Giant panda pedigree data is an important data base for studying the population dynamics of giant pandas. Therefore, it is of great significance for data modeling of giant panda ecosystems from the perspective of panda conservation. Focused on this issue, a data modeling method of giant panda ecosystem based on population dynamics P system was proposed. Based on the giant panda pedigree data released by Chinese Association of Zoological Gardens, the population characteristics of captive pandas were simulated and researched in China Giant Panda Conservation Research Center from individual behavior. The change rules of reproductive parameters were analyzed in detail, and added to the field released module. Eventually, a population dynamic P system for giant panda was designed releasing-to-the-wild with a two-layer nested membrane structure, a collection of objects and a series of evolution rules which is inline with the characteristics of giant panda. For all giant panda, the maximum relative error between the simulation results and the actual data was within ±4.13% and basically controlled within ±2.7% of P system. The experimental results verify the effectiveness and soundness of the proposed model. It can simulate the population dynamic change trend of giant panda and provide the basis for management decision-making.

In the indoor robot localization and 3D dense mapping, the existing methods can not satisfy the requirements of high-precision localization, large-scale and rapid mapping. The ORB-SLAM (Oriented FAST and Rotated BRIEF-Simultaneous Localization And Mapping) algorithm, which has three parallel threads including tracking, map building and relocation, was used to estimate the three-dimensional (3D) pose of the robot. And then 3D dense point cloud was obtained by using the depth camera KINECT. The key frame extraction method in spatial domain was introduced to eliminate redundant frames, and the sub-map method was proposed to reduce the cost of mapping, thereby the whole speed of the algorithm was improved. The experiment results show that the proposed method can locate the robot position accurately in a large range. In the range of 50 meters, the root-mean-square error of the robot is 1.04 m, namely the error is 2%, the overall speed is 11 frame/s, and the localization speed is up to 17 frame/s. The proposed method can meet the requirements of indoor robot localization and 3D dense mapping with high precision, large-scale and rapidity.

In the video based in-air signature verification system, the existed methods cannot meet the requirement of accuracy, real time, robustness for fingertip tracking. To solve this problem, the Tracking-Learning-Detection (TLD) method based on temporal context was proposed. Based on the original TLD algorithm, the temporal context massage, namely the prior knowledge that the movement of fingertip is continuity in two adjacent frames, was introduced to narrow the search range of detection and tracking adaptively, thereby improving tracking speed. The experimental results on 12 public and 1 self-made video sequences show that the improved TLD algorithm can accurately track fingers, and tracking speed can reach 43 frames per secend. Compared with the original TLD tracking algorithm, the accuracy was increased by 15% and the tracking speed was increased more than 100%, which make the proposed method meet the real-time requirements for fingertip tracking.

Concerning the uneven density distributed lightning location data, a lightning nowcasting model based on Ordering Points To Identify the Clustering Structure (OPTICS) algorithm was proposed. The model analyzed continuous period of lightning location data with OPTICS. It effectively filtered out the sparse points that would affect the lightning clouds distribution. Based on the lightning clusters produced by OPTICS, the model used dilate-corrode algorithm to restore real distribution of lightning clouds. Then future lightning location area was predicted according to the moving trend of lightning clouds. Furthermore, to overcome the traditional algorithm's drawback of consuming longer time, adjacent list and improved seed-list updating strategy were introduced into the OPTICS algorithm. The experimental results show that OPTICS based model is more applicable for lightning nowcasting, and achieves higher accuracy and lower time consumption.

Against the massive monitoring data of lightning locating system, a lightning nowcasting model based on Improved Density-Based Spatial Clustering of Application with Noise (IDBSCAN) clustering algorithm was put forward. Based on the lightning location data in real-time monitoring system, this method searched for lightning-density flash point greater than the threshold value of the land, built the cluster with up to the maximum ground flash density, and located the core of the cluster. Besides, with the application of adjacency list search algorithm, time and space consumed for the initial search set of lightning data had been greatly reduced. Furthermore, using regression fitting algorithm, the proposed algorithm can predict the path of movement of lightning cluster. The experimental results show that IDBSCAN algorithm used in the lightning nowcasting is effective.

Several kind of resources sharing scheme were analyzed. By using stachastic petri nets theory, the resources sharing scheme was modeled and mathematically analyzed. Finally,an example demonstrates that absolute resources sharing is superior to partial resources sharing provided pairly competing resources.