Concerning the problem that the numerical prediction method is complex in modeling, the radar echo extrapolation method is easy to generate cumulative error and the model parameters are difficult to set in plateau area, a method for segmenting rainfall clouds in Tibet was proposed based on the improved DeepLab v3. Firstly, the convolutional layers and residual modules in the coding network were used for down-sampling. Then, the multi-scale sampling module was constructed by using the dilated convolution, and the attention mechanism module was added to extract deep high-dimensional features. Finally, the deonvolutional layers in the decoding network were used to restore the feature map resolution. The proposed method was compared with Google semantic segmentation network DeepLab v3 and other models on the validation set. The experimental results show that the method has better segmentation performance and generalization ability, has the rainfall cloud segmented more accurately, and the Mean intersection over union (Miou) reached 0.95, which is 15.54 percentage points higher than that of the original DeepLab v3. On small targets and unbalanced datasets, rainfall clouds can be segmented more accurately by this method, so that the proposed method can provide a reference for the rain cloud monitoring and early warning.

Focused on the issue that the traditional interest area based visualization method can not pay attention to the details in the process of analyzing pilot eye movement data, a visual analysis method of eye movement data based on user-defined interest area was proposed. Firstly, according to the specific analysis task, the self-divison and self-definition of the background image of the task were introduced. Then, multiple auxiliary views and interactive approaches were combined, and an eye movement data visual analysis system for pilot training was designed and implemented to help analysts analyze the difference of eye movement between different pilots. Finally, through case analysis, the effectiveness of the visual analysis method and the practicability of the analysis system were proved. The experimental results show that compared with the traditional method, in the proposed method, the analysts' initiative in the analysis process is increased. The analysts are allowed to explore the local details of the task background in both global and local aspects, making the analysts' analyze the data in multi-angle; the analysts are allowed find the flight students' cognitive difficulties in the training process as a whole, so as to develop more targeted and more effective training courses.

Since New Approach to Multi-Objective A ^* combined with dimensionality reduction technique (NAMOA _dr ^*) algorithm has the phenomenon of plateau exploration, a Random Walk assisted NAMOA _dr ^* (RWNAMOA _dr ^*) algorithm which invoked a random walk procedure was proposed to find an exit (labels with heuristic value not dominated by the last extended label's) when the NAMOA _dr ^*was stuck on a plateau. To determine when NAMOA _dr ^* algorithm was stuck on a plateau exploration, a method of detecting plateau exploration was proposed. When the heuristic value of the extended label was dominated by the last extended label's for continuous m times, NAMOA _dr ^* algorithm was considered to fall into the plateau exploration. In the experiments, a randomly generated grid was used, which was a standard test platform for the evaluation of multi-objective search algorithms. The experimental results reveal that compared with NAMOA _dr ^* algorithm, RWNAMOA _dr ^* algorithm's running time is reduced by 50.69% averagely and its space consuming is reduced by about 10% averagely, which can provide theoretical support for accelerating multi-objective path searching in real life.

Focusing on the issue that releasing frequent sequence patterns and the corresponding true supports may reveal the individuals' privacy when the data set contains sensitive information, a Differential Private Frequent Sequence Mining (DP-FSM) algorithm was proposed. Downward closure property was used to generate a candidate set of sequence patterns, smart truncating based technique was used to sample frequent patterns in the candidate set, and geometric mechanism was utilized to perturb the true supports of each sampled pattern. In addition, to improve the usability of the results, a threshold modification method was proposed to reduce truncation error and propagation error in mining process. The theoretical analysis show that the proposed method is ε-differentially private. The experimental results demonstrate that the proposed method has lower False Negative Rate (FNR) and Relative Support Error (RSE) than that of the comparison algorithm named PFS², thus effectively improving the accuracy of mining results.

When using the way of pattern growth to construct tree structure, the exiting algorithms for mining probabilistic frequent itemsets suffer many problems, such as generating large number of tree nodes, occupying large memory space and having low efficiency. In order to solve these problems, a Progressive Uncertain Frequent Pattern Growth algorithm named PUFP-Growth was proposed. By the way of reading data in the uncertain database tuple by tuple, the proposed algorithm constructed tree structure as compact as Frequent Pattern Tree (FP-Tree) and updated dynamic array of expected value whose header table saved the same itemsets. When all transactions were inserted into the Progressive Uncertain Frequent Pattern tree (PUFP-Tree), all the probabilistic frequent itemsets could be mined by traversing the dynamic array. The experimental results and theoretical analysis show that PUFP-Growth algorithm can find the probabilistic frequent itemsets effectively. Compared with the Uncertain Frequent pattern Growth (UF-Growth) algorithm and Compressed Uncertain Frequent-Pattern Mine (CUFP-Mine) algorithm, the proposed PUFP-Growth algorithm can improve mining efficiency of probabilistic frequent itemsets on uncertain dataset and reduce memory usage to a certain degree.

Based on Ultra-WideBand (UWB) Body Area Network (BAN) and heterogeneous biosensors, a kind of wireless physiological monitoring system was designed, which could acquire and process multiple physiological signals. For reducing the total energy consumption, a low-power scheduling scheme was further proposed so as to provide long-term continuous sensing for wearer's health and safety. According to the physiological state machine of the monitoring system, a coordinator could adaptively determine the biosensor set for next monitoring cycle, and those selected biosensors can cooperatively process the heterogeneous data. The simulation results show that with the same monitoring conditions, the proposed scheme can effectively reduce the biosensor's invalid operation and wireless data transmission, and thus extend the operational lifetime of the BAN-based monitoring system.