187 research outputs found
ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation
Automatic tissue segmentation of fetal brain images is essential for the
quantitative analysis of prenatal neurodevelopment. However, producing
voxel-level annotations of fetal brain imaging is time-consuming and expensive.
To reduce labeling costs, we propose a practical unsupervised domain adaptation
(UDA) setting that adapts the segmentation labels of high-quality fetal brain
atlases to unlabeled fetal brain MRI data from another domain. To address the
task, we propose a new UDA framework based on Appearance and Structure
Consistency, named ASC. We adapt the segmentation model to the appearances of
different domains by constraining the consistency before and after a
frequency-based image transformation, which is to swap the appearance between
brain MRI data and atlases. Consider that even in the same domain, the fetal
brain images of different gestational ages could have significant variations in
the anatomical structures. To make the model adapt to the structural variations
in the target domain, we further encourage prediction consistency under
different structural perturbations. Extensive experiments on FeTA 2021
benchmark demonstrate the effectiveness of our ASC in comparison to
registration-based, semi-supervised learning-based, and existing UDA-based
methods.Comment: MICCAI 2023, released code: https://github.com/lhaof/AS
A large LNG tank technology system āCGTankĀ®ā of CNOOC and its engineering application
AbstractLNG tanks are complex in design and building process and high in costs, so LNG tank technology is one of the most advanced ones in the field of energy, which has been monopolized by foreign companies for a long time. In order to work out LNG tank technology domestically, China National Offshore Oil Corporation (CNOOC for short), the largest LNG importer in China, develops a LNG tank technology system āCGTankĀ®ā successfully in reference to the design and construction experience of domestic and foreign companies, after years of scientific research in tackling difficult problems. This system presents four traits as follows. First, a set of calculation software is developed independently by CNOOC, and the tanks in all operating conditions are calculated after 3D hologram and multi-point contact model of fluid-solid coupling effect is built up. Second, earthquake effect research and inner tank check research are improved innovatively by means of response spectrum analysis after European standards are introduced. Third, it is put forward for the first time that the stress strength discrimination standard is based on the principal stress which is obtained by means of the maximum shearing failure theory. And fourth, a large LNG full-capacity tank technology package with completely independent intellectual property right is established. The āCGTankĀ®ā system was first applied in the Tianjin LNG demonstration project, which has passed all indicator tests and is now in operation smoothly. The project is provided with the core tank design technology by CNOOC Gas and Power Group and with the EPC by CNOOC Engineering Co., Ltd. The independent LNG tank technology can be applied in a wide scope and it is favorable for impelling domestic production of LNG industry completely
Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding
We propose an effective two-stage approach to tackle the problem of
language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task. In
the first stage, we propose an Augmented 2D Temporal Adjacent Network
(Augmented 2D-TAN) to temporally ground the target moment corresponding to the
given description. Primarily, we improve the original 2D-TAN from two aspects:
First, a temporal context-aware Bi-LSTM Aggregation Module is developed to
aggregate clip-level representations, replacing the original max-pooling.
Second, we propose to employ Random Concatenation Augmentation (RCA) mechanism
during the training phase. In the second stage, we use pretrained MDETR model
to generate per-frame bounding boxes via language query, and design a set of
hand-crafted rules to select the best matching bounding box outputted by MDETR
for each frame within the grounded moment.Comment: Best Paper Award at the 3rd Person in Context (PIC) Challenge CVPR
Workshop 202
Experimental Study of Granular Clogging in Two-Dimensional Hopper
We experimentally investigate the clogging process of granular materials in a
two-dimensional hopper, and present a self-consistent physical mechanism of
clogging based on preformed dynamic chain structures in the flow. We found that
these chain structures follow a specific modified restricted random walk, and
clogging occurs when they are mechanically stable enough to withstand the flow
fluctuations, resulting in the formation of an arch at the outlet. We introduce
a simple model which can explain the clogging probability by incorporating an
analytical expression for chain formation and its transition into an arch. Our
results provide insight into the microscopic mechanism of clogging in hopper
flow.Comment: 22 pages, 8 figure
Aggregate Model of District Heating Network for Integrated Energy Dispatch: A Physically Informed Data-Driven Approach
The district heating network (DHN) is essential in enhancing the operational
flexibility of integrated energy systems (IES). Yet, it is hard to obtain an
accurate and concise DHN model for the operation owing to complicated network
features and imperfect measurement. Considering this, this paper proposes a
physically informed data-driven aggregate model (AGM) for DHN, providing a
concise description of the source-load relationship of DHN without exposing
network details. First, we derive the analytical relationship between the state
variables of the source and load nodes of DHN, offering a physical fundament
for the AGM. Second, we propose a physics-informed estimator for AGM that is
robust to low-quality measurement, in which the physical constraints associated
with the parameter normalization and sparsity are embedded to improve the
accuracy and robustness. Finally, we propose a physics-enhanced algorithm to
solve the nonlinear estimator with non-closed constraints efficiently.
Simulation results verify the effectiveness of the proposed method
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