On the Development and Application of FOG

Gyroscope is a type of angular velocity measuring device, which can precisely determine the orientation of moving objects. It was first employed in navigation and later became an inertial navigation instrument widely used in modern aviation, aerospace, and national defense industries. As a vital representative of gyroscope, the fiber-optic gyroscope (FOG) has advantages in terms of compact structure, high precision, high sensitivity, and high environmental adaptability. FOG has been broadly utilized in many fields, and is also a key component of modern navigation instruments. In this paper, the history, classification, performance indicators, and application requirements of gyroscope are briefly summarized. The development history of FOG based on Sagnac effect is described in detail. The three generations of FOG are interferometric FOG, resonant FOG, and stimulated Brillouin scattering FOG. At the same time, this chapter summarizes the development and research situation of FOG in the United States, Japan, France, and other major developing countries, and compares the application of FOG in various international companies

UAV-enabled optimal position selection for secure and precise wireless transmission

In this letter, two unmanned-aerial-vehicle (UAV) optimal position selection schemes are proposed. Based on the proposed schemes, the optimal UAV transmission positions for secure precise wireless transmission (SPWT) are given, where the maximum secrecy rate (SR) can be achieved without artificial noise (AN). In conventional SPWT schemes, the transmission location is not considered which impacts the SR a lot. The proposed schemes find the optimal transmission positions based on putting the eavesdropper at the null point. Thus, the received confidential message energy at the eavesdropper is zero, and the maximum SR achieves. Simulation results show that proposed schemes have improved the SR performance significantly

Simultaneous inference of periods and period-luminosity relations for Mira variable stars

The Period--Luminosity relation (PLR) of Mira variable stars is an important tool to determine astronomical distances. The common approach of estimating the PLR is a two-step procedure that first estimates the Mira periods and then runs a linear regression of magnitude on log period. When the light curves are sparse and noisy, the accuracy of period estimation decreases and can suffer from aliasing effects. Some methods improve accuracy by incorporating complex model structures at the expense of significant computational costs. Another drawback of existing methods is that they only provide point estimation without proper estimation of uncertainty. To overcome these challenges, we develop a hierarchical Bayesian model that simultaneously models the quasi-periodic variations for a collection of Mira light curves while estimating their common PLR. By borrowing strengths through the PLR, our method automatically reduces the aliasing effect, improves the accuracy of period estimation, and is capable of characterizing the estimation uncertainty. We develop a scalable stochastic variational inference algorithm for computation that can effectively deal with the multimodal posterior of period. The effectiveness of the proposed method is demonstrated through simulations, and an application to observations of Miras in the Local Group galaxy M33. Without using ad-hoc period correction tricks, our method achieves a distance estimate of M33 that is consistent with published work. Our method also shows superior robustness to downsampling of the light curves

Safety-oriented planning of expressway truck service areas based on driver demand

Funding This study was supported by the National Natural Science Foundation of China (51978522).Peer reviewedPublisher PD