Abstract:
The present invention relates to an apparatus for recognizing intention of a horse-riding simulator user, and a method thereof, and the apparatus for recognizing intention of a horse-riding simulator user can provide a safe and realistic horse-riding simulation environment to a user by recognizing an aid signal and an intention signal of the user to sense a dangerous situation and accordingly coping with the situation. According to the present invention, it is possible to increase the sense of the real for the user by enabling the horse-riding simulator user to perform similar interaction to actual horse-riding, and to increase effects of horse-riding training using the horse-riding simulator. Particularly, there is an advantage that the dangerous situation is sensed for safe riding, and it is possible to contribute to formation of a related technology market by providing an effective method for recognition of the intention of the horse-riding simulator user.
Abstract:
Provided is a simulator managing apparatus and method for analyzing the posture of a user. The simulator managing apparatus includes: an input configured to acquire environment information and image information with respect to a simulator and a user; a controller configured to recognize a position and a posture of the user by analyzing the information acquired by the input, and to generate control information that includes coaching information for posture correction of the user according to the recognized position and posture; and an output configured to provide the analysis and the control information generated by the controller.
Abstract:
A server for managing quality of a ceramic product and a method thereof are provided. The server for managing quality of a ceramic product includes a memory, a communication module, and a processor connected to the memory and the communication module, in which the processor collects quality-related data including at least one of raw material composition data, process condition data, and property data, generates a training data set through correlation analysis between the raw material composition data, the process condition data, and the property data, and generates at least one of a property prediction model and a raw material composition/process condition inference model using the training data set.