32 research outputs found
Wireless Power Transfer for High-precision Position Detection of Railroad Vehicles
Detection of vehicle position is critical for successful operation of
intelligent transportation system. In case of railroad transportation systems,
position information of railroad vehicles can be detected by GPS, track
circuits, and so on. In this paper, position detection based on tags onto
sleepers of the track is investigated. Position information stored in the tags
is read by a reader placed at the bottom of running railroad vehicle. Due to
limited capacity of battery or its alternative in the tags, power required for
transmission of position information to the reader is harvested by the tags
from the power wirelessly transferred from the reader. Basic mechanism in
wireless power transfer is magnetic induction and power transfer efficiency
according to the relative location of the reader to a tag is discussed with
simulation results. Since power transfer efficiency is significantly affected
by the ferromagnetic material (steel) at the bottom of the railroad vehicle and
the track, magnetic beam shaping by ferrite material is carried out. With the
ferrite material for magnetic beam shaping, degradation of power transfer
efficiency due to the steel is substantially reduced. Based on the experimental
results, successful wireless power transfer to the tag coil is possible when
transmitted power from the reader coil is close to a few watts.Comment: 2015 IEEE Power, Communication and Information Technology Conference
(PCITC) accepted, preprinte
Data Transmission with Reduced Delay for Distributed Acoustic Sensors
This paper proposes a channel access control scheme fit to dense acoustic
sensor nodes in a sensor network. In the considered scenario, multiple acoustic
sensor nodes within communication range of a cluster head are grouped into
clusters. Acoustic sensor nodes in a cluster detect acoustic signals and
convert them into electric signals (packets). Detection by acoustic sensors can
be executed periodically or randomly and random detection by acoustic sensors
is event driven. As a result, each acoustic sensor generates their packets
(50bytes each) periodically or randomly over short time intervals
(400ms~4seconds) and transmits directly to a cluster head (coordinator node).
Our approach proposes to use a slotted carrier sense multiple access. All
acoustic sensor nodes in a cluster are allocated to time slots and the number
of allocated sensor nodes to each time slot is uniform. All sensor nodes
allocated to a time slot listen for packet transmission from the beginning of
the time slot for a duration proportional to their priority. The first node
that detect the channel to be free for its whole window is allowed to transmit.
The order of packet transmissions with the acoustic sensor nodes in the time
slot is autonomously adjusted according to the history of packet transmissions
in the time slot. In simulations, performances of the proposed scheme are
demonstrated by the comparisons with other low rate wireless channel access
schemes.Comment: Accepted to IJDSN, final preprinted versio
Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model
Road infrastructure can affect the occurrence of road accidents. Therefore,
identifying roadway features with high accident probability is crucial. Here,
we introduce image inpainting that can assist authorities in achieving safe
roadway design with minimal intervention in the current roadway structure.
Image inpainting is based on inpainting safe roadway elements in a roadway
image, replacing accident-prone (AP) features by using a diffusion model. After
object-level segmentation, the AP features identified by the properties of
accident hotspots are masked by a human operator and safe roadway elements are
inpainted. With only an average time of 2 min for image inpainting, the
likelihood of an image being classified as an accident hotspot drops by an
average of 11.85%. In addition, safe urban spaces can be designed considering
human factors of commuters such as gaze saliency. Considering this, we
introduce saliency enhancement that suggests chrominance alteration for a safe
road view.Comment: 9 Pages, 6 figures, 4 table
Reinforcement Learning for Predicting Traffic Accidents
As the demand for autonomous driving increases, it is paramount to ensure
safety. Early accident prediction using deep learning methods for driving
safety has recently gained much attention. In this task, early accident
prediction and a point prediction of where the drivers should look are
determined, with the dashcam video as input. We propose to exploit the double
actors and regularized critics (DARC) method, for the first time, on this
accident forecasting platform. We derive inspiration from DARC since it is
currently a state-of-the-art reinforcement learning (RL) model on continuous
action space suitable for accident anticipation. Results show that by utilizing
DARC, we can make predictions 5\% earlier on average while improving in
multiple metrics of precision compared to existing methods. The results imply
that using our RL-based problem formulation could significantly increase the
safety of autonomous driving