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Exploration of the Influence of High-Reactivity Energetic Materials on Microstructure and Performance on Iron-Based Cladding Layer Under Low Laser Power

SSRN

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SSRN 2023年

作者： Liu, Zheng Yang, Yong Sun, Dusheng Han, Jianyu Ma, Shutao Xu, Bin Yuan, Mingyu School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao266520 China Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of Education Qingdao University of Technology Qingdao266520 China SAIC GM Wuling Automobile Co. Ltd. Qingdao Branch 1 Jiangshan Middle Road E&T Zone Qingdao266555 China Qingdao Wushun Automobile Mould Parts Co. Ltd 1689 Taifa Road Huangdao District Qingdao266400 China

Under low laser power conditions, the cladding layer is constrained by inadequate energy density, resulting in incomplete melting of certain powder particles and the occurrence of defects such as cracks and pores within the layer. This paper employs the principle of Self-Propagating High-Temperature Synthesis (SHS) to synergistically integrate high-reactivity energetic materials with metal powder. By external laser energy ignition, the localized combustion of the energetic materials is induced, thereby providing additional heat input during the laser cladding process. Through in-depth analysis of extensive experimental data, the influence of high-reactivity energetic materials on microstructure and performance of alloy cladding layer has been revealed. The research results demonstrate that the inclusion of high-reactivity energetic materials leads to a 450K increase in the maximum temperature of the molten pool, which improves the occurrence of cracks and gas pore defects within the alloy cladding layer. The combustion wave generated by high-reactivity energetic materials induces mixing in the molten pool, resulting in cladding layer grain refinement and an average hardness increase of 80HV1. The inclusion of aluminum leads to the formation of an α-Al2O3 semiconductor oxide film on the alloy surface, enhancing the corrosion resistance of the cladding layer. The friction coefficient decreases from 0.71024 (prior to the addition of high-reactivity energetic materials) to 0.35809, representing a reduction of approximately 49%. © 2023, The Authors. All rights reserved.

关键词： Corrosion resistance

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A Novel Sensitization Mechanism of Stress-Electric-Coupled Biosensor Originated in Magnetostrictive Effect

SSRN

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SSRN 2022年

作者： Sang, Shengbo Ge, Yang Ji, Jianglong Yuan, Zhongyun Dong, Xiushan Zhou, Chuangang Xiao, Pengli Tan, Qiuyun Zhao, Dong MicroNano System Research Center Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education College of Information and Computer Taiyuan University of Technology Taiyuan030024 China School of Electrical Engineering College of Engineering Science Hanyang University Seoul04763 Korea Republic of Shanxi Bethune Hospital Taiyuan030024 China Six-dimensional Artificial Intelligence Biomedical Research Institute Taiyuan030024 China

The stress-electric coupling based on receptor functionalization provides a new paradigm for the development of biosensor. However, the gravity factor of sensitive element usually shows difficult to be avoided due to its suppression to biosensor sensitivity. Herein, a magnetic-sensitization biosensing system with multi-physical field coupling of stress, magnetism and electricity was proposed to highly detect biomolecules. The gravitational interference of sensitive membrane was conquered by doping the TbDyFe with magnetostrictive effect into polydimethylsiloxane (PDMS) and applying magnetic field, showing great contributions to magnetic sensitization. The magnetic sensitization of the biosensor was proved by the collaboration of experiment and simulation. The detections of urine protein and urine sugar associated with kidney disease were realized with limits of detection (LOD) of 0.21 ng/mL and 0.45 nM by using the magnetic-sensitization biosensor. The prepared biosensor exhibited the high specificity, selectivity, stability and repeatability. Especially, the HSA and glucose in real samples can be accurately detect, showing good practicability of the prepared biosensor. This study provides a way to improve the sensitivity of the mechanical biosensor. © 2022, The Authors. All rights reserved.

关键词： Silicones

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Improved Smith Predictive control Design of Aeroengine Based on H∞ algorithm

Improved Smith Predictive Control Design of Aeroengine Based...

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第三十九届中国控制会议

作者： Jie-Jie Li Xian Du Xi-Ming Sun Kun-Zhi Liu Yong-Feng Gao School of Control Science and Engineering Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equiment of Ministry of Education Dalian University of Technology Department of Electrical and Computer Engineering University of California Santa Barbara

Aiming at the network delay problem that exists in the aeroengine distributed control,system stability of the original Hcontroller under different time delays is investigated,and an improved Smith predictive structure is proposed to reduce the influence of ***,according to the control requirements of aeroengine under some typical operating conditions,the appropriate parameters of performance index weighting function are selected to obtain corresponding H output feedback ***,Lyapunov function is constructed to analyze the stability of the original designed system under delays,and the allowable maximum delay that ensures the system stability is determined *** order to reduce the impact of delays and enhance the system robustness,an improved Smith compensator is designed to form a composite controller on the basis of HH ***,taking a turbofan engine control as an example,some numerical simulation cases considering varying-time delay and disturbance are conducted to verify the effectiveness and robustness of the proposed method.

关键词： Aeroengine H∞control time delay stability analysis Smith predictive controller

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Modeling of distributed parameter systems based on independent partial derivative-physics-informed neural network

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Neurocomputing 2025年 642卷

作者： Wang, Lijie Lin, Yangshu Yan, Xinrong Shao, Yuhao Xu, Zuhua Yang, Chao Fan, Haidong Xie, Yurong Zheng, Chenghang State Key Lab of Clean Energy Utilization Institute of Carbon Neutrality State Environmental Protection Engineering Center for Coal-Fired Air Pollution Control Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Zhejiang Baima Lake Laboratory Co. Ltd. Hangzhou310051 China Huadian Electric Power Research Institute Co. Ltd. Hangzhou310030 China

Physics-informed neural networks (PINNs) have attracted considerable interest due to their capacity to incorporate established physical principles, thus facilitating efficient training with a limited amount of observed data. This approach ensures that the results of supervised learning are in accordance with the governing physical laws of the system, thereby enhancing the interpretability of models. However, existing research predominantly employs a single network architecture to simultaneously learn the information of the partial differential equations (PDEs). Such architectures typically require large network sizes to learn the system characteristics, which leads to prolonged training times and inefficiencies. This paper introduces a novel modeling framework, termed the independent partial derivative-physics-informed neural network (IPD-PINN). In contrast to conventional methodologies, IPD-PINN employs a network comprising multiple subnetworks, with each subnetwork dedicated to the learning of individual partial derivatives in the PDEs. The modular network structure allows for flexible and scalable adjustments to the size of each subnetwork, accommodating modeling tasks of varying complexities. Consequently, the entire network is capable of reducing the required training time while maintaining precise modeling accuracy. The efficacy of the proposed IPD-PINN method is demonstrated through two simulation case studies, which highlight its potential to improve the efficiency and accuracy of IPD-PINN modeling. © 2025

关键词： Wave equations

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MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement Learning

arXiv

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arXiv 2021年

作者： Su, Hao Niu, Jianwei Liu, Xuefeng Cui, Jiahe Wan, Ji Wu, Xinghao Wang, Nana State Key Lab of VR Technology and System School of Computer Science and Engineering Beihang University Beijing100000 China Industrial Technology Research Institute School of Information Engineering Zhengzhou University Henan450000 China Hangzhou Innovation Institute Beihang University Zhejiang310000 China

Manga is a fashionable Japanese-style comic form that is composed of black-and-white strokes and is generally displayed as raster images on digital devices. Typical mangas have simple textures, wide lines, and few color gradients, which are vectorizable natures to enjoy the merits of vector graphics, e.g., adaptive resolutions and small file sizes. In this paper, we propose MARVEL (MAnga’s Raster to VEctor Learning), a primitive-wise approach for vectorizing raster mangas by Deep Reinforcement Learning (DRL). Unlike previous learning-based methods which predict vector parameters for an entire image, MARVEL introduces a new perspective that regards an entire manga as a collection of basic primitives—stroke lines, and designs a DRL model to decompose the target image into a primitive sequence for achieving accurate vectorization. To improve vectorization accuracies and decrease file sizes, we further propose a stroke accuracy reward to predict accurate stroke lines, and a pruning mechanism to avoid generating erroneous and repeated strokes. Extensive subjective and objective experiments show that our MARVEL can generate impressive results and reaches the state-of-the-art level. Our code is open-source at: https://***/SwordHolderSH/Mang2Vec. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

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Multi-source Domain Generalization for CSI-based Human Activity Recognition

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IEEE Transactions on Mobile Computing 2025年

作者： Fan, Tianqi Qiu, Sen Gong, Wei Fang, Yuguang Beijing Institute of Technology School of Medical Technology Beijing100000 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian116024 China University of Science and Technology of China Hefei230026 China City University of Hong Kong Department of Computer Science Hong Kong

Domain generalization remains a key challenge in human activity recognition based on channel state information (CSI). Different domains correspond to distinct data distributions, deviating from the typical assumption of independent and identically distributed (i.i.d.) data, which leads to significant performance degradation when models are applied to unseen domains. To address this issue, we propose a novel domain generalization model that integrates meta-learning initialization and an adaptive channel grouping attention mechanism. First, a meta-learning strategy is employed to acquire well-initialized parameters from multiple source domain tasks, enabling the model to implicitly enhance its cross-domain generalization ability. Second, an adaptive grouping attention mechanism is designed in the feature extraction stage to effectively capture the sensitivity differences of different subcarriers to human activities. Meanwhile, a random masking training mechanism is introduced to simulate real-world domain variations and improve model robustness. In addition, a domain adversarial training framework based on the gradient reversal layer (GRL) is adopted to mitigate domain-specific feature dependency, further enhancing the model's generalization capability. We evaluate our proposed method on both a self-collected dataset, which includes human activity data from nine volunteers across six different environments, and a public CSI dataset. The experimental results demonstrate that our method significantly outperforms existing approaches in domain generalization performance, verifying its effectiveness and practical applicability. © 2002-2012 IEEE.

关键词： Feature extraction

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Lyapunov-based Singular Perturbation Results in the Framework of Hybrid Systems ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2027-2032页

作者： Xue-Fang Wang Kun-Zhi Liu Xi-Ming Sun Andrew R. Teel Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian 116024 China Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 USA

Stability properties of singularly perturbed hybrid systems are investigated via Lyapunov functions with assistance from the invariance principle. Both continuously differentiable Lyapunov functions and non-smooth Lyapunov functions are considered. In each case, under appropriate assumptions, uniform asymptotic stability and uniform global asymptotic stability are established. An estimate of the basin of attraction is given for the former property. Two examples are given to illustrate the proposed theoretical results based on continuously differentiable Lyapunov functions. In addition, one example for switched learning inclusions with unstable modes is given to show the effectiveness of the results obtained based on non-smooth Lyapunov functions.

关键词： Singular perturbation hybrid dynamical systems Lyapunov function methods uniform asymptotic stability uniform global asymptotic stability

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Model based reinforcement learning for robot grasping trajectory generation 4

Model based reinforcement learning for robot grasping trajec...

引用

4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019

作者： Xue, Hongxiang Wen, Shuhuan Yang, Chao Liu, Huaping Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and System Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9781728100647

Trajectory generation is a fundamental problem for successful robotic grasping. However, most of the existing work dealt with this problem using supervised learning with a prescribed model. It prevents the developed grasp strategies from being used for new unknown scenarios. In this paper, inspired by the success of the reinforcement learning method, we developed a new method which optimizes grasp trajectories using a policy search method. An important advantage of the proposed method is that the complicated 3D model could be used to generate the multi-dimensional grasp configurations. The developed algorithm has been validated on some typical robotic hand. The experimental results show that the proposed method achieves excellent performance. © 2019 IEEE.

关键词： Trajectories

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Multi-scale pseudo-labels filtering and key pixels adversarial alignment for domain adaptive object detection

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Xing Wei Jiong Xia Fan Yang Chong Zhao Cang Liu Ge Wang Yanke Chen Yang Lu School of Computer and Information Hefei University of Technology China Intelligent Manufacturing Technology Research Institute of Hefei University of Technology China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education China Anhui Mine IOT and Security Monitoring Technology Key Laboratory China

Domain Adaptive Object Detection (DAOD) aims to adapt a detector trained on the labeled source domain to perform effectively on the unlabeled target domain. Most existing DAOD methods commonly employ self-training strategy and adversarial learning. However, the methods based on the self-training strategy often generate unreliable pseudo-labels (e.g., missing detections and false positives) due to the absence of target domain semantics, resulting in suboptimal models. Meanwhile, aligning a large number of unimportant pixels hampers adversarial learning’s ability to capture domain-invariant semantic information. Therefore, we propose the Multi-scale Adversarial Teacher Framework (MATF) based on the common self-training framework (teacher–student framework). Specifically, to mitigate the impact of unreliable pseudo-labels, we propose the Multi-scale Negatives Filtering (MNF) module in the teacher model, which prevents missing detections from damaging the model by filtering out unreliable negative samples at multiple feature scales. In addition, we propose the Multi-scale key-Pixel Discriminator (MKPD), which predicts the distributions of key pixels at multiple feature scales emphasizing the adversarial alignment of key pixels rather than unimportant pixels. Finally, to generate higher-quality pseudo-labels, we introduce the discriminator into the student model to reduce the model’s bias towards the source domain. Unlike most methods, we construct our method on a computationally efficient but less work-intensive one-stage detector. Extensive experiments conducted on DAOD benchmark datasets such as Cityscapes, FoggyCityscapes, KITTI, and Sim10k demonstrate the strong adaptability and effectiveness of MATF.

关键词： Object detection

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Vehicle tracking by detection in UAV aerial video

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Science China(Information Sciences) 2019年第2期62卷 192-194页

作者： Shaohua LIU Suqin WANG Wenhao SHI Haibo LIU Zhaoxin LI Tianlu MAO School of Electronic Engineering University of Beijing Posts and Telecommunications Institute of Electronic and Information Engineering in Guangdong University of Electronic Science and Technology of China School of Control and Computer Engineering North China Electric Power University Beijing Key Lab of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of Sciences

With the maturity and popularity of UAV (unmanned aerial vehicle) technology [1–3], UAV video is becoming an effective supplement to the fixed monitoring video [4, 5]. In the aspect of traffic information acquisition, the advantages of UAV are obvious. UAV can fly not only between the buildings, but also on the freeway, and even into the tunnel, showing the unique flexibility and maneuverability. UAV can control its hovering position artificially and has a high angle shot to get more comprehensive and clearer video *** some emergent situation, such as evacuation caused by typhoons and earthquakes, there are lots of countryside areas without fixed road monitoring cameras. UAV is undoubtedly the best choice to

关键词： UAV Vehicle tracking by detection in UAV aerial video

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