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Machine learning application to predict the electron temperature on the J-TEXT tokamak

Plasma Science and Technology 2021年第8期23卷 47-52页

作者： Jiaolong DONG Jianchao LI Yonghua DING Xiaoqing ZHANG Nengchao WANG Da LI Wei YAN Chengshuo SHEN Ying HE Xiehang REN Donghui XIA International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205People's Republic of China

The reliability of diagnostic systems in tokamak plasma is of great significance for physics researches or fusion *** some diagnostics fail to detect information about the plasma status,such as electron temperature,they can also be obtained by another method:fitted by other diagnostic signals through machine *** paper herein is based on a machine learning method to predict electron temperature,in case the diagnostic systems fail to detect plasma *** fully-connected neural network,utilizing back propagation with two hidden layers,is utilized to estimate plasma electron temperature approximately on the *** input parameters consist of soft x-ray emission intensity,electron density,plasma current,loop voltage,and toroidal magnetic field,while the targets are signals of electron temperature from electron cyclotron emission and x-ray imaging crystal ***,the temperature profile is reconstructed by other diagnostic signals,and the average errors are within 5%.In addition,generalized regression neural network can also achieve this function to estimate the temperature profile with similar *** electron temperature by neural network reveals that machine learning can be used as backup means for plasma information so as to enhance the reliability of diagnostics.

关键词： neural network plasma electron temperature J-TEXT tokamak

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GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing

arXiv

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arXiv 2025年

作者： Ou, Ruizhe Hu, Yuan Zhang, Fan Chen, Jiaxin Liu, Yu Pattern Recognition and Intelligent System Lab School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Institute of Remote Sensing and Geographic Information Systems School of Earth and Space Sciences Peking University Beijing100871 China Peking University Ordos Research Institute of Energy Ordos017000 China

Multi-modal large language models (MLLMs) have achieved remarkable success in image- and region-level remote sensing (RS) image understanding tasks, such as image captioning, visual question answering, and visual grounding. However, existing RS MLLMs lack the pixel-level dialogue capability, which involves responding to user instructions with segmentation masks for specific instances. In this paper, we propose GeoPix, a RS MLLM that extends image understanding capabilities to the pixel level. This is achieved by equipping the MLLM with a mask predictor, which transforms visual features from the vision encoder into masks conditioned on the LLM’s segmentation token embeddings. To facilitate the segmentation of multi-scale objects in RS imagery, a class-wise learnable memory module is integrated into the mask predictor to capture and store class-wise geo-context at the instance level across the entire dataset. In addition, to address the absence of large-scale datasets for training pixel-level RS MLLMs, we construct the GeoPixInstruct dataset, comprising 65,463 images and 140,412 instances, with each instance annotated with text descriptions, bounding boxes, and masks. Furthermore, we develop a two-stage training strategy to balance the distinct requirements of text generation and masks prediction in multi-modal multi-task optimization. Extensive experiments verify the effectiveness and superiority of GeoPix in pixel-level segmentation tasks, while also maintaining competitive performance in image- and region-level benchmarks. The models, dataset, and code are publicly available at https://***/Norman-Ou/GeoPix. © 2025, CC BY.

关键词： Image segmentation

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Rephrasing the Reference for Non-Autoregressive Machine Translation

arXiv

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arXiv 2022年

作者： Shao, Chenze Zhang, Jinchao Zhou, Jie Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Pattern Recognition Center WeChat AI Tencent Inc China

Non-autoregressive neural machine translation (NAT) models suffer from the multi-modality problem that there may exist multiple possible translations of a source sentence, so the reference sentence may be inappropriate for the training when the NAT output is closer to other translations. In response to this problem, we introduce a rephraser to provide a better training target for NAT by rephrasing the reference sentence according to the NAT output. As we train NAT based on the rephraser output rather than the reference sentence, the rephraser output should fit well with the NAT output and not deviate too far from the reference, which can be quantified as reward functions and optimized by reinforcement learning. Experiments on major WMT benchmarks and NAT baselines show that our approach consistently improves the translation quality of NAT. Specifically, our best variant achieves comparable performance to the autoregressive Transformer, while being 14.7 times more efficient in inference. Copyright © 2022, The Authors. All rights reserved.

关键词： Neural machine translation

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High-purity valley-polarized currents induced by bichromatic optical fields in two-dimensional materials

arXiv

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arXiv 2024年

作者： Li, Wenqing Zhu, Xiaosong Li, Liang He, Wanzhu Long, Jie Lan, Pengfei Lu, Peixiang Wuhan National Laboratory for Optoelectronics School of Physics Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China Hubei Optical Fundamental Research Center Wuhan430074 China

Producing currents predominantly from a single valley, namely valley-polarized currents, at optical-cycle timescales is an important aspect of the petahertz valleytronics, yet it remains less developed. This work exhibits the feasibility of achieving this goal using bichromatic optical fields, which allow for the precise control of sub-cycle electron dynamics. The combined effect of the helical and asymmetric waveforms of the optical fields leads to highly asymmetric excitation at different valleys and displacement of the excited electrons concurrently, thereby inducing valley-polarized currents with high valley purity, on the sub-optical-cycle timescale. Inherently, the purity of the currents built up from the optical approach remains high even for materials with short decoherence time. Moreover, the direction of the currents can be precisely controlled by adjusting the relative phase of the bichromatic components. Our work offers a promising avenue for generating and modulating high-purity valley-polarized currents at the femtosecond timescale, facilitating the development of petahertz valleytronics. Copyright © 2024, The Authors. All rights reserved.

关键词： Landforms

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SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution

arXiv

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arXiv 2023年

作者： Nian, Bingkun Tang, Fenghe Ding, Jianrui Zhang, Pingping Yang, Jie Kevin Zhou, S. Liu, Wei Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China China School of Computer Science and Technology Harbin Institute of Technology China School of artificial intelligence Dalian University of Technology China

In this paper, we present a high-performance deep neural network for weak target image segmentation, including medical image segmentation and infrared image segmentation. To this end, this work analyzes the existing dynamic convolutions and proposes dynamic parameter convolution (DPConv). Furthermore, it reevaluates the relationship between reconstruction tasks and segmentation tasks from the perspective of DPConv, leading to the proposal of a dual-network model called the Siamese Reconstruction-Segmentation Network (SRSNet). The proposed model is not only a universal network but also enhances the segmentation performance without altering its structure, leveraging the reconstruction task. Additionally, as the amount of training data for the reconstruction network increases, the performance of the segmentation network also improves synchronously. On seven datasets including five medical datasets and two infrared image datasets, our SRSNet consistently achieves the best segmentation results. The code is released at https://***/fidshu/SRSNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Multi-granulation variable precision rough set based on limited tolerance relation

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2021年第3期83卷 63-74页

作者： Wan, Renxia Yao, Yonghong Kumar, Hussain School of Mathematics and Information Science North Minzu University Ningxia750021 China HongYang Institute for Big Data in Health Fuzhou Fujian350028 China School of Mathematical Sciences Tiangong University Tianjin300387 China The Key Laboratory of Intelligent Information and Data Processing of NingXia Province North Minzu University and Health Big Data Research Institute of North Minzu University Yinchuan750021 China Machine vision and pattern recognition lab University of Regina ReginaSKS4S0A2 Canada

In this paper, the combination of the variable precision rough set and the limited tolerance relation under multi-granularity is explored. As an extension of rough set model, Multi-granularity variable precision limited tolerance rough set model is con-structed. Properties of upper and lower approximation operator and detailed structure of object class through threshold are discussed. Theoretical proofs are given to show the rationality and superiority of the proposed model. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Granulation

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Noise-Informed Diffusion-Generated Image Detection With Anomaly Attention

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IEEE Transactions on information Forensics and Security 2025年 20卷 5256-5268页

作者： Weinan Guan Wei Wang Bo Peng Ziwen He Jing Dong Haonan Cheng School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China New Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China

With the rapid development of image generation technologies, especially the advancement of Diffusion Models, the quality of synthesized images has significantly improved, raising concerns among researchers about information security. To mitigate the malicious abuse of diffusion models, diffusion-generated image detection has proven to be an effective countermeasure. However, a key challenge for forgery detection is generalising to diffusion models not seen during training. In this paper, we address this problem by focusing on image noise. We observe that images from different diffusion models share similar noise patterns, distinct from genuine images. Building upon this insight, we introduce a novel Noise-Aware Self-Attention (NASA) module that focuses on noise regions to capture anomalous patterns. To implement a SOTA detection model, we incorporate NASA into Swin Transformer, forming an novel detection architecture NASA-Swin. Additionally, we employ a cross-modality fusion embedding to combine RGB and noise images, along with a channel mask strategy to enhance feature learning from both modalities. Extensive experiments demonstrate the effectiveness of our approach in enhancing detection capabilities for diffusion-generated images. When encountering unseen generation methods, our approach achieves the state-of-the-art performance.

关键词： Noise Diffusion models Forgery Noise reduction NASA Image reconstruction Transformers Training Image synthesis Electronic mail

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Depth Prior-Based Stable Tensor Decomposition for Video Snow Removal

SSRN

引用

SSRN 2023年

作者： Li, Yongji Wang, Luping Jia, Zhenhong Yang, Jie Kasabov, Nikola School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510275 China College of Information Science and Engineering Xinjiang University Urumqi830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200400 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland1020 New Zealand

Heavy snow seriously degrades the performance of outdoor computer vision systems. Near- and far-field snowflakes in heavy snow videos exhibit distinctly disparate physical properties. To address this issue, this research proposes a video desnowing model that utilizes stable tensor decomposition with snow depth prior information. Initially, the depth of snowflakes is transformed by their speeds calculated from the optical flow field of snowfall. Next, the noise level in backgrounds is available by dense snow. In contrast to previous tensor decomposition (TD), the introduction of a noise term enhances the stability of the depth prior-based stable tensor decomposition (DP-STD) in addressing heavy snowfall. This leads to more efficient and accurate restoration of underlying nonsnow structure. Inspired by biomimicry, the snow on moving objects (MOs) is removed by the adaptive mimesis region of interest (AM-ROI). Both synthetic and real snowfall experimental results show that our proposed desnowing model is more effective than the current SOTA algorithm in removing heavy snow. © 2023, The Authors. All rights reserved.

关键词： Snow

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Learning Invariant Visual Representations for Compositional Zero-Shot Learning

arXiv

引用

arXiv 2022年

作者： Zhang, Tian Liang, Kongming Du, Ruoyi Sun, Xian Ma, Zhanyu Guo, Jun Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications China Aerospace Information Research Institute Chinese Academy of Sciences China

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and a composition into a common embedding space to measure their compatibility score. However, both attributes and objects share the visual representations learned above, leading the model to exploit spurious correlations and bias towards seen pairs. Instead, we reconsider CZSL as an out-of-distribution generalization problem. If an object is treated as a domain, we can learn object-invariant features to recognize the attributes attached to any object reliably. Similarly, attribute-invariant features can also be learned when recognizing the objects with attributes as domains. Specifically, we propose an invariant feature learning framework to align different domains at the representation and gradient levels to capture the intrinsic characteristics associated with the tasks. Experiments on three CZSL benchmarks demonstrate that the proposed method significantly outperforms the previous state-of-the-art. Copyright © 2022, The Authors. All rights reserved.

关键词： Zero-shot learning

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Polarization multiplexing of an ultrafast single-photon detector by optical Kerr gating

Polarization multiplexing of an ultrafast single-photon dete...

引用

2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Kuwana, Takahisa Yabuno, Masahiro China, Fumihiro Miki, Shigehito Terai, Hirotaka Mosley, Peter J. Jin, Rui-Bo Shimizu, Ryosuke The University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Kobe651-2492 Japan Centre for Photonics and Photonic Materials Department of Physics University of Bath BathBA2 7AY United Kingdom Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China

We develop an ultrafast single-photon detector with optical Kerr gating using a photonic crystal fiber. Adopting the polarization-multiplexing technique, we achieved the subpicosecond resolution in the single-photon d...

ISBN: (纸本)9798350350012

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