Abstract: An apparatus may be configured to obtain, for a Siamese neural network having a recurrent neural network (RNN), an initial representation associated with a target object at a first time step and a set of candidate regions at a current time step. The apparatus may determine an updated representation associated with the target object based on the initial representation at the first time step and observed information associated with the target object at a set of previous time steps, and the observed information associated with the target object may be represented by a hidden state of the RNN. The apparatus may output the updated representation associated with the target object for matching with the set of candidate regions at the current time step by the Siamese neural network. The apparatus may determine the updated representation further based on a hidden state at a previous time step.

Abstract: An apparatus may be configured to obtain, for a Siamese neural network having a recurrent neural network (RNN), an initial representation associated with a target object at a first time step and a set of candidate regions at a current time step. The apparatus may determine an updated representation associated with the target object based on the initial representation at the first time step and observed information associated with the target object at a set of previous time steps, and the observed information associated with the target object may be represented by a hidden state of the RNN. The apparatus may output the updated representation associated with the target object for matching with the set of candidate regions at the current time step by the Siamese neural network. The apparatus may determine the updated representation further based on a hidden state at a previous time step.

Abstract: Four different forms of hybrid models of vapor-liquid separation equipment. These are: (i) hybrid models for monitoring the equipment operation based on the plant operating data, (ii) a predictive hybrid model which computes product properties if feed properties are known (iii) a predictive hybrid model which can compute product qualities from the flows entering or leaving the tower without having to know the feed properties, and (iv) a feed properties identification hybrid model.