Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "X-Machine" wg kryterium: Wszystkie pola


Wyświetlanie 1-3 z 3
Tytuł:
A novel method for 3D measurement of RFID multi-tag network using a machine vision system
Autorzy:
Zhuang, X.
Yu, X.
Zhao, Z.
Zhang, W.
Liu, Z.
Lu, D.
Dong, D.
Powiązania:
https://bibliotekanauki.pl/articles/221058.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
3D measurement
RFID multi-tag network
dual-CCD system
neural network
machine vision
Opis:
The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag networks is one of the important issues in the field of RFID, which affects the reading performance of RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multi-tag networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological filtering method are used to process the images. The template matching method is respectively used to determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the 3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance. The BP neural network can predict the reading distances of unknown tag groups and find out the optimal distribution structure of the tag groups corresponding to the maximum reading distance. In the future work, the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.
Źródło:
Metrology and Measurement Systems; 2018, 25, 3; 475-486
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Reconstruction algorithm for obtaining the bending deformation of the base of heavy-duty machine tool using inverse Finite Element Method
Autorzy:
Liu, M.
Zhang, X.
Song, H.
Wang, J.
Zhou, S.
Powiązania:
https://bibliotekanauki.pl/articles/221566.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
inverse Finite Element Method
bending deformation
heavy-duty machine tool
reconstruction algorithm
statically indeterminate structure
Opis:
The field of mechanical manufacturing is becoming more and more demanding on machining accuracy. It is essential to monitor and compensate the deformation of structural parts of a heavy-duty machine tool. The deformation of the base of a heavy-duty machine tool is an important factor that affects machining accuracy. The base is statically indeterminate and complex in load. It is difficult to reconstruct deformation by traditional methods. A reconstruction algorithm for determining bending deformation of the base of a heavy-duty machine tool using inverse Finite Element Method (iFEM) is presented. The base is equivalent to a multi-span beam which is divided into beam elements with support points as nodes. The deflection polynomial order of each element is analysed. According to the boundary conditions, the deformation compatibility conditions and the strain data measured by Fiber Bragg Grating (FBG), the deflection polynomial coefficients of a beam element are determined. Using the coordinate transformation, the deflection equation of the base is obtained. Both numerical verification and experiment were carried out. The deflection obtained by the reconstruction algorithm using iFEM and the actual deflection measured by laser displacement sensors were compared. The accuracy of the reconstruction algorithm is verified.
Źródło:
Metrology and Measurement Systems; 2018, 25, 4; 727-741
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Soft Sensing Method Of LS-SVM Using Temperature Time Series For Gas Flow Measurements
Autorzy:
Xu, W.
Fan, Z.
Cai, M.
Shi, Y.
Tong, X.
Sun, J.
Powiązania:
https://bibliotekanauki.pl/articles/221824.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
gas flow
soft sensor
support vector machine (SVM)
temperature time series
Opis:
This paper proposes a soft sensing method of least squares support vector machine (LS-SVM) using temperature time series for gas flow measurements. A heater unit has been installed on the external wall of a pipeline to generate heat pulses. Dynamic temperature signals have been collected upstream of the heater unit. The temperature time series are the main secondary variables of soft sensing technique for estimating the flow rate. A LS-SVM model is proposed to construct a non-linear relation between the flow rate and temperature time series. To select its inputs, parameters of the measurement system are divided into three categories: blind, invalid and secondary variables. Then the kernel function parameters are optimized to improve estimation accuracy. The experiments have been conducted both in the single-pulse and multiple-pulse heating modes. The results show that estimations are acceptable.
Źródło:
Metrology and Measurement Systems; 2015, 22, 3; 383-392
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies