Deep learning algorithms are widely used for pattern recognition in electronic noses, which are sensor arrays for gas mixtures. One of the challenges of using electronic noses is sensor drift, which can degrade the accuracy of the system over time, even if it is initially trained to accurately estimate concentrations from sensor data. In this paper, an effective drift compensation method is introduced that adds sensor drift information during training of a neural network that estimates gas concentrations. This is achieved by concatenating a calibration feature vector with sensor data and using this as an input to the neural network. The calibration feature vector is generated via a masked-autoencoder-based feature extractor trained with transfer samples, and acts as a prompt to convey sensor drift information. Our method is tested on a 3-year gas sensor array drift dataset, showing that a neural network using our method performs better than other models, including a network with additional fine tuning, demonstrating that our method is efficient at compensating for sensor drift. In this study, the effectiveness of using prompts for network training is confirmed, which better compensates for drifts in new sensor signals than network fine-tuning.

For controller design for systems on manifolds embedded in Euclidean space, it is convenient to utilize a theory that requires a single global coordinate system on the ambient Euclidean space rather than multiple local charts on the manifold or coordinate-free tools from differential geometry. In this article, we apply such a theory to design model predictive tracking controllers for systems whose dynamics evolve on nonflat manifolds and illustrate its efficacy with the fully actuated rigid body attitude control system.

In this paper, we implement a modified SAC [1] algorithm for autonomous driving tasks using the simulator AirSims [2] environment API which provides various weather, collision, and lighting choices. Given current image state and car velocity as our inputs, the task outputs the throttle, brake, and steering angle data and gives the vehicle action instruction through the AirSim control outputs. As autonomous vehicles are more likely to be accepted if they drive like how human would, we at first train our model by imitation learning to provides the pre-trained human-like policy and weights to SAC. During the reinforcement learning, in order to increase the feasible policys robustness, we use ResNet-34 [3] as our actor and critic network architecture in the SAC algorithm.