Aiming at the shortage of the airport gate resources and the disturbance caused by the actual flight arrival and departure time deviation from the planned time, a gate assignment scheduling method was proposed by adding buffer time between the adjacent flights in the same gate. Firstly, a robust gate assignment model with a goal to achieve minimum gate idle time and apron occupancy time was established. Then, a Lagrangian relaxation optimization algorithm based on double targets was designed, and the dual problem in the Lagrangian algorithm was solved by using the subgradient algorithm. Based on the operation data of a hub airport in China, the simulation results show that, compared with those of the original gate assignment scheme, the gate usage amount and the gate idle time of the proposed method is respectively reduced by 15.89% and 7.56%, the gate occupancy rate of the optimization scheme of proposed method is increased by 18.72% and the conflict rate is reduced to 3.57%, proving that the proposed method achieves the purpose of effectively improving the utilization and robustness of airport gates.

To suppress judgment one-sidedness in the existing bearing fault diagnosis method, a bearing fault diagnosis method based on Gibbs sampling was proposed. Firstly, the bearing vibration signal was decomposed by Local Characteristic Scale Decomposition (LCD) to obtain Intrinsic Scale Components (ISC). Secondly, the time domain features were extracted from the bearing vibration signal and ISC, and the time domain features were ranked according to feature sensitivity level. The top ranked features were selected to make up feature sets. Thirdly, feature set training was used to generate a multi-dimensional Gaussian distribution model based on Gibbs sampling. Finally, posterior analysis was used to obtain the probability to realize bearing fault diagnosis. The experimental results show that the diagnostic accuracy of the proposed method reaches 100%; compared with the bearing diagnosis method based on SVM (Support Vector Machine), the diagnostic accuracy is improved by 11.1 percentage points when the number of features is 43. The proposed method can effectively diagnose rolling bearing faults, and it also has good diagnostic effect on high-dimensional and complex bearing fault data.

Concerning the problems of estimating the service time of airport flight support, and the particularity, complexity, and influence factors' uncertainty of flight support service process, an estimation model of flight support service time based on Bayesian Network (BN) was proposed. The knowledge of aviation experts and the machine learning of historical data were combined by the proposed model, and the incremental learning characteristic of BN was used to adjust the BN model dynamically, so as to make itself adapt to new conditions and constantly update the service time estimates of flight support. By using the data selected from a large domestic hub airport information system, the proposed BN model was trained via the Expectation Maximization (EM) algorithm to obtain the test results. The analysis of experimental results and model evaluation show that the proposed method can effectively estimate the service time of flight support and has higher accuracy. In addition, the sensitivity analysis demonstrates that the flight density during flight arrival time has the strongest influence on flight support service time.

In order to solve the system integration problems of high-speed railway integrated dispatching system in highly-distributed, highly heterogeneous environment, system integration framework based on Service Oriented Architecture (SOA) was proposed. The high-speed railway integrated dispatching system structure and its distributed SOA application were constructed based on Service Component Architecture (SCA) and Service Data Object (SDO) technology. The integration of power dispatching subsystem and other scheduling subsystems was achieved based on SCA and SDO technology on Java EE platform. The method fully embodies the openness and cross-platform features of SOA, and it is easy to implement.