Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contai...Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contains three types of noise:the harmonics interference at 50 Hz,high-frequency random noise,and low-frequency noise.We use frequency-domain bandstop filtering to remove the harmonics interference noise,segmentation and extension median filtering,and fitting of fixed extremes in empirical mode decomposition to remove the high-frequency and low-frequency noise,respectively;furthermore,we base the selection of median filtering window size on the variance and skewness coefficient of the data.We first remove the harmonics interference at 50 Hz,then the high-frequency noise,and finally the low-frequency noise.We test the proposed methodology by using theory and experiments,and we find that the three types of noises are removed,the phase and amplitude information of the signal are maintained,and high-quality waveforms are obtained in the time domain.展开更多
Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram o...Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.展开更多
The objective of this paper is to investigate whether mergers create value for shareholders in both the short and long term. For this purpose, 120 announcements of mergers that were registered in Italy during the peri...The objective of this paper is to investigate whether mergers create value for shareholders in both the short and long term. For this purpose, 120 announcements of mergers that were registered in Italy during the period 1994-2006 among listed companies were examined. The short-term analysis was conducted using the event study methodology in order to estimate the cumulative abnormal returns （CARs） in the time window around the announcement date （-10, ＋10）. In this work, the sample of 120 mergers was divided into two sub-samples： the first considers the mergers that were carried out in all sectors of the economy, and the second focuses only on bank mergers. From the results obtained it would appear that, while the sub-sample of all mergers registered a statistically significant value creation for the shareholders of both the bidder and target companies, values also confirmed by combined analysis, the second sub-sample registered negative values for bidder companies and positive values for target companies. Negative values also seem to be confirmed by the results of the combined analysis both at the date of announcement and throughout the entire period of observation. For the long-term analysis, the Buy and Hold Abnormal Returns （BHARs） methodology was used, with which it was possible to observe the returns for three years. In the 36 months following the merger, the portfolios showed a significant destruction of value展开更多
Wavelet denoising is an effective approach to extract fault features from strong background noise.It has been widely used in mechanical fault detection and shown excellent performance.However,traditional thresholds ar...Wavelet denoising is an effective approach to extract fault features from strong background noise.It has been widely used in mechanical fault detection and shown excellent performance.However,traditional thresholds are not suitable for nonstationary signal denoising because they set universal thresholds for different wavelet coefficients.Therefore,a data-driven threshold strategy is proposed in this paper.First,the signal is decomposed into different subbands by wavelet transformation.Then a data-driven threshold is derived by estimating the noise power spectral density in different subbands.Since the data-driven threshold is dependent on the noise estimation and adapted to data,it is more robust and accurate for denoising than traditional thresholds.Meanwhile,sliding window method is adopted to set a flexible local threshold.When this method was applied to simulation signal and an inner race fault diagnostic case of dedusting fan bearing,the proposed method has good result and provides valuable advantages over traditional methods in the fault detection of rotating machines.展开更多
基金supported by the National Natural Science Foundation of China(No.41574127 and No.41227803)
文摘Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contains three types of noise:the harmonics interference at 50 Hz,high-frequency random noise,and low-frequency noise.We use frequency-domain bandstop filtering to remove the harmonics interference noise,segmentation and extension median filtering,and fitting of fixed extremes in empirical mode decomposition to remove the high-frequency and low-frequency noise,respectively;furthermore,we base the selection of median filtering window size on the variance and skewness coefficient of the data.We first remove the harmonics interference at 50 Hz,then the high-frequency noise,and finally the low-frequency noise.We test the proposed methodology by using theory and experiments,and we find that the three types of noises are removed,the phase and amplitude information of the signal are maintained,and high-quality waveforms are obtained in the time domain.
基金The National Natural Science Foundation of China(No.12174053,91938203,11674057,11874109)the Fundamental Research Funds for the Central Universities(No.2242021k30019).
文摘Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.
文摘The objective of this paper is to investigate whether mergers create value for shareholders in both the short and long term. For this purpose, 120 announcements of mergers that were registered in Italy during the period 1994-2006 among listed companies were examined. The short-term analysis was conducted using the event study methodology in order to estimate the cumulative abnormal returns （CARs） in the time window around the announcement date （-10, ＋10）. In this work, the sample of 120 mergers was divided into two sub-samples： the first considers the mergers that were carried out in all sectors of the economy, and the second focuses only on bank mergers. From the results obtained it would appear that, while the sub-sample of all mergers registered a statistically significant value creation for the shareholders of both the bidder and target companies, values also confirmed by combined analysis, the second sub-sample registered negative values for bidder companies and positive values for target companies. Negative values also seem to be confirmed by the results of the combined analysis both at the date of announcement and throughout the entire period of observation. For the long-term analysis, the Buy and Hold Abnormal Returns （BHARs） methodology was used, with which it was possible to observe the returns for three years. In the 36 months following the merger, the portfolios showed a significant destruction of value
基金supported by the National Natural Science Foundation of China(Grant No.51275384)the Key project of National Natural Science Foundation of China(Grant No.51035007)+1 种基金the National Basic Research Program of China("973"Project)(Grant No.2011CB706805)the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110201130001)
文摘Wavelet denoising is an effective approach to extract fault features from strong background noise.It has been widely used in mechanical fault detection and shown excellent performance.However,traditional thresholds are not suitable for nonstationary signal denoising because they set universal thresholds for different wavelet coefficients.Therefore,a data-driven threshold strategy is proposed in this paper.First,the signal is decomposed into different subbands by wavelet transformation.Then a data-driven threshold is derived by estimating the noise power spectral density in different subbands.Since the data-driven threshold is dependent on the noise estimation and adapted to data,it is more robust and accurate for denoising than traditional thresholds.Meanwhile,sliding window method is adopted to set a flexible local threshold.When this method was applied to simulation signal and an inner race fault diagnostic case of dedusting fan bearing,the proposed method has good result and provides valuable advantages over traditional methods in the fault detection of rotating machines.
