In the process of indoor positioning and navigation, a Bayesian network-based floor localization algorithm was proposed for the problem of large error of floor localization when only the pedestrian height displacement considered. Firstly, Extended Kalman Filter (EKF) was adopted to calculate the vertical displacement of the pedestrian by fusing inertial sensor data and barometer data. Then, the acceleration integral features after error compensation was used to detect the corner when the pedestrian went upstairs or downstairs. Finally, Bayesian network was introduced to locate the pedestrian on the most likely floor based on the fusion of walking height and corner information. Experimental results show that, compared with the floor localization algorithm based on height displacement, the proposed algorithm has improved the accuracy of floor localization by 6.81%; and compared with the detection algorithm based on platform, the proposed algorithm has improved the accuracy of floor localization by 14.51%. In addition, the proposed algorithm achieves the accuracy of floor localization by 99.36% in the total 1247 times floor changing experiments.

For the scheduling problem of no cross-over twin Rail-Mounted Gantry (RMG) and container slot selection, considering the safety distance between the two RMGs and the buffer capacity, a coupled model of twin RMG scheduling and container slot selection was proposed with the goal of minimizing the completion time by setting the twin RMG scheduling as the main line and setting the container slot selection as the auxiliary line. The basic idea of it is to set the decision variable to describe the relationship between the tasks. A Genetic Algorithm-Ant Algorithm (GAAA) was designed for solving the coupled model, and the CPLEX was developed for comparisons by analyzing the efficiency in relay mode and mixed mode. The experimental results show that the efficiency in relay mode is better than that of mixed mode when dealing with 8 to 150 container tasks; in small and medium-large sized experiments, the minimum completion time of GAAA is reduced by about 2.65% and 18.50%, respectively; the running time of GAAA is reduced by 88.6% and 99.19% respectively on average compared with CPLEX, which validates the validity of the model.

To avoid the problem of the carton missing in the process of cigarette production, this paper introduced a new method of pattern matching based on machine. Using the method could avoid the effects of the random reflecting light on images. After getting the taboo area of the image, the result of the pattern matching was used to determine whether some cartons miss or not. In addition, the taboo matching method could also adjust the image and get the template image automatically without considering the pattern or color of the carton. The taboo matching would reduce the error detection rate in a real system and provide a way of solving problems of the similar kind.

Detecting records that are approximate duplicates, but not exact duplicates, is one of the key tasks in information integration. Although various algorithms have been presented for detecting duplicated records, strings matching is essential to those algorithms. In self- adaptive information integration algorithm presented by this paper, the hybrid similarity, a comprehensive edit distance and token metric, was used to measure the similar degree between strings. In order to avoid mismatching because of different expressions, the strings in records were partitioned into vocabularies, then were sorted according to their first character. In the process of vocabularies matching, misspellings and abbreviations can be tolerated. The experimental results demonstrate that the self-adaptive approach for information integration achieves higher accuracy than that using Smith-Waterman edit distance and Jaro distance.