6,147 research outputs found
Symbol detection in online handwritten graphics using Faster R-CNN
Symbol detection techniques in online handwritten graphics (e.g. diagrams and
mathematical expressions) consist of methods specifically designed for a single
graphic type. In this work, we evaluate the Faster R-CNN object detection
algorithm as a general method for detection of symbols in handwritten graphics.
We evaluate different configurations of the Faster R-CNN method, and point out
issues relative to the handwritten nature of the data. Considering the online
recognition context, we evaluate efficiency and accuracy trade-offs of using
Deep Neural Networks of different complexities as feature extractors. We
evaluate the method on publicly available flowchart and mathematical expression
(CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used
on both datasets, enabling the possibility of developing general methods for
symbol detection, and furthermore, general graphic understanding methods that
could be built on top of the algorithm.Comment: Submitted to DAS-201
Impact of Service Sector Loads on Renewable Resource Integration
Urban areas consist of a mix of households and services, such as offices,
shops, schools, etc. Yet most urban energy models only consider household load
profiles, omitting the service sector. Realistic assessment of the potential
for renewable resource integration in cities requires models that include
detailed demand and generation profiles. Detailed generation profiles are
available for many resources. Detailed demand profiles, however, are currently
only available for households and not for the service sector. This paper
addresses this gap. The paper (1) proposes a novel approach to devise synthetic
service sector demand profiles based on a combination of a large number of
different data sources, and (2) uses these profiles to study the impact of the
service sector on the potential for renewable resource integration in urban
energy systems, using the Netherlands as a case study. The importance of the
service sector is addressed in a broad range of solar and wind generation
scenarios, and in specific time and weather conditions (in a single scenario).
Results show that including the service sector leads to statistically
significantly better estimations of the potential of renewable resource
integration in urban areas. In specific time and weather conditions, including
the service sector results in estimations that are up to 33% higher than if
only households are considered. The results can be used by researchers to
improve urban energy systems models, and by decision-makers and practitioners
for grid planning, operation and management}.Comment: 32 pages, 7 figures, 4 table
Determinants of frailty development and progression using a multidimensional frailty index: Evidence from the English Longitudinal Study of Ageing
This work was supported by grant number 689592 "my-AHA" from the Horizon 2020 research funding framework of the European Commission (https://ec.europa.eu/programmes/horizon2020/en).Open Access articl
Letter from Nina T. Updythe
Letter concerning a position in the history department at Utah Agricultural Colleg
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