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Water discharge variability of Changjiang(Yangtze) and Huanghe(Yellow) Rivers and its response to climatic changes

Chinese Journal of Oceanology and Limnology 2014年第6期32卷 1392-1405页

作者：张喜林范德江王厚杰杨作升 Key Laboratory of Submarine Geosciences and Technology of Ministry of Education Ocean University of China Key Laboratory of Marine Hydrocarbon Resources and Environment Geology China Geological SurveyMinistry of Land and Resources Qingdao Institute of Marine Geology China Geological Survey

Influences of large-scale climatic phenomena, such as the E1Nifio/La Nifia-Southem Oscillation （ENSO） and the Pacific Decadal Oscillation （PDO）, on the temporal variations of the annual water discharge at the Lijin station in the Huanghe （Yellow） River and at the Datong station in the Changjiang （Yangtze） River were examined. Using the empirical mode decomposition-maximum entropy spectral analysis （EMD- MESA） method, the 2- to 3-year, 8- to 14-year, and 23-year cyclical variations of the annual water discharge at the two stations were discovered. Based on the analysis results, the hydrological time series on the inter- annual to interdecadal scales were constructed. The results indicate that from 1950 to 2011, a significant downward trend occurred in the natural annual water discharge in Huanghe River. However, the changes in water discharge in Changjiang River basin exhibited a slightly upward trend. It indicated that the changes in the river discharge in the Huanghe basin were driven primarily by precipitation. Other factors, such as the precipitation over the Changjiang River tributaries, ice melt and evaporation contributed much more to the increase in the Changjiang River basin. Especially, the impacts of the inter-annual and inter-decadal climate oscillations such as ENSO and PDO could change the long-term patterns of precipitation over the basins of the two major rivers. Generally, low amounts of basin-wide precipitation on interannual to interdecadal scales over the two rivers corresponded to most of the warm ENSO events and the warm phases of the PDO, and vice versa. The positive phases of the PDO and ENSO could lead to reduced precipitation and consequently affect the long-term scale water discharges at the two rivers.

关键词： water discharge Changjiang (Yangtze) River Huanghe (Yellow) River empirical modedecomposition maximum entropy spectral analysis large-scale climate factor

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Application of Local Wave Time-Frequency Spectrum and Neural Networks to Fault Classification in Rotating Machine

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International Journal of Plant Engineering and Management 2005年第1期10卷 36-41页

作者： HAOZhi-hua MAXiao-jiang InstituteofVibrationEngineering DalianUniversityofTechnologyDalian116024P.R.China／／TangshanCollegeTangshan063000P.R.China InstituteofVibrationEngineering DalianUniversityofTechnologyDalian116024P.R.China

A new method of fault analysis and detection by signal classification inrotating machines is presented. The Local Wave time-frequency spectrum which is a new method forprocessing a non-stationary signal is used to produce the representation of the signal. This methodallows the decomposition of one-dimensional signals into intrinsic mode functions (IMFs) usingempirical mode decomposition and the calculation of a meaningful multi-component instantaneousfrequency. Applied to fault signals , it provides new time-frequency attributes. Then the momentsand margins of the time-frequency spectrum are calculated as the feature vectors. The probabilisticneural network is used to classify different fault modes. The accuracy and robustness of theproposed methods is investigated on signals obtained during the different fault modes (early rub,loose, misalignment of the rotor).

关键词： signal classification neural network local wave empirical modedecomposition time-frequency representation

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