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Location prediction has attracted much attention due to its important role in many location-based services. The existing location prediction methods have large trajectory information loss and low prediction accuracy. Hence, they are unsuitable for vehicle location prediction of the intelligent transportation system, which needs small trajectory information loss and high prediction accuracy. To solve the problem, a vehicle location prediction algorithm was proposed in this paper, which is based on a spatiotemporal feature transformation method and a hybrid long short-term memory (LSTM) neural network model. In the algorithm, the transformation method is used to convert a vehicle trajectory into an appropriate input of the neural network model, and then the vehicle location at the next time is predicted by the neural network model. The experimental results show that the trajectory information of an original taxi trajectory is basically reserved by its shadowed taxi trajectory, and the trajectory points of the predicted taxi trajectory are close to those of the shadowed taxi trajectory. It proves that our proposed algorithm effectively reduces the information loss of vehicle trajectory and improves the accuracy of vehicle location prediction. Furthermore, the experimental results also show that the algorithm has a higher distance percentage and a shorter average distance than the other predication models. Therefore, our proposed algorithm is better than the other prediction models in the accuracy of vehicle location predication.

With the development of motion capture technology, it has become a reality to efficiently and quickly obtain realistic human motion information. Motion capture technology has been successfully applied in many fields such as sports competitions, animation games, and film and television production. This article is aimed at studying the application of motion capture technology based on smart sensors in ice and snow sports. Put forward the idea of applying smart sensor-based motion capture technology to ice and snow sports. This article introduces in detail smart sensors, motion capture technology, and related content of ice and snow sports and conducts specific experiments on the application of smart sensor-based motion capture technology in ice and snow sports. The experimental results show that motion capture based on smart sensor technology can help athletes improve their skills and tactics. At the same time, motion capture technology based on smart sensors is also loved by most coaches and athletes, and everyone’s satisfaction with this technology has reached more than 70%.

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<abstract> <p>In recent years, with the rapid development of the economy, in order to stabilize in the market and expand their own business, various companies in the form of various indicators, tangible or intangible to improve the management of the work of workers, speed up the pace of work, take up more work time. This article studies its relationship with stress management from the perspective of psychological capital, in order to achieve prior control of work stress from the perspective of individual positive psychological capital, and provide a new perspective for work stress management in the field of human resource management, and at the same time Enterprises and colleges and universities improve the psychological capital of employees and provide new management models. The unreasonable distribution of work even affects the daily life of management workers and aggravates the working pressure of company management workers. The training process of deep learning is actually the process of repeated forward and reverse calculations of the deep neural network based on the provided data. This process can actually be abstracted, and the deep learning framework is designed to accomplish this task. The existence of a deep learning framework allows users not to fully understand the principles and training process of deep neural networks, but can effectively train the models they want. A long time of high mental state tension leads to a variety of physical and psychological discomfort. If the pressure cannot be alleviated and released, this article extends the health collection equipment of the deep learning to households, continuously records the health status of residents through the mobile Internet, and uses the information resources of the regional residents' health file platform to provide residents with health status evaluation, management and guidance, health care consultation, education and education. A series of personal health management services such as health risk factor assessment. The positive emotion index of managers increased from 18 to 27, and the negative emotion index decreased from 29 to 13. The positive emotion was significantly more than the negative emotion, and the emotional situation was improved.</p> </abstract>