Effect of Brassinolide on Soil Microorganisms in Millet Field Polluted by Tribenuron-Methyl.

Tribenuron-methyl is used to control broad-leaved weeds and has a promising application prospect in millet fields. However, its negative impact on soil ecology cannot be ignored. Brassinosteroids have been widely reported to enhance plant resistance to stress, but information on brassinosteroids for the remediation of pesticide-contaminated soils is limited. Under field conditions, brassinosteroids were applied to explore their effects on the residues of tribenuron-methyl, soil enzyme activity, soil microbiol community, and millet yield. After applying brassinosteroids according to the dose of 150 mL hm-2, the degradation rate of tribenuron-methyl accelerated. Brassinolide stimulated the activities of catalase and dehydrogenase, while the activities of sucrase and alkaline phosphatase were inhibited. The results of high-throughput sequencing showed that brassinosteroids inhibited the growth of Verrucomicrobia, Ascomycota, and Mortierellomycota and promoted the abundance of cyanobacteria. Additionally, brassinosteroids could also significantly increase the diversity index and change the community structure of soil bacteria and fungi. Further, the predicted function results indicated that brassinosteroids changed some metabolic-related ecological functions of the soil. We also found that brassinolide could increase millet yields by 2.4% and 13.6%. This study provides a theoretical basis for the safe use of tribenuron-methyl in millet fields and a new idea for the treatment of pesticide residues in soil.

Scene-Specialized Multitarget Detector with an SMC-PHD Filter and a YOLO Network.

You only look once (YOLO) is one of the most efficient target detection networks. However, the performance of the YOLO network decreases significantly when the variation between the training data and the real data is large. To automatically customize the YOLO network, we suggest a novel transfer learning algorithm with the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter and Gaussian mixture probability hypothesis density (GM-PHD) filter. The proposed framework can automatically customize the YOLO framework with unlabelled target sequences. The frames of the unlabelled target sequences are automatically labelled. The detection probability and clutter density of the SMC-PHD filter and GM-PHD are applied to retrain the YOLO network for occluded targets and clutter. A novel likelihood density with the confidence probability of the YOLO detector and visual context indications is implemented to choose target samples. A simple resampling strategy is proposed for SMC-PHD YOLO to address the weight degeneracy problem. Experiments with different datasets indicate that the proposed framework achieves positive outcomes relative to state-of-the-art frameworks.

Instantaneous Observability of Tightly Coupled SINS/GPS during Maneuvers.

The tightly coupled strapdown inertial navigation system (SINS)/global position system (GPS) has been widely used. The system observability determines whether the system state can be estimated by a filter efficiently or not. In this paper, the observability analysis of a two-channel and a three-channel tightly coupled SINS/GPS are performed, respectively, during arbitrary translational maneuvers and angle maneuvers, where the translational maneuver and angle maneuver are modeled. A novel instantaneous observability matrix (IOM) based on a reconstructed psi-angle model is proposed to make the theoretical analysis simpler, which starts from the observability definition directly. Based on the IOM, a series of theoretical analysis are performed. Analysis results show that almost all kinds of translational maneuver and angle maneuver can make a three-channel system instantaneously observable, but there is no one translational maneuver or angle maneuver can make a two-channel system instantaneously observable. The system's performance is investigated when the system is not instantaneously observable. A series of simulation studies based on EKF are performed to confirm the analytic conclusions.

A Novel Robust H∞ Filter Based on Krein Space Theory in the SINS/CNS Attitude Reference System.

Owing to their numerous merits, such as compact, autonomous and independence, the strapdown inertial navigation system (SINS) and celestial navigation system (CNS) can be used in marine applications. What is more, due to the complementary navigation information obtained from two different kinds of sensors, the accuracy of the SINS/CNS integrated navigation system can be enhanced availably. Thus, the SINS/CNS system is widely used in the marine navigation field. However, the CNS is easily interfered with by the surroundings, which will lead to the output being discontinuous. Thus, the uncertainty problem caused by the lost measurement will reduce the system accuracy. In this paper, a robust H∞ filter based on the Krein space theory is proposed. The Krein space theory is introduced firstly, and then, the linear state and observation models of the SINS/CNS integrated navigation system are established reasonably. By taking the uncertainty problem into account, in this paper, a new robust H∞ filter is proposed to improve the robustness of the integrated system. At last, this new robust filter based on the Krein space theory is estimated by numerical simulations and actual experiments. Additionally, the simulation and experiment results and analysis show that the attitude errors can be reduced by utilizing the proposed robust filter effectively when the measurements are missing discontinuous. Compared to the traditional Kalman filter (KF) method, the accuracy of the SINS/CNS integrated system is improved, verifying the robustness and the availability of the proposed robust H∞ filter.