92 research outputs found
Sense, Model and Identify the Load Signatures of HVAC Systems in Metro Stations
The HVAC systems in subway stations are energy consuming giants, each of
which may consume over 10, 000 Kilowatts per day for cooling and ventilation.
To save energy for the HVAC systems, it is critically important to firstly know
the "load signatures" of the HVAC system, i.e., the quantity of heat imported
from the outdoor environments and by the passengers respectively in different
periods of a day, which will significantly benefit the design of control
policies. In this paper, we present a novel sensing and learning approach to
identify the load signature of the HVAC system in the subway stations. In
particular, sensors and smart meters were deployed to monitor the indoor,
outdoor temperatures, and the energy consumptions of the HVAC system in
real-time. The number of passengers was counted by the ticket checking system.
At the same time, the cooling supply provided by the HVAC system was inferred
via the energy consumption logs of the HVAC system. Since the indoor
temperature variations are driven by the difference of the loads and the
cooling supply, linear regression model was proposed for the load signature,
whose coefficients are derived via a proposed algorithm . We collected real
sensing data and energy log data from HaiDianHuangZhuang Subway station, which
is in line 4 of Beijing from the duration of July 2012 to Sept. 2012. The data
was used to evaluate the coefficients of the regression model. The experiment
results show typical variation signatures of the loads from the passengers and
from the outdoor environments respectively, which provide important contexts
for smart control policies.Comment: 5 pages, 5 figure
Parametric modelling system of gas turbine combustor
U istraživanju numeričke simulacije komore izgaranja plinske turbine postoji niz ponavljajućih operacija kao što su modeliranje i umrežavanje. Ideja je parametarskog projekta da se to pojednostavi. U ovom se radu predstavlja parametarski sustav modeliranja komore izgaranja plinske turbine, uglavnom za sastavnice plamene cijevi, a to uključuje parametarsko modeliranje i stvaranje mreže. Na primjeru vrtložne komore, u radu se detaljno predstavlja parametarski sustav modeliranja. Također se pokazuje da se u bazi podataka nalaze različiti tipovi konstrukcije vrtložne komore i plamene cijevi. Sustav je projektiran prema UG platformi sekundarnog razvoja, temeljenoj na grafičkoj šabloni metode parametarskog projektiranja. Parametarski sustav modeliranja je lako primjenjiv i univerzalan, potrebno je svega nekoliko minuta da se model ažurira, a baza podataka može se ažurirati bilo kada i dodati nove tipove konstrukcije. To je probitak i za projektiranje komore izgaranja i za istraživački rad na optimalizaciji postupka.In the numerical simulation research of gas turbine combustor, there are plenty repetitive operations such as modelling and meshing. Parametric design idea is about simplifying it. This paper establishes a parametric modelling system of gas turbine combustor, mainly for the flame tube components, which includes parametric modelling and simple mesh generation. Taking a swirler as example, this paper introduces the parametric modelling system in detail. It also shows that the database contains different swirler and flame tube structure types. The system is designed under the UG secondary development platform, based on graphics template of parametric design method. The parametric modelling system has good practicability and universality, only takes a few minutes to update a model, and can update the database anytime to add new structure types. It is a utility for aid of both combustion chamber design and performance optimization research work
Visual Commonsense based Heterogeneous Graph Contrastive Learning
How to select relevant key objects and reason about the complex relationships
cross vision and linguistic domain are two key issues in many multi-modality
applications such as visual question answering (VQA). In this work, we
incorporate the visual commonsense information and propose a heterogeneous
graph contrastive learning method to better finish the visual reasoning task.
Our method is designed as a plug-and-play way, so that it can be quickly and
easily combined with a wide range of representative methods. Specifically, our
model contains two key components: the Commonsense-based Contrastive Learning
and the Graph Relation Network. Using contrastive learning, we guide the model
concentrate more on discriminative objects and relevant visual commonsense
attributes. Besides, thanks to the introduction of the Graph Relation Network,
the model reasons about the correlations between homogeneous edges and the
similarities between heterogeneous edges, which makes information transmission
more effective. Extensive experiments on four benchmarks show that our method
greatly improves seven representative VQA models, demonstrating its
effectiveness and generalizability
2D-Shapley: A Framework for Fragmented Data Valuation
Data valuation -- quantifying the contribution of individual data sources to
certain predictive behaviors of a model -- is of great importance to enhancing
the transparency of machine learning and designing incentive systems for data
sharing. Existing work has focused on evaluating data sources with the shared
feature or sample space. How to valuate fragmented data sources of which each
only contains partial features and samples remains an open question. We start
by presenting a method to calculate the counterfactual of removing a fragment
from the aggregated data matrix. Based on the counterfactual calculation, we
further propose 2D-Shapley, a theoretical framework for fragmented data
valuation that uniquely satisfies some appealing axioms in the fragmented data
context. 2D-Shapley empowers a range of new use cases, such as selecting useful
data fragments, providing interpretation for sample-wise data values, and
fine-grained data issue diagnosis.Comment: ICML 202
MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts
Event detection (ED) identifies and classifies event triggers from
unstructured texts, serving as a fundamental task for information extraction.
Despite the remarkable progress achieved in the past several years, most
research efforts focus on detecting events from formal texts (e.g., news
articles, Wikipedia documents, financial announcements). Moreover, the texts in
each dataset are either from a single source or multiple yet relatively
homogeneous sources. With massive amounts of user-generated text accumulating
on the Web and inside enterprises, identifying meaningful events in these
informal texts, usually from multiple heterogeneous sources, has become a
problem of significant practical value. As a pioneering exploration that
expands event detection to the scenarios involving informal and heterogeneous
texts, we propose a new large-scale Chinese event detection dataset based on
user reviews, text conversations, and phone conversations in a leading
e-commerce platform for food service. We carefully investigate the proposed
dataset's textual informality and multi-source heterogeneity characteristics by
inspecting data samples quantitatively and qualitatively. Extensive experiments
with state-of-the-art event detection methods verify the unique challenges
posed by these characteristics, indicating that multi-source informal event
detection remains an open problem and requires further efforts. Our benchmark
and code are released at \url{https://github.com/myeclipse/MUSIED}.Comment: Accepted at EMNLP 202
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
The need for fine-grained perception in autonomous driving systems has
resulted in recently increased research on online semantic segmentation of
single-scan LiDAR. Despite the emerging datasets and technological
advancements, it remains challenging due to three reasons: (1) the need for
near-real-time latency with limited hardware; (2) uneven or even long-tailed
distribution of LiDAR points across space; and (3) an increasing number of
extremely fine-grained semantic classes. In an attempt to jointly tackle all
the aforementioned challenges, we propose a new LiDAR-specific,
nearest-neighbor-free segmentation algorithm - PolarNet. Instead of using
common spherical or bird's-eye-view projection, our polar bird's-eye-view
representation balances the points across grid cells in a polar coordinate
system, indirectly aligning a segmentation network's attention with the
long-tailed distribution of the points along the radial axis. We find that our
encoding scheme greatly increases the mIoU in three drastically different
segmentation datasets of real urban LiDAR single scans while retaining near
real-time throughput.Comment: Accepted by CVPR 2020; Code at
https://github.com/edwardzhou130/PolarSe
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