19,567 research outputs found
Two Approaches to Imputation and Adjustment of Air Quality Data from a Composite Monitoring Network
An analysis of air quality data is provided for the municipal area of Taranto characterized by high environmental risks, due to the massive presence of industrial sites with elevated environmental impact activities. The present study is focused on particulate matter as measured by PM10 concentrations. Preliminary analysis involved addressing several data problems, mainly: (i) an imputation techniques were considered to cope with the large number of missing data, due to both different working periods for groups of monitoring stations and occasional malfunction of PM10 sensors; (ii) due to the use of different validation techniques for each of the three monitoring networks, a calibration procedure was devised to allow for data comparability. Missing data imputation and calibration were addressed by three alternative procedures sharing a leave-one-out type mechanism and based on {\it ad hoc} exploratory tools and on the recursive Bayesian estimation and prediction of spatial linear mixed effects models. The three procedures are introduced by motivating issues and compared in terms of performance
Two Approaches to Imputation and Adjustment of Air Quality Data from a Composite Monitoring Network
An analysis of air quality data is provided for the municipal area of Taranto characterized by high environmental risks, due to the massive presence of industrial sites with elevated environmental impact activities. The present study is focused on particulate matter as measured by PM10 concentrations. Preliminary analysis involved addressing several data problems, mainly: (i) an imputation techniques were considered to cope with the large number of missing data, due to both different working periods for groups of monitoring stations and occasional malfunction of PM10 sensors; (ii) due to the use of different validation techniques for each of the three monitoring networks, a calibration procedure was devised to allow for data comparability. Missing data imputation and calibration were addressed by three alternative procedures sharing a leave-one-out type mechanism and based on {\it ad hoc} exploratory tools and on the recursive Bayesian estimation and prediction of spatial linear mixed effects models. The three procedures are introduced by motivating issues and compared in terms of performance
Multi-View Frame Reconstruction with Conditional GAN
Multi-view frame reconstruction is an important problem particularly when
multiple frames are missing and past and future frames within the camera are
far apart from the missing ones. Realistic coherent frames can still be
reconstructed using corresponding frames from other overlapping cameras. We
propose an adversarial approach to learn the spatio-temporal representation of
the missing frame using conditional Generative Adversarial Network (cGAN). The
conditional input to each cGAN is the preceding or following frames within the
camera or the corresponding frames in other overlapping cameras, all of which
are merged together using a weighted average. Representations learned from
frames within the camera are given more weight compared to the ones learned
from other cameras when they are close to the missing frames and vice versa.
Experiments on two challenging datasets demonstrate that our framework produces
comparable results with the state-of-the-art reconstruction method in a single
camera and achieves promising performance in multi-camera scenario.Comment: 5 pages, 4 figures, 3 tables, Accepted at IEEE Global Conference on
Signal and Information Processing, 201
Learning Human Motion Models for Long-term Predictions
We propose a new architecture for the learning of predictive spatio-temporal
motion models from data alone. Our approach, dubbed the Dropout Autoencoder
LSTM, is capable of synthesizing natural looking motion sequences over long
time horizons without catastrophic drift or motion degradation. The model
consists of two components, a 3-layer recurrent neural network to model
temporal aspects and a novel auto-encoder that is trained to implicitly recover
the spatial structure of the human skeleton via randomly removing information
about joints during training time. This Dropout Autoencoder (D-AE) is then used
to filter each predicted pose of the LSTM, reducing accumulation of error and
hence drift over time. Furthermore, we propose new evaluation protocols to
assess the quality of synthetic motion sequences even for which no ground truth
data exists. The proposed protocols can be used to assess generated sequences
of arbitrary length. Finally, we evaluate our proposed method on two of the
largest motion-capture datasets available to date and show that our model
outperforms the state-of-the-art on a variety of actions, including cyclic and
acyclic motion, and that it can produce natural looking sequences over longer
time horizons than previous methods
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