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Reoxidation of liquid steel and evolution of inclusions during protective atmosphere electroslag remelting of Ce-containing heat-resistant stainless steel

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Journal of Iron and Steel Research International 2024年第8期31卷 1923-1935页

作者： Yu Zhao Cheng-bin shi shijun Wang Peng Ren Jing Li State Key Laboratory of Advanced Metallurgy University of Science and Technology Beijing(USTB)Beijing 100083China

The evolution of Ce-containing inclusions and its correlation with the reoxidation of liquid steel during protective atmosphere electroslag remelting(ESR)of heat-resistant steel were *** reoxidation of liquid steel took place during the ESR,resulting in the oxygen pickup from 0.0014 to 0.0053 wt.%.The inclusions in the consumable electrode,liquid metal pool and remelted ingot are Ce_(2)O_(2)S and Ce_(2)O,inclusions *** inclusions in the remelted ingot are originated from three ways:(I)the original inclusions from the electrode;(Il)the newly formed Ce_(2)O_(2)S and Ce_(2)O_(3) inclusions in the liquid metal pool by reoxidation remaining still in remelted ingot;(IlI)the newly generated Ce2O2S and Ce_(2)O_(3) inclusions during cooling and solidification of liquid *** relative proportions of Ce_(2)O_(3) inclusions in liquid metal pool and remelted ingot are 41% and 76.5%,*** inclusions ranging from 2 to 5μm in the remelted ingot take up 55%,followed by the inclusions smaller than 2μm(43%of total inclusions).The number proportion of the Ce_(2)0,inclusions in the liquid metal pool which were removed by floating up into slag is 1.96×10-5%.

关键词： Non-metallic inclusion Electroslag remelting Rare earth cerium Reoxidation Inclusion evolution

Diffractive neural networks with improved expressive power for gray-scale image classification

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Photonics Research 2024年第6期12卷 1159-1166页

作者： MINJIA ZHENG WENZHE LIU LEI shi JIAN ZI State Key Laboratory of Surface Physics Key Laboratory of Micro-and Nano-Photonic Structures(Ministry of Education)and Department of PhysicsFudan UniversityShanghai 200433China Institute for Nanoelectronic Devices and Quantum Computing Fudan UniversityShanghai 200433China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Shanghai Research Center for Quantum Sciences Shanghai 210315China

In order to harness diffractive neural networks(DNNs)for tasks that better align with real-world computer vision requirements,the incorporation of gray scale is ***,DNNs are not powerful enough to accomplish gray-scale image processing tasks due to limitations in their expressive *** our work,we elucidate the relationship between the improvement in the expressive power of DNNs and the increase in the number of phase modulation layers,as well as the optimization of the Fresnel number,which can describe the diffraction *** demonstrate this point,we numerically trained a double-layer DNN,addressing the prerequisites for intensitybased gray-scale image ***,we experimentally constructed this double-layer DNN based on digital micromirror devices and spatial light modulators,achieving eight-level intensity-based gray-scale image classification for the MNIST and Fashion-MNIST data *** optical system achieved the maximum accuracies of 95.10%and 80.61%,respectively.

关键词： process mirror limitations

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

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Chinese Journal of Mechanical Engineering 2024年第2期37卷 83-99页

作者： Yibo Han Hongtao Ma Yapeng Wang Di shi Yanggang Feng Xianzhong Li Yanjun shi Xilun Ding Wuxiang Zhang School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China Beihang Goer(WeiFang)Intelligent Robot Co. Ltd.Beihang UniversityWeifangChina Research Center of Satellite Technology Harbin Institute of TechnologyHarbinChina Beijing Xinfeng Aerospace Equipment Co. LtdBeijingChina

The lower limb exoskeletons are used to assist wearers in various scenarios such as medical and industrial *** modeling errors of the exoskeleton in different application scenarios pose challenges to the robustness and stability of its control *** Radial Basis Function(RBF)neural network is used widely to compensate for modeling *** order to solve the problem that the current RBF neural network controllers cannot guarantee the asymptotic stability,a neural network robust control algorithm based on computed torque method is proposed in this paper,focusing on trajectory *** innovatively incorporates the robust adaptive term while introducing the RBF neural network term,improving the compensation ability for modeling *** stability of the algorithm is proved by Lyapunov method,and the effectiveness of the robust adaptive term is verified by the *** wearing the exoskeleton under different walking speeds and scenarios were carried out,and the results show that the absolute value of tracking errors of the hip and knee joints of the exoskeleton are consistently less than 1.5°and 2.5°,*** proposed control algorithm effectively compensates for modeling errors and exhibits high robustness.

关键词： Lower limb exoskeleton Model compensation RBF neural network Computed torque method

Evicting and filling attack for linking multiple network addresses of Bitcoin nodes

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Cybersecurity 2024年第3期7卷 49-68页

作者： Huashuang Yang Jinqiao shi Yue Gao Xuebin Wang Yanwei Sun Ruisheng shi Dongbin Wang School of Cyberspace Security Bejing University of Posts and TelecommunicationsBeijingChina Tsinghua University Bejing 100084China Institute of Information Engineering Chinese Academy of SciencesBeijingChina

Bitcoin is a decentralized P2P *** supports users to use pseudonyms instead of network addresses to send and receive transactions at the data layer,hiding users'real network *** transaction tracing attack cuts through the network layer to directly associate each transaction with the network address that issued it,thus revealing the sender's network *** this attack can be mitigated by Bitcoin's network layer privacy *** Bitcoin protects the unlinkability of Bitcoin addresses and there may be a many-to-one relation-ship between addresses and nodes,transactions sent from the same node via different addresses are seen as com-ing from different nodes because attackers can only use addresses as node *** this paper,we proposed the evicting and flling attack to expose the correlations between addresses and cluster transactions sent from different addresses of the same *** attack exploited the unisolation of Bitcoin's incoming connection processing *** particular,an attacker can utilize the shared connection pool and deterministic connection eviction strategy to infer the correlation between incoming and evicting connections,as well as the correlation between releasing and flling *** on inferred results,different addresses of the same node with these connections can be linked together,whether they are of the same or different network *** designed a multi-step attack procedure,and set reasonable attack parameters through analyzing the factors that affect the attack effciency and *** mounted this attack on both our self-run nodes and multi-address nodes in real Bitcoin network,achieving an aver-age accuracy of 96.9% and 82%,***,we found that the attack is also applicable to Zcash,Litecoin,Dogecoin,Bitcoin Cash,and *** analyzed the cost of network-wide attacks,the application scenario,and proposed countermeasures of this attack.

关键词： Security and privacy Bitcoin Address linking

A synthetic microbial community used as a bioinoculant can overcome rice production constraints in acid soils

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Science China(Life Sciences) 2024年第5期67卷 1085-1086页

作者： Maureen O’Callaghan Shengjing shi Resilient Agriculture Group AgResearchLincolnChristcurch 8140New Zealand

A customised synthetic microbial community(SynCom)composed of carefully selected rhizosphere-competent bacterial strains improved rice growth,yield and resistance to soil acidity and Al toxicity.

关键词： soil soils rhizosphere

NeuroPrim:An attention-based model for solving NP-hard spanning tree problems

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Science China Mathematics 2024年第6期67卷 1359-1376页

作者： Yuchen shi Congying Han Tiande Guo School of Mathematical Sciences University of Chinese Academy of SciencesBeijing100049China

Spanning tree problems with specialized constraints can be difficult to solve in real-world scenarios,often requiring intricate algorithmic design and exponential ***,there has been growing interest in end-to-end deep neural networks for solving routing ***,such methods typically produce sequences of vertices,which make it difficult to apply them to general combinatorial optimization problems where the solution set consists of edges,as in various spanning tree *** this paper,we propose NeuroPrim,a novel framework for solving various spanning tree problems by defining a Markov decision process for general combinatorial optimization problems on *** approach reduces the action and state space using Prim's algorithm and trains the resulting model using *** apply our framework to three difficult problems on the Euclidean space:the degree-constrained minimum spanning tree problem,the minimum routing cost spanning tree problem and the Steiner tree problem in *** results on literature instances demonstrate that our model outperforms strong heuristics and achieves small optimality gaps of up to 250 ***,we find that our model has strong generalization ability with no significant degradation observed on problem instances as large as 1,*** results suggest that our framework can be effective for solving a wide range of combinatorial optimization problems beyond spanning tree problems.

关键词： degree-constrained minimum spanning tree problem minimum routing cost spanning tree problem Steiner tree problem in graphs Prim's algorithm reinforcement learning

CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3209-3231页

作者： Weiya shi Shaowen Zhang shiqiang Zhang College of Artificial Intelligence and Big Data Henan University of TechnologyZhengzhou450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou450001China

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural *** these efforts,the detection of small objects in remote sensing remains a formidable *** deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep ***,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection ***,the sensitivity of small objects to the bounding box perturbation further increases the detection *** this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote *** address feature loss in deep layers,we have devised a cross-layer attention fusion *** noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid ***,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)*** efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available *** experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

关键词： Small object detection attention mechanism cross-layer fusion discrete cosine transform

On the Application of Mixed Models of Probability and Convex Set for Time-Variant Reliability Analysis

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1981-1999页

作者： Fangyi Li Dachang Zhu Huimin shi Center for Research on Leading Technology of Special Equipment School of Mechanical and Electric EngineeringGuangzhou UniversityGuangzhou510006China

In time-variant reliability problems,there are a lot of uncertain variables from different ***,it is important to consider these uncertainties in *** addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of *** this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set *** this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability ***,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis *** effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.

关键词： Mixed uncertainty probability model convex model time-variant reliability analysis

Multi-Stream Temporally Enhanced Network for Video Salient Object Detection

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Computers, Materials & Continua 2024年第1期78卷 85-104页

作者： Dan Xu Jiale Ru Jinlong shi School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212100China

Video salient object detection(VSOD)aims at locating the most attractive objects in a video by exploring the spatial and temporal *** poses a challenging task in computer vision,as it involves processing complex spatial data that is also influenced by temporal *** the progress made in existing VSOD models,they still struggle in scenes of great background diversity within and between ***,they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term *** propose a multi-stream temporal enhanced network(MSTENet)to address these *** investigates saliency cues collaboration in the spatial domain with a multi-stream structure to deal with the great background diversity challenge.A straightforward,yet efficient approach for temporal feature extraction is developed to avoid the accumulative noises and reduce time *** distinction between MSTENet and other VSOD methods stems from its incorporation of both foreground supervision and background supervision,facilitating enhanced extraction of collaborative saliency *** notable differentiation is the innovative integration of spatial and temporal features,wherein the temporal module is integrated into the multi-stream structure,enabling comprehensive spatial-temporal interactions within an end-to-end *** experimental results demonstrate that the proposed method achieves state-of-the-art performance on five benchmark datasets while maintaining a real-time speed of 27 fps(Titan XP).Our code and models are available at https://***/RuJiaLe/MSTENet.

关键词： Video salient object detection deep learning temporally enhanced foreground-background collaboration