The imperialist competitive algorithm is a swarm intelligence optimization algorithm with strong local search ability, but excessive local search will lead to the loss of diversity and fall into local optimum. Aiming at this problem, an Imperialist Competitive Algorithm based on Multiple Search Strategy (MSSICA) was proposed. The country was defined as a feasible solution and the kingdoms were defined as four mechanisms of combinatorial artificial chromosome with different characteristics. The block mechanism was used to retain the dominant solution fragment during search and differentiated mechanisms of combinatorial artificial chromosome was used for different empires to search the effective and feasible solution information of different solution spaces. When it come to the local optimum, the multiple search strategy was used to inject a uniformly distributed feasible solution to replace a less advantageous solution to enhance the diversity. Experimental results show that the multiple search strategy can effectively improve diversity of the imperialist competitive algorithm and improve the quality and stability of the solution.

Chinese news title usually consists of a single word to dozens of words. It is difficult to improve the accuracy of news title classification due to the problems such as few characters and sparse features. In order to solve the problems, a new method for text semantic expansion based on word embedding was proposed. Firstly, the news title was expanded into triples consisting of title, subtitle and keywords. The subtitle was constructed by combining the synonym of title and the part of speech filtering method, and the keywords were extracted from the semantic composition of words in multi-scale sliding windows. Then, the Convolutional Neural Network (CNN) model was constructed for categorizing the expanded text. Max pooling and random dropout were used for feature filtering and avoidance of overfitting. Finally, the double-word spliced by title and subtitle, and the multi-keyword set were fed into the model respectively. Experiments were conducted on the news title classification dataset of the Natural Language Processing & Chinese Computing in 2017 (NLP&CC2017). The experimental results show that, the classification precision of the combination model of expanding news title to triples and CNN is 79.42% in 18 categories of news titles, which is 9.5% higher than the original CNN model without expanding, and the convergence rate of model is improved by keywords expansion. The proposed expansion method of triples and the constructed CNN model are verified to be effective.

Focusing on the fact that the order of locations and time play a pivotal role in understanding user mobility patterns for spatio-temporal trajectories, a novel deep representation model for trajectories was proposed. The model considered the characteristics of spatio-temporal trajectories: 1) different orders of locations indicate different user mobility patterns; 2) trajectories tend to be cyclical and change over time. First, two time-ordered locations were combined in location sequence; second, the sequence and its corresponding time bin were combined in the temporal location sequence, which was the basic unit of describing the features of a trajectory; finally, the deep representation model was utilized to train the feature vector for each sequence. To verify the effectiveness of the deep representation model, experiments were designed to apply the temporal location sequence vectors to user mobility patterns mining, and empirical studies were performed on a real check-in dataset of Gowalla. The experimental results confirm that the proposed method is able to discover explicit movement patterns (e.g., working, shopping) and Word2Vec is difficult to discover the valuable patterns.

Concerning the collision problem of the reader in Radio Frequency Identification (RFID) application field, the polling-based frame slot algorithm and the binary bit anti-collision algorithm were compared, and then the improved frame slot algorithm was proposed. First, the frame length was divided into several slots; second, the numbers of tags were dynamically estimated, and the frame length to be transmitted was determined, then the response probability of the electronic label for a slot in the frame was reached a maximum; finally, the minimum system collision probability was reached. The simulation results show that, the system throughput rate of the improved frame slot anti-collision algorithm can be maintained at more than 50%, and in the working scope with a large number of electronic tags throughput rate can reach more than 65%. Compared with the frame slot anti-collision algorithm on average 36% of the system throughput rate, the system throughput rate of improved frame slot algorithm nearly doubles. And the structure is simple, so it is easy to be used in practical applications.

To overcome the defects of the existing algorithms, such as the poor real-time performance, bad effect in sky area and dark dehazed image, a real-time image haze removal algorithm was proposed. Firstly, dark channel prior was used to estimate the rough transmission map. Secondly, the method of optimized guided filtering was used to refine the down-sampled rough transmission map, which can real-time process higher resolution image. Thirdly, refined transmission map was up-sampled and corrected to obtain the final transmission map, which can overcome the defect of bad effect in sky area. Finally, the clear image was got by adaptive brightness adjustment with color restoration. The complexity of the algorithm is only a linear function of the number of input image pixels, which brings a very fast implementation. For the image which resolution is 600×400, the processing time is 80ms.