Showing papers by "Hongkai Zhao published in 2022"
TL;DR: In this article , a shipborne oceanic high-spectral-resolution lidar (HSRL) was used to estimate the depth-resolved diffuse attenuation coefficient Kd and the particulate backscattering coefficient bbp at 532 nm.
Abstract: Lidar techniques present a distinctive ability to resolve vertical structure of optical properties within the upper water column at both day- and night-time. However, accuracy challenges remain for existing lidar instruments due to the ill-posed nature of elastic backscatter lidar retrievals and multiple scattering. Here we demonstrate the high performance of, to the best of our knowledge, the first shipborne oceanic high-spectral-resolution lidar (HSRL) and illustrate a multiple scattering correction algorithm to rigorously address the above challenges in estimating the depth-resolved diffuse attenuation coefficient Kd and the particulate backscattering coefficient bbp at 532 nm. HSRL data were collected during day- and night-time within the coastal areas of East China Sea and South China Sea, which are connected by the Taiwan Strait. Results include vertical profiles from open ocean waters to moderate turbid waters and first lidar continuous observation of diel vertical distribution of thin layers at a fixed station. The root-mean-square relative differences between the HSRL and coincident in situ measurements are 5.6% and 9.1% for Kd and bbp, respectively, corresponding to an improvement of 2.7-13.5 and 4.9-44.1 times, respectively, with respect to elastic backscatter lidar methods. Shipborne oceanic HSRLs with high performance are expected to be of paramount importance for the construction of 3D map of ocean ecosystem.
8 citations
TL;DR: A data driven and data adaptive approach based on local regression and global consistency is proposed for stable PDE identification.
Abstract: In this work we study the problem about learning a partial differential equation (PDE) from its solution data. PDEs of various types are used as examples to illustrate how much the solution data can reveal the PDE operator depending on the underlying operator and initial data. A data driven and data adaptive approach based on local regression and global consistency is proposed for stable PDE identification. Numerical experiments are provided to verify our analysis and demonstrate the performance of the proposed algorithms.
4 citations
TL;DR: In this paper , a data-driven approach for model reduction and approximation of an arbitrary solution of a PDE without knowing the underlying PDE is designed. But it depends on the growth rate of the eigenvalues, µ n , of L in terms of n .
Abstract: Linear evolution PDE ∂ t u ( x, t ) = −L u , where L is a strongly elliptic operator independent of time, is studied as an example to show if one can superpose snapshots of a single (or a finite number of) solution(s) to construct an arbitrary solution. Our study shows that it depends on the growth rate of the eigenvalues, µ n , of L in terms of n . When the statement is true, a simple data-driven approach for model reduction and approximation of an arbitrary solution of a PDE without knowing the underlying PDE is designed. Numerical experiments are presented to corroborate our analysis.
TL;DR: In this article , the adaptive robust cubature Kalman filtering (CKF) algorithm was employed for human attitude analysis based on wearable inertial sensors with time-varying state-process noise.
Abstract: Attitude analysis and recognition can be applied in wearable computing for medical assistance, motor-function assessment and dexterous human-robot interaction. The main problems, however, are serious drift and instability during traditional motion measurement fusion methods due to the high dynamic complexity of limb movements. To the best of our knowledge, it is the first attempt to employ an adaptive robust cubature Kalman filtering algorithm in the human attitude analysis based on wearable inertial sensors with time-varying state-process noise. Experiment results show that the adaptive robust CKF algorithm based on quaternion and gyroscope error modeling proposed in this paper can solve motion attitude solution. Lastly, we compare our method with CKF and EKF algorithm, the proposed algorithm can effectively improve the precision of attitude analysis.