4,810 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data
Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a dataâdriven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the householdâlevel water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Timeâofâuse and intensityâofâuse differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201
Global Trajectory Optimisation : Can We Prune the Solution Space When Considering Deep Space Manoeuvres? [Final Report]
This document contains a report on the work done under the ESA/Ariadna study 06/4101 on the global optimization of space trajectories with multiple gravity assist (GA) and deep space manoeuvres (DSM). The study was performed by a joint team of scientists from the University of Reading and the University of Glasgow
Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery
Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm.
Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58â0.81) was higher than the other lines (r = 0.21â0.59) included in this study with different genetic backgrounds.
Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing
Species Identification and Profiling of Complex Microbial Communities Using Shotgun Illumina Sequencing of 16S rRNA Amplicon Sequences
The high throughput and cost-effectiveness afforded by short-read sequencing
technologies, in principle, enable researchers to perform 16S rRNA profiling of
complex microbial communities at unprecedented depth and resolution. Existing
Illumina sequencing protocols are, however, limited by the fraction of the 16S
rRNA gene that is interrogated and therefore limit the resolution and quality
of the profiling. To address this, we present the design of a novel protocol
for shotgun Illumina sequencing of the bacterial 16S rRNA gene, optimized to
capture more than 90% of sequences in the Greengenes database and with nearly
twice the resolution of existing protocols. Using several in silico and
experimental datasets, we demonstrate that despite the presence of multiple
variable and conserved regions, the resulting shotgun sequences can be used to
accurately quantify the diversity of complex microbial communities. The
reconstruction of a significant fraction of the 16S rRNA gene also enabled high
precision (>90%) in species-level identification thereby opening up potential
application of this approach for clinical microbial characterization.Comment: 17 pages, 2 tables, 2 figures, supplementary materia
Reduction of false sharing by using process affinity in page-based distributed shared memory mutiprocessor systems
In page-based distributed shared memory systems, a large page size makes efficient use of interconnection network, but increases the chance of false sharing, while a small page size reduces the level of false sharing but results in an inefficient use of the network. This paper proposes a technique that uses process affinity to achieve data pages clustering so as to optimize the temporal data locality on DSM systems, and therefore reduces the chance of false sharing and improves the data locality. To quantify the degree of process affinity for a piece of data, a measure called process affinity index is used that indicates the closeness between this piece of data and the process. Simulation results show that process affinity technique improves the execution performance as page size increases due to the effective reduction of fair sharing. In the best case an order of magnitude performance improvement is achieved.published_or_final_versio
Mining typical load profiles in buildings to support energy management in the smart city context
Mining typical load profiles in buildings to
drive energy management strategies is a fundamental
task
to be addressed in a smart
city environment. In this work,
a general framework
on load profiles characterisation in buildings based on the
recent
scientific
literature
is proposed
. The
process
relies on the combination of different pattern recognition and classification algorithms in order
to provide a robust insight of the energy usage patterns at different level
s and at different scales (from single building to stock of
buildings).
Several im
plications related to energy profiling in buildings, including tariff design, demand side management and
advanced energy diagnos
is are discussed.
Moreover,
a robust methodology
to mine typical energy patterns to
support advanced
energy
diagnosis
in buildin
gs is introduced
by analysing the monitored energy consumption of
a cooling/heating mechanical room
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