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Wyszukujesz frazę "Li, Xia" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
A discrete variable based methodology for topology optimization considering spatially uneven manufacturing errors
Autorzy:
Xia, Meng
Li, Jing
Powiązania:
https://bibliotekanauki.pl/articles/27309953.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
interval field
manufacturing errors
morphological operations
topology optimization
Opis:
Manufacturing errors (MEs) are unavoidable in product fabrication. The omnipresence of manufacturing errors (MEs) in product engineering necessitates the development of robust optimization methodologies. In this research, a novel approach based on the morphological operations and interval field (MOIF) theory is proposed to address MEs in the discrete-variable-based topology optimization procedures. On the basis of a methodology for deterministic topology optimization (TO) based on the Min-Cut, MOIF introduces morphological operations to generate geometrical variations, while the dimension of the structuring element is dynamically set by the interval field function’s output. The effectiveness of the proposed approach as a powerful tool for accounting for spatially uneven ME in the TOs has been demonstrated.
Źródło:
Archives of Electrical Engineering; 2023, 72, 4; 1121--1133
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on speed control of high speed trains based on hybrid modeling
Autorzy:
Hou, Tao
Tang, Li.
Niu, Hong-xia
Zhao, Tingyang
Powiązania:
https://bibliotekanauki.pl/articles/27311802.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
high-speed train
hybrid modeling
speed control
error compensation
pociąg ekspresowy
modelowanie hybrydowe
kontrola prędkości
kompensacja błędów
Opis:
With the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed trains lacks exploration of the combination of train operation data information and physical model, resulting in low system modeling accuracy, which impacts the effectiveness of speed control and the operation of high-speed trains. To further increase the dynamic modeling accuracy of high-speed train operation and the high-speed train's speed control effect, a high-speed train speed control method based on hybrid modeling of mechanism and data drive is put forward. Firstly, a model of the high-speed train's mechanism was created by analyzing the train's dynamics. Secondly, the improved kernel-principal component regression algorithm was used to create a data-driven model using the actual operation data of the CRH3 (China Railway High-speed 3) high-speed train from Huashan North Railway Station to Xi'an North Railway Station of "Zhengxi High-speed Railway," completing the mechanism model compensation and the error correction of the speed of the actual operation process of the high-speed train, and realizing the hybrid modeling of mechanism and data-driven. Finally, the prediction Fuzzy PID control algorithm was developed based on the natural line and train characteristics to complete the train speed control simulation under the hybrid model and the mechanism model, respectively. In addition, analysis and comparison analysis were conducted. The results indicate that, compared to the high-speed train speed control based on the mechanism model, the high-speed train speed control based on hybrid modeling is more accurate, with an average speed control error reduced by 69.42%. This can effectively reduce the speed control error, improve the speed control effect and operation efficiency, and demonstrate the efficacy of the hybrid modeling and algorithm. The research results can provide a new ideal of multi-model fusion modeling for the dynamic modeling of high-speed train operation, further improve control objectives such as safety, comfort, and efficiency of high-speed train operation, and provide a reference for automatic driving and intelligent driving of high-speed trains.
Źródło:
Archives of Transport; 2023, 66, 2; 77--82
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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