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Wyszukujesz frazę "Zhen, Hao" wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
Camera-based PHM method in rotating machinery equipment micro-action scenarios
Autorzy:
Junfeng, An
Liu, Jiqiang
Zhen, Hao
Mengmeng, Lu
Powiązania:
https://bibliotekanauki.pl/articles/24200809.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
deep learning
condition monitoring
Rmcad
anomaly detection
defect early warning
Opis:
The health operation of rotating machinery guarantees safety of the project. To ensure a good operating environment, current subway equipment inspections frequency is high, resulting in a waste of resources. Small abnormal changes in mechanical equipment will also contribute to the development of mechanical component defects, which will ultimately lead to the failure of the equipment. Therefore, mechanical equipment defects should be detected and diagnosed as soon as possible. Through the use of graphic processing and deep learning, this paper proposes Rmcad Framework with three aspects: condition monitoring, anomaly detection, defect early warning. Using a network algorithm, this paper proposes an improved model that has the characteristics of two-stream and multi-loss functions, which improves the accuracy of detection. Additionally, a defect warning method is constructed to improve the perception ability of equipment before failure occurs and reduce the frequency of frequent maintenance by detecting anomalies according to the degree of opening.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 1; art. no. 10
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Flotation separation of cassiterite and chlorite using carboxymethyl cellulose as a depressant
Autorzy:
Hu, Yang
Ying, Luo Hong
Zhang, Ying
Wei, Lu Kuan
Hao, Guan Zhen
Powiązania:
https://bibliotekanauki.pl/articles/2175422.pdf
Data publikacji:
2022
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
cassiterite
chlorite
sodium oleate
carboxymethyl cellulose
selective inhibition
Opis:
The nature and mechanism of interaction between carboxymethyl cellulose (CMC) with cassiterite (and chlorite surfaces) and their effects on the flotation separation process of cassiterite (from chlorite) were investigated by micro-flotation tests, surface adsorption experiments, zeta potential measurements, solution chemical calculation, infrared spectroscopy, and X-ray photo-electron spectroscopy (XPS). The results from single mineral tests revealed that CMC exhibited good selective inhibition effects with cassiterites and chlorites. When the dosage was 12.5 mg/L at pH 8, cassiterite and chlorite recovery was 92.2% and 6.3%, respectively. The artificial mixed ore test revealed that the flotation separation effect was the best when the dosage of CMC was 6.5 mg/L. Cassiterite used during the studies was 75.1% pure. The recovery was 82.8%. The interaction between CMC and the cassiterite surface led to a shift in the zeta potential toward the negative direction. CMC was weakly adsorbed on the cassiterite surface. There was no significant impact on the subsequent collection of sodium oleate. The concentration of C atom increased post interaction, and the potential shifted toward the negative direction. Characteristic CMC peaks were observed at this point. Hydrogen bonds and weak chemisorption interactions between CMC and chlorite affected the interaction between sodium oleate and the chlorite surface. It also affected the flotation results. The cassiterite and chlorite were separated effectively.
Źródło:
Physicochemical Problems of Mineral Processing; 2022, 58, 6; art. no. 155141
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
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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