Focusing on the issues that the relationships between the stations are affected by the sparse distribution of surface meteorological stations and it is difficult to infer the strengths of relationships between the stations， a Deep learning Model for multi-station temperature prediction combined with MOD11A1 and surface meteorological station data was proposed， namely GDM， which included Spatio-Temporal Attention （TSA） ， Double Graph neural Long Short-Term Memory （DG-LSTM） network encoding and Edge-Node transform Gated Recurrent Unit （EN-GRU） decoding modules. Firstly， TSA module was utilized to extract MOD11A1 image features and form the temperature time series of multiple virtual meteorological stations， so as to alleviate the impact of sparse distribution of surface meteorological stations on the relationships between the stations. Secondly， DG-LSTM encoder was used to calculate the strengths of the relationships among surface meteorological stations and virtual meteorological stations via fusing two sets of temperature time series. Finally， EN-GRU decoder was adopted to model the temperature time series relationships between surface meteorological stations through combining the inter-station relationship strengths. Experimental results show that compared with 2-Dimensional Convolutional Neural Network （2D-CNN）， Long Short-Term Memory-Fully Connected network （LSTM-FC）， Long Short-Term Memory neural network Extended （LSTME） and Long Short-Term Memory and AdaBoost network （LSTM-AdaBoost）， GDM has the Average Absolute Error （MAE） of temperature prediction in 24 hours at 10 surface meteorological stations reduced by 0.383 ℃， 0.184 ℃， 0.178 ℃ and 0.164 ℃ respectively. It can be seen that GDM can improve the prediction accuracy of the temperature for meteorological stations in the next 24 hours.

Errors are common to the system. In safety-critical systems, quantitative analysis of errors is necessary. However, the previous reasoning and verification methods rarely consider errors. The errors are usually described with the interval numbers, so that the linear assertion was spread and the concept of linear error assertion was given. Furthermore, combined with the properties of convex set, a method to solve the vertices of linear error assertion was proposed, and the correctness of this method was proved. By analyzing the related concepts and theorems, the problem to judge whether there was implication relationship between linear error assertions was converted to the problem to judge whether the vertices of the precursor assertion were contained in the zero set of the successor assertion, so as to give the easy-to-program steps of judging the implication relationship between linear error assertions. Finally, the application of this method to train acceleration was given, and the correctness of the method was tested with the large-scale random examples. Compared with the reasoning methods without error semantics, this method has advantages in the field of reasoning and verification of systems with error parameters.

For the issue that the robustness of the traditional audio watermarking algorithm based on Empirical Mode Decomposition (EMD) is not strong, an blind audio watermarking algorithm based on the extremum of Intrinsic Mode Function (IMF) was presented. The original audio signal was segmented firstly, and the audio frame was decomposed to a series of IMFs by EMD. Watermarking bits and synchronization code were embedded in the extremum of the last IMF by mean quantization. The embedding payload of the proposed method was 46.9~50.3 b/s, and the watermarked audio signal keeps the perceptive quality of the original audio signal. Several signal attacks such as adding noise, MP3 compression, re-sampling, filtering and cropping were imposed on the watermarked audio. The extracted watermarking bit changed a little, which shows the robustness of the proposed scheme. Compared with time domain and wavelet domain methods, the proposed method can resist 32 kb/s MP3 compression attack with high embedding payload.

Traditional power allocation schemes have ignored channel estimation errors and circuit energy consumption. To solve this problem, an improved green joint optimization scheme was proposed in this paper. On the premise of guaranteeing user's QoS (Quality of Service), energy efficiency and time efficiency for relay selection and each relay's power allocation were jointly optimized in the improved scheme, with taking channel estimation errors and relay circuit energy consumption into consideration. In the end, the closed solutions of transmit power of the source node and relay nodes were obtained. The simulation results show that the proposed scheme performs 30% better than traditional optimization scheme in energy efficiency with high SNR (Signal-to-Noise Ratio) and has a close performance with low SNR.

The traditional Super-Resolution (SR) algorithm, based on sparse dictionary pairs, is slow in training speed, poor in dictionary quality and low in feature matching accuracy. In view of these disadvantages, a super-resolution algorithm based on the improved sparse coding was proposed. In this algorithm, a Morphological Component Analysis (MCA) method with adaptive threshold was used to extract picture feature, and Principal Component Analysis (PCA) algorithm was employed to reduce the dimensionality of training sets. In this way, the effectiveness of the feature extraction was improved, the training time of dictionary was shortened and the over-fitting phenomenon was reduced. An improved sparse K-Singular Value Decomposition (K-SVD) algorithm was adopted to train low-resolution dictionary, and the super-resolution dictionary was solved by utilizing overlapping relation, which enforced the effectiveness and self-adaptability of the dictionary. Meanwhile, the training speed was greatly increased. Through the reconstruction of color images in the Lab color space, the degradation of the reconstructed image quality, which may be caused by the color channel's correlation, was avoided. Compared with traditional methods, this proposed approach can get better high-resolution images and higher computational efficiency.

Precise train stopping is a key technology of automatic train operation. On the basis of analyzing the train stopping phase, the delay characteristics of brake model and constraint conditions of train characteristics were considered, using generalized predictive control theory, a multi-objective predictive controller with constraints was designed taking account of train speed and distance as control targets and combining the control constraint conditions. The simulation results show that the proposed controller can accurately track the train stopping curve to achieve high precision stopping requirements and higher comfort.

It is necessary to generate the quorums as soon as possible in large-scale fully distributed system for its mutual exclusion problem. Based on the theory of relaxed cyclic difference set, the definition of second relaxed cyclic difference set was proposed. After researching the new concepts, the subtraction steps in previously classical methods can be changed into summation steps. Furthermore, a lot of summation steps can be cut down by the recurrence relation deduced from the summation steps. The time complexity of this algorithm is just only O(n) and the size of the symmetric quorums is still close to 2n^(1/2).