Massenstrombestimmung durch Messung des Bremsmoments an einem Fluid schwingenden Fluegelrad

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A novel biomimetic sensor system for vibration source perception of autonomous underwater vehicles based on artificial lateral lines

The perception of vibration sources can be used to detect, classify, locate, and track autonomous underwater vehicles (AUVs), which is of great importance for ocean scientific research and naval applications. The artificial lateral lines system (ALLS) is a promising technique to sense underwater vibration sources. However, most current ALLS research focuses on perception mechanism and biomimetic sensor design. The design of a systematic ALLS that is ready for practical applications is still an unsolved problem. To this end, a novel biomimetic sensor system is proposed in this work for the purpose of developing a practical ALLS for AUVs. In order to determine the distribution of the developed biomimetic sensors in the AUVs, hydromechanics modelling and simulation of the artificial lateral lines were implemented to investigate the pressure response mechanisms of the AUVs in terms of the position, frequency and amplitude of the vibration source(s). Subsequently, an experimental AUV was equipped with biomimetic sensors to evaluate the performance of the vibration source perception. Experimental tests were conducted to analyze the relationship between the measured AUV pressure and the distance, frequency and amplitude of the vibration source. Analysis results demonstrate that the experimental measurements were consistent with simulation results. Based on the relationship between the sensor measurements and the vibration source, a neural network model was used to identify the coordinates, frequency and amplitude of the vibration source, producing an identification accuracy of 93%. Hence, the proposed ALLS is effective for vibration source perception of AUVs

The application of the principle of wave superposition in ultrasonic measurement of lubricant film thickness

The principle of resonance is often applied in the ultrasonic measurement of the thickness of lubricant films. The information data relating to the thickness of the lubricant film involves the acquisition of the resonance frequency of a continuous wave with the application of a continuum model. However, ultrasonic signals are often times, generated by piezoelectric elements but these exhibit some variations from the continuous wave because they are dispersed pulses that have a wide range of frequency bandwidths. In this paper, the authors have applied the principle of wave superposition for the measurement of lubricant film thickness. This principle exhibits the capability to generate the resonant mechanisms of the pulse waves. The implemented theoretical analysis in this research work, depicted that the principle of wave superposition may demonstrate more applicability than the continuum model under the condition of the partial inclusion of the reflected pulse waves. The application of this principle facilitated the analysis of the resonant mechanisms of the pulse wave from the phase variations of the reflected echoes. It followed the further investigations into the influence of both the number of the reflected echoes and the materials of the solid layers on the film resonance method. The results of these investigations were validated using experimental results

A robust registration method for autonomous driving pose estimation in urban dynamic environment using LiDAR

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively

Measurement and prediction of granite damage evolution in deep mine seams using acoustic emission

With unceasing increase of mining depth and development intensity, mining disasters such as rock burst have been increasing frequently, which often result in catastrophic accidents. Therefore, it is imperative to accurately forecast underground disasters. Previous research has suggested that the combination of drill-hole pressure relief and acoustic emission (AE) monitoring serves as an effective measure method towards the forecasting and prevention of disastrous accidents. However, the AE evolution mechanism of underground rock damages remains a challenge; more specifically, the relationships among the drilling hole positions, depths and diameters, and the stress-strain and AE characteristics of the rocks are discussed little in the literature. In order to bridge this research gap, the particle flow code (PFC2D) is employed to systemically investigate the hidden patterns among the mechanical properties, AE and damage evolution of the rock mass with different positions, depths and diameters of the drilling holes. Analysis results demonstrate that the drilling position influences the rock stress-strain and AE characteristics in the plastic deformation stage and the residual stage while the hole depth affects the drilling process. More specifically, the initial AE strength, AE impact at the peak moment, AE fluctuations and induction time are significantly influenced by the drilling position and depth. Furthermore, the drilling position and depth change the evolution law in the damage acceleration and stable development stages, while the hole diameter has little effect on the AE signal during the rock drilling process

Improved neural network control approach for a humanoid arm

This study extended the knowledge over the improvement of the control performance for a seven degrees-of-freedom (7DOF) humanoid arm. An improved adaptive Gaussian radius basic function neural network (RBFNN) approach was proposed to ensure the reliability and stability of the humanoid arm control. Considering model uncertainties, the established dynamic model for the humanoid arm was divided into a nominal model and an error model. The error model was approximated by the RBFNN learning to compensate the uncertainties. The contribution of this study mainly concentrates on employing fruit fly optimization algorithm (FOA) to optimize the basic width parameter of the RBFNN, which can enhance the capability of the error approximation speed. Additionally, the output weights of the neural network were adjusted using the Lyapunov stability theory to improve the robustness of the RBFN-based error model. The simulation and experiment results demonstrate that the proposed approach is able to optimize the system state with less tracking errors, regulate the uncertain nonlinear dynamic characteristics, and effectively reduce unexpected interferences