Gunma Nikken Polytechnic College

This paper analyzes the current network technology in higher vocational college sports teaching management and its development prospects. The development of computer network technology makes colleges and universities pay more and more attention to students' physical quality. At this time, integrating network technology into the process of physical education teaching can improve the teaching quality and efficiency. The application of network technology in educational administration can also make the physical education teaching management system be effectively developed to meet the needs of system operation and the completion of the project. This paper analyzes the reasons for the application of network technology in Physical Education in higher vocational colleges, the current situation of the application of physical education, the application research of physical education, the application and development prospect of educational administration management, in order to play a reference role for the management and development of physical education in Higher Vocational colleges.

In the industrial use of stereolithography, precision is always a problem. The basic phenomenon of solidification shrinkage has not been sufficiently investigated. This study aims at clarifying the initial linear shrinkage of cured resin in a minute volume. Experimental equipment has been developed which measures the time history of the single strand in situ in a stereolithography machine. An analysis model of the time history of a minute volume linear shrinkage was shown using the measured shrinkage of a cured line segment. The relation between the time history of the linear shrinkage and temperature was measured and the shrinkage in the minute volume after irradiation was found to result due to temperature variation. Deformation and linear shrinkage were measured with two scanning orders to control the thermal distribution in layer forming. The effects of thermal distribution were also observed in one layer forming.

In this paper, a hybrid model of multy-layer neural network combined with a finite-impulse-response filter is proposed for a nonlinear time series prediction. We introduce an important analysis of the input sequence to determine the effective minimum combination of the input samples and hidden neurons. Through computer simulations, using both sunspot and computer generated time series, the proposed analysis has shown its effectiveness and the proposed predictor has demonstrated its superiority. It is of a faster convergence and smaller residual error than the conventional nonlinear predictor.KeywordsNetwork SizeInput SequenceHide NeuronInput SampleInput DimensionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.