A numerical model of a coupled bubble jet and wall was built on the assumption of potential flow and calculated by the boundary integral method. A three-dimensional computing program was then developed. Starting with ...A numerical model of a coupled bubble jet and wall was built on the assumption of potential flow and calculated by the boundary integral method. A three-dimensional computing program was then developed. Starting with the basic phenomenon of the interaction between a bubble and a wall, the dynamics of bubbles near rigid walls were studied systematically with the program. Calculated results agreed well with experimental results. The relationship between the Bjerknes effect of a wall and characteristic parameters was then studied and the calculated results of various cases were compared and discussed with the Blake criterion based on the Kelvin-impulse theory. Our analyses show that the angle of the jet’s direction and the pressure on the rigid wall have a close relationship with collapse force and the bubble’s characteristic parameters. From this, the application range of Blake criterion can be determined. This paper aims to provide a basis for future research on the dynamics of bubbles near a wall.展开更多
Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which ...Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform （SIFT） algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval （CBIR） applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis （LDA） is used to transform the local features to global features while reducing di- mensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.展开更多
基金the National Natural Science Foundation of China under Grant No. 50779007the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20070217074)+1 种基金the Defence Advance Research Program of Science and Technology of Ship Industry under Grant No. 07J1.1.6Harbin Engineering University Foundation under Grant No. HEUFT07069
文摘A numerical model of a coupled bubble jet and wall was built on the assumption of potential flow and calculated by the boundary integral method. A three-dimensional computing program was then developed. Starting with the basic phenomenon of the interaction between a bubble and a wall, the dynamics of bubbles near rigid walls were studied systematically with the program. Calculated results agreed well with experimental results. The relationship between the Bjerknes effect of a wall and characteristic parameters was then studied and the calculated results of various cases were compared and discussed with the Blake criterion based on the Kelvin-impulse theory. Our analyses show that the angle of the jet’s direction and the pressure on the rigid wall have a close relationship with collapse force and the bubble’s characteristic parameters. From this, the application range of Blake criterion can be determined. This paper aims to provide a basis for future research on the dynamics of bubbles near a wall.
文摘Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform （SIFT） algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval （CBIR） applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis （LDA） is used to transform the local features to global features while reducing di- mensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.
