Search Results for author: Chen Jiang

Found 9 papers, 6 papers with code

One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction

1 code implementation15 Aug 2022 Hong Peng, Chen Jiang, Jing Cheng, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu

At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the large Hankel matrix to capture the internal distribution among different patches.

WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction

1 code implementation8 May 2022 Zongjiang Tu, Die Liu, Xiaoqing Wang, Chen Jiang, Minghui Zhang, Shanshan Wang, Qiegen Liu, Dong Liang

Deep learning based parallel Imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI).

K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction

1 code implementation21 Mar 2022 Zongjiang Tu, Chen Jiang, Yu Guan, Shanshan Wang, Jijun Liu, Qiegen Liu, Dong Liang

Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible.

MRI Reconstruction

Asteroseismic modelling of solar-type stars: A deeper look at the treatment of initial helium abundance

no code implementations15 Oct 2020 Benard Nsamba, Nuno Moedas, Tiago L. Campante, Margarida S. Cunha, Antonio García Hernández, Juan C. Suárez, Mário J. P. F. G. Monteiro, João Fernandes, Chen Jiang, Babatunde Akinsanmi

Adopting 35 low-mass, solar-type stars with multi-year Kepler photometry from the asteroseismic "LEGACY" sample, we explore the systematic uncertainties on the inferred stellar parameters (i. e., radius, mass, and age) arising from the treatment of the initial helium abundance in stellar model grids .

Solar and Stellar Astrophysics

A Semismooth-Newton's-Method-Based Linearization and Approximation Approach for Kernel Support Vector Machines

no code implementations21 Jul 2020 Chen Jiang, Qingna Li

Motivated by this question and the idea in linearization framework, in this paper, we focus on the $L_2$-loss kernel SVM and propose a semismooth Newton's method based linearization and approximation approach for it.

Understanding Contexts Inside Robot and Human Manipulation Tasks through a Vision-Language Model and Ontology System in a Video Stream

1 code implementation2 Mar 2020 Chen Jiang, Masood Dehghan, Martin Jagersand

In this paper, to model the intended concepts of manipulation, we present a vision dataset under a strictly constrained knowledge domain for both robot and human manipulations, where manipulation concepts and relations are stored by an ontology system in a taxonomic manner.

Language Modelling

Bridging Visual Perception with Contextual Semantics for Understanding Robot Manipulation Tasks

no code implementations16 Sep 2019 Chen Jiang, Martin Jagersand

Using the framework, we present a case study where robot performs manipulation actions in a kitchen environment, bridging visual perception with contextual semantics using the generated dynamic knowledge graphs.

Common Sense Reasoning Knowledge Graphs +1

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