To deal with the lung segmentation problem under complex conditions, Random Walk algorithm was applied to automatic lung segmentation. Firstly, according to the anatomical and imaging characteristics of the chest Computed Tomography (CT) images, foreground and background seeds were selected respectively. Then, CT image was segmented roughly by using the Random Walk algorithm and the approximate mask of lung area was extracted. Next, through implementing mathematical morphology operations to the mask, foreground and background seeds were further adjusted to adapt to the actually complicated situations. Finally, the fine segmentation of lung parenchyma for chest CT image was implemented by using the Random Walk algorithm again. The experimental results demonstrate that, compared with the gold standard, the Mean Absolute Distance (MAD) is 0.44±0.13 mm, the Dice Coefficient (DC) is 99.21%±0.38%. Compared with the other lung segmentation methods, the proposed method are significantly improved in accuracy of segmentation. The experimental results show that the proposed method can solve the difficult cases of the lung segmentation, and ensure the integrity, accuracy, real-time and robustness of the segmentation. Meanwhile, the results and time of the proposed method can meet the clinical needs.

In order to effectively master protocol interactive behavior, a method to automatically build protocol interactive process based on message sequence chart was proposed. Firstly, according to the characteristics of the protocol interactive process, the dependency graph was defined to represent the partial order of events in message sequence, and the network flows were converted to dependency graphs. Secondly, the basic message sequences were used to describe protocol interactive behavior fragments, and the basic message sequences were mined by defining event maximum suffix. Finally, the maximum dependency graphs that were found out were connected and merged to build a message sequence chart. The experimental results show that the proposed method has a high accuracy and the built message sequence chart can visually represent the protocol interactive process.

Aiming at the problem of pulmonary small nodules was difficult to identify, a method using fuzzy C-means clustering algorithm to analyse the lung Region Of Interest (ROI) was presented. An improved Fuzzy C-Means clustering algorithm based on Plurality of Weight (PWFCM) was presented to enhance the accurate rate and speed of small nodules recognition. To improve the convergence, each sample and its features were weighted and a new membership constraint was introduced. The low sensitivity from the uneven ROI data was decreased by using a double clustering strategy. The experimental results tested on the real CT image data show that PWFCM algorithm can detect lung nodules with a higher sensitivity and lower false positive rate.

To solve the problems existing in BLP (Bell-LaPadula) model, such as information aggregation leakage, excessive privileges of trusted subject and the deficiency of integrity, with reference to the application requirement of hierarchical file protection, an information aggregation leakage proof model named IALP (Information Aggregation Leakage Proof) was proposed based on assignment partition. First of all, the cause of information aggregation leakage and the current research situation were discussed. Secondly, on the basis of assignments partition, the knowledgeable degree of subject and the information weight of object were quantized, and the relatively trusted subject was proposed. Security axioms and state transition rules were given. Finally, the theoretical proof, application examples and analysis indicate that IALP can control the knowable degree of the subject towards the object set with the aggregation leakage relation, and limits the privilege of trusted subject and enhances the integrity to some extent.

Concerning the security issues in the current road transport logistics, an early-warning model of road transport logistics was proposed. First, five influence aspects of road transport logistics were discussed, including people, vehicles, roads, environment and carried goods. Then the early-warning indexes and the weight of each index were determined using the Analytic Hierarchy Process (AHP) and the expert scoring method. Finally, based on the multi-level Fuzzy Comprehensive Evaluation (FCE) method, the early-warning model of road transport logistics was constructed by selecting proper fuzzy operators. With the model, the early-warning information of road transport logistics could be obtained by calculating the early-warning level. The experiment shows that constructing the early-warning model of logistics transport by FCE is feasible. It realizes the early-warning in the transport process.