摘要 :
Aiming at the problems of poor teaching effect and large deviation of teaching quality evaluation results in the current second classroom, this paper puts forward the second classroom teaching quality evaluation method based on th...
展开
Aiming at the problems of poor teaching effect and large deviation of teaching quality evaluation results in the current second classroom, this paper puts forward the second classroom teaching quality evaluation method based on the multi-faceted Rasch model, uses the multi-faceted Rasch model to set the second classroom teaching ability evaluation index, standardize the evaluation scale and construct the evaluation model, so as to ensure the accuracy of teaching evaluation and improve the second classroom teaching quality, Finally, experiments show that the second classroom teaching quality evaluation method based on multi-faceted Rasch model has high accuracy, can better correct the evaluation deviation value and ensure the teaching quality.
收起
摘要 :
Aiming at the problems of poor teaching effect and large deviation of teaching quality evaluation results in the current second classroom, this paper puts forward the second classroom teaching quality evaluation method based on th...
展开
Aiming at the problems of poor teaching effect and large deviation of teaching quality evaluation results in the current second classroom, this paper puts forward the second classroom teaching quality evaluation method based on the multi-faceted Rasch model, uses the multi-faceted Rasch model to set the second classroom teaching ability evaluation index, standardize the evaluation scale and construct the evaluation model, so as to ensure the accuracy of teaching evaluation and improve the second classroom teaching quality, Finally, experiments show that the second classroom teaching quality evaluation method based on multi-faceted Rasch model has high accuracy, can better correct the evaluation deviation value and ensure the teaching quality.
收起
摘要 :
The mechanical reducer gear has the advantages of good meshing performance, strong bearing capacity and smooth transmission, which has been widely used in the mechanical transmission system. But the traditional design cycle of mec...
展开
The mechanical reducer gear has the advantages of good meshing performance, strong bearing capacity and smooth transmission, which has been widely used in the mechanical transmission system. But the traditional design cycle of mechanical reducer gear parameters is long, especially the gear calibration analysis, the calculation process is cumbersome and labor intensity is high, which leads to the incomplete interface of mechanical reducer gear model. In order to improve the analysis accuracy, a lot of correction work is needed. Therefore, based on particle swarm optimization algorithm for mechanical reducer gear parametric modeling research. Based on SolidWorks 3D software, the parametric development of spur gear, modified helical gear, bevel gear, internal gear, tooth root transition curve and helix is carried out. The finite element contact theory is introduced to calculate the contact problem of two cylinders, and compared with the classical Hertz theory, the correctness of the theory is verified. On this basis, the influence of friction on the contact stress and meshing strength of gears is obtained by setting different sliding friction coefficients, and compared with the theoretical calculation results, the correctness of the finite element analysis is verified.
收起
摘要 :
The mechanical reducer gear has the advantages of good meshing performance, strong bearing capacity and smooth transmission, which has been widely used in the mechanical transmission system. But the traditional design cycle of mec...
展开
The mechanical reducer gear has the advantages of good meshing performance, strong bearing capacity and smooth transmission, which has been widely used in the mechanical transmission system. But the traditional design cycle of mechanical reducer gear parameters is long, especially the gear calibration analysis, the calculation process is cumbersome and labor intensity is high, which leads to the incomplete interface of mechanical reducer gear model. In order to improve the analysis accuracy, a lot of correction work is needed. Therefore, based on particle swarm optimization algorithm for mechanical reducer gear parametric modeling research. Based on SolidWorks 3D software, the parametric development of spur gear, modified helical gear, bevel gear, internal gear, tooth root transition curve and helix is carried out. The finite element contact theory is introduced to calculate the contact problem of two cylinders, and compared with the classical Hertz theory, the correctness of the theory is verified. On this basis, the influence of friction on the contact stress and meshing strength of gears is obtained by setting different sliding friction coefficients, and compared with the theoretical calculation results, the correctness of the finite element analysis is verified.
收起
摘要 :
In order to better improve the effect of mechanical motion trajectory tracking control and ensure the quality and safety of mechanical operation, a mechanical motion trajectory tracking control method based on convolution neural n...
展开
In order to better improve the effect of mechanical motion trajectory tracking control and ensure the quality and safety of mechanical operation, a mechanical motion trajectory tracking control method based on convolution neural network is proposed. In the process of mechanical motion trajectory tracking control, due to the influence of small disturbance piecewise linear error, the mechanical system will produce multivariable nonlinear motion phenomenon in the operation process. Boundary layer tracking error is easy to occur. In order to solve the above problem, the convolution neural network is used to optimize the mechanical trajectory tracking control algorithm, and the simulation model of mechanical motion environment is established. The spatial coordinates of the trajectory are abstracted as the virtual world of genetic population, and the spatial grid structure characteristics are obtained. According to the matching conditions and parameters of convolution neural network, an error tracking integral term is designed and improved. By accurately tracking the mechanical trajectory, the accuracy of path planning and autonomous positioning is effectively improved. The experimental results show that the control method based on convolution neural network has the advantages of fast convergence speed, high accuracy and high calculation accuracy.
收起
摘要 :
In order to better improve the effect of mechanical motion trajectory tracking control and ensure the quality and safety of mechanical operation, a mechanical motion trajectory tracking control method based on convolution neural n...
展开
In order to better improve the effect of mechanical motion trajectory tracking control and ensure the quality and safety of mechanical operation, a mechanical motion trajectory tracking control method based on convolution neural network is proposed. In the process of mechanical motion trajectory tracking control, due to the influence of small disturbance piecewise linear error, the mechanical system will produce multivariable nonlinear motion phenomenon in the operation process. Boundary layer tracking error is easy to occur. In order to solve the above problem, the convolution neural network is used to optimize the mechanical trajectory tracking control algorithm, and the simulation model of mechanical motion environment is established. The spatial coordinates of the trajectory are abstracted as the virtual world of genetic population, and the spatial grid structure characteristics are obtained. According to the matching conditions and parameters of convolution neural network, an error tracking integral term is designed and improved. By accurately tracking the mechanical trajectory, the accuracy of path planning and autonomous positioning is effectively improved. The experimental results show that the control method based on convolution neural network has the advantages of fast convergence speed, high accuracy and high calculation accuracy.
收起
摘要 :
Mathematical morphology is a nonlinear signal processing method. It has no signal amplitude shift and attenuation of phase information when signals are processed. Therefore it has been widely used in the power system, such as harm...
展开
Mathematical morphology is a nonlinear signal processing method. It has no signal amplitude shift and attenuation of phase information when signals are processed. Therefore it has been widely used in the power system, such as harmonic analysis of transient signals, singularity detection and de-noising, detection of power quality, fault diagnosis, relay protection and fault location and so on. But it has not yet been applied to electric energy measurement of nonlinear signal. This paper proposes an algorithm used morphological wavelet to calculate the power in condition of non-linear load when considering the characteristics of multi-resolution analysis of wavelet transform. Morphological wavelet has the advantage of fast computing speed, accurate detection of edge information, moreover the performance of signal analysis is better than pure wavelet transform. It is proved that the method can accurately detect various nonlinear signals, extract fundamental energy, improve the accuracy of measurement, and reduce computation time through simulation analysis. Because of the rapidity of its computing, it can also be used for real-time monitoring of the signal.
收起
摘要 :
Mathematical morphology is a nonlinear signal processing method. It has no signal amplitude shift and attenuation of phase information when signals are processed. Therefore it has been widely used in the power system, such as harm...
展开
Mathematical morphology is a nonlinear signal processing method. It has no signal amplitude shift and attenuation of phase information when signals are processed. Therefore it has been widely used in the power system, such as harmonic analysis of transient signals, singularity detection and de-noising, detection of power quality, fault diagnosis, relay protection and fault location and so on. But it has not yet been applied to electric energy measurement of nonlinear signal. This paper proposes an algorithm used morphological wavelet to calculate the power in condition of non-linear load when considering the characteristics of multi-resolution analysis of wavelet transform. Morphological wavelet has the advantage of fast computing speed, accurate detection of edge information, moreover the performance of signal analysis is better than pure wavelet transform. It is proved that the method can accurately detect various nonlinear signals, extract fundamental energy, improve the accuracy of measurement, and reduce computation time through simulation analysis. Because of the rapidity of its computing, it can also be used for real-time monitoring of the signal.
收起
摘要 :
The fault type of the conventional multi-fault identification system of mechanical bearings is not clear, leading to the system's high load rate. A multi-fault identification system of mechanical bearings based on machine vision i...
展开
The fault type of the conventional multi-fault identification system of mechanical bearings is not clear, leading to the system's high load rate. A multi-fault identification system of mechanical bearings based on machine vision is designed. Hardware part: take the linear voltage signal of the equipment as the primary signal source, optimize the peripheral circuit; Software part: Introduce coupling model theory, improve the abnormal detection process of the mechanical bearing, identify multiple fault types, ensure power transmission between components, calculate the fault frequency of bearing inner ring, use the fault prediction function of machine vision design software. Experimental results: The average load rate of the multi-fault recognition system of mechanical bearings in this paper and the other two types of the multi-type fault identification system of mechanical bearing is 40.501%, 51.970%, and 51.100%, which proves that the multi-fault identification system of mechanical bearings combined with machine vision technology has high reliability.
收起
摘要 :
The fault type of the conventional multi-fault identification system of mechanical bearings is not clear, leading to the system's high load rate. A multi-fault identification system of mechanical bearings based on machine vision i...
展开
The fault type of the conventional multi-fault identification system of mechanical bearings is not clear, leading to the system's high load rate. A multi-fault identification system of mechanical bearings based on machine vision is designed. Hardware part: take the linear voltage signal of the equipment as the primary signal source, optimize the peripheral circuit; Software part: Introduce coupling model theory, improve the abnormal detection process of the mechanical bearing, identify multiple fault types, ensure power transmission between components, calculate the fault frequency of bearing inner ring, use the fault prediction function of machine vision design software. Experimental results: The average load rate of the multi-fault recognition system of mechanical bearings in this paper and the other two types of the multi-type fault identification system of mechanical bearing is 40.501%, 51.970%, and 51.100%, which proves that the multi-fault identification system of mechanical bearings combined with machine vision technology has high reliability.
收起
原文获取