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Colloidal Synthesis of Semiconductor Films for Efficient Photoelectrochemical Hydrogen Generation

Energy Material Advances 2023年第1期4卷 200-209页

作者： Akang Chen Xinyuan Li Jiale Wang Jiatao Zhang Beijing Key Laboratory of Construction-Tailorable Advanced Functional Materials and Green Applications Key Laboratory of Medical Molecule Science and Pharmaceutical EngineeringMinistry of Industry and Information TechnologyMOE Key Laboratory of Cluster ScienceSchool of Chemistry and Chemical EngineeringSchool of Materials Science&EngineeringBeijing Institute of TechnologyBeijing 100081China

The semiconductor-based photoanodes have shown great potential on photoelectrochemical(PEC)hydrogen *** to the pristine semiconductor,photoanodes fabricated with doped semiconductors exhibit modulated bandgap structure and enhanced charge separation efficiency,demonstrating improved optoelectronic *** this work,we develop a colloidal cation exchange(CE)strategy on versatile synthesis of heterovalent doped chalcogenide semiconductor thin films with high surface *** Ag-doped CdSe(CdSe:Ag)thin films as an example,the organized centimeter-scale CdSe:Ag films with nanometer-scale thickness(thickness around 80 nm,length×width around 1.5 cm×1.2 cm)exhibit enhanced optical absorbance ability and charge carrier density by tuning the energy levels of conduction and valence bands as well as improved electrical conductivity by Ag dopants compared to the pristine CdSe film obtained by the vapor-phase vacuum deposition *** the meantime,the surface roughness of the as-prepared semiconductor thin films is also increased with abundantly exposed active sites to facilitate accessibility to water for hydrogen generation and suppress photogenerated carrier *** CdSe:Ag film photoanodes exhibit superb PEC hydrogen generation performance with a photocurrent density of 0.56 mA/cm^(2) at 1.23 V versus reversible hydrogen electrode,which is nearly 3 times higher than the pristine CdSe *** work provides a new strategy on colloidal synthesis of photoelectrodes with modulated heterovalent doping and surface roughness for PEC applications.

关键词： roughness film synthesis

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A Situation Awareness Model with Human-Machine Collaboration

A Situation Awareness Model with Human-Machine Collaboration

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Meng, Xiangxia Li, Tingting Diao, Lianwang Science and Technology On Information Systems Engineering Laboratory Nanjing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

To improve the understanding of battlefield situation, the mechanisms of human cognition, it is necessary to investigate the models and techniques associated with situation awareness. Based on a model with human-machine collaboration, the process completed by machine is discussed, while two mechanisms including attention mechanism and memory mechanism, and three methods are studied to realize situation awareness using mechine intelligence. The attention mechanism can be divided into purpose-drived and data-drived, while memory mechanism includes long-term memory and short-term memory. The three methods are situation awareness based on knowledge, the method on multi-level battlefield, and the method with hypothesis-testing. These approaches can contribute to achieving deeper understanding on the situation awareness, which can be helpful for the construction of command and control systems. © 2022 IEEE.

关键词： Command and control systems

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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Computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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FEDAF: frequency enhanced decomposed attention free transformer for long time series forecasting

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Neural Computing and Applications 2024年第26期36卷 16271-16288页

作者： Yang, Xuekang Li, Hui Huang, Xiang Feng, Xingyu School of Automation Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China Science and Technology on Information System Engineering Laboratory Jiangsu Province Nanjing210046 China School of Information and Electronic Engineering Zhejiang Gongshang University Zhejiang Province Hangzhou310018 China

Long time series forecasting (LTSF), which involves modeling relationships within long time series to predict future values, has extensive applications in domains such as weather forecasting, financial analysis, and traffic prediction. Recently, numerous transformer-based models have been developed to address the challenges in LTSF. These models employ methods such as sparse attention to alleviate the inefficiencies associated with the attention mechanism and utilize decomposition architecture to enhance the predictability of the series. However, these complexity reduction methods necessitate additional calculations, and the series decomposition architecture overlooks the random components. To overcome these limitations, this paper proposes the Frequency Enhanced Decomposed Attention Free Transformer (FEDAF). FEDAF introduces two variants of the Frequency Enhanced Attention Free Mechanism (FEAFM), namely FEAFM-s and FEAFM-c, which seamlessly replace self-attention and cross-attention. Both variants perform calculations in the frequency domain without incurring additional costs, with the time and space complexity of FEAFM-s being O(LlogL). Additionally, FEDAF incorporates a time series decomposition architecture that considers random components. Unlike other models that solely decompose the series into trend and seasonal components, FEDAF also eliminates random terms by applying Fourier denoising. Our study quantifies data drift and validates that the proposed decomposition structure can mitigate the adverse effects caused by data shift. Overall, FEDAF demonstrates superior forecasting performance compared to state-of-the-art models across various domains, achieving a remarkable improvement of 19.49% for Traffic in particular. Furthermore, an efficiency analysis reveals that FEAFM enhances space efficiency by 12.8% compared to the vanilla attention mechanism and improves time efficiency by 43.63% compared to other attention mechanism variants. © The Author(s), under

关键词： Weather forecasting

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Fast compressed sensing spectral measurement with adaptive gradient multiscale resolution

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Chinese Physics B 2024年第2期33卷 298-304页

作者：蓝若明刘雪峰李天平白成杰 School of Physics and Electronics Shandong Normal UniversityJinan 250014China Key Laboratory of Electronics and Information Technology for Space Systems National Space Science CenterChinese Academy of SciencesBeijing 100190China

We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed *** method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution *** the characteristics of the human retina system,the resolution distribution follows the principle of gradually *** system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a *** system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.

关键词： spectrometer compressed sensing adaptive gradient multiscale resolution fast measurement

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A Deep Learning Approach to Industrial Corrosion Detection

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Computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

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A Review on Binary Code Analysis Datasets 18th

A Review on Binary Code Analysis Datasets

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18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Sea Ice Type Recognition Method based on Convolutional Neural Network for GNSS-R Delay Doppler Images 4

A Sea Ice Type Recognition Method based on Convolutional Neu...

引用

4th International Conference on Big Data and Artificial Intelligence and Software engineering, ICBASE 2023

作者： Cheng, Xing Xie, Heng He, Shanbao Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Ministry of Information Industry School of Information and Communication Engineering Beijing China

ISBN: (纸本)9798350329490

Remote sensing (RS) of sea ice types is of great significance for global climate research. A convolutional neural network (CNN) based sea ice type recognition method for delay Doppler map (DDM) of global navigation satellite system reflection (GNSS-R) is proposed in this paper. In the proposed method, the sea ice type recognition problem is transformed into an image recognition problem by utilizing the difference between the DDM images corresponding to the first-year and multi-year ice, and the CNN model is used to recognize the images. Experimental results show that our proposed CNN based method for identifying sea ice types has excellent performance. © 2023 IEEE.

关键词： Sea ice

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Cooperative decision-making algorithm with efficient convergence for UCAV formation in beyond-visual-range air combat based on multi-agent reinforcement learning

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Chinese Journal of Aeronautics 2024年第8期37卷 311-328页

作者： Yaoming ZHOU Fan YANG Chaoyue ZHANG Shida LI Yongchao WANG School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China

Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air *** Multi-Agent Reinforcement Learning(MARL)shows outstanding performance in cooperative decision-making,it is challenging for existing MARL algorithms to quickly converge to an optimal strategy for UCAV formation in BVR air combat where confrontation is complicated and reward is extremely sparse and *** to solve this problem,this paper proposes an Advantage Highlight Multi-Agent Proximal Policy Optimization(AHMAPPO)***,at every step,the AHMAPPO records the degree to which the best formation exceeds the average of formations in parallel environments and carries out additional advantage sampling according to ***,the sampling result is introduced into the updating process of the actor network to improve its optimization ***,the simulation results reveal that compared with some state-of-the-art MARL algorithms,the AHMAPPO can obtain a more excellent strategy utilizing fewer sample episodes in the UCAV formation BVR air combat simulation environment built in this paper,which can reflect the critical features of BVR air *** AHMAPPO can significantly increase the convergence efficiency of the strategy for UCAV formation in BVR air combat,with a maximum increase of 81.5%relative to other algorithms.

关键词： Unmanned combat aerial vehicle(UCAV)formation Decision-making Beyond-visual-range(BVR)air combat Advantage highlight Multi-agent reinforcement learning(MARL)

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A high-speed true random number generator based on Ag/SiNx/n-Si memristor

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Frontiers of physics 2024年第1期19卷 241-250页

作者： Xiaobing Yan Zixuan Zhang Zhiyuan Guan Ziliang Fang Yinxing Zhang Jianhui Zhao Jiameng Sun Xu Han Jiangzhen Niu Lulu Wang Xiaotong Jia Yiduo Shao Zhen Zhao Zhenqiang Guo Bing Bai Institute of Life Science and Green Development Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei ProvinceCollege of Electron and Information EngineeringHebei UniversityBaoding 071002China

The intrinsic variability of memristor switching behavior can be used as a natural source of randomness,this variability is valuable for safe applications in hardware,such as the true random number generator(TRNG).However,the speed of TRNG is still be further ***,we propose a reliable Ag/SiNx/n-Si volatile memristor,which exhibits a typical threshold switching device with stable repeat ability and fast switching *** volatile-memristor-based TRNG is combined with nonlinear feedback shift register(NFSR)to form a new type of high-speed dual output ***,the bit generation rate reaches a high speed of 112 kb/*** addition,this new TRNG passed all 15 National Institute of Standards and technology(NIST)randomness tests without post-processing steps,proving its performance as a hardware security *** work shows that the SiNx-based volatile memristor can realize TRNG and has great potential in hardware network security.

关键词： volatile memristor true random number generator(TRNG) delay time threshold switching device

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