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Found 22 papers, 4 papers with code
Hybrid Parallel Collaborative Simulation Framework Integrating Device Physics with Circuit Dynamics for PDAE-Modeled Power Electronic Equipment
To bridge this gap, this paper proposes a Hybrid-Parallel Collaborative (HPC) framework specifically designed to analyze the Partial Differential Algebraic Equation (PDAE) modeled power electronic equipment, integrating the device physics and circuit dynamics.
7 Tesla multimodal MRI dataset of ex-vivo human brain
Ex-vivo MRI offers invaluable insights into the complexity of the human brain, enabling high-resolution anatomical delineation and integration with histopathology, and thus, contributes to both basic and clinical studies on normal and pathological brains.
AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI
Accurate automatic tissue segmentation in fetal brain MRI is a crucial step in clinical diagnosis but remains challenging, particularly due to the dynamically changing anatomy and tissue contrast during fetal development.
A Multi-scenario Attention-based Generative Model for Personalized Blood Pressure Time Series Forecasting
Continuous blood pressure (BP) monitoring is essential for timely diagnosis and intervention in critical care settings.
SCREENER: A general framework for task-specific experiment design in quantitative MRI
Results demonstrate SCREENER outperforms previous ad hoc and optimized protocols under clinical signal-to-noise ratio (SNR) conditions, achieving significant improvement, both in binary classification tasks, e. g. from 67% to 89%, and in a multi-class classification task, from 46% to 59%.
Approximating Voltage Stability Boundary Under High Variability of Renewables Using Differential Geometry
This paper proposes a novel method rooted in differential geometry to approximate the voltage stability boundary of power systems under high variability of renewable generation.
InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update
Our experiments with three GNN models on four large graphs demonstrate that InkStream accelerates by 2. 5-427$\times$ on a CPU cluster and 2. 4-343$\times$ on two different GPU clusters while producing identical outputs as GNN model inference on the latest graph snapshot.
A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank Approximation
Conclusion: The proposed CP-based MA method showed superior performance compared to SVD-based methods with higher sensitivity to motion, success rate, and lower baseline bias, and can be used as a prior step to improve fetal brain reconstruction.
Quantum Power Flows: From Theory to Practice
Climate change is becoming one of the greatest challenges to the sustainable development of modern society.
Cyber-resilient Automatic Generation Control for Systems of AC Microgrids
In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber securty solutions.
A microstructure estimation Transformer inspired by sparse representation for diffusion MRI
Thus, the METSC is composed with three stages, an embedding stage, a sparse representation stage, and a mapping stage.
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
Meta Attention For Off-Policy Actor-Critic
In this paper, we propose a meta attention method for state-based reinforcement learning tasks, which combines attention mechanism and meta-learning based on the Off-Policy Actor-Critic framework.
DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control
Then, to efficiently utilize the historical state information of the intersection, we design a sequence model with the temporal convolutional network (TCN) to capture the historical information and further merge it with the spatial information to improve its performance.
Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks
Reinforcement learning (RL) is always the preferred embodiment to construct the control strategy of complex tasks, like asymmetric assembly tasks.
Feature-Based Compliance Control for Peg-in-Hole Assembly with Clearance or Interference Fit
This paper aims at solving mass precise peg-in-hole assembly.
Performance Comparison between Reconfigurable Intelligent Surface and Relays: Theoretical Methods and a Perspective from Operator
On the other hand, a relay node in a traditional relay network has to be active, which indicates that it will consume energy when it is relaying the signal or information between the source and destination nodes.
Information Theory Information Theory
Compressive Circular Polarization Snapshot Spectral Imaging
A compressive sensing based circular polarization snapshot spectral imaging system is proposed in this paper to acquire two-dimensional spatial, one-dimensional circular polarization (the right and left circular polarization), and one-dimensional spectral information, simultaneously.
Compressive Sensing
Optics
Image and Video Processing
78A10 (Primary)
I.4.2; I.4.4; I.4.5
Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization
In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering.
Kernel principal component analysis network for image classification
In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed.
Conditional Random Fields with High-Order Features for Sequence Labeling
Dependencies among neighbouring labels in a sequence is an important source of information for sequence labeling problems.
