The dual cycle bridge detection of piezoresistive triaxial accelerometer based on MEMS technology

A cycle bridge detection method, which uses a piezoresistive triaxial accelerometer, has been described innovatively. This method just uses eight resistors to form a cycle detection bridge, which can detect the signal of the three directions for real time. It breaks the law of the ordinary independent Wheatstone bridge detection method, which uses at least 12 resistors and each four resistors connected as a Wheatstone bridge to detect the output signal from a specific direction. In order to verify the feasibility of this method, the modeling and simulating of the sensor structure have been conducted by ANSYS, then the dual cycle bridge detection method and independent Wheatstone bridge detection method are compared, the result shows that the former method can improve the sensitivity of the sensor effectively. The sensitivity of the x, y-axis used in the former method is two times that of the sensor used in the latter method, and the sensitivity of the z-axis is four times. At the same time, it can also reduce the cross-axis coupling degree of the sensor used in the dual cycle bridge detection method. In addition, a signal amplifier circuit and adder circuit have been provided, Finally, the test result of the ＂eight-beams/mass＂ triaxial accelerometer, which is based on the dual cycle bridge detection method and the related circuits, have been provided. The results of the test and the theoretical analysis are consistent, on the whole.

Fast Cycle Structure Detection for Power Grids Based on Graph Computing

The cycle structure in a power grid may lower the stability of the network;thus,it is of great significance to accu-rately and timely detect cycles in power grid networks.However,detecting possible cycles in a large-scale network can be highly time consuming and computationally intensive.In addition,since the power grid's topology changes over time,cycles can appear and disappear,and it can be difficult to monitor them in real time.In traditional computing systems,cycle detection requires considerable computational resources,making real-time cycle detection in large-scale power grids an impossible task.Graph computing has shown excellent performance in many areas and has solved many practical graph-related problems,such as power flow calculation and state estimation.In this article,a cycle detection method,the Paton method,is implemented and optimized on a graph computing platform.Two cases are used to test its performance in an actual power grid topology scenario.The results show that the graph computing-based Paton method reduces the time consumption by at least 60%compared to that of other methods.

Combined GPS/GLONASS Data Processing

To obtain the GLONASS satellite position at an epoch other than reference time,the satellite’s equation of motion has to be integrated with broadcasting ephemerides.The iterative detecting and repairing method of cycle slips based on triple difference residuals for combined GPS/GLONASS positioning and the iterative ambiguity resolution approach suitable for combined post processing positioning are discussed systematically.Experiments show that millimeter accuracy can be achieved in short baselines with a few hours’ dual frequency or even single frequency GPS/GLONASS carrier phase observations,and the precision of dual frequency observations is distinctly higher than that of single frequency observations.