Abstract: Methods for generating a partial three-dimensional representation of a person are disclosed herein. In one aspect, a computer-implemented method comprises obtaining depth data of the person captured from a stationary depth camera scanning around the person; segmenting the depth data into a first segment; mapping the depth data of the first segment to a plurality of point clouds; performing pairwise registration on the point clouds of the first segment; segmenting the depth data into a second segment; mapping the depth data of the second segment to a plurality of point clouds; performing pairwise registration on the point clouds of the second segment; and merging the registered point clouds of the first and second segments to generate the partial 3D representation of the person.

Abstract: The present disclosure provides a method for predicting safety interlock failure and a system for producing a roll package. The method for predicting safety interlock failure includes: obtaining, by a model for predicting safety interlock failure, real-time operation data of the production equipment to perform prediction; predicting, by the model for predicting safety interlock failure, whether the safety interlock device fails according to fault shutdown alarm information and non-fault shield alarm information in the real-time operation data, and generating a prediction result; and if the prediction result indicates that the safety interlock device fails, generating early warning information, wherein the fault shutdown alarm information is alarm information generated when the safety shield is opened to shut down the production equipment, and the non-fault shield alarm information is alarm information generated when the safety shield is opened or closed after the production equipment is shut down.

Abstract: The present disclosure relates to artificial intelligence, and proposes a cooperative adversarial network. A loss function is set at a lower layer of the cooperative adversarial network, and is used to learn a domain discriminating feature. In addition, a cooperative adversarial target function includes the loss function and a domain invariant loss function that is set at a last layer (that is, a higher layer) of the cooperative adversarial network, to learn both the domain discriminating feature and a domain-invariant feature. Further, an enhanced collaborative adversarial network is proposed. Based on the collaborative adversarial network, target domain data is added to training of the collaborative adversarial network, an adaptive threshold is set based on precision of a task model, to select a target domain training sample, network confidence is discriminated based on a domain, and a weight of the target domain training sample is set.