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arXiv 2023年

作者： Kuang, Yang Shen, Yedan Hu, Guanghui Guangdong University of Technology China School of Mathematics and Information Science Guangzhou University China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University China Department of Mathematics Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications University of Macau China Zhuhai UM Science and Technology Research Institute Zhuhai China

Chemical accuracy serves as an important metric for assessing the effectiveness of the numerical method in Kohn-Sham density functional theory. It is found that to achieve chemical accuracy, not only the Kohn-Sham wavefunctions but also the Hartree potential, should be approximated accurately. Under the adaptive finite element framework, this can be implemented by constructing the a posteriori error indicator based on approximations of the aforementioned two quantities. However, this way results in a large amount of computational cost. To reduce the computational cost, we propose a novel multi-mesh adaptive method, in which the Kohn-Sham equation and the Poisson equation are solved in two different meshes on the same computational domain, respectively. With the proposed method, chemical accuracy can be achieved with less computational consumption compared with the adaptive method on a single mesh, as demonstrated in a number of numerical experiments. © 2023, CC BY.

关键词： Poisson equation

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2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model for WMH Segmentation from 3D MR Brain Image

2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lan Huang Yinglu Sun Ziqi Zhao Chunjie Guo Yan Wang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China Department of Radiology the First Hospital of Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Segmenting brain white matter hyperintensities (WMH) from 3D Magnetic Resonance (MR) images is crucial for the diagnosis, treatment, and prognosis of Multiple Sclerosis (MS). Unlike common 2D images, this task is more challenging and time-consuming. Classical deep learning methods for 3D image segmentation face two main challenges: 1) Pure 3D networks have more parameters and are prone to overfitting. 2) When using 2D networks to segment slices of 3D images, the lack of 3D structural information results in suboptimal segmentation after reconstruction. To address these difficulties, we propose the 2.5D ASF-UNet, which employs the 2.5D workflow and uses adjacent slices as the input for 2D segmentation network ASF-UNet. In ASF-UNet, separate down-sampling paths are used for the adjacent slices, and the Local Spatial Attention Module (LSAM) is designed to more effectively integrate 3D spatial information into the 2D network. Additionally, the Conv_Spectral_Block (CSB) is designed to extract and integrate local and global features. It allows the model to capture global spatial structures while preserving detailed information. Experimental results on MICCAI MSSEG 2016 and Local MS datasets show that 2.5D ASF-UNet achieve better segmentation performance than other deep learning methods.

关键词： Deep learning Image segmentation Solid modeling Three-dimensional displays Multiple sclerosis Feature extraction Brain modeling White matter Prognostics and health management Overfitting

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Effect of current density on groundwater arsenite removal performance using air cathode electrocoagulation

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Frontiers of Environmental science & engineering 2021年第6期15卷 211-219页

作者： Yanxiao Si Fang Zhang Chen Hong Guanghe Li Haichuan Zhang Dun Liu School of Environment and State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua UniversityBeijing 100084China Surface Engineering Division Sinopec Petroleum Exploration and Production Research InstituteBeijing 100083China School of Energy and Environmental Engineering University of Science and Technology BeijingBeijing 100083China

Naturally occurring arsenic enrichment in groundwater poses a huge threat to human *** cathode electrocoagulation(ACEC)has recently been proposed to enhance As(Ⅲ)oxidation and lower energy *** this study,ACEC,EC/O_(2) and EC/N_(2) were evaluated with different current densities from 1 to 8 mA/cm^(2) to investigate the effect on As(Ⅲ)removal in different redox *** density had no appreciable effect on arsenic removal efficiency given the same charge in ACEC because the concentration ratio of Fe/H2O_(2) under different current densities remained ***,in EC/O_(2) and EC/N_(2),As(Ⅲ)removal was inhibited at higher current densities(4–8 mA/cm^(2)),likely because more Fe(Ⅱ)competed with As(Ⅲ)for the oxidant,leading to less effective oxidation of As(Ⅲ).In all EC systems,the$OH units generated per power consumption reached the highest value at the lowest current *** with other EC systems,the ACEC system showed lower energy consumption at all current densities due to the low energy consumption of the electrode reaction and more free radical generation.A lower current density saved more energy at the expense of time,showing the trade-off relationship between energy consumption and removal *** operation costs for As(Ⅲ)removal under optimal conditions were calculated as 0.028$/m^(3) for ACEC,0.030$/m^(3) for EC/O_(2),and 0.085$/m^(3) for EC/N_(2)

关键词： Electrocoagulation Air cathode Arsenic Current density Energy consumption

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The Control Design of Spaee Flying Net Robot System Based on RBF Network

The Control Design of Spaee Flying Net Robot System Based on...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Xu Liu, Yuanhe Liang, Yan'gang Li, Kebo Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions College of Aerospace Science and Engineering National University of Defense Technology Changsha China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions College of Aerospace Science National University Defense Technology Changsha China

ISBN: (纸本)9781665426473

Space Flying Net Robot System (SFNRS) is a new capture method of space debris. In this paper, the SFNRS dynamic equations are established by using the lumped mass method. The previous studies about controller design assumed that the mass of the maneuvering unit in SFNRS is known and a constant while it is unknown in fact. In addition, the upper boundary of tension in the space net is often assumed as a known constant, but it is difficult to get the value. In this paper, the radial basis function (RBF) network is used to design a controller in order to overcome the shortcomings above Finally, the simulation is carried out to verify the effectiveness and the robustness of the proposed method. © 2021 IEEE

关键词： Controllers

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

Automated Detection of Epileptic Spikes and Seizures Incorpo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao Anhui University School of Artificial Intelligence Hefei230601 China Hefei Comprehensive National Science Center Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei230088 China Sanbo Brain Hospital Capital Medical University Beijing Key Laboratory of Epilepsy Department of Neurosurgery Beijing China Peking University McGovern Institute for Brain Research Beijing China Peking University Center for Mri Research Academy for Advanced Interdisciplinary Studies Beijing China Peking University Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Beijing China Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China

ISBN: (纸本)9798350386226

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal's intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor's spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. © 2024 IEEE.

关键词： Magnetoencephalography

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Cavity dynamics of water drop impact onto immiscible oil pool with different viscosity

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Acta Mechanica Sinica 2021年第3期37卷 447-455,I0002页

作者： Muhammad Mohasan Anas Bin Aqeel Pengyu Lv Yantao Yang Huiling Duan SKLTCS and Department of Mechanics and Engineering Science BIC-ESATCollege of EngineeringPeking UniversityBeijing 100871China National University of Sciences and Technology H-12IslamabadPakistan Key Laboratory of High Energy Density Physics Simulation Center for Applied Physics and TechnologyPeking UniversityBeijing 100871China

In this work,we numerically study the impact of a water droplet onto a deep oil *** fluids are immiscible and the viscosity of the pool liquid is changed *** focus on the cavity dynamics during the impact and especially the effects of the pool liquid viscosity and the impacting *** the parameter range explored,we identify the regime where splashing occurs with corolla breaking into droplets,and the regime where no splashing is *** is found for the time evolution of cavity depth for fixed impact velocity and different viscosity,if the cavity depth and time are nondimensionalized by the maximal depth and the time when the maximal depth is *** power-law scalings are also proposed to describe the dependence of the maximal cavity depth and the corresponding time on the impact velocity and pool liquid viscosity,in the term of Froude and Reynolds numbers.

关键词： Drop impact Immiscible fluids Cavity dynamics

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FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification

FastClass: A Time-Efficient Approach to Weakly-Supervised Te...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Xia, Tingyu Wang, Yue Tian, Yuan Chang, Yi School of Artificial Intelligence Jilin University China School of Information and Library Science University of North Carolina Chapel Hill United States Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China

Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tasks. However, these methods not only rely on carefully-crafted class descriptions to obtain class-specific keywords but also require substantial amount of unlabeled data and takes a long time to train. This paper proposes FastClass, an efficient weakly-supervised classification approach. It uses dense text representation to retrieve class-relevant documents from external unlabeled corpus and selects an optimal subset to train a classifier. Compared to keyword-driven methods, our approach is less reliant on initial class descriptions as it no longer needs to expand each class description into a set of class-specific keywords. Experiments on a wide range of classification tasks show that the proposed approach frequently outperforms keyword-driven models in terms of classification accuracy and often enjoys orders-of-magnitude faster training speed. © 2022 Association for computational Linguistics.

关键词： Classification (of information)

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Internal Energy Recycling in Fapbi3/Mxene for Enhanced Photocatalytic H2 Evolution

SSRN

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SSRN 2024年

作者： Wang, Feng Guo, Ziwang Liu, Lulu Gong, Rui Jia, Guangri Xu, Yanchao Wei, Shuting Zhang, Dantong Ding, Baofu Cui, Xiaoqiang State Key Laboratory of Automotive Simulation and Control School of Materials Science and Engineering Key Laboratory of Automobile Materials of MOE Jilin University 2699 Qianjin Street Changchun130012 China Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology CAS Shenzhen518055 China Key Laboratory of Materials Design and Quantum Simulation School of Science Changchun University No.6543 Satellite Road Changchun130022 China Faculty of Materials Science and Energy Engineering Shenzhen University of Advanced Technology Guangdong Shenzhen518055 China

Carrier recombination is one of the main factors in lowering charge separation efficiency, and thus severely restricts photocatalytic performance. In this work, we demonstrate a composite system of FAPbI3/MXene/Pt, featuring an internal energy cycling from the non-radiative fluorescence resonance energy transfer (FRET) between perovskite and MXene. That is, the energy of the emitted fluorescent photons is reabsorbed by each other. Recycling process exploits the energy released by carrier recombination and increases efficiency of charge separation. As a result, the composite of FAPbI3/MXene/Pt possesses a rapid H2 evolution rate of 2394 µmol g-1 h-1 under 1.5 AM simulated sunlight, which is more than one order of magnitude higher than that of FAPbI3/Pt (64 µmol g-1 h-1). The proposed insight not only provides an efficient strategy to promote photocatalytic H2 evolution based on perovskite-based composite, but also opens up opportunities to design other novel heterogeneous catalytic materials and promote their application potential in various catalytic fields. © 2024, The Authors. All rights reserved.

关键词： Perovskite

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Learning generalizable agents via saliency-guided features decorrelation 23

Learning generalizable agents via saliency-guided features d...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Sili Huang Yanchao Sun Jifeng Hu Siyuan Guo Hechang Chen Yi Chang Lichao Sun Bo Yang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education and School of Artificial Intelligence Jilin University China Department of Computer Science University of Maryland College Park School of Artificial Intelligence Jilin University China Lehigh University Bethlehem Pennsylvania Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The variations can arise in both task-irrelevant features, such as background noise, and task-relevant features, such as robot configurations, that are related to the optimal decisions. To achieve generalization in both situations, agents are required to accurately understand the impact of changed features on the decisions, i.e., establishing the true associations between changed features and decisions in the policy model. However, due to the inherent correlations among features in the state space, the associations between features and decisions become entangled, making it difficult for the policy to distinguish them. To this end, we propose Saliency-Guided Features Decorrelation (SGFD) to eliminate these correlations through sample reweighting. Concretely, SGFD consists of two core techniques: Random Fourier Functions (RFF) and the saliency map. RFF is utilized to estimate the complex non-linear correlations in high-dimensional images, while the saliency map is designed to identify the changed features. Under the guidance of the saliency map, SGFD employs sample reweighting to minimize the estimated correlations related to changed features, thereby achieving decorrelation in visual RL tasks. Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations.

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Novel Amorphous Feooh-Modified Co9s8 Nanosheets with Enhanced Oxygen Evolution Reaction Activity

SSRN

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SSRN 2024年

作者： Wang, Chong Tu, Huanlu Hao, Zeyu Li, Yaxin Xu, Jian Hu, Xiaoying Yu, Shansheng Tian, Hongwei Key Laboratory of Automobile Materials of MOE School of Materials Science and Engineering Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials Jilin University Jilin Changchun130012 China College of Science Laboratory of Materials Design and Quantum Simulation Changchun University Jilin Changchun130022 China

Efficient oxygen evolution reaction (OER) is important for water electrolysis and advanced hydrogen production energy. However, the sluggish kinetics of this reaction require significant overpotentials, leading to high energy consumption. Therefore, developing OER electrocatalysts with exceptional performance and long-term durability is crucial for enhancing the energy efficiency and cost-effectiveness of the hydrogen production process. In this paper, novel FeOOH/Co9S8 catalysts were prepared through a two-step hydrothermal reaction followed by one-step electrodeposition on nickel foam substrates for an alkaline oxygen evolution reaction (OER). The as-obtained catalysts possessed abundant non-homogeneous interfaces between FeOOH and Co9S8 nanosheets, conducive to optimized coordination environments of Fe and Co sites through the redistribution of interfacial charges. This strengthened the chemisorption of oxygenated intermediates, leading to accelerated reaction kinetics, abundant active sites, and enhanced OER properties. The optimized electrocatalyst FeOOH/Co9S8 achieved a current density of 10 mA cm-2 at an overpotential of 248 mV coupled with good stability for over 140 h. Overall, a novel approach for producing effective and durable alkaline dielectric OER electrocatalysts was proposed, useful for future manufacturing of advanced energy devices. © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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