To solve the problems of large space, low transmission efficiency, high maintenance frequency in existing coal mine belt conveyor transmission system, a kind of direct drive structure using outer-rotor Permanent Magnet Synchronous Motor (outer-rotor PMSM) was proposed, and a model-free adaptive control with cascade structure was applied to the speed control system of belt conveyor. According to the requirements of mine belt conveyor running, the detailed design parameters and mathematical model of this motor were given, and the ideal speed curves of starting and the steady state were set. By using model-free adaptive control algorithm, a cascade model-free adaptive control law was designed, and the cascade control system structure was also given. The simulation of the ideal starting curve in direct-drive system in coal mine belt conveyor for outer-rotor PMSM was conducted using Matlab software. The results show that the cascade model-free adaptive control algorithm reduces the speed tracking error and improves control precision, which suppresses the system noise and the disturbance for load changing effectively. This method achieves good startup and steady-state characteristics of belt conveyor.

The paper proposed a method to solve the shortcomings of the current Wireless Sensor Network (WSN) three-dimensional localization algorithm in terms of accuracy and complexity. The raster scan was used to resolve the projection cross domain of the neighboring anchor nodes on the two coordinate planes, and got the corresponding positions of the unknown nodes on the two coordinate planes, thus ultimately realizing the three-dimensional position estimate. Finally, the locations of the unknown nodes in three-dimension were estimated. The simulation result shows that when 200 sensor nodes were deployed randomly confined to the space of 100m*100m*100m, the coverage ratio of unknown nodes reached 99.1%, and the relative error decreased to 0.5533. The use of projection reduced the complexity of the algorithm efficiently.

This paper proposed a new algorithm to estimate Gauss and mean curvature on triangular meshes based on NURBS fitting. In this algorithm, double neighbors were selected for local fitting in the first place which involved border checking. For parameterization, directly projection method was used. Then by using least square NURBS fitting, surface function was acquired, which followed the traditional curvature estimation method. Through tests on meshes considering different resolution and noises, final conclusion can be drawn that our algorithm has good results and is more stable than other algorithms, thus is more applicable.