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Found 6 papers, 1 papers with code
Improving speech recognition models with small samples for air traffic control systems
By virtue of the common terminology used in the ATC domain, the transfer learning task can be regarded as a sub-domain adaption task, in which the transferred model is optimized using a joint corpus consisting of baseline samples and new transcribed samples from the target dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Non-recurrent Traffic Congestion Detection with a Coupled Scalable Bayesian Robust Tensor Factorization Model
However, it remains a challenging task to construct an analytical framework through which the natural spatial-temporal structural properties of multivariable traffic information can be effectively represented and exploited to better understand and detect NRTC.
Platoon trajectories generation: A unidirectional interconnected LSTM-based car following model
However, the traffic micro-simulation accuracy of car following models in a platoon level, especially during traffic oscillations, still needs to be enhanced.
Differential Variable Speed Limits Control for Freeway Recurrent Bottlenecks via Deep Reinforcement learning
In this paper, we propose a more effective deep reinforcement learning (DRL) model for differential variable speed limits (DVSL) control, in which the dynamic and different speed limits among lanes can be imposed.
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework
An 1-dimension CNN is exploited to capture spatial features of traffic flow, and two LSTMs are utilized to mine the short-term variability and periodicities of traffic flow.
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE).
