In this paper, we give an up-to-date survey on physically-based fluid animation research. As one of the most popular approaches to simulate realistic fluid effects, physically-based fluid animation has spurred a large...In this paper, we give an up-to-date survey on physically-based fluid animation research. As one of the most popular approaches to simulate realistic fluid effects, physically-based fluid animation has spurred a large number of new results in recent years. We classify and discuss the existing methods within three categories： Lagrangian method, Eulerian method and Lattice-Boltzmann method. We then introduce techniques for seven different kinds of special fluid effects. Finally we review the latest hot research areas and point out some future research trends, including surface tracking, fluid control, hybrid method, model reduction, etc.展开更多
Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the stru...Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.展开更多
基金Supported partially by the National Basic Research Program of China (Grant No. 2009CB320804)the National High-Tech Research & Development Program of China (Grant No. 2006AA01Z307)
文摘In this paper, we give an up-to-date survey on physically-based fluid animation research. As one of the most popular approaches to simulate realistic fluid effects, physically-based fluid animation has spurred a large number of new results in recent years. We classify and discuss the existing methods within three categories： Lagrangian method, Eulerian method and Lattice-Boltzmann method. We then introduce techniques for seven different kinds of special fluid effects. Finally we review the latest hot research areas and point out some future research trends, including surface tracking, fluid control, hybrid method, model reduction, etc.
基金supported by the National Natural Science Foundation of China under Grant Nos.91120305,61272251the National Basic Research 973 Program of China under Grant No.2015CB856004
文摘Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.
