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Found 16 papers, 0 papers with code
An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications
In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications.
A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving
Experimental results show that the HMMs trained with the continuous characterization of mobility features can give a higher prediction accuracy when they are used for predicting driving intentions.
Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks
Therefore, a framework combining with a deep clustering (DeepCluster) module is developed for STTP at largescale networks in this paper.
Patent Analytics Based on Feature Vector Space Model: A Case of IoT
In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN).
Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing
By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing.
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model.
Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids
Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid.
LSTM-based Anomaly Detection for Non-linear Dynamical System
Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability.
Federated Reinforcement Learning: Techniques, Applications, and Open Challenges
This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL).
Min-Max Latency Optimization Based on Sensed Position State Information in Internet of Vehicles
The dual-function radar communication (DFRC) is an essential technology in Internet of Vehicles (IoV).
Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information
In this context, the value of V2X communications for DRL-based platoon controllers is studied with an emphasis on the tradeoff between the gain of including exogenous information in the system state for reducing uncertainty and the performance erosion due to the curse-of-dimensionality.
Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning
Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management.
Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming
Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle.
Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning
In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL).
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control
It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay.
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation
We focused on the PC sub-problem and proposed the MTCC-PC algorithm to learn an optimal PC policy given an RRA policy.
