475 research outputs found
Part-aware Prototype Network for Few-shot Semantic Segmentation
Few-shot semantic segmentation aims to learn to segment new object classes
with only a few annotated examples, which has a wide range of real-world
applications. Most existing methods either focus on the restrictive setting of
one-way few-shot segmentation or suffer from incomplete coverage of object
regions. In this paper, we propose a novel few-shot semantic segmentation
framework based on the prototype representation. Our key idea is to decompose
the holistic class representation into a set of part-aware prototypes, capable
of capturing diverse and fine-grained object features. In addition, we propose
to leverage unlabeled data to enrich our part-aware prototypes, resulting in
better modeling of intra-class variations of semantic objects. We develop a
novel graph neural network model to generate and enhance the proposed
part-aware prototypes based on labeled and unlabeled images. Extensive
experimental evaluations on two benchmarks show that our method outperforms the
prior art with a sizable margin.Comment: ECCV-202
Fine-Granularity Transmission Distortion Modeling for Video Packet Scheduling Over Mesh Networks
Digital Object Identifier 10.1109/TMM.2009.2036290Packet scheduling is a critical component in multi-session video streaming over mesh networks. Different video packets have different levels of contribution to the overall video presentation quality at the receiver side. In this work, we develop a
fine-granularity transmission distortion model for the encoder to predict the quality degradation of decoded videos caused by lost
video packets. Based on this packet-level transmission distortion model, we propose a content-and-deadline-aware scheduling (CDAS) scheme for multi-session video streaming over multi-hop mesh networks, where content priority, queuing delays, and dynamic network transmission conditions are jointly considered for each video packet. Our extensive experimental results demonstrate that the proposed transmission distortion model and the CDAS scheme significantly improve the performance of multi-session video streaming over mesh networks
Understanding gas transport mechanisms in shale gas reservoir: Pore network modelling approach
This report summarizes the recent findings on gas transport mechanisms in shale gas reservoir by pore network modelling. Multi-scale pore network model was developed to accurately characterize the shale pore structure. The pore network single component gas transport model was established considering the gas slippage and real gas property. The gas transport mechanisms in shale pore systems were elaborated on this basis. A multicomponent hydrocarbon pore network transport model was further proposed considering the influences of capillary pressure and fluid occurrence on fugacity balance. The hydrocarbon composition and pore structure influences on condensate gas transport were analyzed. These results provide valuable insights on gas transport mechanisms in shale gas reservoir.Cited as: Song, W., Yao, J., Zhang, K., Yang, Y., Sun, H. Understanding gas transport mechanisms in shale gas reservoir: Pore network modelling approach. Advances in Geo-Energy Research, 2022, 6(4): 359-360. https://doi.org/10.46690/ager.2022.04.1
Machine learning for online sea ice bias correction within global ice-ocean simulations
In this study we perform online sea ice bias correction within a GFDL global
ice-ocean model. For this, we use a convolutional neural network (CNN) which
was developed in a previous study (Gregory et al., 2023) for the purpose of
predicting sea ice concentration (SIC) data assimilation (DA) increments. An
initial implementation of the CNN shows systematic improvements in SIC biases
relative to the free-running model, however large summertime errors remain. We
show that these residual errors can be significantly improved with a data
augmentation approach, in which sequential CNN and DA corrections are applied
to a new simulation over the training period. This then provides a new training
data set with which to refine the weights of the initial network. We propose
that this machine-learned correction scheme could be utilized for generating
improved initial conditions, and also for real-time sea ice bias correction
within seasonal-to-subseasonal sea ice forecasts
Numerical simulation on structural safety and dynamic response of coal mine rescue ball with gas explosion load using Arbitrary Lagrangian-Eulerian (ALE) algorithm
Coal mine rescue devices, which can supply miners underground with fundamental shelters after gas explosion, are essential for safety production of coal mines. In this paper, a novel and composite structure-rescue antiknock ball for coal mine rescue is designed. Further, the structural safety and dynamic response under gas explosion of the antiknock ball is investigated by ALE algorithm. To achieve this goal, the ALE finite element method is described in dynamic form, and governing equations and the finite element expressions of the ALE algorithm are derived. 3 balls with different structures are designed and dynamic response analysis has been conducted in a semi-closed tunnel with explosive load of pre-mixed gas/air mixture by using ALE algorithm based on explicit nonlinear dynamic program LS-DYNA. Displacement field, stress field and energy transmission laws are analyzed and compared via theoretical calculations. Results show that the cabin door, emergency door and spherical shell are important components of the rescue ball. The 3# composite ball is the optimization structure that can delay the shock effect of the gas explosion load on a coal mine rescue system; the simulation results can provide reference data for coal mine rescue system design
Deep learning of systematic sea ice model errors from data assimilation increments
Data assimilation is often viewed as a framework for correcting short-term
error growth in dynamical climate model forecasts. When viewed on the time
scales of climate however, these short-term corrections, or analysis
increments, can closely mirror the systematic bias patterns of the dynamical
model. In this study, we use convolutional neural networks (CNNs) to learn a
mapping from model state variables to analysis increments, in order to showcase
the feasibility of a data-driven model parameterization which can predict
state-dependent model errors. We undertake this problem using an ice-ocean data
assimilation system within the Seamless system for Prediction and EArth system
Research (SPEAR) model, developed at the Geophysical Fluid Dynamics Laboratory,
which assimilates satellite observations of sea ice concentration every 5 days
between 1982--2017. The CNN then takes inputs of data assimilation forecast
states and tendencies, and makes predictions of the corresponding sea ice
concentration increments. Specifically, the inputs are states and tendencies of
sea ice concentration, sea-surface temperature, ice velocities, ice thickness,
net shortwave radiation, ice-surface skin temperature, sea-surface salinity, as
well as a land-sea mask. We find the CNN is able to make skillful predictions
of the increments in both the Arctic and Antarctic and across all seasons, with
skill that consistently exceeds that of a climatological increment prediction.
This suggests that the CNN could be used to reduce sea ice biases in
free-running SPEAR simulations, either as a sea ice parameterization or an
online bias correction tool for numerical sea ice forecasts.Comment: 38 pages, 8 figures, 10 supplementary figure
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