IABC: An iteratively automatic machine learning boosted hand-eye calibration method for laser displacement sensor

An on-machine measuring (OMM) system with a laser displacement sensor (LDS) is designed for measuring free-form surfaces of hypersonic aircraft’s radomes. To improve the measurement accuracy of the OMM system, a novel Iteratively Automatic machine learning Boosted hand-eye Calibration (IABC) method is proposed. Both the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via IABC. Firstly, a new objective function is derived, containing analytical parameters of the hand-eye relationship and LDS errors. Then, a hybrid calibration model composed of two kernels is proposed to solve the objective function. One kernel is the analytical kernel designed for solving analytical parameters. Another kernel is the automatic machine learning (AutoML) kernel designed to model LDS errors. The two kernels are connected with stepwise iterations to find the best calibration results. Compared with traditional methods, hand-eye experiments show that IABC reduces the calibration RMSE by about 50%. Verification experiments show that IABC reduces the measurement deviations by about 25%-50% and RMSEs within 40%. Even when the training data are obviously less than the test data, IABC performs well. Experiments demonstrate that IABC is more accurate than traditional hand-eye methods.     

STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.     

加工装配误差影响下偏转射流压力伺服阀静态特性分析

电液压力伺服阀是电液压力伺服控制系统的核心控制元件，广泛应用于航空、航天、军事等领域。区别于流量伺服阀，压力伺服阀在滑阀放大器的设计上多采用带有压力控制容腔的三通阀结构，不同的滑阀结构使得现有的偏转射流流量伺服阀仿真模型难以满足压力伺服阀性能预测的需求。本文基于AMESim平台建立了偏转射流压力伺服阀的仿真模型，并通过实验对仿真模型进行了验证，验证结果表明仿真模型能够准确地描述压力伺服阀的静态特性。最后，通过仿真模型分析了加工装配误差影响下压力伺服阀输出性能差异，仿真结果可为偏转射流压力伺服阀的性能预测和结构设计提供参考。