水中上升气泡体积变化率的图像分析技术

水中上升气泡的体积变化率是舰船自消隐特种气幕技术等诸多研究的重要基础。鉴于当前对这一体积变化率研究的紧迫需求,提出并较为深入地研究了水中上升气泡体积变化率的图像分析技术。首先,在理论研究的基础上,专门建立了分析计算的数学模型;进而给出了分析的实施方法,即利用摄像法获取水中上升气泡的图像序列,并从中得出所需图像的相关信息,再利用建立的模型即可求出其体积变化率;同时,设计了专门的实验,初步验证了这一分析技术的可行性。

Discrimination for minimal hepatic encephalopathy based on Bayesian modeling of default mode network

In order to classify the minimal hepatic encephalopathy （MHE） patients from healthy controls, the independent component analysis （ICA） is used to generate the default mode network （DMN） from resting-state functional magnetic resonance imaging （fMRI）. Then a Bayesian voxel- wised method, graphical-model-based multivariate analysis （GAMMA）, is used to explore the associations between abnormal functional integration within DMN and clinical variable. Without any prior knowledge, five machine learning methods, namely, support vector machines （SVMs）, classification and regression trees （ CART ）, logistic regression, the Bayesian network, and C4.5, are applied to the classification. The functional integration patterns were alternative within DMN, which have the power to predict MHE with an accuracy of 98%. The GAMMA method generating functional integration patterns within DMN can become a simple, objective, and common imaging biomarker for detecting MIIE and can serve as a supplement to the existing diagnostic methods.

Modified image analytical solutions for ground displacement using nonuniform convergence model

Based on the image theory,the analytical solutions of tunneling-induced ground displacement were derived in conjunction with the nonuniform convergence model.The reasonable value of Poisson ratio in the analytical solution was discussed.The ground settlement width parameter which could reflect the ground condition was introduced to modify the analytical solutions proposed above,and new analytical solutions were presented.To evaluate the validity of the present solutions using the nonuniform convergence model,the results were compared with the observed values for four engineering projects,including 38 measured data of ground settlement.The agreement shows that the present solutions using the nonuniform convergence model are effective for evaluating the tunneling-induced ground displacements.

Fusing PLSA model and Markov random fields for automatic image annotation

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image.The novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation.To demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.