In order to improve the accuracy and the Out Of Vocabulary (OOV) recognition rate of the Chinese word segmentation, a Chinese word segmentation system based on character representation learning method was proposed. Firstly, the word in the text was mapped to a vector in a high-dimentioanl vecter space using Skip-gram model; then the K-means clustering algorithm was used to acquire clusters of the word vector, and the clustering results were regarded as features of Conditional Random Fields (CRF) model for training. Finally the CRF model was used for word segmentation and OOV recognition. The influences of the word vector dimensions, the number of clusters and different cluster algorithm on word segmentation were analyzed. Experiments were conducted on the 4th CCF Conference on Natural Language Processing & Chinese Computing (NLPCC2015) corpus. Experimental results show that the proposed system can effectively improve Chinese short text segmentation performance without using external knowledge, the F-value and the OOV recognition rate achieve to 95.67% and 94.78% respectively.

It is difficult to maintain a balance between accuracy and real-time performance of lane line recognition, thus a new lane line recognition method was proposed based on region division. Firstly, an improved OTSU algorithm was applied to segment the edge image; then, feature points in that edge image were extracted by using Progressive Probabilistic Hough Transform (PPHT) algorithm and fitted as a line by using Least Square Method (LSM). Finally, all fitted lines were judged and the possible lines were chosen by using an anti-interference algorithm. Comparative experiments were conducted with three other algorithms mentioned in the references. In addition, an evaluation model was put forward to assess the performance of the algorithms when dealing with 500 typical lane images. Meanwhile, by calculating the average overhead time on processing each frame of a 1 min 26 s video, the response time of the algorithm was evaluated. The experimental results show that three indexes including precision, recall rate and F value of the proposed algorithm are better than the comparison algorithm, and the proposed algorithm also meets the requirement of real-time processing.

The reliability of Embedded System Hardware (ESH) is very important, which is directly related to the quality and longevity of the embedded system. To analyze the reliability of ESH, it was studied on the perspective of hardware using Copula function. At first, abstract formalization of the ESH was defined from composition level. Then reliability modeling of each function module of the ESH was given by considering integration of hardware and software, as well as using Copulas function to establish the reliability model of ESH. Finally, the parameters of the proposed reliability model were estimated, and a specific calculation example by using this proposed model was put forward and compared with some other Copulas functions. The result shows that the proposed model using Copula function is effective.