Xinwei Liu

Shenyang Ligong University

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Yu‐Hong Dai

Academy of Mathematics and Systems Science

Yakui Huang

Hebei University of Technology

Jie Sun

Tianjin Medical University General Hospital

Sanqing Su

Xi'an University of Architecture and Technology

Meidan Que

Xi'an University of Architecture and Technology

Junting Li

Xi'an University of Architecture and Technology

Christophe Charrier

Université de Caen Normandie

Fuliang Zuo

Xi'an University of Architecture and Technology

Lingfu Yang

Xi'an University of Architecture and Technology

Huiqi Zheng

Xi'an University of Architecture and Technology

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Chinese Academy of Sciences

114

University of Chinese Academy of Sciences

Tsinghua University

Shanghai Jiao Tong University

Peking University

Xi'an University of Architecture and Technology

Zhejiang University

Huazhong University of Science and Technology

Nanjing University of Posts and Telecommunications

Hebei University of Technology

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Box constrained total generalized variation model and primal-dual algorithm for Poisson noise removal

Journal of Pseudo-Differential Operators and Applications (2019)

Yehu Lv Xinwei Liu

Total variation denoising

Regularization

Saddle point

10.1007/s11868-019-00317-y

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Citations (1)

Exact penalty method for D-stationary point of nonlinear optimization

arXiv (Cornell University) (2023)

Xinwei Liu Yu‐Hong Dai

We consider the nonlinear optimization problem with least $\ell_1$-norm measure of constraint violations and introduce the concepts of the D-stationary point, the DL-stationary point and the DZ-stationary point with the help of exact penalty function. If the stationary point is feasible, they correspond to the Fritz-John stationary point, the KKT stationary point and the singular stationary point, respectively. In order to show the usefulness of the new stationary points, we propose a new exact penalty sequential quadratic programming (SQP) method with inner and outer iterations and analyze its global and local convergence. The proposed method admits convergence to a D-stationary point and rapid infeasibility detection without driving the penalty parameter to zero, which demonstrates the commentary given in [SIAM J. Optim., 20 (2010), 2281--2299] and can be thought to be a supplement of the theory of nonlinear optimization on rapid detection of infeasibility. Some illustrative examples and preliminary numerical results demonstrate that the proposed method is robust and efficient in solving infeasible nonlinear problems and a degenerate problem without LICQ in the literature.

Stationary point

Sequential quadratic programming

Karush–Kuhn–Tucker conditions

Penalty Method

Saddle point

10.48550/arxiv.2309.13816

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Ion Mobility Separation Using a Dual-LIT Miniature Mass Spectrometer

Analytical Chemistry (2020)

Jingjin Fan Penglong Lian Ming Li Xinwei Liu X. K. Zhou

Ion mobility (IM) has been increasingly used in combination with mass spectrometry (MS) for chemical and biological analysis. While implementation of IM with MS usually requires complex instrumentation with delicate controls, in this study we explored the potential of performing IM separation using dual-linear ion traps (LITs) in a miniature mass spectrometer, which was originally developed for performing comprehensive MS/MS scan functions with a simple instrumentation configuration. The IM separation was achieved by ion transfer between the LITs with dynamic gas flow. Its performance was characterized for analysis of a broad range of chemical and biological compounds including small organic compounds such as trisaccharides, raffinose, cellotriose, and melezitose, as well as protein conformers. The demonstrated technique serves as another example of developing powerful hybrid instrument functions with simple configurations and miniaturized sizes.

Instrumentation

Ion-mobility spectrometry

10.1021/acs.analchem.9b04271

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Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal

Lecture notes in computer science (2022)

Xinwei Liu Jian Liu Yang Bai Jindong Gu Tao Chen

10.1007/978-3-031-19781-9_1

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Citations (12)

A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network

Digital Signal Processing (2022)

Pan Shang Xinwei Liu Chengqing Yu Guangxi Yan Qingqing Xiang

Ensemble Learning

Adjacency matrix

Adjacency list

10.1016/j.dsp.2022.103419

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Citations (42)

Watching China’s Democratic Process from the Mode of Thinking

Journal of Hebei Polytechnic University (2006)

Xinwei Liu

Democracy is a new thing to China,though a lot of remarkable personages had democratic ideal and made enormous efforts for this more than 100 years ago,but the democratic process of China still lacks the experience of developing.Its basic reason lies in the Chinese mode of thinking was not changed,lack democratic consciousness and democracy way of thinking.Therefore, developing democracy must change the thinking ways against democracy in China’s traditions,not only the system strengthened democracy is ensured but also from the basic education,improves national quality to start with.

Mode (computer interface)

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細目検出のための改良3ステップ非接触指紋画像強調法【Powered by NICT】

IEEE Conference Proceedings (2016)

Xinwei Liu Pedersen Marius Charrier Christophe Cheikh Faouzi Alaya Patrick Bours

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Experimental study of droplets impacting on a solid surface

Journal of Beijing University of Chemical Technology (2019)

Feng Chen Dengfei Wang Cai ZiQi Gao ZhengMing Xinwei Liu

Solid surface

10.13543/j.bhxbzr.2019.04.003

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A new software for gravity gradiometry data forwarding and inversion

IOP Conference Series Earth and Environmental Science (2021)

Zhenlong Hou Jikang Wei Xinwei Liu

Abstract A new software has been developed specially for gravity gradiometry data forwarding and joint inversion. It is developed with hybrid programming technique and some powerful function libraries, including language of Python and C, Matplotlib, PyQt and so on. The software could help the users with graphical user interface, implement the data import/export and some management, the simple visualization in 2/3 dimension, modeling for gravity exploration data, and 3D density inversion. One of the advantages of this software is combining the popular development techniques to make the research of the forwarding and inversion easily, another one is simple secondary development, which allows the users to add their algorithms to the platform. The software is mainly used for the teaching and scientific research purposes, suitable for the geophysical scientists, engineers and graduate student.

Python

Graphical user interface

10.1088/1755-1315/660/1/012138

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Stimulated Motion Suppression (STMS): a New Approach to Break the Resolution Barrier for Ion Trap Mass Spectrometry

Journal of the American Society for Mass Spectrometry (2018)

X. K. Zhou Xinwei Liu Spencer Chiang Wenbo Cao Ming Li

Ion trap is an excellent platform to perform tandem mass spectrometry (MS/MS), but has an intrinsic drawback in resolving power. Using ion resonant ejection as an example, the resolution degradation can be largely attributed to the broadening of the resonant frequency band (RFB) between ion motion and driving alternative-current (AC). To solve this problem, stimulated motion suppression (STMS) was developed. The key idea of STMS is the use of two suppression alternative-current (SAC) signals, which both have reversed initial phases to the main AC. The SACs can block the unexpected sideband ion resonances (or ejections), therefore playing a key role in sharpening the RFB. The proof-of-concept has been demonstrated through ion trajectory simulations and validated experimentally. STMS provides a new and versatile means for the improvement of the ion trap resolution, which for a long time has reached the bottleneck through conventional methods, e.g., increasing the radio-frequency (RF) voltage and decreasing the mass scan rate. At the end, it is worth noting that the idea of STMS is very general and principally can be applied in any RF device for the purposes of high-resolution mass analysis and ion isolation.

10.1007/s13361-018-1995-x

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Citations (4)