In order to improve the accuracy of visibility forecast, especially the accuracy of low-visibility forecast, an ensemble learning model based on random forest and LightGBM for visibility forecast was proposed. Firstly, based on the meteorological forecast data of the numerical modeling system, combined with meteorological observation data and PM _2.5 concentration observation data, the random forest method was used to construct the feature vectors. Secondly, for the missing data with different time spans, three missing value processing methods were designed to replace the missing values, and then the data sample set with good continuity for training and testing was created. Finally, a visibility forecast model based on LightGBM was established, and its parameters were optimized by using the network search method. The proposed model was compared to Support Vector Machine(SVM), Multiple Linear Regression(MLR) and Artificial Neural Network(ANN) on performance. Experimental results show that for different levels of visibility, the proposed visibility forecast model based on LightGBM algorithm obtains the highest Threat Score(TS); when the visibility is less than 2 km, the average correlation coefficient between the visibility values of observation stations predicted by the model and the observation values of visibility of observation stations is 0.75, the average mean square error between them is 6.49. It can be seen that the forecast model based on LightGBM can effectively improve the accuracy of visibility forecast.

Concerning the problem that the performance analysis method of Luby Transform (LT) codes under Gaussian elimination decoding algorithm is complicated and inaccurate, a novel performance analysis method based on probability transfer function was proposed. Firstly, for two LT codes with simple uniform degree distribution, the precise performance was studied and its quantitative expression was given. Secondly, the general LT code was investigated, and a simple but effective qualitative analysis method was proposed. Finally, the simulation work was done to verify the new method. In the comparison experiments with the traditional method which only gives the upper and the lower bounds of the rank of generated matrix, the maximum error of performance analysis results for simple uniform degree LT codes reduces to 0.0124, and the complexity of general LT codes decrease to O( k ²). Theoretical analysis shows that the proposed method can effectively guide the optimization design of LT codes in communication area.

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.

Feature representation and metric learning are fundamental problems in person re-identification. In the feature representation, the existing methods cannot describe the pedestrian well for massive variations in viewpoint. In order to solve this problem, the Color Name (CN) feature was combined with the color and texture features. To extract histograms for image features, the image was divided into zones and blocks. In the metric learning, the traditional kernel Local Fisher Discriminant Analysis (kLFDA) method mapped all query images into the same feature space, which disregards the importance of different regions of the query image. For this reason, the features were grouped by region based on the kLFDA, and the importance of different regions of the image was described by the method of Query-Adaptive Late Fusion (QALF). Experimental results on the VIPeR and iLIDS datasets show that the extracted features are superior to the original feature; meanwhile, the improved method of metric learning can effectively increase the accuracy of person re-identification.

Since the traditional wavelet threshold functions have some drawbacks such as the non-continuity on the points of threshold, and large deviation of estimated wavelet coefficient, distortion and Gibbs phenomenon occur after denoising. To overcome these drawbacks, an improved threshold function was proposed. Compared with the hard, soft threshold functions and the existing improved threshold function, the proposed function not only is easy to be calculated, but also has the superior mathematical characteristics.To verify its advantages, a series of simulation experiments were performed, the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) values were compared with other different denoising methods.The experimental results indicate that it is better than above mentioned denoising methods in both the visual effects and the performance of PSNR and MSE.

An improved measure of similarity based on the Hamming distance for measuring the degree of similarity between intuitionistic fuzzy rough sets was proposed on the basis of analyzing the deficiency of the existing similarity measure method. This method solved the problem of inaccurate similarity measure by adding the hesitancy degree and weight. Firstly, a similarity measure method for the degree of similarity between two intuitionistic fuzzy rough elements was given, and several important characters of it were revealed. Furthermore, a similarity measure method based on the Hamming distance for the degree of similarity between intuitionistic fuzzy rough sets was presented. This method was proved to have the same characters. At last, this improved similarity measure method is confirmed to be more reasonable and effective by examples.