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Revealing the atomic mechanism of diamond–iron interfacial reaction

Carbon Energy 2024年第3期6卷 255-263页

作者： Yalun Ku Kun Xu Longbin Yan Kuikui Zhang Dongsheng Song Xing Li Shunfang Li Shaobo Cheng Chongxin Shan Henan Key Laboratory of Diamond Optoelectronic Materials and Devices Key Laboratory of Material PhysicsMinistry of EducationSchool of Physics and MicroelectronicsZhengzhou UniversityZhengzhouChina Department of Mechanical Engineering Stanford UniversityStanfordCaliforniaUSA Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefeiChina Institute of Quantum Materials and Physics Henan Academy of SciencesZhengzhouChina

Diamond,with ultrahigh hardness,high wear resistance,high thermal conductivity,and so forth,has attracted worldwide ***,researchers found emergent reactions at the interfaces between diamond and ferrous materials,which significantly affects the performance of diamond-based ***,combing experiments and theoretical calculations,taking diamond–iron(Fe)interface as a prototype,the counter-diffusion mechanism of Fe/carbon atoms has been ***,it is identified that Fe and diamond first form a coherent interface,and then Fe atoms diffuse into diamond and prefer the carbon vacancies ***,the relaxed carbon atoms diffuse into the Fe lattice,forming Fe_(3)***,graphite is observed at the Fe_(3)C surface when Fe_(3)C is over-saturated by carbon *** present findings are expected to offer new insights into the atomic mechanism for diamondferrous material's interfacial reactions,benefiting diamond-based device applications.

关键词： coherent interface counter-diffusion diamond iron phase transition

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Grape Leaf Disease Detection and Recognition Method Based on Improved Model RTDETR-DRA 4

Grape Leaf Disease Detection and Recognition Method Based on...

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4th International Conference on Electronic information engineering and Computer technology, EIECT 2024

作者： Chen, Zhiyuan Yang, Yanhua School of Information Science & Engineering Yunnan University Yunnan China School of Information Science & Engineering University Key Laboratory of Internet of Things Technology and Application Yunnan University Yunnan China

ISBN: (纸本)9798331528850

In recent years, the rapid development of artificial intelligence has revolutionized agriculture, particularly in the detection and identification of plant diseases. Grape leaf diseases pose a significant threat to grape production, directly impacting yield and quality. To address the limitations of existing disease detection models, such as high computational cost and inefficient deployment on resource-limited devices, we propose an improved model based on RTDETR, named RTDETR-DRA. This model integrates the DRBNCSPELAN module, leveraging multi-path feature fusion and channel split design to optimize real-time detection accuracy while minimizing computational cost. Experimental results show that RTDETR-DRA outperforms the original RTDETR model, achieving higher accuracy in disease detection with significantly reduced parameters and GFLOPs, making it suitable for lightweight deployment on platforms like mobile devices. Our proposed method contributes to the advancement of efficient and precise plant disease detection technologies. © 2024 IEEE.

关键词： Software design

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HCU-Net: An Efficient Fully Convolutional Neural Network for Thyroid Nodules Ultrasound Image Segmentation

HCU-Net: An Efficient Fully Convolutional Neural Network for...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Ren, Mengxi Liu, Jun School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665410205

Thyroid nodules, which can manifest as solitary or multiple growths, are among the most prevalent diseases with a high incidence rate. When examining thyroid nodules, ultrasound is typically the first step. Ultrasound image segmentation of thyroid nodules can assist physicians in diagnosis. However, the following three factors limit the development of thyroid nodule segmentation: (1) most of the existing studies optimize the segmentation of single nodules, and the incidence of multiple nodules is higher, so the segmentation of multiple nodules is also very important;(2) the ultrasound image of thyroid nodules is difficult to segment because of its low resolution and fuzzy boundary;(3) Due to the small data set of thyroid nodule ultrasound, in such a small dataset, attention mechanisms may be prone to overfitting, resulting in poorer generalization performance. Therefore, the attention-based model may not be suitable for the segmentation of thyroid nodules. Hence, we propose Hybrid Convolutional U-Net (HCU-Net), a simple and strong fully convolutional neural network for the segmentation of single or multiple thyroid nodules. HCU-Net incorporates a large receptive field to ensure accurate and stable segmentation. Specifically, we design Hybrid Branch Block to enhance the network's feature extraction capability for single or multiple nodules in ultrasound images by integrating multi-scale features. Furthermore, we add Enhanced ConvNeXt Blocks to the U-Net's final layer, enabling the extraction of global contextual information for segmentation performance. The Experimental results show that our proposed method can effectively improve these issues on two publicly available datasets. In addition, compared to attention-based models, our network is simpler and more suitable for small datasets like ultrasound images. © 2024 IEEE.

关键词： Ultrasonic imaging

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Gradient-structure-enhanced dielectric energy storage performance of flexible nanocomposites containing controlled preparation of defective TiO_(2) and ferroelectric KNbO_(3) nanosheets

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Nano Research 2024年第5期17卷 4079-4088页

作者： Yan Wang Lili Zhao Ruicong Chen Wenhui Zhao Dengwei Hu Haoran Wang Bin Cui Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education Shaanxi Key Laboratory of Physico–Inorganic Chemistry Xi’an Key Laboratory of Functional Supramolecular Structure and MaterialsCollege of Chemistry and Materials ScienceNorthwest UniversityXi’an 710127China School of Information Science and Technology Northwest UniversityXi’an 710127China Engineering Research Center for Titanium Based Functional Materials and Devices in Universities of Shaanxi Province Faculty of Chemistry and Chemical EngineeringBaoji University of Arts and SciencesBaoji 721013China

Next generation power system needs dielectrics with increased dielectric energy ***,the low energy density of dielectrics limits their ***,an asymmetric trilayered nanocomposite,with a transition layer(TL),an insulation layer(IL),and a polarization layer(PL),is designed based on poly(vinylidene fluoride)-polymethyl methacrylate(PVDF-PMMA)matrix using KNbO_(3)(KN)and TiO_(2)(TO)as the *** morphology and defect control of the two-dimensional nano KN and nano TO fillers are realized via a hydrothermal method to increase the composite breakdown strength(E_(b))and the composite energy density(U_(e)).The asymmetric trilayered structure leads to a gradient electric field distribution,and the KN and TO nanosheets block charges transfer along z *** a result,the development path of the electrical trees is greatly curved,and E_(b) is effectively *** the Ue value of the nanocomposites reaches 17.79 J·cm^(-3) at 523 MV·m^(-1).On the basis,the composite Ue is further improved by defect control in TO *** nanocomposite KN/TO/PVDF-PMMA containing TO with less oxygen vacancy concentration(calcined at oxygen atmosphere)acquires a high Ue of 21.61 J·cm^(-3) at 548 MV·m^(-1).This study provides an idea for improving the energy storage performance by combining the design of the composite dielectric structure and the control of nanofillers’defect and morphology.

关键词： asymmetric trilayered structure two-dimensional energy density oxygen vacancies breakdown strength dielectric

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Fast Hybrid Far/Near-Field Beam Training For Extremely Large-Scale Millimeter Wave/Terahertz systems

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IEEE Transactions on Communications 2024年第6期73卷 3956-3970页

作者： Wang, Hongwei Fang, Jun Duan, Huiping Li, Hongbin University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Electronic Science and Technology of China School of Information and Communications Engineering Chengdu611731 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

In this paper, we consider the problem of downlink beam training for extremely large-scale millimeter wave (mmWave)/Terahertz (THz) systems, where the far-field assumption which treats wavefronts as planar waves may not hold valid. For such hybrid far/near-field channels, beam training needs to identify the best beam alignment on a two-dimensional angle-range domain. An exhaustive search scheme sequentially scanning the entire angle-range space incurs a high training overhead. To address this issue, in this paper, we propose an efficient hybrid far/near-field beam training method. By utilizing the approximate orthogonality of near-field steering vectors of the same effective distance, we devise a multi-directional beam training sequence which can more efficiently scan the entire angle-range space. Based on the devised beam training sequence, we develop a simple estimation method at the receiver that can simultaneously identify the angle and the range associated with the dominant path. Simulation results show that the proposed method achieves better performance than the exhaustive search scheme, while with a much lower overhead cost. The proposed method also presents a clear advantage over other existing state-of-the-art hybrid far/near-field beam training methods in terms of performance and generality. © 1972-2012 IEEE.

关键词： Wavefronts

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In-Vehicle Network Injection Attacks Detection Based on Feature Selection and Classification

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Automotive Innovation 2024年第1期7卷 138-149页

作者： Haojie Ji Liyong Wang Hongmao Qin Yinghui Wang Junjie Zhang Biao Chen Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100192China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Hefei Innovation Research Institute Beihang UniversityHefei 230012China

Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle *** transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols ***,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal ***,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network *** this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection *** consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is *** addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal *** paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data *** experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.

关键词： Classification algorithm Anomaly detection In-vehicle network Feature extraction Injecting attack

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Discrete Whale Optimization Algorithm for Capacitated Vehicle Routing Problem

Discrete Whale Optimization Algorithm for Capacitated Vehicl...

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2023 China Automation Congress, CAC 2023

作者： Chen, Huiyu Sun, Lijun Chen, Tianfei College of Information Science and Engineering Henan University of Technology Henan China Henan University of Technology Zhengzhou Key Laboratory of Machine Perception and Intelligent System Henan China

ISBN: (纸本)9798350303759

Capacitated vehicle routing problem (CVRP) is a classical combinatorial optimization problem, which plays an important role in the logistics field and belongs to the NP-hard problem. Discrete whale optimization algorithm (DWOA) with a routing first and grouping later solution generating technique is proposed to handle the CVRP. The algorithm redefines the classical whale optimization algorithm's encircling prey method, bubble-net attacking method, and exploration phase method. Through the improved whale optimization algorithm, the optimal planning of logistics distribution vehicle routing is realized. Finally, an experimental demonstration of the DWOA algorithm's success offers a novel approach for resolving CVRP. © 2023 IEEE.

关键词： Vehicle routing

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Brillouin Klein space and half-turn space in three-dimensional acoustic crystals

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science Bulletin 2024年第13期69卷 2050-2058页

作者： Zhenxiao Zhu Linyun Yang Jien Wu Yan Meng Xiang Xi Bei Yan Jingming Chen Jiuyang Lu Xueqin Huang Weiyin Deng Ce Shang Perry Ping Shum Yihao Yang Hongsheng Chen Kexin Xiang Gui-Geng Liu Zhengyou Liu Zhen Gao State Key Laboratory of Optical Fiber and Cable Manufacture Technology Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Physics and Optoelectronics South China University of TechnologyGuangzhou 510640China Key Laboratory of Artificial Micro-and Nanostructures of Ministry of Education and School of Physics and Technology Wuhan UniversityWuhan 430072China King Abdullah University of Science and Technology(KAUST) Physical Science and Engineering Division(PSE)Thuwal 23955-6900Saudi Arabia Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationZJU-Hangzhou Global Science and Technology Innovation CenterCollege of Information Science and Electronic EngineeringZJU-UIUC InstituteZhejiang UniversityHangzhou 310027China Division of Physics and Applied Physics School of Physical and Mathematical SciencesNanyang Technological UniversitySingapore 637371Singapore Institute for Advanced Studies Wuhan UniversityWuhan 430072China

The Bloch band theory and Brillouin zone(BZ)that characterize wave-like behaviors in periodic mediums are two cornerstones of contemporary physics,ranging from condensed matter to topological *** theoretical breakthrough revealed that,under the projective symmetry algebra enforced by artificial gauge fields,the usual two-dimensional(2D)BZ(orientable Brillouin two-torus)can be fundamentally modified to a non-orientable Brillouin Klein bottle with radically distinct manifold ***,the physical consequence of artificial gauge fields on the more general three-dimensional(3D)BZ(orientable Brillouin three-torus)was so far ***,we theoretically discovered and experimentally observed that the fundamental domain and topology of the usual 3D BZ can be reduced to a non-orientable Brillouin Klein space or an orientable Brillouin half-turn space in a 3D acoustic crystal with artificial gauge *** experimentally identify peculiar 3D momentum-space non-symmorphic screw rotation and glide reflection symmetries in the measured band ***,we experimentally demonstrate a novel stacked weak Klein bottle insulator featuring a nonzero Z2 topological invariant and self-collimated topological surface states at two opposite surfaces related by a nonlocal twist,radically distinct from all previous 3D topological *** discovery not only fundamentally modifies the fundamental domain and topology of 3D BZ,but also opens the door towards a wealth of previously overlooked momentum-space multidimensional manifold topologies and novel gaugesymmetry-enriched topological physics and robust acoustic wave manipulations beyond the existing paradigms.

关键词： Momentum-space non-symmorphic symmetry Brillouin Klein space and half-turn space Acoustic Klein bottle insulator Self-collimated topological surface states

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Port-controllable routing of orbital angular momentum modes using a rotatable diffractive neural network

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science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 154-163页

作者： Junmin Liu Jiafu Chen Qingji Zeng Zemin Liang Xinping Wu Xin Zhao Jiangnan Xiao Huapeng Ye Ze Dong Dianyuan Fan Shuqing Chen Institute of Microscale Optoelectronics Shenzhen UniversityShenzhen 518060China College of New Materials and New Energies Shenzhen Technology UniversityShenzhen 518118China Terahertz Technology Innovation Research Institute Shanghai Key Laboratory of Modern Optical SystemTerahertz Science Cooperative Innovation CenterSchool of Optical-Electrical Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology&Institute of Electronic Paper Displays South China Academy of Advanced OptoelectronicsSouth China Normal UniversityGuangzhou 510006China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China

Orbital angular momentum(OAM)modes provide an additional orthogonal physical dimension,offering transformative potential for enhancing optical communication *** significant progress in mode multiplexing,the development of robust communication networks faces persistent challenges,particularly in effectively routing and controlling these multiplexed channels among network *** tackle these dilemmas,we propose a rotatable diffractive neural network(R-DNN)strategy and demonstrate its capability for port-controllable OAM mode *** leveraging the correlation between the orthogonal evolution of OAM modes in free space and phase modulations during propagation,the R-DNN precisely shapes the spatial evolution of mode fields through multiple rotatable phase layers,enabling efficient routing to specific output *** approach exploits the interaction of secondary wavelets with the relative states of the rotatable layers,allowing on-demand control of mode evolution paths and enhancing routing *** a proof of concept,we developed a tri-functional router that successfully directs three OAM modes to individually controllable output *** router achieves an average intermode crosstalk of less than−16.4 dB across three functional states,one-dimensional,two-dimensional,and cross-connected switching,while supporting the routing of 5.85 Tbit/s quadrature phase-shift keying *** results highlight the R-DNN’s effectiveness in achieving precise and controllable OAM mode manipulation,paving the way for advanced applications in mode-multiplexed communication networks and beyond.

关键词： orbital angular momentum mode mode routing diffractive neural network

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A fully-customized dataflow engine for 3D earthquake simulation with a complex topography

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science China(information sciences) 2022年第5期65卷 65-80页

作者： Bingwei CHEN Haohuan FU Wayne LUK Guangwen YANG Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University National Supercomputer Center in Wuxi Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua University Department of Computing Imperial College London

With HPC(high performance computing) evolving into the exascale era, improvements in computing performance and power efficiency have become increasingly more important. Based on our previous work on enabling earthquake simulations on a large scale on Sunway Taihu Light, we further explore other possibilities to improve the application through a fully-customized hardware design on reconfigurable FPGA(field programmable gate array) devices. We investigate the feasibility and the potential benefits of a complete fixed-point design. We first perform a coarse-resolution-based simulation to analyze the representation range and precision needed to capture both the total energy and the energy distribution of variables over space and time. We then derive a complete fixed-point design that identifies the suitable bitwidth for major categories of variables and dynamically represents the range through a dynamic scaling scheme. Finally, we use the optimized fixed-point design to run a case of the Wenchuan earthquake to demonstrate the potential of supporting large-scale scientific simulations on FPGA devices. The results demonstrate that an 18-bit fixed-point design already provides an almost identical description of the seismic events in the Wenchuan scenario down to a single-precision floating-point version and provides sustainable performance equivalent to 13.1 Intel Xeon Gold 6154 18-core CPUs or 2.10 Sunway 260-core processors, with performance per watt(power efficiency) improved by 15.3 and 3.72 times compared with the Intel Xeon Gold 6154 18-core CPUs and the Sunway 260-core processors, respectively.

关键词： reconfigurable computing earthquake simulation FPGA hardware-software co-design architecture exploration

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