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Effect of Mn alloying on the hydrogen-assisted cracking behavior in multiphase/duplex stainless steel

材料科学技术（英文版） 2025年第4期207卷 126-141页

作者： Menghao Liu Cuiwei Du Xiaogang Li Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China Key Laboratory for Corrosion and Protection of the Ministry of Education Institute of Advanced Materials & TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China

There remains debate on whether Mn is beneficial or detrimental to hydrogen embrittlement in stainless *** this work,a series of stainless steels were designed to study the change of hydrogen embrit-tlement sensitivity,crack propagation,and hydrogen trapping behaviors upon Mn *** results suggest that adding 4 wt.％Mn increased hydrogen embrittlement susceptibility,whereas adding 8 wt.％Mn decreased hydrogen embrittlement *** banded α'-martensite through austenitic grain is the main reason for the increased hydrogen embrittlement sensitivity when adding 4 wt.％Mn,by ad-sorbing hydrogen,promoting crack initiation,and accelerating crack propagation.

关键词： Hydrogen-assisted cracking Multiphase stainless steel Manganese

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Cancer classification with data augmentation based on generative adversarial networks

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Frontiers of Computer Science 2022年第2期16卷 69-79页

作者： Kaimin WEI Tianqi LI Feiran HUANG Jinpeng CHEN Zefan HE College of Information Science and Technology Jinan UniversityGuangzhou 510632China Guangdong Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Software Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Accurate diagnosis is a significant step in cancer *** learning can support doctors in prognosis decision-making,and its performance is always weakened by the high dimension and small quantity of genetic ***,deep learning can effectively process the high dimensional data with ***,the problem of inadequate data remains unsolved and has lowered the performance of deep *** end it,we propose a generative adversarial model that uses non target cancer data to help target generator *** use the reconstruction loss to further stabilize model training and improve the quality of generated *** also present a cancer classification model to optimize classification *** results prove that mean absolute error of cancer gene made by our model is 19.3%lower than DC-GAN,and the classification accuracy rate of our produced data is higher than the data created by *** for the classification model,the classification accuracy of our model reaches 92.6%,which is 7.6%higher than the model without any generated data.

关键词： data mining cancer data analysis deep learning generative adversarial networks

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Declined trends of chlorophyll a in the South China Sea over 2005–2019 from remote sensing reconstruction

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Acta Oceanologica Sinica 2023年第1期42卷 12-24页

作者： Tianhao Wang Yu Sun Hua Su Wenfang Lu School of Marine Sciences Sun Yat-Sen UniversityZhuhai519000China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai519000China College of Ocean and Earth Sciences Xiamen UniversityXiamen361102China State Key Laboratory of Marine Environmental Science Xiamen UniversityXiamen361102China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou UniversityFuzhou350108China National&Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou UniversityFuzhou350108China

Chlorophyll a concentration(CHL)is an important proxy of the marine ecological environment and phytoplankton ***-term trends in CHL of the South China Sea(SCS)reflect the changes in the ecosystem’s productivity and functionality in the regional carbon *** this study,we applied a previously reconstructed 15-a(2005–2019)CHL product,which has a complete coverage at 4 km and daily resolutions,to analyze the long-term trends of CHL in the *** regression was used to elaborate on the long-term trends of high,median,and low CHL values,as an extended method of conventional linear *** results showed downward trends of the SCS CHL for the 75th,50th,and 25th quantile in the past 15 a,which were−0.0040 mg/(m^(3)·a)(−1.62%per year),−0.0023 mg/(m^(3)·a)(−1.10%per year),and−0.0019 mg/(m^(3)·a)(−1.01%per year).The negative trends in winter(November to March)were more prominent than those in summer(May to September).In terms of spatial distribution,the downward trend was more significant in regions with higher *** led to a reduced standard deviation of CHL over time and *** further explored the influence of various dynamic factors on CHL trends for the entire SCS and two typical systems(winter Luzon Strait(LZ)and summer Vietnam Upwelling System(SV))with single-variate linear regression and multivariate Random Forest *** multivariate analysis suggested the CHL trend pattern can be best explained by the trends of wind speed and mixed-layer *** divergent importance of controlling factors for LZ and SV can explain the different CHL trends for the two *** study expanded our understanding of the long-term changes of CHL in the SCS and provided a reference for investigating changes in the marine ecosystem.

关键词： chlorophyll a concentration quantile trends remote sensing reconstruction South China Sea

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Exploiting Multiple Features for Hash Codes Learning with Semantic-Alignment-Promoting Variational Auto-encoder 12th

Exploiting Multiple Features for Hash Codes Learning with Se...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Chen, Jiayang Su, Qinliang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Beijing China

ISBN: (纸本)9783031446924

Semantic hashing is an effective technique to empower information retrieval. Currently, considerable efforts have been dedicated to generating high-quality hash codes by modeling document features using generative models and other approaches. However, most of these methods rely solely on a single type of feature, such as TFIDF features, BERT embeddings, etc. As different types of features have distinct but complementary information of documents, e.g. TFIDF mainly contains the keywords information and BERT focuses on the semantics, hash codes generated solely from either may not capture the full essence of the documents. To overcome this challenge, we propose a semantic-alignment-promoting variational auto-encoder to generate hash codes from multiple document features. Specifically, a VAE-based generative model is first developed to model the multiple features. Then, we propose a semantic-alignment-promoting inference network to estimate the parameters of the variational posterior from multiple features. Additionally, the quality of hash codes is further improved by promoting the semantic alignment between the hash codes of connected documents in a constructed connection graph. The results of extensive experiments on three public datasets demonstrate that our proposed model significantly outperforms current state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Application of remote sensing based on algorithm process technology inspection processing method

Application of remote sensing based on algorithm process tec...

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2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Ge, Qiang Jiang, Kejian Han, Hongyu Liu, Huaizhou Zhang, Jing Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475001 China School of Computer and Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9781510664616

Aiming at a series of problems such as low evaluation efficiency of traditional algorithms, cumbersome process operation, high use cost and no complete algorithm inspection process system, this paper proposes an algorithm flow technology inspection processing system and method based on remote sensing applications, mainly including satellite product processing, algorithm processing, sample feature selection, sample extraction, and algorithm processing results comparison, It mainly realizes the functions of input, output and parameter adjustment required by different function functions, and designs an interface interaction system that adapts to the input and output elements required by different function modules. It realizes the modular, plug-in and componentized design of interface interaction of function modules, and obtains the evaluation system of algorithm credibility. © 2023 SPIE.

关键词： Inspection

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Hash Function Based on Quantum Walks with Two-Step Memory

Hash Function Based on Quantum Walks with Two-Step Memory

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2024 International Conference on Computing, Machine Learning and data Science, CMLDS 2024

作者： Zhou, Qing Lu, Songfeng Yang, Hao Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9798400716393

We propose a new quantum-walk-based hash function QHF2M by combining two types of quantum walks with two-step memory and numerically test its statistical performance. The test result shows that QHF2M is on a par with the state-of-the-art hash functions based on discrete quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property. Moreover, both unequal and equivalent coin angles used by the underlying quantum walks can lead to near-ideal statistical performance. This result indicates that neither using quantum walks with different memory length nor adopting distinct coin angles is a necessary condition for constructing good quantum-walk-with-memory-based hash functions. © 2024 ACM.

关键词： Hash functions

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Advance of Thyroid Nodule Ultrasound Diagnosis Based on Deep Learning 29th

Advance of Thyroid Nodule Ultrasound Diagnosis Based on Deep...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Wan, Huiling Chen, Shuwen Ni, Yiyang Qi, Shaojia Qu, Hui School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China

ISBN: (纸本)9783031449468

The incidence of thyroid nodules is increasing rapidly worldwide. In order to reduce the burden on doctors and improve the accuracy of diagnosis, ultrasound imaging plays an important role in the diagnosis and follow-up of thyroid nodules. In recent years, artificial intelligence (AI) technology based on machine learning and deep learning has improved significantly. With the help of medical imaging screening tools, physicians can make clinical decisions about thyroid nodules more efficiently and provide the best protection for patients as early as possible. This paper mainly discusses the research progress of computer vision and related algorithms in ultrasonic imaging of thyroid nodules in recent years from the aspects of image preprocessing, detection and segmentation of focal areas, feature extraction, and identification of benign/malignant nodules. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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DDoS Detection and Defense Based on FLAD and SDN 20

DDoS Detection and Defense Based on FLAD and SDN

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20th International Conference on Natural Computation, Fuzzy Systems and knowledge Discovery, ICNC-FSKD 2024

作者： Dong, Jie Fang, Wenyu Zheng, Wanling Liu, Jinkun Liu, Yanhua College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China

ISBN: (纸本)9798350356328

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm. © 2024 IEEE.

关键词： Differential privacy

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Correction to: Chaos suppression through Chaos enhancement (Nonlinear Dynamics, (2024), 10.1007/s11071-024-10426-z)

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Nonlinear Dynamics 2024年第10期113卷 12349-12350页

作者： Li, Lin Li, Jizhou Miyoshi, Takemasa Prediction Science Laboratory RIKEN Cluster for Pioneering Research Kobe Japan Data Assimilation Research Team RIKEN Center for Computational Science Kobe Japan Failure Mechanics and Engineering Disaster Prevention Key Laboratory of Sichuan Province College of Architecture and Environment Sichuan University Chengdu China Wako Japan

The article "Chaos suppression through Chaos enhancement", written by Li, L., Li, J. & Miyoshi, T., was originally published electronically on the publisher’s internet portal on 06 October 2024 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 07 November 2024 to © The Author(s) 2024 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://***/licenses/by/4.0. Open access funding enabled and organized by Projekt DEAL. © The Author(s) 2024.

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