Search Results for author: Haijun Yu

Found 11 papers, 3 papers with code

On the Influence of Smoothness Constraints in Computed Tomography Motion Compensation

no code implementations29 May 2024 Mareike Thies, Fabian Wagner, Noah Maul, Siyuan Mei, Mingxuan Gu, Laura Pfaff, Nastassia Vysotskaya, Haijun Yu, Andreas Maier

This study analyzes the influence of a spline-based motion model within an existing rigid motion compensation algorithm for cone-beam CT on the recoverable motion frequencies.

Anatomy Computed Tomography (CT) +1

OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios

no code implementations21 Sep 2023 Zhisheng Wang, Zihan Deng, Fenglin Liu, Yixing Huang, Haijun Yu, Junning Cui

The second uses multiple networks to train different directional Hilbert filtering models for DBP images of multiple linear scannings, respectively, and then overlays the reconstructed results, i. e., Multiple Networks Overlaying (MNetO).

Constructing Custom Thermodynamics Using Deep Learning

1 code implementation8 Aug 2023 Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi, Eleonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov, Kedar Hippalgaonkar, Qianxiao Li

Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate.

Physical Intuition

BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction

no code implementations30 May 2023 Zhisheng Wang, Haijun Yu, Yixing Huang, Shunli Wang, Song Ni, Zongfeng Li, Fenglin Liu, Junning Cui

Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields.


OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle

1 code implementation6 Sep 2020 Haijun Yu, Xinyuan Tian, Weinan E, Qianxiao Li

We further apply this method to study Rayleigh-Benard convection and learn Lorenz-like low dimensional autonomous reduced order models that capture both qualitative and quantitative properties of the underlying dynamics.

ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations

2 code implementations7 Nov 2019 Shanshan Tang, Bo Li, Haijun Yu

As spectral accuracy is hard to obtain by direct training of deep neural networks, ChebNets provide a practical way to obtain spectral accuracy, it is expected to be useful in real applications that require efficient approximations of smooth functions.

PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units

no code implementations9 Sep 2019 Bo Li, Shanshan Tang, Haijun Yu

In this paper, we construct deep neural networks with rectified power units (RePU), which can give better approximations for smooth functions.

DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT

no code implementations6 May 2019 Weiwen Wu, Haijun Yu, Peijun Chen, Fulin Luo, Fenglin Liu, Qian Wang, Yining Zhu, Yanbo Zhang, Jian Feng, Hengyong Yu

Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique.

Computed Tomography (CT) Dictionary Learning +1

Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units

no code implementations14 Mar 2019 Bo Li, Shanshan Tang, Haijun Yu

Comparing to the results on ReLU network, the sizes of RePU networks required to approximate functions in Sobolev space and Korobov space with an error tolerance $\varepsilon$, by our constructive proofs, are in general $\mathcal{O}(\log \frac{1}{\varepsilon})$ times smaller than the sizes of corresponding ReLU networks.

Numerical Analysis

Application of Bounded Total Variation Denoising in Urban Traffic Analysis

no code implementations4 Aug 2018 Shanshan Tang, Haijun Yu

While it is believed that denoising is not always necessary in many big data applications, we show in this paper that denoising is helpful in urban traffic analysis by applying the method of bounded total variation denoising to the urban road traffic prediction and clustering problem.

Clustering Denoising +1

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