With the in-depth research and extensive application of outlier detection technology, more and more excellent algorithms have been proposed. However, the existing outlier detection algorithms still use the evaluation metrics of traditional classification, which leads to the problems of singleness and poor adaptability of evaluation metrics. To solve these problems, the first type of High True positive rate-Area Under Curve (HT_AUC) and the second type of Low False positive rate-Area Under Curve (LF_AUC) were proposed. First, the commonly used outlier detection evaluation metrics were analyzed to illustrate their advantages and disadvantages as well as applicable scenarios. Then, based on the existing Area Under Curve (AUC) method, the HT_AUC and the LF_AUC were proposed aiming at the high True Positive Rate (TPR) demand and low False Positive Rate (FPR) demand respectively, so as to provide more suitable metrics for performance evaluation as well as quantization and integration of outlier detection algorithms. Experimental results on real-world datasets show that the proposed method is able to better satisfy the demands of the first type of high true rate and the second type of low false positive rate than the traditional evaluation metrics.

Aiming at high manual dependence and frequent Aggregation and Disaggregation (AD) of existing model AD trigger mechanisms, an intelligent trigger mechanism based on focus-area multi-entity temporal outlier detection algorithm was proposed. Firstly, the focus-areas were divided based on attention neighbors. Secondly, the outlier score of focus-area was obtained by calculating the k-distance outlier score of entities in a focus-area. Finally, a trigger mechanism for AD was constructed based on strongest-focus-area threshold decision method. The experimental results on real dataset show that, compared with the traditional single-entity temporal outlier detection algorithms, the proposed algorithm improves the performance of Precision, Recall and F1-score by more than 10 percentage points. The proposed algorithm can not only judge the trigger time of the AD operation in time, but also enable the simulation system to intelligently detect the simulation entities with emergency situation and meet the requirements of multi-resolution modeling.

The current deep learning-based salient object detection algorithms fail to produce accurate object boundaries, which makes the regions along object contours blurred and inaccurate. To solve the problem, a salient object detection algorithm based on multi-task deep learning model was proposed. Firstly, based on deep Convolutional Neural Network (CNN), a multi-task model was used to separately learn region and boundary features of a salient object. Secondly, the detected object boundaries were utilized to produce a number of region candidates. After that the region candidates were re-ranked and their weights were computed by combining the results of salient region detection. Finally, the entire saliency map was extracted. The experimental results on three widely-used benchmarks show that the proposed method achieves better accuracy. According to F-measure, the proposed method averagely outperforms the deep learning-based algorithm by 1.9%, while lowers the Mean Absolutely Error (MAE) by 12.6%.

The diffeomorphism is a differential transformation with smooth and invertible properties, which leading to topology preservation between anatomic individuals while avoiding physically implausible phenomena during MRI image registration. In order to yield a more plausible diffeomorphism for spatial transformation, nonlinear structure of high-dimensional data was considered, and an MRI image registration using manifold learning based on adaptive tangent space was put forward. Firstly, Symmetric Positive Definite (SPD) covariance matrices were constructed by voxels from an MRI image, then to form a Lie group manifold. Secondly, tangent space on the Lie group was used to locally approximate nonlinear structure of the Lie group manifold. Thirdly, the local linear approximation was adaptively optimized by selecting appropriate neighborhoods for each sample voxel, therefore the linearization degree of tangent space was improved, the local nonlinearization structure of manifold was highly preserved, and the best optimal diffeomorphism could be obtained. Numerical comparative experiments were conducted on both synthetic data and clinical data. Experimental results show that compared with the existing algorithm, the proposed algorithm obtains a higher degree of topology preservation on a dense high-dimensional deformation field, and finally improves the registration accuracy.

In order to alleviate the problem of inaccurate noise identifying and blurred restoration in image edges and details, a novel algorithm based on weighted Spatial Local Outlier Measure (SLOM) was proposed for removing random-valued impulse noise, namely WSLOM-EPR. Based on optimized spatial distance difference, the mean and standard deviation of neighborhood were introduced to set up a noise detection method for reflecting local characters in image edges, which could improve the precision of noise identification in edges. According to the precision detection results, the Edge-Preserving Regularization (EPR) function was optimized to improve the computation efficiency and preserving capability of edges and details. The simulation results showed that, with 40% to 60% noisy level, the overall performance in noise points detection was better than that of the contrast detection algorithms, which can maintain a good balance in false detection and miss detection of noise. The Peak Signal-to-Noise Ratios (PSNR) of WSLOM-EPR was better than that of the most of the contrast algorithms, and the restoring image had clear and continuous edges. Experimental results show that WSLOM-EPR can improve detection precision and preserve more edges and details information.

In order to simulate interactions of fluids with solid boundaries, a boundary handling algorithm based on weakly compressible Smoothed Particle Hydrodynamics (SPH) was presented. First, a novel volume-weighted function was introduced to solve the density estimation errors in non-uniformly sampled solid boundary regions. Then, a new boundary force computation model was proposed to avoid penetration without position correction of fluid particles. Last, an improved fluid pressure force model was proposed to enforce the weak incompressibility constraint. The experimental results show that the proposed method can effectively solve the stability problem of interactions of weakly compressible fluids and non-uniformly sampled solid boundaries using position correction-based boundary handling method. In addition, only the positions of boundary particles are needed, thus the memory as well as the extra computation due to position correction can be saved.