2,319 research outputs found
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks
Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study
Group) was established to initiate discussions on new IEEE 802.11 features.
Coordinated control methods of the access points (APs) in the wireless local
area networks (WLANs) are discussed in EHT Study Group. The present study
proposes a deep reinforcement learning-based channel allocation scheme using
graph convolutional networks (GCNs). As a deep reinforcement learning method,
we use a well-known method double deep Q-network. In densely deployed WLANs,
the number of the available topologies of APs is extremely high, and thus we
extract the features of the topological structures based on GCNs. We apply GCNs
to a contention graph where APs within their carrier sensing ranges are
connected to extract the features of carrier sensing relationships.
Additionally, to improve the learning speed especially in an early stage of
learning, we employ a game theory-based method to collect the training data
independently of the neural network model. The simulation results indicate that
the proposed method can appropriately control the channels when compared to
extant methods
Inferring transportation modes from GPS trajectories using a convolutional neural network
Identifying the distribution of users' transportation modes is an essential
part of travel demand analysis and transportation planning. With the advent of
ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach
for inferring commuters' mobility mode(s) is to leverage their GPS
trajectories. A majority of studies have proposed mode inference models based
on hand-crafted features and traditional machine learning algorithms. However,
manual features engender some major drawbacks including vulnerability to
traffic and environmental conditions as well as possessing human's bias in
creating efficient features. One way to overcome these issues is by utilizing
Convolutional Neural Network (CNN) schemes that are capable of automatically
driving high-level features from the raw input. Accordingly, in this paper, we
take advantage of CNN architectures so as to predict travel modes based on only
raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
and train. Our key contribution is designing the layout of the CNN's input
layer in such a way that not only is adaptable with the CNN schemes but
represents fundamental motion characteristics of a moving object including
speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
quality of GPS logs through several data preprocessing steps. Using the clean
input layer, a variety of CNN configurations are evaluated to achieve the best
CNN architecture. The highest accuracy of 84.8% has been achieved through the
ensemble of the best CNN configuration. In this research, we contrast our
methodology with traditional machine learning algorithms as well as the seminal
and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C:
Emerging Technologie
On the efficient representation and execution of deep acoustic models
In this paper we present a simple and computationally efficient quantization
scheme that enables us to reduce the resolution of the parameters of a neural
network from 32-bit floating point values to 8-bit integer values. The proposed
quantization scheme leads to significant memory savings and enables the use of
optimized hardware instructions for integer arithmetic, thus significantly
reducing the cost of inference. Finally, we propose a "quantization aware"
training process that applies the proposed scheme during network training and
find that it allows us to recover most of the loss in accuracy introduced by
quantization. We validate the proposed techniques by applying them to a long
short-term memory-based acoustic model on an open-ended large vocabulary speech
recognition task.Comment: Accepted conference paper: "The Annual Conference of the
International Speech Communication Association (Interspeech), 2016
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