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Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification

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Defence Technology（防务技术） 2024年第1期31卷 375-386页

作者： Jie Zhao Zhongjiao shi Yuchen Wang Wei Wang Beijing Institute of Technology Beijing 100081China

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system ***,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference ***,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F ***,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression *** long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be ***,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.

关键词： Rotor-missile Adaptive control Parameter identification Quaternion control

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

Study on a probabilistic algorithm for the forming and 3D characterization of special-shaped surfaces under profile grinding

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Advances in Manufacturing 2024年第2期12卷 288-299页

作者： Zhao-Qing Zhang Kai-Ning shi Yao-Yao shi Yi-Hui Song Zhe He Ya-Song Pu School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an710072People’s Republic of China Key Laboratory of High Performance Manufacturing for Aero Engine(Northwestern Polytechnical University) Ministry of Industry and Information TechnologyXi’an710072People’s Republic of China Engineering Research Center of Advanced Manufacturing Technology for Aero Engine(Northwestern Polytechnical University) Ministry of EducationXi’an710072People’s Republic of China

Profile grinding is the most crucial method for the ultra-precision machining of special-shaped ***,profile grinding produces a unique machining profile,and many random factors in the machining process lead to complex surface *** this study,the structural and probabilistic characteristics of the profile grinding of a special-shaped surface were analyzed,and a probabilistic algorithm for the forming and 3D characterization of special-shaped surfaces under profile grinding was *** forming process of a GH738 blade tenon tooth surface was considered as an example to demonstrate the *** comparison results showed that the simulation results had similar surface characteristics to the measurement results,and the relative error range of the 3D roughness parameter was 0.21%–19.76%,indicating an accurate prediction and characterization of the complex special-shaped surface under the action of multiple factors.

关键词： Special-shaped surface Profile grinding Surface morphology 3D characterization

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

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

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

Regulation of Tumor Microenvironment Under Malignant Conditions:Application of nMOFs in Sonodynamic Therapy

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Chemical Research in Chinese Universities 2024年第4期40卷 611-626页

作者： YANG Yilin YAN Fei shi Zhan State Key Laboratory of Inorganic Synthesis and Preparative Chemistry College of ChemistryJilin UniversityChangchun130012P.R.China

In this review,we delve into the intricate regulation of the tumor microenvironment(TME)under malignant conditions and explore the transformative potential of nanoscale metal-organic frameworks(nMOFs)in the realm of sonodynamic therapy(SDT).The TME serves as a dynamic milieu influencing tumor progression and therapeutic response,presenting formidable challenges,such as hypoxia,acidity,excess hydrogen peroxide,high expression of glutathione,and *** the exceptional attributes of nMOFs,including their tunable structures and biocompatibility,holds immense promise for enhancing SDT efficacy and reshaping the TME *** integrating nMOFs with SDT,researchers aim to assemble multiple functionalities in a single platform that enhance tumor cell eradication while counteracting unfavorable TME conditions and immune *** potential of nMOFs to revolutionize tumor therapies by precisely targeting TME and overcoming therapeutic barriers is underscored by an in-depth analysis of recent breakthroughs in the use of nMOFs-based sonosensitizers to remodulate TME to amplify the efficacy of SDT.

关键词： Sonodynamic therapy Metal-organic framework Tumor microenvironment Modification of sonosensitizer

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

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