A three-degrees-of-freedom model, including surge, sway and yaw motion, with differential thrusters is proposed to describe unmanned surface vehicle (USV) dynamics in this study. The experiment is carried out in the Qing Huai River and the data obtained from different zigzag trajectories are filtered by a Gaussian filtering method. A physics-informed neural network (PINN) is proposed to identify the dynamic models of the USV. PINNs combine the advantages of data-driven machine learning and physical models. They can also embed the speed and steering models into the loss function, which can significantly retain all types of information. Compared with traditional neural networks, the results show that the PINN has better generalization ability in predicting the surge and sway velocities and rotation speed with only limited training data.
AUV (autonomous underwater vehicles) are required to have long-term and high-precision positioning capability relative to seabed targets in most deep-sea exploration tasks. However, acoustic positioning error is positively correlated with its operating range and inertial navigation has inevitable accumulated time errors, neither of which provide precise AUV positions. TAN (terrain aided navigation) directly calculates the AUV position to the seabed terrain coordinate system by tracking the seabed topographic characteristics, which can guide the AUV to seabed target accurately. However, the initial TAN positioning error will increase with the AUV operation depth, which causes a large PF (particle filter) initialization error and particle coverage interval, and will affect the convergence and stability of TAN. To solve this problem, we first propose a TAP (terrain aided position) confidence interval model. We then use the confidence interval to constrain the initial particles to a smaller range. Finally, the validity of the algorithm is verified by playback simulation with ship-borne multi-beam sonar sensor measured data. The results show that the TAP confidence interval can reduce the coverage of the initial particle, and can improve the convergence speed and filtering accuracy of the TAN.
Brassica crops include various edible vegetable and plant oil crops, and their production is limited by low temperature beyond their tolerant capability. The key regulators of low‐temperature resistance in Brassica remain largely unexplored. To identify posttranscriptional regulators of plant response to low temperature, we performed small RNA profiling, and found that 16 known miRNAs responded to cold treatment in Brassica rapa. The cold response of seven of those miRNAs were further confirmed by qRT‐PCR and/or northern blot analyses. In parallel, a genome‐wide association study of 220 accessions of Brassica napus identified four candidate MIRNA genes, all of which were cold‐responsive, at the loci associated with low‐temperature resistance. Specifically, these large‐scale data analyses revealed a link between miR1885 and the plant response to low temperature in both B. rapa and B. napus. Using 5′ rapid amplification of cDNA ends approach, we validated that miR1885 can cleave its putative target gene transcripts, Bn.TIR.A09 and Bn.TNL.A03, in B. napus. Furthermore, overexpression of miR1885 in Semiwinter type B. napus decreased the mRNA abundance of Bn.TIR.A09 and Bn.TNL.A03 and resulted in increased sensitivity to low temperature. Knocking down of miR1885 in Spring type B. napus led to increased mRNA abundance of its targets and improved rapeseed tolerance to low temperature. Together, our results suggested that the loci of miR1885 and its targets could be potential candidates for the molecular breeding of low temperature‐tolerant Spring type Brassica crops.
Predicting the maneuvering motion of an unmanned surface vehicle (USV) plays an important role in intelligent applications. To more precisely predict this empirically, this study proposes a method based on the support vector regression with a mixed kernel function (MK-SVR) combined with the polynomial kernel (PK) function and radial basis function (RBF). A mathematical model of the maneuvering of the USV was established and subjected to a zig-zag test on the DW-uBoat USV platform to obtain the test data. Cross-validation was used to optimize the parameters of SVR and determine suitable weight coefficients in the MK function to ensure the adaptive adjustment of the proposed method. The PK-SVR, RBF-SVR, and MK-SVR methods were used to identify the dynamics of the USV and build the corresponding predictive models. A comparison of the results of the predictions with experimental data confirmed the limitations of the SVR with a single kernel function in terms of forecasting different parameters of motion of the USV while verifying the validity of the MK-SVR based on data collected from a full-scale test. The results show that the MK-SVR method combines the advantages of the local and global kernel functions to offer a better predictive performance and generalization ability than SVR based on the nuclear kernel function. The purpose of this manuscript is to propose a novel method of dynamics identification for USV, which can help us establish a more precise USV dynamic model to design and verify an excellent motion controller.
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