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SSRN 2023年

作者： Huang, Xuefei Tuo, Jingyi Chen, Guang Wang, Hui An, Xuguang Wang, Fu Zhang, Qiang College of Materials Science and Engineering Sichuan University Chengdu610065 China Science and Technology on Reactor Fuel and Materials Laboratory Nuclear Power Institute of China Chengdu610041 China Interdisciplinary Materials Research Center Institute for Advanced Study Chengdu University China Science and Technology on Surface Physics and Chemistry Laboratory Mianyang621908 China State Key Laboratory of Environment-friendly Energy Materials School of Materials Science and Engineering Southwest University of Science and Technology Mianyang621010 China China Nuclear Power Operation Technology Corporation. LTD Hubei Wuhan430223 China

The effects of Nb on the steam oxidation kinetic behavior of FeCrAl alloy during two different heating stages, i.e., the initial heating and the subsequent holding stage, were studied. Nb addition significantly decreased the weight gain at the holding stage. Nb addition also promoted the formation of Al2O3 and suppressed the growth of Fe and Cr oxides, resulting in the thinner oxide film in the heating stage. Also, the oxide film is much easier to wrinkle in the subsequent holding stage. In addition, the formation of Nb2O5 promotes the formation of discontinuous granular Al2O3 in oxide films and substrates. © 2023, The Authors. All rights reserved.

关键词： Oxide films

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Radar HRRP Open Set Recognition Using Hierarchical Prototype Learning

Radar HRRP Open Set Recognition Using Hierarchical Prototype...

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IEEE International Conference on Radar

作者： Yichen Liu Yanhua Wang Liang Zhang Binhua Yu Xiaojing Yang Yu-ang Zhang Le Zheng Difan Zou School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Department of Computer Science University of Hong Kong Hongkong China HKU Musketeers Foundation Institute of Data Science University of Hong Kong Hongkong China

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

High-resolution range profiles (HRRP) are increasingly employed in radar automatic target recognition (RATR). In practical applications of RATR, the environment is open and dynamic, and the recognition model is likely to encounter targets of unseen categories. However, traditional methods focus on a close-set scenario, where all categories in the test set are known during training. To bridge this gap, this paper proposes an open-set recognition (OSR) method based on hierarchical classification, developed from prototype learning. This method improves the generalization performance for recognizing known categories and identifies unknown categories while providing information about how the unknown categories relate to the known ones. Specifically, first, a four-layer category hierarchy is built based on prior knowledge to guide the training and testing stages. Next, we propose a hierarchical prototype loss (HPL) to constrain the feature space extracted by the prototype network so that the feature distribution of objects is consistent with the hierarchical category structure. Lastly, the trained prototype network makes predictions following the hierarchical structure. This operation could provide relationship information between the unknown and known categories. Extensive experiments on measured HRRP data validate the effectiveness of our proposed method for open-set recognition.

关键词： Training Bridges Target recognition Prototypes Radar Feature extraction Testing

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Attribute Reduction Method Based on Improved Granular Ball Neighborhood Rough Set 7

Attribute Reduction Method Based on Improved Granular Ball N...

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7th International Conference on Cloud Computing and Big data Analytics, ICCCBDA 2022

作者： Xia, Han Chen, Zizhong Wu, Yanmin Qi, Jinli Chongqing Key Laboratory of Computer Science and Technology Chongqing University of Posts and Telecommunications ChongQing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing China ChongQing College of Electronic Engineering Department of General Education and International Studies ChongQing China

ISBN: (纸本)9781665487115

Feature reduction is an important aspect of big data analytics today, and neighborhood rough set is a classic attribute reduction method. The traditional neighborhood rough set finds out the radius suitable for problem solving by specifying the radius of the neighborhood or using a grid search method. There are a lot of overlapping calculations in the neighborhood calculation, and the calculation complexity is high. The granular ball neighbor-hood rough set can adaptively generate different neighborhood radius, and obtains better algorithm efficiency and learning effect than the traditional neighborhood rough set. However, due to the overlap problem in the process of granular ball generation, the granular ball neighborhood rough set still exists the overlap calculation of neighborhoods. For this reason, this paper solves the problem of granular ball overlap by detecting heterogeneous ball overlap and splitting, and thus designs an improved granular ball neighbor-hood rough set attribute reduction method. Experiments show that the improved granular ball neighborhood rough set has higher classification accuracy on the public benchmark dataset than the classic neighborhood rough set and the standard granular ball neighborhood rough set. © 2022 IEEE.

关键词： Cloud computing Costs Search methods Rough sets Big data Benchmark testing Classification algorithms

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Mechanical Properties of Tungsten Fiber Reinforced Tungsten Composite Prepared by Powder Extrusion Printing and High-Pressurehigh-Temperature Combined Process

SSRN

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SSRN 2023年

作者： Zhao, Tianyu Wu, Chuan Li, Jialin Tang, Jun Chen, Jiming Liu, Xiang Tao, Qiang Liu, Zhaodong Cheng, Jiaen Wang, Pinghuai Du, Juan Key Laboratory of Radiation Physics and Technology of Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu610064 China Southwestern Institute of Physics Chengdu610064 China State Key Laboratory of Superhard Materials Synergetic Extreme Condition User Facility Jinlin University Changchun130012 China

Tungsten is the one of the most promising candidate materials for PFM, due to its high melting point, high thermal conductivity and low sputtering rate. However, the serious brittleness at room temperature limits its application in engineering. A novel Wf/W composites contains commercially available tungsten wire mesh (covered with graphite coating) with a mass ratio of 1% as the fiber reinforcement phase was prepared by PEP and HPHT combined process. It has the relative density more than 99% and the maximum hardness up to 574±10 HV. Besides, its compressive strength is up to 1530 MPa, and the plastic deformation strain is as high as 15.8%, both are at an excellent level comparing to the existing research. This material showed great potential for practical application as future PFM. © 2023, The Authors. All rights reserved.

关键词： Compressive strength

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges

Unleashing the Potential of Decentralized Federated Learning...

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Haibo Cheng Wenjian Liu Youyang Qu Kexue Fu Qinyuan Wang Faculty of Data Science City University of Macau Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL’s current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling.

关键词： Surveys data privacy Systematics Federated learning Medical services data models Servers Security Reliability Systematic literature review

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Redundancy Reduction of Musculoskeletal Model for Robots with Group Sparse Neural Network 6

Redundancy Reduction of Musculoskeletal Model for Robots wit...

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6th International Conference on Control and Robotics Engineering, ICCRE 2021

作者： Zhong, Shanlin Zhang, Jinhan Nie, Xiangli Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Science Beijing China CETC Key Laboratory of Data Link Technology Xi'an China

ISBN: (纸本)9780738126128

Creating robots with similar behavior and appearance to humans has been a long-term dream for roboticists. However, the highly redundant characteristic of the muscle system brings tremendous difficulty for the control and manufacture of the musculoskeletal robot. In this paper, we propose a heuristic pruning-based recurrent neural network method with group sparse regularization to reduce redundancy of muscles in musculoskeletal model, which can maintain the motion accuracy of musculoskeletal robots without activating the redundant muscles. Experiments on an upper extremity musculoskeletal model validate the effectiveness of the proposed method. © 2021 IEEE.

关键词： Recurrent neural networks

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SAV-SE: Scene-aware Audio-Visual Speech Enhancement with Selective State Space Model

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IEEE Journal on Selected Topics in Signal Processing 2025年第4期19卷 623-634页

作者： Qian, Xinyuan Gao, Jiaran Zhang, Yaodan Zhang, Qiquan Liu, Hexin Garcia, Leibny Paola Li, Haizhou University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia Nanyang Technological University College of Computing and Data Science Singapore Singapore Johns Hopkins University CLSP and HLT-COE United States Chinese University of HongKong Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen51872 China

Speech enhancement plays an essential role in various applications, and the integration of visual information has been demonstrated to bring substantial advantages. However, the majority of current research concentrates on the examination of facial and lip movements, which can be compromised or entirely inaccessible in scenarios where occlusions occur or when the camera view is distant. Whereas contextual visual cues from the surrounding environment have been overlooked: for example, when we see a dog bark, our brain has the innate ability to discern and filter out the barking noise. To this end, in this paper, we introduce a novel task, i.e. Scene-aware Audio-Visual Speech Enhancement SAV-SE. To our best knowledge, this is the first proposal to use rich contextual information from synchronized video as auxiliary cues to indicate the type of noise, which eventually improves the speech enhancement performance. Specifically, we propose the VC-S $^{2}$ E method, which incorporates the Conformer and Mamba modules for their complementary strengths. Extensive experiments are conducted on public MUSIC, AVSpeech and AudioSet datasets, where the results demonstrate the superiority of VC-S $^{2}$ E over other competitive methods. We will make the source code publicly available. Project demo page: https://***/ © 2007-2012 IEEE.

关键词： Speech enhancement

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Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation

arXiv

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arXiv 2024年

作者： Li, Wenda Chen, Kaixuan Liu, Shunyu Zheng, Tongya Huang, Wenjie Song, Mingli State Key Laboratory of Blockchain and Security Zhejiang University China School of Software Technology Zhejiang University China Institute of Blockchain and Data Security China Big Graph Center School of Computer and Computing Science Hangzhou City University China College of Computer Science and Technology Zhejiang University Hangzhou China

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information. Moreover, the limited number of nodes in each mini-batch restricts the model’s capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks. The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node representation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm. Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https://***/l-wd/LGMformer. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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Meso-scale Discovery Assay Detects the Changes of Plasma Cytokine Levels in Mice after Low or High LET Ionizing Irradiation

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Biomedical and Environmental sciences 2021年第7期34卷 540-551页

作者： JIA Rong CHEN Ya Xiong DU Ya Rong HU Bu Rong Key Laboratory of Space Radiobiology of Gansu Province&CAS Key Laboratory of Heavy Ion Radiation Biology and Medicine Institute of Modern PhysicsChinese Academy of SciencesLanzhou 730000GansuChina School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100039China Department of Radiation Medicine School of Public Health and ManagementWenzhou Medical UniversityWenzhou 325035ZhejiangChina

Objective To obtain precise data on the changes in the levels of 29 cytokines in mice after high or low linear energy transfer(LET)irradiation and to develop an accurate model of radiation exposure based on the cytokine levels after *** Plasma samples harvested from mice at different time points after carbon-ion or X-ray irradiation were analyzed using meso-scale discovery(MSD),a high-throughput and sensitive electrochemiluminescence measurement *** estimation equations were set up using multiple linear regression *** The relative levels of IL-6 at 1 h,IL-5 and IL-6 at 24 h,and IL-5,IL-6 and IL-15 at 7 d after irradiation with two intensities increased *** minimum measured levels of IL-5,IL-6 and IL-15 were up to 4.0076 pg/mL,16.4538 pg/mL and 0.4150 pg/mL,*** addition,dose estimation models were established and *** The MSD assay can provide more accurate data regarding the changes in the levels of the cytokines IL-5,IL-6 and *** cytokines could meet the essential criteria for radiosensitive biomarkers and can be used as radiation *** prediction models can conveniently and accurately estimate the exposure dose in irradiated organism.

关键词： MSD assay Low or high LET irradiation Mouse plasma Cytokines Dose estimation model

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Three-Dimension Tio2@Nife-Layered Double Hydroxide Core-Shell Heterostructures for Enhanced Photocatalytic Phenol Hydroxylation

SSRN

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SSRN 2024年

作者： Wu, Yixiong Xu, Yanqi Li, Cunjun Wang, Hai Wang, Xinyu Qin, Aimiao Qin, Haiqing Wang, Linjiang College of Materials Science and Engineering Key Laboratory of New Processing Technology for Nonferrous Metals and Materials Ministry of Education Guilin University of Technology Guilin541004 China Collaborative Innovation Center for Exploration of Nonferrous Metal Deposits and Efficient Utilization of Resources Guilin University of Technology Guilin541004 China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal University Guilin541004 China College of Physics and Technology Guangxi Normal University Guilin541004 China School of Materials and Energy University of Electronic Science and Technology of China Chengdu611731 China Geology and Mining Co. Ltd. Guilin541004 China

One-step selectively photocatalytic phenol hydroxylation to produce Dihydroxybenzenes (DHB) is an attractive and challenging strategy. However, the rapid recombination of photogenerated carriers existed in single component photocatalyst seriously hinders the photocatalytic efficiency. The heterojunction strategy can optimize the band structure of the photocatalyst, and effectively improve the carriers’ separation and transfer during the photocatalytic process. Herein, we successfully designed and prepared a 3D TiO2@Ni3Fe1-LDH core-shell heterostructure photocatalyst by coupling TiO2 nanorods with Ni3Fe1-LDH nanosheets for the photocatalytic phenol hydroxylation to produce DHB. The results showed that the 3D TiO2@Ni3Fe1-LDH core-shell heterostructure had a suitable energy band structure for the photocatalytic phenol hydroxylation and the heterostructure accelerated the carriers’ separation and transfer. Meanwhile, the high porosity and abundant active sites were conducive to the adsorption of H2O2 and the photogenerated carriers transfer efficiency from the photocatalyst to H2O2. The 3D TiO2@Ni3Fe1-LDH core-shell heterostructure showed excellent photocatalytic performance of phenol hydroxylation, where the phenol conversion was 48.6%, and the selectivity of DHB was 81.1% at 25 °C and 4 h of light illumination. Furthermore, the as-prepared photocatalysts exhibited good stability after four cycles of experiment. This work provided a convenient and efficient method for the green synthesis of DHB. © 2024, The Authors. All rights reserved.

关键词： Nanorods

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