187 research outputs found
A hierarchical semantic segmentation framework for computer vision-based bridge damage detection
Computer vision-based damage detection using remote cameras and unmanned
aerial vehicles (UAVs) enables efficient and low-cost bridge health monitoring
that reduces labor costs and the needs for sensor installation and maintenance.
By leveraging recent semantic image segmentation approaches, we are able to
find regions of critical structural components and recognize damage at the
pixel level using images as the only input. However, existing methods perform
poorly when detecting small damages (e.g., cracks and exposed rebars) and thin
objects with limited image samples, especially when the components of interest
are highly imbalanced. To this end, this paper introduces a semantic
segmentation framework that imposes the hierarchical semantic relationship
between component category and damage types. For example, certain concrete
cracks only present on bridge columns and therefore the non-column region will
be masked out when detecting such damages. In this way, the damage detection
model could focus on learning features from possible damaged regions only and
avoid the effects of other irrelevant regions. We also utilize multi-scale
augmentation that provides views with different scales that preserves
contextual information of each image without losing the ability of handling
small and thin objects. Furthermore, the proposed framework employs important
sampling that repeatedly samples images containing rare components (e.g.,
railway sleeper and exposed rebars) to provide more data samples, which
addresses the imbalanced data challenge
Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring
We introduce a physics-guided signal processing approach to extract a
damage-sensitive and domain-invariant (DS & DI) feature from acceleration
response data of a vehicle traveling over a bridge to assess bridge health.
Motivated by indirect sensing methods' benefits, such as low-cost and
low-maintenance, vehicle-vibration-based bridge health monitoring has been
studied to efficiently monitor bridges in real-time. Yet applying this approach
is challenging because 1) physics-based features extracted manually are
generally not damage-sensitive, and 2) features from machine learning
techniques are often not applicable to different bridges. Thus, we formulate a
vehicle bridge interaction system model and find a physics-guided DS & DI
feature, which can be extracted using the synchrosqueezed wavelet transform
representing non-stationary signals as intrinsic-mode-type components. We
validate the effectiveness of the proposed feature with simulated experiments.
Compared to conventional time- and frequency-domain features, our feature
provides the best damage quantification and localization results across
different bridges in five of six experiments.Comment: To appear in Proc. ICASSP2020, May 04-08, 2020, Barcelona, Spain.
IEE
COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility
Demand flexibility is increasingly important for power grids. Careful
coordination of thermostatically controlled loads (TCLs) can modulate energy
demand, decrease operating costs, and increase grid resiliency. We propose a
novel distributed control framework for the Coordination Of HeterOgeneous
Residential Thermostatically controlled loads (COHORT). COHORT is a practical,
scalable, and versatile solution that coordinates a population of TCLs to
jointly optimize a grid-level objective, while satisfying each TCL's end-use
requirements and operational constraints. To achieve that, we decompose the
grid-scale problem into subproblems and coordinate their solutions to find the
global optimum using the alternating direction method of multipliers (ADMM).
The TCLs' local problems are distributed to and computed in parallel at each
TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses
combinatorial and non-convex constraints, we characterize these constraints as
a convex set through relaxation, thereby making COHORT computationally viable
over long planning horizons. After coordination, each TCL is responsible for
its own control and tracks the agreed-upon power trajectory with its preferred
strategy. In this work, we translate continuous power back to discrete on/off
actuation, using pulse width modulation. COHORT is generalizable to a wide
range of grid objectives, which we demonstrate through three distinct use
cases: generation following, minimizing ramping, and peak load curtailment. In
a notable experiment, we validated our approach through a hardware-in-the-loop
simulation, including a real-world air conditioner (AC) controlled via a smart
thermostat, and simulated instances of ACs modeled after real-world data
traces. During the 15-day experimental period, COHORT reduced daily peak loads
by an average of 12.5% and maintained comfortable temperatures.Comment: Accepted to ACM BuildSys 2020; 10 page
Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations
Water adsorption and dissociation processes on pristine low-index TiO interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO surfaces, based on three density-functional-theory approximations. Here we show the water dissociation free energies on seven pristine TiO surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase (101) and rutile (100) have mostly molecular adsorption, while the simulations of rutile (110) sensitively depend on the slab thickness and molecular adsorption is preferred with thick slabs. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. These mechanisms can be rationalized based on the arrangements of water molecules on the different surfaces. Our finding thus demonstrates that the different pristine TiO surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces
What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Training control policies in simulation is more appealing than on real robots
directly, as it allows for exploring diverse states in a safe and efficient
manner. Yet, robot simulators inevitably exhibit disparities from the real
world, yielding inaccuracies that manifest as the simulation-to-real gap.
Existing literature has proposed to close this gap by actively modifying
specific simulator parameters to align the simulated data with real-world
observations. However, the set of tunable parameters is usually manually
selected to reduce the search space in a case-by-case manner, which is hard to
scale up for complex systems and requires extensive domain knowledge. To
address the scalability issue and automate the parameter-tuning process, we
introduce an approach that aligns the simulator with the real world by
discovering the causal relationship between the environment parameters and the
sim-to-real gap. Concretely, our method learns a differentiable mapping from
the environment parameters to the differences between simulated and real-world
robot-object trajectories. This mapping is governed by a simultaneously-learned
causal graph to help prune the search space of parameters, provide better
interpretability, and improve generalization. We perform experiments to achieve
both sim-to-sim and sim-to-real transfer, and show that our method has
significant improvements in trajectory alignment and task success rate over
strong baselines in a challenging manipulation task
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