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2022 Conference on Infrared, Millimeter, Terahertz Waves and Applications, IMT 2022

作者： Fu, Jie Qi, Heng Wang, Shaofei Zheng, Guofeng Qi, Tao Zhang, Peng Luoyang Electro-Optical Equipment Research Institute of AVIC Henan Luoyang China Science and Technology on Electro-Optic Control Laboratory Henan Luoyang China North Institute for Scientific and Technical Information Beijing China

ISBN: (纸本)9781510662476

As an important branch of lidar technology, laser 3D imaging technology can be used to detect the outside of the target features, and in the military vehicles, aircraft to be practical application, has been the world’s military attention and recognized. Laser 3D imaging can detect and display the information such as the appearance and outline of the target object, and can be combined with image processing technology, GPS technology, and pattern recognition technology, to finally realize the position and external details of the target object. Compared with the traditional two-dimensional remote sensing and image photography technology, it can more truly and carefully reflect the characteristics of the target object, so the three-dimensional imaging lidar technology has great development prospects. This paper introduces the principle of 3D imaging lidar, working system and the key technology in the development process, and analyzed the development trend and characteristics of 3D imaging lidar, comparing the application of 3D imaging lidar technology at home and abroad, and finally puts forward the development trend of 3D imaging lidar on the airborne platform. © 2023 SPIE.

关键词： optical radar

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Dynamic event-triggered fault-tolerant cooperative resilient tracking control with prescribed performance for UAVs

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science China(Information sciences) 2024年第8期67卷 67-84页

作者： Rong YUAN Zhengcai AN Shuyi SHAO Mou CHEN Mihai LUNGU College of Automation Engineering Nanjing University of Aeronautics and Astronautics Science and Technology on Electro-optic Control Laboratory Faculty of Electrical Engineering University of Craiova Aerospace Engineering Doctoral School University Politehnica of Bucharest

In this paper, a resilient tracking control scheme with cooperative collision avoidance performance is studied for the fixed-wing unmanned aerial vehicle(UAV) leader-follower formation in the presence of actuator failures and external disturbances. Firstly, based on the control objectives of UAV formation tracking and collision avoidance, the transformation tracking errors are obtained using the prescribed performance control technique. Next, a fault detection mechanism is introduced to determine if there is the actuator fault. Subsequently, the event-triggered resilient fault observers are designed based on a dynamic eventtriggered mechanism to estimate actuator faults. Furthermore, the prescribed performance functions and the H∞ performance index are employed to ensure the UAV formation collision-free and mitigate the impact of disturbances. Moreover, the resilient controller is designed to minimize the effect of the perturbations for the control gain and the fault observer gain on the system. The stability of the system is also proven through the Lyapunov stability analysis, and the controller gains are calculated by solving the linear matrix ***, the validity of the proposed control strategy is demonstrated by the simulation analysis.

关键词： UAV formation cooperative collision avoidance event-triggered fault observer prescribed performance resilient control

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Pedestrian Re-identification Method Based on Bilateral Feature Extraction Network and Re-ranking

Pedestrian Re-identification Method Based on Bilateral Featu...

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2021 International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2021

作者： Zhang, Xiaxia Li, Ning Zhang, Ruixin Li, Guoqiang Science and Technology on Electro-Optic Control Laboratory Nanjing China 613th Research Institute of Aviation Industry of China Science and Technology on Electro-Optic Control Laboratory Luoyang China

ISBN: (纸本)9781665424905

In recent year, convolution neural network(CNN) has been widely used in the field of pedestrian re-identification. However, most models based on CNNs need complex calculation and the features extracted by these models are easy to ignore the shallow detail information, which leads to insufficient recognition for pedestrians with small differences. Aim at the above problems, we propose a bilateral feature extraction network(BiFeNet). The network includes two parts: detail branch and semantic branch. Detail branch consists of simple convolution operations and down-sampling to obtain high-resolution feature maps with rich detail information. Semantic branch uses a relatively deep network to obtain strong semantic information. For loss function, we use Circle loss that unifies the two basic learning paradigms. Circle loss can offer a more flexible optimization towards a more definite convergence target. Finally, the K nearest neighbor method is used to reorder the initial list obtained by using Euclidean distance to make more similar matches ranked higher, thereby improving the accuracy of re-identification. On Market-1501, DukeMTMC-ReID and CUHK03 datasets, the mAP of our proposed method reached 89.53%, 77.67% and 80.54%, respectively. Experimental results show that our proposed method can extract more recognizable features and improve the accuracy of pedestrian re-identification. © 2021 IEEE.

关键词： Semantics

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Research on micro-cantilever calibration method based on nano indentation technology 8

Research on micro-cantilever calibration method based on nan...

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8th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Wang, Shaofei Luoyang Electro-Optical Equipment Research Institute of AVIC Henan Luoyang China Science and Technology on Electro-Optic Control Laboratory Henan Luoyang China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

With the deepening of the related research work of atomic force microscope (AFM), people can not only be satisfied with the morphology measurement function of AFM.[1,2] How to use AFM to carry out micro force measurement has gradually become one of the hotspots of AFM application research. Among them, the elastic coefficient of AFM micro cantilever is an important bridge connecting the AFM input signal and the force on the needle tip.[3] Therefore, the measurement of elastic coefficient of micro cantilever is an important premise for micro force measurement using AFM. According to the characteristics of rectangular micro cantilever and the practical problems faced in AFM measurement, the measurement technology of elastic coefficient of AFM micro cantilever based on nano indentation measurement technology is studied in this paper. The theoretical analysis and experimental results show that the nano indentation method can be used to calibrate the elastic coefficient of micro cantilever. © 2022 SPIE

关键词： Calibration

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Formation Tracking Stabilization control for Fixed-wing UAVs with Planar Motion 5

Formation Tracking Stabilization Control for Fixed-wing UAVs...

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5th International Symposium on Autonomous Systems, ISAS 2022

作者： Gao, Ruizhou He, Chenghua Wang, Zengmao Peng, Xiuhui Science and Technology on Electro-optic Control Laboratory Luoyang471000 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9781665487085

This paper investigates the formation tracking stabilization problem for fixed-wing UAVs with planar motion under a directed graph. Assuming that the constant reference position and heading is only available to a subset of followers, a specified-time distributed estimator is designed to obtain the constant configuration of the leader for each follower. Utilizing the estimated desired configuration and defined formation shape, the saturated linear speed and angular speed inputs are designed, respectively. The novelty of this paper lies in the realization of the required pose formation tracking stabilization shape under the condition that the designed linear speed remains positive for all time. Finally, the feasibility of the designed formation tracking stabilization control law is illustrated by the numerical simulation. © 2022 IEEE.

关键词： Stabilization

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Helmet Pose Measurement Method Based on Feature Learning 6

Helmet Pose Measurement Method Based on Feature Learning

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6th International Conference on Information Communication and Signal Processing, ICICSP 2023

作者： Fu, Gaobo Zhao, Xinbo Zou, Xiaochun Yan, Linjie Zhang, Baoshang School of Software Northwestern Polytechnical University Xi'an China Science and Technology on Electro-optic Control Laboratory Luoyang China School of Computer Science Northwestern Polytechnical University Xi'an China School of Electronic Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350339994

In modern aviation systems, the helmet sight (HMS) assists the pilot to cooperate with the warplane to quickly complete the target tracking and aiming, and the helmet pose measurement is the core technology of the helmet sight. Since the helmet pose measurement technology involved in aviation safety, this technology is kept confidential at home and abroad, and there is a lack of open source helmet data set and high-precision helmet posture measurement algorithm. For this purpose, this paper produced the first helmet dataset, Helment 8K, and proposed the CSPDarkNet-Ht helmet pose measurement algorithm based on feature learning. The target is to form the helmet dataset image on the helmet model according to the unique feature points of the helmet model design. Helment 8K data set is made by building a helmet model with 3dmax and then rendering 8282 helmet images at different angles with targets. The CSPDarkNet-Ht algorithm divides the yaw angle and pitch angle of the helmet pose angle into two sub-tasks for classification and regression. Firstly, The algorithm uses CSPDarkNet to extract the low, medium and high semantic information of the target features for the backbone network. Then, different semantic feature layers are classified from different angles and regression of the highest semantic feature layer is performed to obtain the predicted pose angles. Finally, the loss function was optimized to include the cross-entropy loss of the five coarse and fine categories as the total loss. Moreover, the average absolute error of the angular prediction on the Helment 8K dataset is 0.5, and the prediction accuracy is 91.09% within 1 degree error, which is consistent with the accurate measurement of the helmet pose. © 2023 IEEE.

关键词： helmet pose measurement hybrid coarse-fine classification target

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Design and preparation of aluminum alloy with high thermal conductivity based on CALPHAD and first-principles calculation

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China Foundry 2022年第3期19卷 225-237页

作者： Ye Wang Hui-jun Kang Yu Guo Hong-tao Chen Mao-liang Hu Ze-sheng Ji School of Materials Science and Chemical Engineering Harbin University of Science and TechnologyHarbin 150004China Key Laboratory of Materials Modification by Laser Ion and Electron Beams(Dalian University of Technology)Ministry of EducationDalian 116024LiaoningChina Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian 116024LiaoningChina School of Science and Engineering Huzhou CollegeHuzhou 313000ZhejiangChina

To obtain the aluminum alloy with high thermal and mechanical properties,the effects of alloying elements and the second phases on the thermal conductivity of Al alloys were investigated by CALPHAD and first-principles calculation,*** properties of the second phases,including Young's modulus,Poisson's ratio and minimum thermal conductivity,were systematically *** show that the ranking order of the effects of the alloying elements on the thermal conductivity is Mg>Cu>Fe>Si,and for Al-12Si alloys,the mathematical model of the relationship between the alloying elements and the thermal conductivity can be expressed as λ=ax^(2)-bx+c when the second phase precipitates in the *** kinds of ternary phases of Al-Fe-Si have higher deformation resistance,rigidity,theoretical hardness,Debye temperature and thermal conductivity than the other phases which possibly exist in the Al-12Si *** on the guidance of CALPHAD and first-principles calculation,the optimized chemical composition of Al alloy with high conductivity is Al-11.5Si-0.4Fe-0.2Mg(wt.%)with a thermal conductivity of 137.50 W·m^(-1)·K^(-1)and a hardness of 81.3 HBW.

关键词： design and preparation CALPHAD first-principles calculation thermal conductivity aluminum alloy

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RGB to L∗a∗b∗ Color Prediction Model Based on Color Cards 8th

RGB to L∗a∗b∗ Color Prediction Model Based on Color Cards

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8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

作者： Zhang, Yong Zou, Jie Ma, Chao Gu, Yunpeng Ma, Jianwei College of Information Engineering Henan University of Science and Technology Luoyang471000 China Science and Technology on Electro-Optic Control Laboratory AVIC Optronics Luoyang471000 China

ISBN: (纸本)9789819980208

Color is a critical characteristic indicating the traits of an object and is widely used in computer vision technology. In order to ensure uniformity and device independence of color in image processing, standardized and normative color management are needed. L∗a∗b∗ color space is a device-independent expression of color values because it is uniform and independent of hardware devices. Therefore, in this paper, we compare four prediction models from RGB to L∗a∗b∗. The experimental platform was built to acquire digital images with Pantone color cards as sample data and extract RGB value. The real L∗a∗b∗ value is measured by colorimeter. And preprocess the original RGB value and L∗a∗b∗ value data, and establish four prediction models: linear, quadratic polynomial, neural network and extreme learning machine, by comparing preprocessed data with real data. Using four evaluation metrics (MAE, MSE, RMSE and R2), and scatter fit plots were used to compare results. The results show that the model based on extreme learning machine has the highest prediction accuracy, with MAE reaching 0.021, 0.017 and 0.010, MSE 0.0006, 0.0005 and 0.0001, RMSE 0.026, 0.021 and 0.031, R2 0.96, 0.97 and 0.96, respectively. Indicates that the extreme learning machine has good potential for application. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Size effect of amorphous layers on radiation resistance in Cu/Nb multilayers

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Journal of Materials science & technology 2024年第30期197卷 25-31页

作者： Zhe Yan Wenfan Yang Jingyu Pang Jiahao Yao Jian Zhang Lixin Yang Shijian Zheng Jian Wang Xiuliang Ma Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology School of Materials Science and EngineeringHebei University of TechnologyTianjin 300130China Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China School of Energy Research Xiamen UniversityXiamen 361005China Mechanical and Materials Engineering University of Nebraska-LincolnLincolnNE 68588USA Bay Area Center for Electron Microscopy Songshan Lake Materials LaboratoryDongguan 523808China Institute of Physics Chinese Academy of SciencesBeijing 100190China

In nuclear reactors,the irradiation of metal materials by high-energy particles results in a substantial number of point defects,i.e.,vacancies and interstitials[1-3].High-density accumulation of point defects leads to the formation of defect clusters,including voids,bubbles,and dislocation loops[4-6].These radiation defects contribute to the degradation of structural integrity and perfor-mance,such as hardening,embrittlement,swelling,and surface de-terioration[7-9].Therefore,the regulation and elimination of radi-ation defects have been a long-standing and serious challenge for nuclear materials.

关键词： resistance amorphous swelling

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FAANet: Feature-Augmented Attention Network for Surface Defect Detection of Metal Workpieces 8th

FAANet: Feature-Augmented Attention Network for Surface Defe...

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8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

作者： Gu, Yunpeng Zou, Jie Ma, Chao Zhang, Yong Zhang, Mengtong Ma, Jianwei College of Information Engineering Henan University of Science and Technology Luoyang471000 China Science and Technology on Electro-optic Control Laboratory AVIC Optronics Luoyang471000 China

ISBN: (纸本)9789819980178

Accurate segmentation of defects on the surface of metal workpieces is challenging due to the large-scale differences and complex morphological features. To address the problem of low accuracy in segmenting metal workpiece surface defects in complex scenes, we propose a Feature-Augmented Attention Network (FAANet) by studying the types of workpiece surface defects. The network adopts an encoder-decoder structure. In the encoding stage, VGG16 is used as the backbone to extract features at different depths of the metal workpiece surface. Moreover, a feature augmentation module is designed to refine the feature maps at different levels for the characteristics of the information contained in the features at different depths, emphasizing the detailed features, such as the edges of the defective targets. In the decoding stage, an efficient channel attention mechanism is fused in the network to make full use of the feature map with rich information after feature fusion. More relevant features are extracted through the interaction of different channel information to improve information utilization as well as segmentation accuracy. For the sample imbalance problem, a loss function supervised training model incorporating focal loss is designed to mitigate the negative impact of sample imbalance on the model performance. The results show that the proposed method achieves better segmentation results in detecting surface defects on metal workpieces in complex scenes than existing prevailed methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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