Wei Zheng
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View article: Development and Verification of an Online Monitoring Ionization Chamber for Dose Measurement in a Small-Sized Betatron
Development and Verification of an Online Monitoring Ionization Chamber for Dose Measurement in a Small-Sized Betatron Open
Online radiation dose monitoring is critical for the safe operation of accelerators. Although commercial dose monitors are well-developed, integrating an ionization chamber directly within a small-sized Betatron magnet remains challenging.…
View article: Adaptive anomaly detection disruption prediction starting from first discharge on tokamak
Adaptive anomaly detection disruption prediction starting from first discharge on tokamak Open
Plasma disruption presents a significant challenge in tokamak fusion, especially in large-size devices like ITER, where it causes severe damage. While current data-driven machine learning methods perform well in disruption prediction, they…
View article: Adaptive anomaly detection disruption prediction starting from first discharge on tokamak
Adaptive anomaly detection disruption prediction starting from first discharge on tokamak Open
Plasma disruption presents a significant challenge in tokamak fusion, especially in large-size devices like ITER, where it causes severe damage. While current data-driven machine learning methods perform well in disruption prediction, they…
View article: Obtaining frequency-time diagram from perturbation signal-time diagram
Obtaining frequency-time diagram from perturbation signal-time diagram Open
In the field of nuclear fusion energy development, magnetic confinement tokamak reactors, which use strong magnetic fields to confine high-temperature plasmas, are crucial for sustainable and clean fusion energy. Disruption caused by plasm…
View article: High-beta disruption prediction study on HL-2A with instance-based transfer learning
High-beta disruption prediction study on HL-2A with instance-based transfer learning Open
Unmitigated disruptions pose a much more serious threat when large-scale tokamaks are operating in the high performance regime. Machine learning based disruption predictors can exhibit impressive performance. However, their effectiveness i…
View article: Cross-tokamak disruption prediction based on domain adaptation
Cross-tokamak disruption prediction based on domain adaptation Open
The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated…
View article: Overview of the recent experimental research on the J-TEXT tokamak
Overview of the recent experimental research on the J-TEXT tokamak Open
The J-TEXT capability is enhanced compared to two years ago with several upgrades of its diagnostics and the increase of electron cyclotron resonance heating (ECRH) power to 1 MW. With the application of electron cyclotron wave (ECW), the …
View article: Progress of the electron cyclotron resonance heating system and the related experiments on J-TEXT
Progress of the electron cyclotron resonance heating system and the related experiments on J-TEXT Open
To augment the capabilities of the J-TEXT tokamak, efforts were undertaken in 2017 to commence the construction of an electron cyclotron resonance heating (ECRH) system. A significant milestone was achieved in 2019 when the successful oper…
View article: Tokamak plasma disruption precursor onset time study based on semi-supervised anomaly detection
Tokamak plasma disruption precursor onset time study based on semi-supervised anomaly detection Open
Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven me…
View article: Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder
Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder Open
In the initial stages of operation for future tokamak, facing limited data availability, deploying data-driven disruption predictors requires optimal performance with minimal use of new device data. This paper studies the issue of data uti…
View article: Disruption prediction for future tokamaks using parameter-based transfer learning
Disruption prediction for future tokamaks using parameter-based transfer learning Open
Tokamaks are the most promising way for nuclear fusion reactors. Disruption in tokamaks is a violent event that terminates a confined plasma and causes unacceptable damage to the device. Machine learning models have been widely used to pre…
View article: IDP-PGFE: an interpretable disruption predictor based on physics-guided feature extraction
IDP-PGFE: an interpretable disruption predictor based on physics-guided feature extraction Open
Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. If a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors.…
View article: Sliding Mode Control for Parallel DC–DC Converter Network Systems With Uniform Quantization and Discretization Effects
Sliding Mode Control for Parallel DC–DC Converter Network Systems With Uniform Quantization and Discretization Effects Open
In this paper, a uniform quantization design method for the parallel text DC-DC converter network control system (NCS) is proposed in combination with the sliding mode control (SMC) strategy to solve the problem that performing the system …
View article: Advances in physics and applications of 3D magnetic perturbations on the J-TEXT tokamak
Advances in physics and applications of 3D magnetic perturbations on the J-TEXT tokamak Open
In the last two years, three major technical improvements have been made on J-TEXT in supporting of the expanded operation regions and diagnostic capabilities. (1) The successful commission of the 105 GHz/500 kW/1 s electron cyclotron reso…
View article: Meteorlogical outliers detection based on artificial intelligence
Meteorlogical outliers detection based on artificial intelligence Open
To precisely evaluate the effect of artificial precipitation of Project Tianshui, anomaly data within the large dataset collected is supposed to be detected and dealt with reasonably, to enhance the analysis and prediction of the data. Usi…
View article: Disruption prevention using rotating resonant magnetic perturbation on J-TEXT
Disruption prevention using rotating resonant magnetic perturbation on J-TEXT Open
Major plasma disruption is one of the most critical issues to be solved for tokamak fusion reactors. Experiments to prevent mode locking and subsequent disruptions have been carried out on the J-TEXT tokamak using rotating resonant magneti…
View article: Overview of the recent experimental research on the J-TEXT tokamak
Overview of the recent experimental research on the J-TEXT tokamak Open
Recent J-TEXT research has highlighted the significance of the role that non-axisymmetric magnetic perturbations, so called three-dimensional (3D) magnetic perturbation (MP) fields, play in a fundamentally 2D concept, i.e. tokamaks. This p…