Wave steepness is an important characteristic of a high sea state, and is widely applied on wave propagations at ports, ships, offshore platforms, and CO2 circulation in the ocean. Obtaining wave steepness is a diffic...Wave steepness is an important characteristic of a high sea state, and is widely applied on wave propagations at ports, ships, offshore platforms, and CO2 circulation in the ocean. Obtaining wave steepness is a difficult task that depends heavily on theoretical research on wavelength distribution and direct observations. Development of remote-sensing techniques provides new opportunities to study wave steepness. At present, two formulas are proposed to estimate wave steepness from QuikSCAT and ERS-1/2 scatterometer data. We found that wave steepness retrieving is not affected by radar band, and polarization method, and that relationship of wave steepness with radar backscattering cross section is similar to that with wind. Therefore, we adopted and modified a genetic algorithm for relating wave steepness with radar backscattering cross section. Results show that the root-mean-square error of the wave steepness retrieved is 0.005 in two cases from ERS-1/2 scatterometer data and from QuikSCAT scatterometer data.展开更多
This paper studies an existing 13.8 kilovolt distribution network which, serves an oil production field spread over an area of approximately 60 kilometers square, in order to locate any fault that may occur anywhere i...This paper studies an existing 13.8 kilovolt distribution network which, serves an oil production field spread over an area of approximately 60 kilometers square, in order to locate any fault that may occur anywhere in the network using fuzzy c-mean classification techniques. In addition, Sections 5 and 6 introduce two different methods for normalizing data and selecting the optimum number of clusters in order to classify data. Results and conclusions are given to show the feasibility for the suggested fault location method. Suggestion for future related research has been provided in Section 8.展开更多
基金Supported by the National High Technology Research and Development Program of China(863Program)(No.2008AA09Z102)Data were provided by the European Space Agency
文摘Wave steepness is an important characteristic of a high sea state, and is widely applied on wave propagations at ports, ships, offshore platforms, and CO2 circulation in the ocean. Obtaining wave steepness is a difficult task that depends heavily on theoretical research on wavelength distribution and direct observations. Development of remote-sensing techniques provides new opportunities to study wave steepness. At present, two formulas are proposed to estimate wave steepness from QuikSCAT and ERS-1/2 scatterometer data. We found that wave steepness retrieving is not affected by radar band, and polarization method, and that relationship of wave steepness with radar backscattering cross section is similar to that with wind. Therefore, we adopted and modified a genetic algorithm for relating wave steepness with radar backscattering cross section. Results show that the root-mean-square error of the wave steepness retrieved is 0.005 in two cases from ERS-1/2 scatterometer data and from QuikSCAT scatterometer data.
文摘This paper studies an existing 13.8 kilovolt distribution network which, serves an oil production field spread over an area of approximately 60 kilometers square, in order to locate any fault that may occur anywhere in the network using fuzzy c-mean classification techniques. In addition, Sections 5 and 6 introduce two different methods for normalizing data and selecting the optimum number of clusters in order to classify data. Results and conclusions are given to show the feasibility for the suggested fault location method. Suggestion for future related research has been provided in Section 8.
