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arXiv 2021年

作者： Li, Yue Zhou, Xuyang Colnaghi, Timoteo Wei, Ye Marek, Andreas Li, Hongxiang Bauer, Stefan Rampp, Markus Stephenson, Leigh T. Max-Planck Institut für Eisenforschung GmbH Max-Planck-Straße 1 Düsseldorf40237 Germany Max Planck Computing and Data Facility Gießenbachstraße 2 Garching85748 Germany State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing100083 China Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany

Nanoscale L12-type ordered structures are widely used in face-centred cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyse the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type d¢–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Implementing Feedforward Neural Network Using DNA Strand Displacement Reactions

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Nano 2020年第1期16卷

作者： Siyan Zhu Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 P. R. China School of Computer Science and Technology Dalian University of Technology Dalian 116024 P. R. China

The ability of neural networks to process information intelligently has allowed them to be successfully applied in the fields of information processing, controls, engineering, medicine, and economics. The brain-like working mode of a neural network gives it incomparable advantages in solving complex nonlinear problems compared with other methods. In this paper, we propose a feedforward DNA neural network framework based on an enzyme-free, entropy-driven DNA reaction network that uses a modular design. A multiplication gate, an addition gate, a subtraction gate, and a threshold gate module based on the DNA strand displacement principle are cascaded into a single DNA neuron, and the neuron cascade is used to form a feedforward transfer neural network. We use this feedforward neural network to realize XOR logic operation and full adder logic operation, which proves that the molecular neural network system based on DNA strand displacement can carry out complex nonlinear operation and reflects the powerful potential of building these molecular neural networks.

关键词： Molecular programming DNA strand displacement neural network chemical reaction networks

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Electrospinning of nanofibres

NATURE REVIEWS METHODS PRIMERS

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NATURE REVIEWS METHODS PRIMERS 2024年第1期4卷 1页

作者： [Anonymous] Key Laboratory of Textile Science & Technology Ministry of Education College of Textiles Donghua University Shanghai China Center for Nanotechnology & Sustainability Department of Mechanical Engineering College of Design and Engineering National University of Singapore Singapore Singapore School of Engineering Institute for Materials and Processes The University of Edinburgh Edinburgh UK Center for Advanced Intelligent Materials Universiti Malaysia Pahang Al-Sultan Abdullah Kuantan Malaysia Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Al-Sultan Abdullah Kuantan Malaysia

This PrimeView highlights applications of electrospun nanofibres

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Anomalous Polarization-Switching Phenomena and Noteworthy Pyroelectricity in Ferroelectric Hf0.5zr0.5o2 Polycrystalline Films

SSRN

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SSRN 2023年

作者： Wang, Xudong Zhou, Yong Xiong, Ke Wu, Shuyu Tu, Luqi Chen, Yan Wu, Shuaiqin Zeng, Jinhua Zheng, Yuqing Gu, Shiqun Luo, Man Lin, Tie Shen, Hong Meng, Xiangjian Liu, Qi Chu, Junhao Wang, Jianlu State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China Key Laboratory of Microelectronics Device and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Frontier Institute of Chip and System Fudan University Shanghai200433 China Shanghai Frontier Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai200433 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences Hangzhou330106 China BirenTech Shanghai201114 China Jiangsu Key Laboratory of ASIC Design School of Information Science and Technology Nantong University Jiangsu Nantong226019 China

Hafnia-based oxide thin films exhibit unconventional robust ferroelectricity at the nanoscale, paving the way for advancements in next-generation nanoelectronics. However, the stability and dynamics of ferroelectric polarization in hafnia-based oxides films, particularly at the nanoscale, warrant further exploration. In this study, we delve into the factors influencing the formation of the ferroelectric phase. Our findings illuminate that the confinement effect of the capped layer enhances the stabilization of the metastable orthorhombic phase, thereby bolstering its ferroelectricity and endurance. Furthermore, we identify an unprecedented polarization-switching behavior in HfO2-based ferroelectric films, characterized by a decrease in the remanent polarization and an increase in the coercive field as the temperature drops. This peculiar phenomenon can be ascribed to the reduction in the long-range interaction of polarization dipoles upon cooling, as evidenced by the dielectric spectrum. The diminished long-range interaction results in the emergence of nanodomains and a re-entrant relaxer behavior, offering novel insights into the origin of ferroelectricity and the stability of ferroelectric polarization in hafnia-based thin films. Given the commendable compatibility of Hf0.5Zr0.5O2 films with Si substrates and a pyroelectric coefficient of -62.1 μC/m2·K, they emerge as a prime candidate for sensor device applications. © 2023, The Authors. All rights reserved.

关键词： Thin films

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A 2-bit graphene encoder based on the plasmon-induced transparency effect and its sensing characteristics

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Results in Physics 2024年 59卷

作者： Zhu, Aijun Li, Zongteng Hou, Weigang Yang, Xinghua Cheng, Lei Hu, Cong Qiao, Fei Mahapatra, Rabi Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin 541004 China Shanxi Key Laboratory of Advanced Semiconductor Optoelectronic Devices and Integrated Systems Jincheng Shanxi 048000 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing 400000 China College of Science Beijing Forestry University Beijing 100083 China Jincheng Research Institute of Opto-mechatronics Industry Jincheng Shanxi 048000 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guilin 541004 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Computer Science and Engineering Texas A&M University College Station 77843-3112 TX United States

A 2-bit encoder based on the plasmon-induced transparency (PIT) effect was proposed and investigated in the range from 2 to 7 THz. The Lorentz oscillation coupling model was utilized to confirm the simulation. By tuning the Fermi levels of cross1 and cross2, the 2-bit encoder was achieved. And the minimum MD was 94.73 %, the minimum ER was 12.77 dB, and the maximum IL was 0.32 dB. As a sensor, the structure had a sensitivity of up to 2.33 THz/RIU. In addition, due to the insensitivities to incidence angle and polarization angle, the encoder could be utilized to complex environments. Therefore, these results reveals that our work is significant in terahertz encoders, optical switches, sensors, slow light devices, and modulators. © 2024 The Author(s)

关键词： 2-bit encoder Metamaterial Plasmon-induced transparency (PIT) effect Plasmonic-graphene Terahertz (THz)

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RETRACTED ARTICLE:Thermal,mechanical,and tribological properties of sodium-montmorillonite-nanoparticle-reinforced polyethersulfone and polytetrafluoroethylene ternary composites

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Friction 2022年第7期10卷 1134-1134页

作者： Shicheng YAN Yahong XUE Yahui HU Shoujun WANG Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China College of Mechanical Engineering Tianjin University of TechnologyTianjin 300384China

The Editor-in-Chief has retracted this article[1]because a large number of figures as well significant parts of the text overlap with the 2017 *** by Zhen Zuo[2].All authors agree to this retraction.[1]Yan S C,Xue Y H,Hu Y H,Wang S ***,mechanical,and tribological properties of sodium-montmorillonite-nanoparticlereinforced polyethersulfone and polytetrafluoroethylene ternary ***(2020).https://***/10.1007/s40544-019-0343-x.[2]Zuo *** on tribological property and friction interface interaction of PES/PTFE ***.D ***(China):Yanshan University,2017.

关键词： friction Friction tribological

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Numerical Investigation on Thermal Characteristics and Energy Efficiency of Lithium-Ion Battery Under Different Charging Stresses

SSRN

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SSRN 2024年

作者： Xie, Wenlong Gao, Xinlei Zhang, Zhengjie Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China Department of Mechanical Engineering Imperial College London LondonSW7 2AZ United Kingdom School of Transportation Science and engineering Beihang University Beijing100191 China

The thermal characteristics of lithium-ion batteries during the charging process significantly impact their electrochemical performance and safety, particularly under high-rate or extreme temperature conditions. In this study, an electrochemical-thermal coupling model for a 2.5 Ah LiNi0.8Co0.1Mn0.1O2 18650 lithium-ion battery is proposed and experimentally validated. The heat generation and variation patterns of the electrodes at different charging rates and temperatures are evaluated, and the influence mechanisms of various electrode design parameters on charging energy efficiency are elucidated. The results reveal that high-rate charging increases the proportion of irreversible heat in the total heat generation, primarily due to the increase in ohmic heating at the positive electrode. Ambient temperature is less sensitive to heat generation than the C-rate, but low temperatures can also increase irreversible heat and are associated with an increase in positive polarization heat. Analyzing thermal characteristics at the electrode level can contribute to the optimization of battery design and the improvement of thermal safety. Electrode architectures, including electrode thickness, active material porosity, particle radius and negative/positive electrode capacity (N/P) ratio, are determined to analyze their effects on the heat generation of various components within the battery. Thick electrodes or active materials with high porosity induce significant ohmic heating and increase irreversible heat, with a slight effect on reversible heat, which is detrimental to energy efficiency. Large particle radii primarily increase reaction heat and polarization heat, and a high N/P ratio reduces the battery heat generation and is related to the reduction of negative reaction heat. Notably, variations in both two parameters have limited impact on to energy efficiency. Therefore, implementing appropriate thermal management across various charging scenarios is crucial for achievin

关键词： Lithium-ion batteries

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Significant Wave Height Prediction Based on VMD-SA-MLP-BP

Significant Wave Height Prediction Based on VMD-SA-MLP-BP

引用

Chinese Automation Congress (CAC)

作者： Yining Wu Runfeng Zhang Fang Liu Zhenzhong Liu Jianguo Wu Haonan Dong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Yichang Research Institute of Testing Technology Yichang China Tianjin Hanhailanfan Marine Technology Co. Ltd. Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

Waves seriously impact port construction, worldwide route planning, military activities, and wave power generation. To improve the accuracy of significant wave height prediction, we proposed a novel prediction method, a multi-layer perceptron combined with a backpropagation adjustment (MLP-BP) prediction model that combines mutation mode decomposition (VMD) and a simulated annealing optimization algorithm (SA). Firstly, we use the variable modulus method to decompose the significant wave height sequence data and transform the wave height sequence into multiple different sub-modes (IMF) to reduce the complexity and non-stationarity of the data. Secondly, the simulated annealing algorithm (SA) is used to optimize the weight and bias of the MLP-BP neural network to find the optimal parameter configuration and improve the performance and generalization ability of the prediction model. Finally, the decomposed sub-modal components of each significant wave height sequence are inserted into the MLP-BP neural network and the predicted values of each element are summed to obtain the final significant wave height prediction. The prediction results of the SSA-MLP-BP, PSO-MLP-BP, SA-MLP-BP, and VMD-SA-MLP-BP were compared and demonstrated that the VMD-SA-MLP-BP model performed best. The MAE, MAPE, MSE, RMSE, and R2 of the prediction evaluation indexes were 0.036 m, 11.7%, 0.004 m 2 , 0.067 m, and 0.983, respectively, which performed well in predicting significant wave height.

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Research on Hot Isostatic Pressing Process Of Cfetr Wccb Components

SSRN

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SSRN 2022年

作者： Shen, Xu Liu, Jia-Qin Wu, Yu-Cheng Wu, Jie-Feng Liu, Song-Lin Zhu, Xiao-Yong Jiang, Bei-Yan School of Materials Science and Engineering Hefei University of Technology Hefei230009 China Institute of Plasma Physics Chinese Academy of Sciences Hefei230031 China Engineering Research Center of Advanced Composite Materials Design & Application of Anhui Province Hefei University of Technology Hefei230051 China Anhui Province Key Laboratory of Special Welding Technology Huainan232000 China Institute of Intelligent Manufacture Hefei University of Technology Hefei230051 China

Chinese Fusion Engineering Testing Reactor (CFETR) is a superconducting magnet tokamak reactor of equivalent size and complementary functionality to ITER. The water-cooled ceramic breeder (WCCB) cladding designed by the Institute of Plasma Physics, Chinese Academy of Sciences is one of the candidate cladding concepts for CFETR. As an important part, the WCCB directly determines the safe operation of the fusion reactor. In the WCCB manufacturing technology, the hot isostatic pressing process is applied to the manufacture of WCCB due to its unique technical characteristics. This paper introduces the process research and inspection during the development of WCCB components, and these results will be the guidance to the manufacture of WCCB Components. © 2022, The Authors. All rights reserved.

关键词： Superconducting magnets

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