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Regulating Effect of Substrate Temperature on Sputteringgrown Ge/Si QDs under Low Ge Deposition

Journal of Wuhan University of technology(Materials science) 2024年第4期39卷 888-894页

作者：舒启江 YANG Linjing LIU Hongxing 黄鹏儒 Institute of Information Yunnan University of Chinese MedicineKunming 650500China Guangxi Key Laboratory of Information Materials and Guangxi Collaborative Innovation Center of Structure and Property for New Energy and Materials School of Material Science&EngineeringGuilin University of Electronic TechnologyGuilin 541004China

The effect of deposition temperature on the morphology and optoelectronic performance of Ge/Si QDs grown by magnetron sputtering under low Ge deposition(~4 nm)was investigated by atomic force microscopy,Raman spectroscopy,and photoluminescence(PL)*** experimental results indicate that temperatures higher than 750℃effectively increase the crystallization rate and surface smoothness of the Si buffer layer,and temperatures higher than 600℃significantly enhance the migration ability of Ge atoms,thus increasing the probability of Ge atoms meeting and nucleating to form QDs on Si buffer layer,but an excessively high temperature will cause the QDs to undergo an Ostwald ripening process and thus develop into super large *** addition,some PL peaks were observed in samples containing small-sized,high-density Ge QDs,the photoelectric properties reflected by these peaks were in good agreement with the corresponding structural characteristics of the grown *** results demonstrate the viability of preparing high-quality QDs by magnetron sputtering at high deposition rate,and the temperature effect is expected to work in conjunction with other controllable factors to further regulate QD growth,which paves an effective way for the industrial production of QDs that can be used in future devices.

关键词： Ge/Si QDs deposition temperatures evolution law photoelectric performance

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Collaboratively enhancing electrochemical properties of LiNi_(0.83)Co_(0.11)Mn_(0.06)O_(2) through doping and coating of quadrivalent elements

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Rare Metals 2023年第12期42卷 4103-4114页

作者： Zhao-Zhe Yu Gui-Quan Zhao Fang-Li Ji Hao Tong Qi-Lin Tong Hua-Cheng Li Yan Cheng Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology Guilin University of Electronic TechnologyGuilin 541004China Engineering Research Center of Electronic Information Materials and Devices Ministry of EducationGuilin University of Electronic TechnologyGuilin 541004China Guangxi CNGR New Energy Science&Technology Co. Ltd.Qinzhou 535000China

Ni-rich layered oxides(Ni>80%)with high energy density have become a mainstream cathode material for Li-ion ***,irreversible phase transitions and interface instability are deep-seated challenges in commercializing Ni-rich *** study used a collaborative modification strategy involving doping and coating with quadrivalent elements to construct Ni-rich *** particular,introducing tetravalent Zr makes the valence change of Ni(2+to 4+)more accessible to complete spontaneously during the charging and discharging processes,which significantly suppresses the cationic mixing and irreversible phase transition(H2?H3).Combining the strategy of constructing CeO_(2) coatings on the surface and interfacial spinel-like phases improves the Li+diffusion kinetics and interfacial ***,part of the strongly oxidizing four-valence Ce^(4+)diffuses to the surface layer,further increasing the average valence state of ***,LiNi_(0.83)Co_(0.11)Mn_(0.06)O_(2)(NCM)-Zr@Ce achieves 78.5%outstanding retention at1.0C after 200 cycles within 3.0-4.3 V compared to unmodified NCM with 41.4%*** improved cyclic stability can be attributed to the collaborative modification strategy of the quadrivalent elements,which provides an effective synergistic modification strategy for developing high-performance Li-ion battery cathode materials.

关键词： Collaborative modification strategy Quadrivalent elements Phase transitions Structural degradation Ni-rich layered oxides

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Adaptive Tracking Algorithm for Hypersonic Maneuvering Target Based on Bi-satellite Positioning

Adaptive Tracking Algorithm for Hypersonic Maneuvering Targe...

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2024 China Automation Congress, CAC 2024

作者： Fu, Zhumu Wan, Dongfeng Wang, Zhikai Tao, Fazhan School Information Engineering Henan University of Science and Technology National Key Laboratory of Air-based Information Perception and Fusion Luoyang China

ISBN: (纸本)9798350368604

Due to the hypersonic maneuvering target’s fast flight speed and wide trajectory range, it is impractical to track its entire path using ground-based radar. Based on the Bi-satellite positioning measurement model with line of sight intersection, this paper integrate the Strong Tracking Filter (STF) with the Unscented Kalman Filter (UKF) algorithm, incorporating two adaptive coefficients to adjust the filter gain and noise covariance matrix dynamically. This approach aims to track and estimate the trajectory of a hypersonic glide vehicles (HGVs) based on observations from two LEO early warning satellites within the space-based infrared system. Through simulation, this paper presents the results of tracking target trajectories using an adaptive strong tracking Unscented Kalman Filter (ASTUKF) algorithm based on Bi-satellite positioning. The simulation and experimental results demonstrate that the proposed algorithm achieves a 28.95% and 50.36% lower total speed RMSE compared to the other two algorithms, respectively. Similarly, the total position RMSE is reduced by 30.46% and 51.99%, respectively, when compared with the same algorithms. © 2024 IEEE.

关键词： Kalman filters

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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Computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within Computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Joint self-supervised and reference-guided learning for depth inpainting

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Computational Visual Media 2022年第4期8卷 597-612页

作者： Heng Wu Kui Fu Yifan Zhao Haokun Song Jia Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Depth information can benefit various computer vision tasks on both images and ***,depth maps may suffer from invalid values in many pixels,and also large *** improve such data,we propose a joint self-supervised and reference-guided learning approach for depth *** the self-supervised learning strategy,we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge *** module alternately learns a convolutional dictionary and sparse coding from a corrupted depth ***,both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map,which is effectively smoothed using local contextual *** reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth *** thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large *** summary,our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent *** results show that the proposed approach works well for both invalid value restoration and large hole inpainting.

关键词： depth inpainting self-supervised learning reference-guided learning

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A healable, mechanically robust and ultrastretchable ionic conductive elastomer for durably wearable sensor

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Nano Research 2024年第4期17卷 3369-3378页

作者： Guoxian Zhang Chunmei Li JiaoJun Tan Mingqi Wang Yafeng Ren Feijie Ge Qiuyu Zhang Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology School of Chemistry and Chemical EngineeringNorthwestern Polytechnical UniversityXi'an 710072China College of Bioresources Chemical and Materials Engineering National Demonstration Center for Experimental Light Chemistry Engineering EducationShaanxi University of Science&TechnologyXi’an 710021China

The ionic conductive elastomers show great promise in multifunctional wearable electronics,but they currently suffer from liquid leakage/evaporation or mechanical *** ionic conductive elastomers integrating non-volatility,mechanical robustness,superior ionic conductivity,and ultra-stretchability remains urgent and ***,we developed a healable,robust,and conductive elastomer via impregnating free ionic liquids(ILs)into the ILs-multigrafted poly(urethane-urea)(PUU)elastomer networks.A crucial strategy in the molecular design is that imidazolium cations are largely introduced by double-modification of PUU and centipede-like structures are obtained,which can lock the impregnated ILs through strong ionic *** this system,the PUU matrix contributes outstanding mechanical properties,while the hydrogen bonds and ionic interactions endow the elastomer with self-healing ability,conductivity,as well as non-volatility and *** fabricated ionic conductive elastomers show good conductivity(3.8×10^(-6) S·cm^(-1)),high mechanical properties,including tensile stress(4.64 MPa),elongation(1470%),and excellent healing ability(repairing efficiency of 90%after healing at room temperature for 12 h).Significantly,the conductive elastomers have excellent antifatigue properties,and demonstrate a highly reproducible response after 1000 uninterrupted extension-release *** work provides a promising strategy to prepare ionic conductive elastomers with excellent mechanical properties and stable sensing capacity,and further promote the development of mechanically adaptable intelligent sensors.

关键词： ionic conductive elastomers poly(urethane-urea)(PUU) ionic interactions centipede-like self-healing

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ATFS: A deep learning framework for angle transformation and face swapping of face de-identification

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Multimedia Tools and Applications 2024年第12期83卷 36797-36822页

作者： Tsai, Chwei-Shyong Wu, Hsien-Chu Chen, Wei-Ting Ying, Josh Jia-Ching Department of Management Information Systems National Chung Hsing University Taichung402 Taiwan Language Center National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan

This paper proposes an effective framework for improving angle transformation and face replacement. This ATFS framework aims to solve the image distortion caused by angled face swapping in face de-identification. In the ATFS framework, TransNet and SWGAN were proposed based on generative adversarial networks. When TransNet uses neural networks to reconstruct images, it combines the predicted facial points. As a result, Transnet can locate the target’s facial features and generate multi-angle transformed images to achieve angle transformation. The SWGAN applies a complicated neural network that combines residual operations and self-attention modules to extract the input image features and replace the face region of the input image with the target image to achieve face de-identification. Both TransNet and SWGAN adopt discriminators and various loss functions to train the neural network and accurately accelerate the training process. The experimental results show that the proposed method can maintain high-quality images and avoid image distortion during face swapping for image angle transformation compared to previously proposed methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Generative adversarial networks

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Radar Probing Optimization for Joint Beamforming and UAV Trajectory Design in UAV-Enabled Integrated Sensing and Communication

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IEEE Transactions on Communications 2024年第6期73卷 4469-4485页

作者： Liu, Yaxi Mao, Wencan He, Boxin Huangfu, Wei Huang, Tianyao Zhang, Haijun Long, Keping University Of Science And Technology Beijing Beijing Advanced Innovation Center For Materials Genome Engineering Beijing Engineering And Technology Research Center For Convergence Networks And Ubiquitous Services School Of Computer And Communication Engineering Beijing100083 China University Of Science And Technology Shunde Innovation School Beijing China National Institute Of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task;ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An adaptive multitasking optimization algorithm based on population distribution

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Mathematical Biosciences and engineering 2024年第2期21卷 2432-2457页

作者： Li, Xiaoyu Wang, Lei Jiang, Qiaoyong Xu, Qingzheng School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China School of Electronic and Information Engineering Ankang University Ankang725000 China The Key Laboratory of Industrial Automation of Shaanxi Province Shaanxi University of Technology Hanzhong723001 China College of Information and Communication National University of Defense Technology Wuhan430019 China

Evolutionary multitasking optimization (EMTO) handles multiple tasks simultaneously by transferring and sharing valuable knowledge from other relevant tasks. How to effectively identify transferred knowledge and reduce negative knowledge transfer are two key issues in EMTO. Many existing EMTO algorithms treat the elite solutions in tasks as transferred knowledge between tasks. However, these algorithms may not be effective enough when the global optimums of the tasks are far apart. In this paper, we study an adaptive evolutionary multitasking optimization algorithm based on population distribution information to find valuable transferred knowledge and weaken the negative transfer between tasks. In this paper, we first divide each task population into K sub-populations based on the fitness values of the individuals, and then the maximum mean discrepancy (MMD) is utilized to calculate the distribution difference between each sub-population in the source task and the subpopulation where the best solution of the target task is located. Among the sub-populations of the source task, the sub-population with the smallest MMD value is selected, and the individuals in it are used as transferred individuals. In this way, the solution chosen for the transfer may be an elite solution or some other solution. In addition, an improved randomized interaction probability is also included in the proposed algorithm to adjust the intensity of inter-task interactions. The experimental results on two multitasking test suites demonstrate that the proposed algorithm achieves high solution accuracy and fast convergence for most problems, especially for problems with low relevance. © 2024 the Author(s).

关键词： Optimization

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Strain concentration factor of heterogeneous materials and analytical influence functions based on Eshelby tensor

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Theoretical & Applied Mechanics Letters 2024年第4期14卷 306-313页

作者： Shanqiao Huang Zifeng Yuan HEDPS Center for Applied Physics and TechnologyDepartment of Mechanics and Engineering SciencePeking UniversityBeijing 100871China State Key Laboratory for Turbulence and Complex Systems Peking UniversityBeijing 100871China

In this manuscript,Eshelby tensor is employed to assess the strain concentration that arises in the matrix phase at the interface,offering precise values and locations of maximum strain under specific loading conditions for both spherical and cylindrical *** compared to numerical simulation results,the analytical predictions grounded in the Eshelby tensor exhibit satisfactory *** an analytical calculation method based on Eshelby tensor for the elastic strain influence functions of reduced-order homogenization(ROH)method is developed and adopted on particle-reinforced and fibrous composites,presenting its feasibility and advantage on off-line stage calculation of ROH *** error analyses between analytical and numerical results are *** numerical results also exhibit the necessity of finer interface partitioning to obtain the response on micro-scale with higher resolution.

关键词： Eshelby tensor Strain concentration Composite structures Reduced-order-homogenization Influence functions

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