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.

The characteristics of massiveness,dynamics,and lightweight devices for network devices in the Internet of Things(IoT)are inherently connected and exist simultaneously. To satisfy these three characteristics simultaneously,a Blockchain-Based IoT Access Control(BBIAC)framework was proposed. Firstly,the BBIAC model under this framework was proposed,the concept of attribute was introduced into the process of IoT authorization to realize the model's support for massiveness;the distributed structure and identity authentication method of blockchain provide the support of dynamics for the model. Secondly,the complete workflow of BBIAC model was introduced. Thirdly,the formal safety assessment of BBIAC was performed by Colored Petri Network(CPN),and the security of the BBIAC model was proved. Experimental results show that BBIAC is suitable for IoT environments with characteristics of massiveness,dynamics and lightweight devices.

In the environment with unknown dynamic obstacle moving and target point, the radius of the repulsive force is often larger than the radius of the obstacle when the path is planned by the artificial potential field method, which leads to the collision of the dynamic obstacle with the robot. An improved dynamic path planning strategy of artificial potential field based on Morphine algorithm and non-completely waiting strategy was proposed. The non-completely waiting strategy was adopted when the dynamic obstacle collided with the robot on a side. The Morphine algorithm was used to localize the path when the dynamic obstacle collided with the robot face to face. Moreover, the rolling window theory was introduced to improve the accuracy of avoiding dynamic obstacles. Through the simulation tests, compared with the traditional artificial potential field, the proposed algorithm is shortened by 12 steps in the event of a side collision and 6 steps in the event of a face-to-face collision. Therefore, the improved algorithm is more effective in path smoothness and planning steps.

Domain Name System (DNS) provides domain name resolution service, i.e., converting domain names to IP addresses. Malicious domain detection is mainly for discovering illegal activities and ensuring the normal operation of the domain name servers. Prior work on malicious domain name detection was summarized, and a new machine learning based malicious domain detection algorithm for exploiting multiple-dimensional features was further proposed. With respect to domain name lexical features, more fine-grained features were extracted, such as the conversion frequency of the numbers and letters and the maximum length of continuous letters. As for the network attribute features, more attentions were paid to the name servers, such as the quantity, and the degree of dispersion. The experimental results show that the accuracy, recall rate, F1 value of the proposed method reaches 99.8%, which means a better performance on malicious domain name detection.

In order to overcome the non-person entity's interference, the difficulties in selection of feature words and muti-person influence on target personal relation extraction, this paper proposed person judgment based on decision tree, relation feature word generation based on minimum set cover and statistical approach based on three-layer sentence pattern rules. In the first step, 18 features were extracted from attribute files of China Conference on Machine Learning (CCML) competition 2015, C4.5 decision was used as the classifier, then 98.2% of recall rate and 92.6% of precision rate were acquired. The results of this step were used as the next step's input. Next, the algorithm based on minimum set cover was used. The feature word set covers all the personal relations as the scale of feature word set is maintained at a proper level, which is used to identify the relation type in text headline. In the last step, a method based on statistics of three-layer sentence pattern rules was used to filter small proportion rules and specify the sentence pattern rules based on positive and negative proportions to judge whether the personal relation is correct or not. The experimental result shows the approach acquires 82.9% in recall rate and 74.4% in precision rate and 78.4% in F1-measure, so the proposed method can be applied to personal relation extraction from text headlines, which helps to construct personal relation knowledge graph.

A new Kinect depth map pretreatment algorithm was presented for the lower accuracy problem compared with the original depth information in the field of three-Dimensional (3D) scene measurement for robot's perception. Firstly, a measuring and sampling model of the depth map was developed to realize the Monte Carlo uncertainty evaluation model. Secondly, the depth value intervals were calculated to judge and filter the noise pixels. Finally, noise points were repaired with mean-value of the estimation intervals. The experimental results show that the algorithm can effectively suppress and repair the noise pixels while keeping the depth gradient and values of non-noise pixels. The Mean Square Error (MSE) of depth map after preprocessing is reduced by 15.25% to 28.79%, and the object profiles remain unchanged compared with the JBF (Joint Bilateral Filtering) based on color and depth map. Therefore, it achieves the purpose of improving the depth information accuracy in 3D scenes.

The checking of bullet's primer is the most important step in controlling the quality of bullet products. In order to segment the image of bullet's primer surface defect accurately, a new method of image segmentation was proposed. According to the checking requirement and the properties of cartridge's bottom, firstly, the image of bullet's primer was ascertained approximately to be detected, and Log operator was applied to extract the circle edge of primer. After analyzing both advantages and disadvantages of the Hough transform and the least square method, a new algorithm of circle detection combined improved Hough transform and the least square method was proposed, by which the center of circle and radius were acquired accurately. Finally, the image of primer circle was extracted by the parameters of circle, the primer surface defect was segmented by threshold, and the results of segmentation were optimized by mathematical morphology. The experimental results show that the proposed method is of accuracy and robustness in the application of bullet's primer surface defect segmentation. The average wrong segmentation rate is below 10%, and the average deviation is less than 17 pixels.