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606 results on '"Shen Biao"'

1. Dynamic Task Scheduling Method for Space Crowdsourcing

Author

SHEN Biao, SHEN Li-wei, LI Yi

Subjects
space crowdsourcing, task allocation, task scheduling, route planning, genetic algorithm, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

Space crowdsourcing is used to solve offline crowdsourcing tasks with time and space constraints,and it has developed rapidly in recent years.Task scheduling is an important research direction of space crowdsourcing.The difficulty lies in the dynamic uncertainty of tasks and workers in the scheduling process.In order to efficiently perform task scheduling,a dynamic task scheduling method for space crowdsourcing that considers the uncertainty of tasks and workers at the same time is proposed.The method has been improved in three aspects.First,the factors that need to be considered for scheduling are expanded.In addition to considering the uncertainty of the temporal and spatial attributes of the newly added tasks,it also considers the uncertainty of the transportation mode and temporal and spatial attributes of the newly added workers.Then,the scheduling strategy is improved.By using the aggregate scheduling strategy,the dynamically added tasks are aggregated first,and then the task allocation and path optimization are performed.Compared with the traditional non-aggregated scheduling,the calculation time is significantly reduced.The last aspect is to improve the scheduling algorithm.Based on the traditional genetic algorithm,the task allocation and path optimization operations are performed iteratively.Compared with the scheduling algorithm that first allocates tasks and then optimizes the path,it improves the accuracy of the optimal results.In addition,a simulation platform for dynamic scheduling of space crowdsourcing task paths based on real map navigation is designed and implemented,and the method is verified by this platform.

Published
2022
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2. Overview of recent experimental results on the EAST Tokamak

Author

Gong, X, Team and Collaborators, on behalf of EAST, Team:, The EAST, Song, Yuntao, Wan, Baonian, Li, Jiangang, Wan, Yuanxi, Wu, Xinchao, Liu, Fukun, Chen, Junling, Hu, Jiansheng, Xu, Guosheng, Lu, Kun, Gong, Xianzu, Xiao, Bingjia, Wu, Yu, Gao, Xiang, Yao, Damao, Xiang, Nong, Hu, Liqun, Hu, Chundong, Wu, Jiefeng, Shen, Biao, Gao, Ge, Huang, Yiyun, Xu, Liuwei, Zhang, Qiyong, Bae, Cheonho, Cao, Bin, Cao, Lei, Chang, Jiafeng, Chen, Dalong, Chen, Ran, Chen, Xiaojiao, Chen, Yebin, Chen, Yue, Cheng, Yunxin, Cheng, Yong, Ding, Bojiang, Ding, Fang, Ding, Rui, Du, Shijun, Duan, Yanmin, Fu, Jia, Gao, Daming, Gao, Wei, Gu, Yongqi, Guo, Bin, Guo, Fei, Guo, Yong, Han, Xiaofeng, He, Shiying, Hu, Ailan, Hu, Chang, Hu, Guanghai, Hu, Huaichuan, Hu, Qingsheng, Hu, Yanlan, Hu, Zhenhua, Huang, Juan, Huang, Liansheng, Huang, Ming, Huang, Ronglin, Ji, Xiang, Jia, Hua, Jiang, Caichao, Jie, Yinxian, Ju, Songqing, Kong, Defeng, Li, Erzhong, Li, Guoqiang, Li, Jiahong, Li, Junjun, Li, Miaohui, Li, Pan, Li, Kedong, Li, Shi, Li, Yadong, Liang, Lizhen, Liao, Yanchuan, Lin, Shiyao, Lin, Xin, Ling, Bili, Liu, Haiqing, Liu, Huajun, Liu, Jianwen, Liu, Liang, Liu, Shaocheng, Liu, Sheng, Liu, Wenbin, Liu, Xiaoju, Liu, Xiaoyan, Liu, Yong, Liu, Zhihong, Liu, Zhimin, Lu, Jianhua, Luo, Zhengping, Ma, Dengkui, Mao, Huafeng, and Ma, Wendong

Subjects
Nuclear and Plasma Physics, Physical Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Fluids & Plasmas, Nuclear and plasma physics
Abstract

Since the last IAEA-FEC in 2021, significant progress on the development of long pulse steady state scenario and its related key physics and technologies have been achieved, including the reproducible 403 s long-pulse steady-state H-mode plasma with pure radio frequency (RF) power heating. A thousand-second time scale (∼1056 s) fully non-inductive plasma with high injected energy up to 1.73 GJ has also been achieved. The EAST operational regime of high βP has been significantly extended (H98y2 > 1.3, βP ∼ 4.0, βN ∼ 2.4 and ne/nGW ∼ 1.0) using RF and neutral beam injection (NBI). The full edge localized mode suppression using the n = 4 resonant magnetic perturbations has been achieved in ITER-like standard type-I ELMy H-mode plasmas with q95 ≈ 3.1 on EAST, extrapolating favorably to the ITER baseline scenario. The sustained large ELM control and stable partial detachment have been achieved with Ne seeding. The underlying physics of plasma-beta effect for error field penetration, where toroidal effect dominates, is disclosed by comparing the results in cylindrical theory and MARS-Q simulation in EAST. Breakdown and plasma initiation at low toroidal electric fields (

Published
2024

3. Adaptive Anomaly Detection Disruption Prediction Starting from First Discharge on Tokamak

Author

Ai, Xinkun, Zheng, Wei, Zhang, Ming, Ding, Yonghua, Chen, Dalong, Chen, Zhongyong, Guo, Bihao, Shen, Chengshuo, Wang, Nengchao, Yang, Zhoujun, Chen, Zhipeng, Pan, Yuan, Shen, Biao, and Xiao, Binjia

Subjects
Physics - Plasma Physics
Abstract

Plasma disruption presents a significant challenge in tokamak fusion, where it can cause severe damage and economic losses. Current disruption predictors mainly rely on data-driven methods, requiring extensive discharge data for training. However, future tokamaks require disruption prediction from the first shot, posing challenges of data scarcity during the early operation period. In this period disruption prediction aims to support safe exploration of operation range and accumulate necessary data to develop advanced prediction models. Thus, predictors must adapt to evolving plasma environments during this exploration phase. To address these issues, this study proposes a cross-tokamak adaptive deployment method using the Enhanced Convolutional Autoencoder Anomaly Detection (E-CAAD) predictor, enabling disruption prediction from the first shot of new devices. Experimental results indicate the ability of E-CAAD model trained on existing devices to effectively differentiate between disruption precursors and non-disruption samples on new devices, proving the feasibility of model cross-device transfer. Building upon this, adaptive learning from scratch and threshold adaptive adjustment strategies are proposed to achieve model cross-device transfer. The adaptive learning from scratch strategy enables the predictor to use scarce data during the early operation of the new device while rapidly adapting to changes in operation environment. The threshold adaptive adjustment strategy addresses the challenge of selecting warning thresholds on new devices where validation set is lacking, ensuring that the warning thresholds adapt to changes in the operation environment. Finally, experiments transferring the model from J-TEXT to EAST exhibit comparable performance to EAST models trained with ample data, achieving a TPR of 85.88% and a FPR of 6.15%, with a 20ms reserved MGI system reaction time., Comment: 18 pages, 7 figures

Published
2024

5. Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder

Author

Ai, Xinkun, Zheng, Wei, Zhang, Ming, Ding, Yonghua, Chen, Dalong, Chen, Zhongyong, Shen, Chengshuo, Guo, Bihao, Wang, Nengchao, Yang, Zhoujun, Chen, Zhipeng, Pan, Yuan, Shen, Biao, Xiao, Binjia, and team, J-TEXT

Subjects
Physics - Plasma Physics
Abstract

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 utilization in data-driven disruption predictor during cross tokamak deployment. Current predictors primarily employ supervised learning methods and require a large number of disruption and non-disruption shots for training. However, the scarcity and high cost of obtaining disruption shots for future tokamaks result in imbalanced training datasets, reducing the performance of supervised learning predictors. To solve this problem, we propose the Enhanced Convolutional Autoencoder Anomaly Detection (E-CAAD) predictor. E-CAAD can be only trained by normal samples from non-disruption shots and can also be trained by disruption precursor samples when disruption shots occur. This model not only overcomes the sample imbalance in supervised learning predictors, but also overcomes the inefficient dataset utilization faced by traditional anomaly detection predictors that cannot use disruption precursor samples for training, making it more suitable for the unpredictable datasets of future tokamaks. Compared to traditional anomaly detection predictor, the E-CAAD predictor performs better in disruption prediction and is deployed faster on new devices. Additionally, we explore strategies to accelerate deployment of E-CAAD predictor on the new device by using data from existing devices. Two deployment strategies are presented: mixing data from existing devices and fine-tuning the predictor trained on existing devices. Our comparisons indicate that the data from existing device can accelerate the deployment of predictor on new device. Notably, the fine-tuning strategy yields the fastest deployment on new device among the designed strategies., Comment: 19 pages, 8 figures

Published
2023
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7. Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation

Author

Shen, Chengshuo, Zheng, Wei, Guo, Bihao, Ding, Yonghua, Chen, Dalong, Ai, Xinkun, Xue, Fengming, Zhong, Yu, Wang, Nengchao, Shen, Biao, Xiao, Binjia, Chen, Zhongyong, Pan, Yuan, and team, J-TEXT

Subjects
Physics - Plasma Physics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

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 a novel approach to predict disruption in a future tokamak using only a few discharges. The first step is to use the existing understanding of physics to extract physics-guided features from the diagnostic signals of each tokamak, called physics-guided feature extraction (PGFE). The second step is to align a few data from the future tokamak (target domain) and a large amount of data from existing tokamak (source domain) based on a domain adaptation algorithm called CORrelation ALignment (CORAL). It is the first attempt at applying domain adaptation in the task of disruption prediction. PGFE has been successfully applied in J-TEXT to predict disruption with excellent performance. PGFE can also reduce the data volume requirements due to extracting the less device-specific features, thereby establishing a solid foundation for cross-tokamak disruption prediction. We have further improved CORAL (supervised CORAL, S-CORAL) to enhance its appropriateness in feature alignment for the disruption prediction task. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak, which has a large gap in the ranges of plasma parameters. The utilization of the S-CORAL improves the disruption prediction performance on future tokamak. Through interpretable analysis, we discovered that the learned knowledge of the disruption prediction model through this approach exhibits more similarities to the model trained on large data volumes of future tokamak., Comment: 17 pages, 9 figures

Published
2023

11. The first plasma breakdown in Thailand Tokamak - 1 using the Ohmic heating and double swing flux methods based on magnetic field analysis

Author

Suksaengpanomrung, Suebsak, Chen, Dalong, Poolyarat, Nopporn, Luo, Zhengping, Shen, Biao, Tamman, Arlee, Huang, Yiyun, Wonghabut, Pasit, Nilgumhang, Kewalee, Promping, Jiraporn, Dangtip, Somsak, Wisitsorasak, Apiwat, Sangaroon, Siriyaporn, Chatthong, Boonyarit, Klaywittapat, Ponkris, Nupangtha, Wasin, Wijaikum, Apiwat, and Onjun, Thawatchai

Published
2025
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12. Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor

Author

Zheng, Wei, Xue, Fengming, Zhang, Ming, Chen, Zhongyong, Shen, Chengshuo, Ai, Xinkun, Wang, Nengchao, Chen, Dalong, Guo, Bihao, Ding, Yonghua, Chen, Zhipeng, Yang, Zhoujun, Shen, Biao, Xiao, Bingjia, and Pan, Yuan

Subjects
Physics - Plasma Physics, Computer Science - Machine Learning
Abstract

Predicting disruptions across different tokamaks is a great obstacle to overcome. Future tokamaks can hardly tolerate disruptions at high performance discharge. Few disruption discharges at high performance can hardly compose an abundant training set, which makes it difficult for current data-driven methods to obtain an acceptable result. A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem. The key is a disruption prediction model containing a feature extractor that is able to extract common disruption precursor traces in tokamak diagnostic data, and a transferable disruption classifier. Based on the concerns above, the paper first presents a deep fusion feature extractor designed specifically for extracting disruption precursor features from common diagnostics on tokamaks according to currently known precursors of disruption, providing a promising foundation for transferable models. The fusion feature extractor is proved by comparing with manual feature extraction on J-TEXT. Based on the feature extractor trained on J-TEXT, the disruption prediction model was transferred to EAST data with mere 20 discharges from EAST experiment. The performance is comparable with a model trained with 1896 discharges from EAST. From the comparison among other model training scenarios, transfer learning showed its potential in predicting disruptions across different tokamaks.

Published
2022

21. Recent advances in EAST physics experiments in support of steady-state operation for ITER and CFETR

Author

Wan, BN, Liang, Y, Gong, XZ, Xiang, N, Xu, GS, Sun, Y, Wang, L, Qian, JP, Liu, HQ, Zeng, L, Zhang, L, Zhang, XJ, Ding, BJ, Zang, Q, Lyu, B, Garofalo, AM, Ekedahl, A, Li, MH, Ding, F, Ding, SY, Du, HF, Kong, DF, Yu, Y, Yang, Y, Luo, ZP, Huang, J, Zhang, T, Zhang, Y, Li, GQ, Xia, TY, Wan, Baonian, Li, Jiangang, Wan, Yuanxi, Song, Yuntao, Zhang, Xiaodong, Fu, Peng, Guo, Houyang, Liang, Yunfeng, Gong, Xianzhu, Xu, Guosheng, Xiao, Bingjia, Wu, Yu, Gao, Xiang, Yao, Damao, Xiang, Nong, Hu, Liqun, Shan, Jiafang, Zhao, Yanping, Luo, Guangnan, Hu, Chundong, Wu, Jiefeng, Hu, Jiansheng, Shen, Biao, Ji, Zhenshan, Gao, Ge, Huang, Yiyun, Xu, Liuwei, Zhang, Qiyong, Zhuang, Ming, Liu, Fukun, Zhao, Junyu, Chen, Junling, Cao, Bin, Cao, Lei, Chang, Jiafeng, Chen, Kaiyun, Chen, Ran, Chen, Yebin, Cheng, Anyi, Cheng, Yong, Dai, Yu, Deng, Wei, Deng, Xu, Ding, Bojiang, Ding, Fang, Ding, Rui, Ding, Siye, Du, Shijun, Duan, Yanmin, Feng, Jianqiang, Gan, Kaifu, Gao, Daming, Gao, Qingsheng, Gao, Wei, Gu, Yongqi, Guo, Yong, Han, Xiaofeng, Hu, Ailan, Hu, Chang, Hu, Guanghai, Hu, Huaichuan, Hu, Liangbin, Hu, Qingsheng, Hu, Yanlan, Hu, Zhenhua, Huang, Juan, Huang, Liansheng, Huang, Ming, Huang, Ronglin, and Ji, Xiang

Subjects
steady state, long pulse, high bootstrap current fraction, tokamak, Fluids & Plasmas, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Atomic, Molecular, Nuclear, Particle and Plasma Physics
Published
2019

30. Orthogonal double V‐linear frequency modulation waveform for simultaneous polarimetric measurement and its non‐linear processing.

Author

Shen, Biao, Zhang, Runzhe, Yang, Ziyuan, and Liu, Tao

Subjects
*SIGNAL detection, *SECOND harmonic generation, *RADAR, *SIGNALS & signaling
Abstract

It has become an interesting topic for modern radar possessing capabilities of polarimetric measurement, high‐resolution imaging, and good delay‐Doppler resolution. The linear frequency modulation (LFM) scheme is widely used in radar systems as it can achieve both high Doppler tolerance and resolution. Based on the LFM scheme, a novel waveform is proposed, namely, the orthogonal double V‐linear frequency modulation (ODV‐LFM), which exhibits the capability to mitigate the influence of delay‐Doppler coupling and reduces the risk of false targets and ghosts in multiple‐target scenes. Based on the ODV‐LFM signal, a simultaneous polarimetric measurement method is proposed to obtain fully polarimetric information of the target. According to the unique chirp rate characteristic of the ODV‐LFM signal, the pointwise linear and non‐linear processing methods are proposed to enhance delay‐Doppler resolution, suppress sidelobes, and achieve high‐resolution imaging without substantially compromising the signal detection performance. Finally, simulated experiments are conducted to demonstrate the superiority of the proposed waveform and methods. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Cross-tokamak Disruption Prediction based on domain adaptation

Author

Shen, Chengshuo, primary, Zheng, Wei, additional, Guo, Bihao, additional, Ding, Yong Hua, additional, Chen, Dalong, additional, Ai, Xinkun, additional, Xue, Fengming, additional, Zhong, Yu, additional, Wang, Nengchao, additional, Shen, Biao, additional, Xiao, Bingjia, additional, Chen, Zhongyong, additional, and Pan, Yuan, additional

Published
2024
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36. Unraveling fertilization effects on the dynamics of arbuscular mycorrhizal fungal community in the Qinghai-Tibet Alpine Meadow

Author

Sub Ecology and Biodiversity, Ecology and Biodiversity, Liu, Longfei, Ren, Yi, Sun, Shuo, Liu, Chen, Ding, Kairui, Li, Rong, Zhang, Pengfei, Shen, Biao, Ravanbakhsh, Mohammadhossein, Xiong, Wu, Shen, Qirong, Sub Ecology and Biodiversity, Ecology and Biodiversity, Liu, Longfei, Ren, Yi, Sun, Shuo, Liu, Chen, Ding, Kairui, Li, Rong, Zhang, Pengfei, Shen, Biao, Ravanbakhsh, Mohammadhossein, Xiong, Wu, and Shen, Qirong

Published
2024

37. Learnable Gabor Convolutional Networks

Author

Zhong, Guoqiang, Gao, Wei, Jiao, Wencong, Shen, Biao, Xia, Dongdong, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gedeon, Tom, editor, Wong, Kok Wai, editor, and Lee, Minho, editor

Published
2019
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42. Statistical analysis of locked mode induced disruption in EAST

Author

Zhou, Wei-Ran, primary, Deng, Guo-Hong, additional, Sun, You-Wen, additional, Wang, Hui-Hui, additional, Zhou, Deng, additional, Shi, Tong-Hui, additional, Gu, Shuai, additional, Ye, Cheng, additional, Ma, Qun, additional, Zang, Qing, additional, He, Kai-Yang, additional, Chen, Da-Long, additional, Shen, Biao, additional, Jia, Man-Ni, additional, Luo, Zheng-Ping, additional, Liu, Hai-Qing, additional, Zhou, Zi-Qiang, additional, and Zhang, Tao, additional

Published
2024
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