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Evidence of carbide-assisted in situ-directional recrystallization of GH4099 superalloy fabricated by laser powder bed fusion

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材料科学技术（英文版） 2025年第12期215卷 99-110页

作者： Senli Li Jian Liu Han Zhao Jun Lv Liang Ma Yi Tan Pengting Li School of Materials Science and Engineering Dalian University of TechnologyDalian 116024China National Key Laboratory of Aerospace Flight Technology Beijing 100074China School of Mechanical and Aerospace Engineering Dalian University of TechnologyDalian 116024China

Columnar grains offer considerable advantages in terms of microstructure for resisting high-temperature low-cycle *** additive manufacturing,the formation of fine columnar grains is ***-ever,post-heat treatment often transforms these grains into equiaxed *** study aimed to tailor the grain morphology by controlling the precipitation of *** balancing the restraining effects of carbide pinning and grain growth,we achieved carbide-assisted in situ-directional *** process preserved the columnar grains created via laser powder bed fusion,even after high-temperature heat *** approach emphasizes promoting the longitudinal growth of columnar grains while preventing their ***,we characterized the evolution of carbides and γʹ precipitates and examined their role in nucleation and growth during *** study supports the via-bility of carbide-assisted in situ-directional recrystallization in additive manufacturing alloys,introducing an innovative strategy for microstructure *** implementation of carbon stabilization(CS)treatment to control the carbide distribution led to a 40％improvement in the creep life at 900 ℃ and 150 MPa.

关键词： Additive manufacturing Carbide Directional recrystallization Superalloy

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Co-doped cryptomelane-type manganese oxide in situ grown on a nickel foam substrate for high humidity ozone decomposition

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Journal of Environmental sciences 2025年第2期148卷 529-540页

作者： Haoyuan Liang Xu Wang Hui Wang Zhenping Qu Key Laboratory of Industrial Ecology and Environmental Engineering School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China

Monolithic catalysts with excellent O_(3)catalytic decomposition performance were prepared by in situ loading of Co-doped KMn_(8)O_(16)on the surface of nickel *** triple-layer structure with Co-doped KMn_(8)O_(16)/Ni6MnO_(8)/Ni foam was grown spontaneously on the surface of nickel foam by tuning the molar ratio of KMnO_(4)to Co(NO_(3))_(2)·6H_(2)O ***,the formed Ni6MnO_(8)structure between KMn_(8)O_(16)and nickel foam during in situ synthesis process effectively protected nickel foam from further etching,which significantly enhanced the reaction stability of *** optimum amount of Co doping in KMn_(8)O_(16)was available when the molar ratio of Mn to Co species in the precursor solution was 2:*** the Mn2Co1 catalyst had abundant oxygen vacancies and excellent hydrophobicity,thus creating outstanding O_(3)decomposition *** O_(3)conversion under dry conditions and relative humidity of 65%,90%over a period of 5 hr was 100%,94%and 80%with the space velocity of 28,000 hr^(−1),*** in situ constructed Co-doped KMn_(8)O_(16)/Ni foam catalyst showed the advantages of low price and gradual applicability of the preparation process,which provided an opportunity for the design of monolithic catalyst for O_(3)catalytic decomposition.

关键词： Co-doped cryptomelane-type manganese O_(3)decomposition High humidity Nickel foam Monolithic catalyst

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A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2135-2153页

作者： Xuerui Zhang Zhongyang Han Jun Zhao School of Control Science and Engineering Dalian University of TechnologyDalian 116024China IEEE

Ingredient optimization plays a pivotal role in the copper industry,for which it is closely related to the concentrate utilization rate,stability of furnace conditions,and the quality of copper *** acquire a practical ingredient plan,which should exhibit long duration time with sufficient utilization and feeding stability for real applications,an ingredient plan optimization model is proposed in this study to effectively guarantee continuous production and stable furnace *** address the complex challenges posed by this integer programming model,including multiple coupling feeding stages,intricate constraints,and significant non-linearity,a multi-stage differential-multifactorial evolution algorithm is *** the proposed algorithm,the differential evolutionary(DE)algorithm is improved in three aspects to efficiently tackle challenges when optimizing the proposed ***,unlike traditional time-consuming serial approaches,the multifactorial evolutionary algorithm is utilized to optimize multiple complex models contained in the population of evolutionary algorithm caused by the feeding stability in a parallel ***,a repair algorithm is employed to adjust infeasible ingredient lists in a timely *** addition,a local search strategy taking feedback from the current optima and considering the different positions of global optimum is developed to avoiding premature convergence of the differential evolutionary ***,the simulation experiments considering different planning horizons using real data from the copper industry in China are conducted,which demonstrates the superiority of the proposed method on feeding duration and stability compared with other commonly deployed *** is practically helpful for reducing material cost as well as increasing production profit for the copper industry.

关键词： Copper industry differential-multifactorial evolution ingredient optimization multi-stage optimization

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Achieving high strength and ductile eutectic high-entropy alloy with directional lamellar via laser additive manufacturing

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Journal of Materials science and technology 2025年 235卷 70-80页

作者： Amar, Abdukadir Wang, Mingliang Huang, Rui Zhang, Lingkun Lu, Yiping School of Materials Science and Engineering Dalian University of Technology Dalian116024 China School of Materials Science and Engineering Dalian University of Technology Dalian116024 China

Developing high-strength and ductile metallic parts with designable shapes is an unfading research topic for material science and engineering. As a revolutionary technology, additive manufacturing (AM) provides a new pathway for producing complex-shaped metallic parts with the possibility of in situ tailoring their microstructure. However, AM is not always ideally applicable for all metals and alloys. Eutectic high entropy alloys (EHEAs) contain both the advantages of the eutectic alloys and high entropy alloys (HEAs), and EHEAs show significant potential in AM due to their excellent mechanical properties and good fluidity. Herein, heterogeneous and ultra-fine eutectic lamellar microstructure with directional growth along the deposition direction (DD) was obtained by adjusting the process parameters of AM to improve the strength and ductility of EHEAs. Compared with the as-cast sample, the simultaneous increment in both strength and ductility is achieved by AM. Combination of strength and ductility of the AM sample tensile along the DD direction (yield strength σy=1115 MPa, ultimate tensile strength σUTS=1417 MPa, ultimate tensile strain ΕU=23 %) in this work was superior to most of the additive manufactured alloys and comparable to the thermomechanical-treated EHEAs with the best mechanical properties. The high strength and good ductility of the AM were mainly attributed to the ultra-fine lamellar nature and fully constrained soft and hard lamellar microstructure, which produces an obvious hetero-deformation induced (HDI) strengthening and high crack buffering effect during the deformation. This work provides a new possibility to achieve high strength and ductile complex-shaped metallic parts via designing directional lamellar eutectic structures by AM. © 2025

关键词： Ductility

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Lexicon-Augmented Cross-Domain Chinese Word Segmentation with Graph Convolutional Network

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Chinese Journal of Electronics 2022年第5期31卷 949-957页

作者： YU Hao HUANG Kaiyu WANG Yu HUANG Degen School of Computer Science and Technology Dalian University of Technology School of Foreign Languages Dalian University of Technology

Existing neural approaches have achieved significant progress for Chinese word segmentation(CWS). The performances of these methods tend to drop dramatically in the cross-domain scenarios due to the data distribution mismatch across domains and the out of vocabulary words problem. To address these two issues,proposes a lexicon-augmented graph convolutional network for cross-domain CWS. The novel model can capture the information of word boundaries from all candidate words and utilize domain lexicons to alleviate the distribution gap across domains. Experimental results on the cross-domain CWS datasets(SIGHAN-2010 and TCM)show that the proposed method successfully models information of domain lexicons for neural CWS approaches and helps to achieve competitive performance for crossdomain CWS. The two problems of cross-domain CWS can be effectively solved through various interactions between characters and candidate words based on ***, experiments on the CWS benchmarks(Bakeoff-2005) also demonstrate the robustness and efficiency of the proposed method.

关键词： cross-domain CWS datasets candidate words neural CWS approaches vocabulary words problem text analysis vocabulary convolutional neural nets domain lexicons natural language processing lexicon-augmented cross-domain Chinese word segmentation graph neural networks lexicon-augmented graph convolutional network learning (artificial intelligence) cross-domain scenarios word processing word boundaries

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A recover-then-discriminate framework for robust anomaly detection

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science China(Information sciences) 2025年第4期68卷 300-318页

作者： Peng XING Dong ZHANG Jinhui TANG Zechao LI School of Computer Science and Engineering Nanjing University of Science and Technology Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology

Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.

关键词： recovery network HOG prompt discriminative network self-correlation loss anomaly detection

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Labeling-based centrality approaches for identifying critical edges on temporal graphs

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Frontiers of computer science 2025年第2期19卷 89-104页

作者： Tianming ZHANG Jie ZHAO Cibo YU Lu CHEN Yunjun GAO Bin CAO Jing FAN Ge YU School of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China School of Computer Science and Technology Zhejiang UniversityHangzhou 310013China Department of Computer Science Northeastern UniversityShenyang 110819China

Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shortest distances from the edge to all the other *** betweenness centrality ranks which edges are significant based on the fraction of all-pairs shortest paths that pass through the ***,extensive research efforts go into centrality computation over general graphs that omit time ***,numerous real-world networks are modeled as temporal graphs,where the nodes are related to each other at different time *** temporal property is important and should not be neglected because it guides the flow of information in the *** state of affairs motivates the paper’s study of edge centrality computation methods on temporal *** introduce the concepts of the label,and label dominance relation,and then propose multi-thread parallel labeling-based methods on OpenMP to efficiently compute edge closeness and betweenness centralities *** types of optimal temporal *** edge closeness centrality computation,a time segmentation strategy and two observations are presented to aggregate some related temporal edges for uniform *** edge betweenness centrality computation,to improve efficiency,temporal edge dependency formulas,a labeling-based forward-backward scanning strategy,and a compression-based optimization method are further proposed to iteratively accumulate centrality *** experiments using 13 real temporal graphs are conducted to provide detailed insights into the efficiency and effectiveness of the proposed *** with state-ofthe-art methods,labeling-based methods are capable of up to two orders of magnitude speedup.

关键词： temporal graph closeness centrality between-ness centrality temporal path

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Efficient Untargeted White-Box Adversarial Attacks Based on Simple Initialization

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Chinese Journal of Electronics 2024年第4期33卷 979-988页

作者： Yunyi ZHOU Haichang GAO Jianping HE Shudong ZHANG Zihui WU School of Computer Science and Technology Xidian University

Adversarial examples(AEs) are an additive amalgamation of clean examples and artificially malicious perturbations. Attackers often leverage random noise and multiple random restarts to initialize perturbation starting points, thereby increasing the diversity of AEs. Given the non-convex nature of the loss function, employing randomness to augment the attack's success rate may lead to considerable computational overhead. To overcome this challenge,we introduce the one-hot mean square error loss to guide the initialization. This loss is combined with the strongest first-order attack, the projected gradient descent, alongside a dynamic attack step size adjustment strategy to form a comprehensive attack process. Through experimental validation, we demonstrate that our method outperforms baseline attacks in constrained attack budget scenarios and regular experimental settings. This establishes it as a reliable measure for assessing the robustness of deep learning models. We explore the broader application of this initialization strategy in enhancing the defense impact of few-shot classification models. We aspire to provide valuable insights for the community in designing attack and defense mechanisms.

关键词： Deep learning Additives Perturbation methods Noise Mean square error methods Robustness Glass box

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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Event-based nonsingular fixed-time containment control for nonlinear multiagent systems with dynamic uncertainties

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science China(Information sciences) 2025年第5期68卷 413-414页

作者： Yuanbo SU Qihe SHAN Tieshan LI C.L.Philip CHEN Navigation College Dalian Maritime University School of Automation Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering South China University of Technology

Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear multiagent systems (MASs) has received considerable attention,for example [1,2].Although the valued studies in [1,2] investigate containment control problems for MASs subject to nonlinearities,the proposed distributed nonlinear protocols only achieve the asymptotic *** a crucial performance indicator for distributed containment control of MASs,the fast convergence is conducive to achieving better control accuracy [3].The work in [4] first addresses the backstepping-based adaptive fuzzy fixed-time containment tracking problem for nonlinear high-order MASs with unknown external ***,the designed fixedtime control protocol [4] cannot escape the singularity problem in the backstepping-based adaptive control *** is well known,the singularity problem has become an inherent problem in the adaptive fixed-time control design,which may cause the unbounded control inputs and even the instability of controlled ***,how to solve the nonsingular fixed-time containment control problem for nonlinear MASs is still open and awaits breakthrough to the best of our knowledge.

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