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Wyszukujesz frazę "Zhang, Hao" wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
An accurate and stable pose estimation method for planar cases considering the line constraints between every two points
Autorzy:
Zhang, Zimiao
Zhang, Hao
Zhang, Fumin
Zhang, Shihai
Powiązania:
https://bibliotekanauki.pl/articles/27311739.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
pose estimation
line constraints
coplanar
non-iterative
Opis:
The current solutions for pose estimation problems using coplanar feature points (PnP problems) can be divided into non-iterative and iterative solutions. The accuracy, stability, and efficiency of iterative methods are unsatisfactory. Therefore, non-iterative methods have become more popular. However, the non-iterative methods only consider the correspondence of the feature points with their 2D projections. They ignore the constraints formed between feature points. This results in lower pose estimation accuracy and stability. In this work, we proposed an accurate and stable pose estimation method considering the line constraints between every two feature points. Our method has two steps. In the first step, we solved the pose non-iteratively, considering the correspondence of the 3D feature points with their 2D projections and the line constraints formed by every two feature points. In the second step, the pose was refined by minimizing the re-projection errors with one iteration, further improving accuracy and stability. Simulation and actual experiment results show that our method’s accuracy, stability, and computational efficiency are better than the other existing pose estimation methods. In the -45° to +45° measuring range, the maximum angle measurement error is no more than 0.039°, and the average angle measurement error is no more than 0.016°. In the 0 mm to, 30 mm measuring range, the maximum displacement measurement error is no more than 0.049 mm, and the average displacement measurement error is no more than 0.012 mm. Compared to other current pose estimation methods, our method is the most efficient based on guaranteeing measurement accuracy and stability.
Źródło:
Metrology and Measurement Systems; 2023, 30, 2; 235--258
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Parameter identification approach using improved teaching and learning based optimization for hub motor considering temperature rise
Autorzy:
Li, Yong
Wang, Yuan
Zhang, Taohua
Hu, Han
Wu, Hao
Powiązania:
https://bibliotekanauki.pl/articles/2203368.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
parameters identification
teaching–learning-based optimization
hub motor
temperature rise
Opis:
Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance, -axis inductance, -axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, -axis inductance, -axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified.
Źródło:
Metrology and Measurement Systems; 2023, 30, 1; 99--115
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
RFID tag group recognition based on motion blur estimation and YOLOv2 improved by Gaussian algorithm
Autorzy:
Li, Lin
Yu, Xiao-Lei
Liu, Zhen-Lu
Zhao, Zhi-Min
Zhang, Ke
Zhou, Shan-Hao
Powiązania:
https://bibliotekanauki.pl/articles/2051852.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
RFID
YOLOv2
neural network
GRNN
Opis:
Effective recognition of tags in the dynamic measurement system would significantly improve the reading performance of the tag group, but the blurred outline and appearance of tag images captured in motion seriously limit the effectiveness of the existing tag group recognition. Thus, this paper proposes passive tag group recognition in the dynamic environment based on motion blur estimation and improved YOLOv2. Firstly, blur angles are estimated with a Gabor filter, and blur lengths are estimated through nonlinear modelling of a Generalized Regression Neural Network (GRNN). Secondly, tag recognition based on YOLOv2 improved by a Gaussian algorithm is proposed. The features of the tag group are analyzed by the Gaussian algorithm, the region of interest of the dynamic tag is effectively framed, and the tag foreground is extracted; Secondly, the data set of tag groups are trained by the end-to-end YOLOv2 algorithm for secondary screening and recognition, and finally the specific locations of tags are framed to meet the effective identification of tag groups in different scenes. A considerable number of experiments illustrate that the fusion algorithm can significantly improve recognition accuracy. Combined with the reading distance, the research presented in this paper can more accurately optimize the three-dimensional structure of the tag group, improve the reading performance of the tag group, and avoid the interference and collision of tags in the communication channel. Compared with the previous template matching algorithm, the tag group recognition ability put forward in this paper is improved by at least 13.9%, and its reading performance is improved by at least 6.2% as shown in many experiments.
Źródło:
Metrology and Measurement Systems; 2022, 29, 1; 53-74
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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