effectively distinguishing and extracting stable background signals from transient foreground events.The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period,then constructs a repeating segment model.Through comparison with the amplitude spectrum of the original signal,repeating patterns are extracted and a soft time-frequency mask is generated.Findings–After adaptive thresholding processing,the target signal is separated.Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.Originality/value–A REPET method with adaptive threshold is proposed,which adopts the dynamic threshold adjustment mechanism,adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal.It also lays the foundation for transformer fault detection based on acoustic fingerprinting.
文摘We provide a Landau theory of the coupled charge density wave and spin density wave order parameters to investigate the possibility of coexistence or competition between the two ordering processes occurring in the two-dimensional doped antiferromagnet.We find that the two ordering processes can coexist only in a certain region of coupling parameters,andthe one with lower transition temperature can only coexist together with the other one with higher transition temperature and can not be stabilized separately.Phase diagrams in different situations are given.
文摘基于1982-2017年NCEP_CFSv2(NCEP Climate Forecast System version 2)模式预测资料对黑龙江省夏季降水进行降尺度预测。通过分析黑龙江省夏季降水与同期环流因子的关系、模式对关键区环流因子的预测,选取模式模拟与再分析资料相关较好、黑龙江降水实况与再分析资料关系较好的环流因子作为预测因子,结合最优子集回归法筛选因子,建立降尺度预测模型,最后采用交叉检验法进行预测效果检验和独立样本预测。结果表明:模式降尺度预测与实况的距平符号-致率为69%,6 a独立样本预测中有5 a预测正确,优于目前的业务预测效果。进-步研究发现,在模式能够准确预测环流因子的情况下,模式降尺度可以较好地预测黑龙江省夏季降水的趋势。此外,模式降尺度在拉尼娜年预测效果较好。
