35 research outputs found
A Deep Network Model for Paraphrase Detection in Short Text Messages
This paper is concerned with paraphrase detection. The ability to detect
similar sentences written in natural language is crucial for several
applications, such as text mining, text summarization, plagiarism detection,
authorship authentication and question answering. Given two sentences, the
objective is to detect whether they are semantically identical. An important
insight from this work is that existing paraphrase systems perform well when
applied on clean texts, but they do not necessarily deliver good performance
against noisy texts. Challenges with paraphrase detection on user generated
short texts, such as Twitter, include language irregularity and noise. To cope
with these challenges, we propose a novel deep neural network-based approach
that relies on coarse-grained sentence modeling using a convolutional neural
network and a long short-term memory model, combined with a specific
fine-grained word-level similarity matching model. Our experimental results
show that the proposed approach outperforms existing state-of-the-art
approaches on user-generated noisy social media data, such as Twitter texts,
and achieves highly competitive performance on a cleaner corpus
Global Transformer Architecture for Indoor Room Temperature Forecasting
A thorough regulation of building energy systems translates in relevant energy savings and in a better comfort for the occupants. Algorithms to predict the thermal state of a building on a certain time horizon with a good confidence are essential for the implementation of effective control systems. This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings, aiming at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems. Recent advancements in deep learning have enabled the development of more sophisticated forecasting models compared to traditional feedback control systems. The proposed global Transformer architecture can be trained on the entire dataset encompassing all rooms, eliminating the need for multiple room-specific models, significantly improving predictive performance, and simplifying deployment and maintenance. Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings. The proposed approach provides a novel solution to enhance the accuracy and efficiency of temperature forecasting, serving as a valuable tool to optimize energy consumption and decrease greenhouse gas emissions in the building sector.publishedVersio
Global Transformer Architecture for Indoor Room Temperature Forecasting
A thorough regulation of building energy systems translates in relevant
energy savings and in a better comfort for the occupants. Algorithms to predict
the thermal state of a building on a certain time horizon with a good
confidence are essential for the implementation of effective control systems.
This work presents a global Transformer architecture for indoor temperature
forecasting in multi-room buildings, aiming at optimizing energy consumption
and reducing greenhouse gas emissions associated with HVAC systems. Recent
advancements in deep learning have enabled the development of more
sophisticated forecasting models compared to traditional feedback control
systems. The proposed global Transformer architecture can be trained on the
entire dataset encompassing all rooms, eliminating the need for multiple
room-specific models, significantly improving predictive performance, and
simplifying deployment and maintenance. Notably, this study is the first to
apply a Transformer architecture for indoor temperature forecasting in
multi-room buildings. The proposed approach provides a novel solution to
enhance the accuracy and efficiency of temperature forecasting, serving as a
valuable tool to optimize energy consumption and decrease greenhouse gas
emissions in the building sector
Configurable convolutional neural networks for real-time pedestrian-level wind prediction in urban environments
Urbanization has underscored the importance of understanding the pedestrian
wind environment in urban and architectural design contexts. Pedestrian Wind
Comfort (PWC) focuses on the effects of wind on the safety and comfort of
pedestrians and cyclists, given the influence of urban structures on the local
microclimate. Traditional Computational Fluid Dynamics (CFD) methods used for
PWC analysis have limitations in computation, cost, and time. Deep-learning
models have the potential to significantly speed up this process. The
prevailing state-of-the-art methodologies largely rely on GAN-based models,
such as pix2pix, which have exhibited training instability issues. In contrast,
our work introduces a convolutional neural network (CNN) approach based on the
U-Net architecture, offering a more stable and streamlined solution. The
process of generating a wind flow prediction at pedestrian level is
reformulated from a 3D CFD simulation into a 2D image-to-image translation
task, using the projected building heights as input. Testing on standard
consumer hardware shows that our model can efficiently predict wind velocities
in urban settings in real time. Further tests on different configurations of
the model, combined with a Pareto front analysis, helped identify the trade-off
between accuracy and computational efficiency. This CNN-based approach provides
a fast and efficient method for PWC analysis, potentially aiding in more
efficient urban design processes