345 research outputs found
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
- …