Current Issue : October - December Volume : 2019 Issue Number : 4 Articles : 5 Articles
Micro electro mechanical system (MEMS) inertial sensors have advantages, including small size and low power consumption.The\nperformances of Micro Inertial measurement unit (IMU), which is composed of MEMS inertial sensors, degrade, and error, will\nbecome larger in high dynamic environment. In order to solve the problem, a novel combined calibrationmethod for compensating\nthe deterministic error of MEMS sensors is proposed. Considering the rotation of different sensitive axes in high dynamic and low\ndynamic environment, the compounded calibration based on fuzzy neural network (FNN) is adopted to identify the coupling\ncoefficients to eliminate the adverse coupling effects between different rotation axes. Furthermore, the self-developedMicro IMU\nand magnetometer are applied in attitude estimation system. Considering the large attitude error occurred in most cases, the\napproach utilizing the estimation of error quaternion vector could avoid the calculation error due to inaccurate modeling in the\nskew symmetric matrix that comprises attitude error vector components.The intelligent Kalman filter (IKF) based on complexity\nstate equation of error quaternion is designed to improve the performance by adjusting the parameters of filter on line. The\nexperimental results show that the proposed approach could have a higher level of stability and accuracy in comparison to other\nattitude estimation algorithms....
Neat polyurethane (PU) specimens and composites of polyurethane with variable amounts of multiwalled carbon nanotubes\n(MWCNTs) were subjected to tensile tests, stress relaxation tests, and strain rate jumps. Since the already published data about the\neffect of carbon nanotubes addition to polymer matrix are somewhat contradictory, great care was taken to understand the\nmechanical properties of neat PU specimens. the studies revealed that the tensile curves of neat PU are substantially influenced by\nseveral factors, such as strain rate, age, and thickness of the specimens. The addition of MWCNTs into the PU matrix had a\nnegligible effect on the mechanical properties of composites at low strains..................
The fatigue damage of rock is an important factor affecting the stability of rock structure. In this paper, the mechanical response of\ncoal under cyclic loading was studied. In order to accurately describe the deformation characteristics of coal under cyclic loading,\nan elastic-plastic model of coal based on the theory of subloading surface was established and verified by experiments. The model\ncan well reflect the Mancin effect and ratcheting effect of coal samples, which is basically consistent with the actual deformation\ncharacteristics of coal, and the theoretical value and experimental value are in good agreement. At the same time, the cyclic\nresponse characteristics of specimens under strain load disturbance were analyzed. The results show that the specific strain\ndisturbance can only cause a certain damage to coal and the area of hysteresis loop decreases first, then stabilizes, and then\nincreases as the number of cycles increases. In addition, the damage factor Dn in the model was analyzed in this paper. Dn, which\ncan accurately describe the damage process of coal, accurately locate the time point of disturbance load change, and has greater\nsensitivity to coal failure, is helpful to improve the accuracy of the stability judgment of coal structure and ensure the safety of\nengineering. The above results are of great significance for strengthening the understanding of coal mass instability process and\nmode under cyclic loading....
This paper examined the temperature changes from the COordinated Regional climate Downscaling Experiment (CORDEX) over\nthe Middle East and North Africa (MENA) domain called CORDEX-MENA. )e focus is on the Arabian Peninsula in the 21st\ncentury, using data from three Coupled Model Intercomparison Project Phase 5 (CMIP5) models downscaled by RegCM4, a\nregional climate model. The analysis includes surface observations along with RegCM4 simulations and changes in threshold\nbased on extreme temperature at the end of the 21st century relative to the base period (1971-2000).............
The welding of the same parts has same welding trajectory, so welding process has strong repeatability. In this paper, aiming at\nthe repeatability of welding process, an iterative learning controller is designed to achieve the control of weld quality. Due to\nthe extremely variable welding environment and the presence of noise interferences and load disturbances, it is easy to cause the\njumping change in parameters and even the structure of the welding system.Therefore, the idea of multiplemodel adaptive control\n(MMAC) is introduced into iterative learning control (ILC), and amultiple model iterative learning control (MMILC) algorithm is\ndesigned according to model of weld pool dynamic process in gas tungsten arc welding (GTAW). Besides, the convergence of the\nalgorithm is analyzed for two cases: fixed parameters and jumping parameters. It turns out that the MMILC can not only utilize\nthe repetitive information effectively in the welding process to achieve high precision tracking control of weld seam in limited time\ninterval, but also realize the multiple model switching according to different working conditions to improve the welding quality....
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