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In this perspective, we outline that a space borne gravitational wave detector network combining LISA and Taiji can be used to measure the Hubble constant with an uncertainty less than 0.5% in ten years, compared with the network of the ground based gravitational wave detectors which can measure the Hubble constant within a 2% uncertainty in the next five years by the standard siren method. Taiji is a Chinese space borne gravitational wave detection mission planned for launch in the early 2030 s. The pilot satellite mission Taiji-1 has been launched in August 2019 to verify the feasibility of Taiji. The results of a few technologies tested on Taiji-1 are presented in this paper. Gravitational wave astronomy has opened the door to test general relativity and the effect of gravity in the Universe. The authors present the capabilities of an overlap between space gravitational wave detectors LISA and Taiji to constrain the Hubble constant to 0.5%, in 10 years, and what can be learned from the satellite pilot Taiji-1 launched in 2019.

To the question of networked control system with random time-delay in both forward and feedback channels, a new class of Smith predictor combined to Fuzzy PID control is presented. It sets up a new Smith forecast model and designs the parameter self-tuning fuzzy PID controller. The results of system simulation using MATLAB show that the network control system of the proposed scheme has better dynamic and stability, and in the case of model miss-matching has high robustness.

The remote control system based on DTMF transmission was discussed. It used a SCM (Single-chip microcomputer) and voice chip to realize remote communication control, which voice prompts can point out and feedback state information of the controlled side for different operations. Using this method, users in the outdoor can use any a DTMF phone including mobile, fixed telephone, according to the voice prompt, to carry out remote control of the switching and timing functions of the various home appliances. The system is practical, reliable, and can be widely used in household appliances or other places of various control devices. It will be the promising future technology products.

Based on Machine Vision, the paper research a method to detect obstacles and calculate the distance between the obstacle and moving vehicles. First it uses the shadow detection method to determine approximate location of the obstacle and adopt corner detection method to detect the obstacle contour information. Then, the minimum bounding rectangle method is used to accurately framed obstacles vehicles. Finally, based on the principle of the distance in the camera, the obstacle distance measurement formula is built. Experimental results show that the algorithm can accurately locate obstacles and measure the actual distance between the moving vehicle and the obstacles.

Based on a large number of statistical results, we study the prediction of solar proton events in depth. By summarizing the relations of the proton events with the area, position, McIntosh structure, magnetic structure of the active region, and the number of solar flare bursts which happened in the active region two days before, a short-term prediction model of solar proton events is built on the lines of artificial neural network. Tests on 12 samples later than 2000 indicate a prediction accuracy of about 83%. Further test predictions are made on the proton events which occurred in Jan.–Apr. 2002. It is found that all the 6 events that occurred in this period are correctly predicted. Among them, 3 are predicted 3 days ahead, 2 events — 2 days ahead, and 1 event — 1 day ahead.