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When a satellite equipped with large angular momentum rotational payload is in orbit for a long time, the angular momentum generated by the detection head of the rotational payload is mainly compensated by the attitude and orbit control system, the rotation characteristic will directly affect the satellite attitude stability. When the rotation characteristic of the detection head deviates greatly from the design index, it will affect the attitude stability of the satellite and cause the attitude of the satellite to be unstable. Based on the analysis of on-orbit data, in order to suppress the rotation characteristics of the payload detection head, the paper designed a method for feedforward compensation using reaction wheel. Finally, through on-orbit tests and data analysis, the validity and practicability of the design algorithm are verified.

Devices and their faults increasingly bring a great difficulty to grid operators. In order to solve troubles caused by equipment fault diagnosis, this paper conducts a research on the intelligent image recognition and diagnosis technology of key external insulation devices based on deep learning.

This paper presents a binocular vision-aided inertial navigation system of mobile robot for positioning and navigation in the GPS-denied environment with fuzzy map. The system consists of two common cameras and a low-cost inertial measurement unit (IMU). The stereo camera inertial measurement unit (SC-IMU) hardware is designed and fixed on the mobile robot to collect related data. A novel fusion method is proposed according to the two modules operating at different data rates, which fuses the current position of robot acquired by the IMU and the binocular camera system, and achieves the intermittent correction for the localization of inertial navigation system (INS). We show experimental results to validate the reliability and effectiveness of the proposed algorithm.

The inertial navigation system(INS) composed of inertial measurement unit with low cost and high positioning speed is widely applied to indoor positioning, but as a result of interference of magnetic field, the constant error accumulating make it impossible to obtain high accuracy. Ultrasound sensor has high measuring accuracy in a simple indoor environment, so as to compensate the accumulative errors of INS. In this paper, four ultrasonic ranging elements are used to expanding indoor robot's measuring range while Kalman filter is in use for information fusion algorithm. After applying this multiple sensors with INS method, good practicability for its relatively high measurement precision is obtained.

Previous research on consecutive systems focused on the model with linear consecutive connected elements. A circular consecutive k-out-of-r-from-n system is proposed in this paper as a generalization of circular consecutive k-out-of-n system. The model is further generalized to be a multi-state circular sliding window system. Two algorithms based on universal generating function technique are proposed to evaluate the system reliability of the two proposed models. The model and algorithms are illustrated through examples.