In this study, the modified Sauer cavitation model and Kirchhoff-Ffowcs Williams and Hawkings (K-FWH) acoustic model were adopted to numerically simulate the unsteady cavitation flow field and the noise of a threedimensional NACA66 hydrofoil at a constant cavitation number. The aim of the study is to conduct and analyze the noise performance of a hydrofoil and also determine the characteristics of the sound pressure spectrum, sound power spectrum, and noise changes at different monitoring points. The noise change, sound pressure spectrum, and power spectrum characteristics were estimated at different monitoring points, such as the suction side, pressure side, and tail of the hydrofoil. The noise characteristics and change law of the NACA66 hydrofoil under a constant cavitation number are presented. The results show that hydrofoil cavitation takes on a certain degree of pulsation and periodicity. Under the condition of a constant cavitation number, as the attack angle increases, the cavitation area of the hydrofoil becomes longer and thicker, and the initial position of cavitation moves forward. When the inflow velocity increases, the cavitation noise and the cavitation area change more drastically and have a superposition tendency toward the downstream. The novelty is that the study presents important calculations and analyses regarding the noise performance of a hydrofoil, characteristics of the sound pressure spectrum, and sound power spectrum and noise changes at different monitoring points. The article may be useful for specialists in the field of engineering and physics.

To reduce the random error of microelectromechanical system (MEMS) gyroscope, a hybrid method combining improved empirical mode decomposition (EMD) and least squares algorithm (LS) is proposed. Firstly, based on the multiple screening mechanism, intrinsic mode functions (IMFs) from the first decomposition are divided into noise IMFs, strong noise mixed IMFs, weak noise mixed IMFs and signal IMFs. Secondly, according to their characteristics, they are processed again. IMFs from the second decomposition are divided into noise IMFs and signal IMFs. Finally, useful signal is gathered to obtain the final denoising signal. Compared with some other denoising methods proposed in recent years, the experimental results show that the proposed method has obvious advantages in suppressing random error, greatly improving the signal quality and improving the accuracy of inertial navigation.

In recent years, due to the proliferation of inertial measurement units (IMUs) in mobile devices such as smartphones, attitude estimation using inertial and magnetic sensors has been the subject of considerable research. Traditional methods involve probabilistic and iterative state estimation; however, these approaches do not generalize well over continuously changing motion dynamics and environmental conditions. Therefore, this paper proposes a deep learning-based approach for attitude estimation. This approach segments data from sensors into different windows and estimates attitude by separately extracting local features and global features from sensor data using a residual network (ResNet18) and a long short-term memory network (LSTM). To improve the accuracy of attitude estimation, a multi-scale attention mechanism is designed within ResNet18 to capture finer temporal information in the sensor data. The experimental results indicate that the accuracy of attitude estimation using this method surpasses that of other methods proposed in recent years.

Donghua steel continuous casting-rolling (DSCCR) line is a new endless rolling line in which tunnel heating furnace is added before and after roughing mills to change the temperature field of slab and intermediate slab, but this change will affect the microstructure and properties of hot rolled plate. Therefore, the microstructure evolution, mechanical properties, texture analysis, hole expanding and earing test of 2.0 mm thick hot rolled plate produced by DSCCR line at different final rolling temperature of 860°C, 840°C and 820°C are studied. The results show that with the decrease of final rolling temperature, there is an obvious layered microstructure distribution along the thickness direction, and the surface coarse grain area gradually expands inward, at the same time the morphology of cementite also changed from large multi domain lamellar pearlite and long rod cementite to small single domain lamellar pearlite and short rod cementite. The engineering stress-strain curves have discontinuous yield with the yield elongation of 4-5% and the elongations are more than 35%. EBSD analysis shows that small angle grain boundaries and deformed grains increase significantly with the decrease of final rolling temperature, and are mainly distributed in fine grain area. Hole expanding and earing tests show that with the decrease of final rolling temperature, the earing performance decreased but the limiting hole expanding ratio is similar.