Abstract: Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.

Abstract: Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.

Abstract: Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.

Abstract: Embodiments of the innovation relate to an emotional quality estimation device comprising a controller having a memory and a processor, the controller configured to execute a training engine with labelled training data to train a neural network and generate a classroom analysis machine, the labelled training data including historical video data and an associated classroom quality score table; receive a classroom observation video from a classroom environment; execute the classroom analysis machine relative to the classroom observation video from the classroom environment to generate an emotional quality score relating to the emotional quality of the classroom environment; and output the emotional quality score for the classroom environment.

Abstract: Embodiments of the innovation relate to an emotional quality estimation device comprising a controller having a memory and a processor, the controller configured to execute a training engine with labelled training data to train a neural network and generate a classroom analysis machine, the labelled training data including historical video data and an associated classroom quality score table; receive a classroom observation video from a classroom environment; execute the classroom analysis machine relative to the classroom observation video from the classroom environment to generate an emotional quality score relating to the emotional quality of the classroom environment; and output the emotional quality score for the classroom environment.