Abstract: A purpose of the present invention is to improve precision when estimating the solution to a task expressed in a graph. A model generation device according to one embodiment of the present invention performs machine learning of an estimation module provided with a feature-incorporated network and an estimator. The feature-incorporated network is provided with a plurality of feature-incorporated layers. An encoder for each feature-incorporated layer is configured so as to incorporate the feature amount of the outflow edge and inflow edge of each vertex after reflecting the relationship with other vertices. Moreover, the encoder for each feature-incorporated layer is configured so as to derive the relative feature amount for each edge based on the feature amount for all of the edges inputted for a target vertex, without calculating the weighted sum of the features of the edges adjacent to each vertex.

Abstract: A technique capable of implementing more efficient connection between users is provided. A matching system includes: storage means configured to hold a question set including a plurality of questions; acquisition means configured to present one or more questions included in the question set to a user and acquires one or more answers to the presented questions from the user; first matching processing means configured to execute first matching processing based on one or more questions answered by the user; and second matching processing means configured to execute second matching processing based on content of one or more answers included in the one or more answers from the user when a result of the first matching processing satisfies a first determination condition.

Abstract: An unmanned aerial system includes a remote controller device and an unmanned aerial vehicle. A user input on the remote controller device indicates a flight command requested by a user. The remote controller device determines a current position and/or orientation of the remote controller device in response to the flight command from the user. The current position and/or orientation is sent to the vehicle. The vehicle responsively determines a desired orientation of the unmanned aerial vehicle as a function of the current position and/or orientation of the remote controller device and operates a lift mechanism to execute a flight operation based on the desired orientation of the unmanned aerial vehicle and the current position of the remote controller device.

Abstract: An elevator health state online monitoring method includes: acquiring parameters such as a position, a three-axis acceleration, an inclination angle, and a noise of a car in a running state of an elevator, left and right brake temperatures of a traction machine, and an environment temperature. Parameter measuring devices are installed on a top of the car and at the brake side of the traction machine. The parameters are collected to a cloud database through a wireless network after being acquired, and a judgment result is given through background automatic analysis. The method is mainly used for monitoring whether key mechanical parts of the elevator are abnormal or not, including guide rail deformation, guide shoe abrasion, car sliding, brake abrasion, adhesion, and the like. This way the health state of the elevator can be automatically monitored, and early warning can be carried out in time when abnormity is found.

Abstract: An unmanned aerial system includes a remote controller device and an unmanned aerial vehicle. A user input on the remote controller device indicates a flight command requested by a user. The remote controller device determines a current position and/or orientation of the remote controller device in response to the flight command from the user. The current position and/or orientation is sent to the vehicle. The vehicle responsively determines a desired orientation of the unmanned aerial vehicle as a function of the current position and/or orientation of the remote controller device and operates a lift mechanism to execute a flight operation based on the desired orientation of the unmanned aerial vehicle and the current position of the remote controller device.