A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the pr...A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two.展开更多
Although research on a field investigation about quantification drawdown of ground water wells has hitherto been conducted with emphasis on Sana'a basin which is 3 200 km2 in area characterized by general hazard i...Although research on a field investigation about quantification drawdown of ground water wells has hitherto been conducted with emphasis on Sana'a basin which is 3 200 km2 in area characterized by general hazard in quantity and quality of water,there exist uncertainties about the size of the hazardous annual decline in the level of underground water. So the authors are trying to assess reliable hazard data on the depth of ground-water which were obtained by measuring static water level. The data set are twenty six wells from 9 regions which were selected to represent Sana'a basin and collected during the course of the 20 months groundwater monitoring survey from January 2008 to January 2009. The results show that the average drawdown during 20 months to be 3.22 m with an average 0.16 m per month and 2 m per year.展开更多
Target dimension is important information in underwater target classification. An intrinsic mode characteristic extraction method in underwater cylindrical shell acoustic radiation was studied in this paper based on t...Target dimension is important information in underwater target classification. An intrinsic mode characteristic extraction method in underwater cylindrical shell acoustic radiation was studied in this paper based on the mechanism of shell vibration to gain the information about its dimension instead of accurate inversion processing. The underwater cylindrical shell vibration and acoustic radiation were first analyzed using mode decomposition to solve the wave equation. The characteristic of acoustic radiation was studied with different cylindrical shell lengths, radii, thickness, excitation points and fine structures. Simulation results show that the intrinsic mode in acoustic radiation spectrum correlates closely with the geometry dimensions of cylindrical shells. Through multifaceted analysis, the strongest intrinsic mode characteristic extracted from underwater shell acoustic radiated signal was most likely relevant to the radiated source radius. Then, partial information about unknown source dimension could be gained from intrinsic mode characteristic in passive sonar applications for underwater target classification. Experimental data processing results verified the effectiveness of the method in this paper.展开更多
基金Supported by the Major State Basic Research Development Program of China under Grant No. 5132103ZZT32.
文摘A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two.
文摘Although research on a field investigation about quantification drawdown of ground water wells has hitherto been conducted with emphasis on Sana'a basin which is 3 200 km2 in area characterized by general hazard in quantity and quality of water,there exist uncertainties about the size of the hazardous annual decline in the level of underground water. So the authors are trying to assess reliable hazard data on the depth of ground-water which were obtained by measuring static water level. The data set are twenty six wells from 9 regions which were selected to represent Sana'a basin and collected during the course of the 20 months groundwater monitoring survey from January 2008 to January 2009. The results show that the average drawdown during 20 months to be 3.22 m with an average 0.16 m per month and 2 m per year.
基金supported by the Project of the Key Laboratory of Science and Technology on Underwater Test and Control(Grant No.9140C260505120C26104)the National Natural Science Foundation of China(Grant No. 11104029)
文摘Target dimension is important information in underwater target classification. An intrinsic mode characteristic extraction method in underwater cylindrical shell acoustic radiation was studied in this paper based on the mechanism of shell vibration to gain the information about its dimension instead of accurate inversion processing. The underwater cylindrical shell vibration and acoustic radiation were first analyzed using mode decomposition to solve the wave equation. The characteristic of acoustic radiation was studied with different cylindrical shell lengths, radii, thickness, excitation points and fine structures. Simulation results show that the intrinsic mode in acoustic radiation spectrum correlates closely with the geometry dimensions of cylindrical shells. Through multifaceted analysis, the strongest intrinsic mode characteristic extracted from underwater shell acoustic radiated signal was most likely relevant to the radiated source radius. Then, partial information about unknown source dimension could be gained from intrinsic mode characteristic in passive sonar applications for underwater target classification. Experimental data processing results verified the effectiveness of the method in this paper.
