Position error correction of position sensitive detector by least squares support vector machine

2005 ◽  
Author(s):  
Xiaodong Wang ◽  
Meiying Ye
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Error Correction ◽  
Position Error ◽  
Position Sensitive Detector ◽  
Support Vector ◽  
Sensitive Detector ◽  
Position Sensitive

Hydrostatic Fiber Optic Liquid Level Sensor with position sensitive detector

Metrologiya ◽  
2020 ◽  
pp. 38-51
Author(s):  
V. N. Astapov ◽  
I. N. Kozlova
Keyword(s):  
Fiber Optic ◽  
Effective Area ◽  
Raw Material ◽  
Position Sensitive Detector ◽  
Fiber Optic Sensor ◽  
Liquid Level ◽  
Problem Solution ◽  
Sensitive Detector ◽  
Position Sensitive ◽  
Triangulation Sensor

This article presents the rationale and methodology for developing an intrinsically safe device, namely, a hydrostatic fiber optic sensor with a position-sensitive detector for monitoring the level of oil products in large-capacity tanks at oil depots and during pumping in a raw material warehouses. This device suitable for continuous monitoring of the liquid level, based on the measurement of a hydrostatic column of liquid with automatic offset of changes in the density of the liquid. Offset is carried out by means of a displacer (a fully submerged float), inside which a housing with a position-sensitive detector (PSD) is integrated. Theoretical validation of the bellows suspension usage for a displacer is given. During filling a container with a liquid whose level is measured, liquid bellows, the movement of which is recorded by an optical triangulation sensor using the reflected infrared ray incident on the bottom of the bellows. The principle of the triangulation sensor operation is based on the geometric properties of the triangles. The pulses of infrared radiation come through a fiber optic cable. In order to measure the movement of the surface (the bottom of the bellows) by measuring the movement of the reflected beam, a position-sensitive detector is used, which is located in a remote controller. In this device for the intrinsic safety problem solution, optical inputs of a fiber optic flat cable are located in the active zone of the sensor, which is connected to the optical inputs of a position-sensitive detector, operated on the principles of photoelectric effect. The light spot moving along the sensitive zone and converted by the detector into a one-dimensional signal proportional to the distance to the object. hydrostatically applies pressure over the entire effective area of the measuring

Control of the phase composition of advanced calcium phosphates using an x-ray diffractometer with a curved position-sensitive detector

2020 ◽  
Vol 86 (6) ◽  
pp. 29-35
Author(s):  
V. P. Sirotinkin ◽  
O. V. Baranov ◽  
A. Yu. Fedotov ◽  
S. M. Barinov
Keyword(s):  
Phase Composition ◽  
Calcium Phosphates ◽  
Position Sensitive Detector ◽  
Sensitive Detector ◽  
X Ray Diffraction ◽  
X Ray ◽  
Impurity Phases ◽  
Position Sensitive ◽  
Diffraction Peaks ◽  
Diffraction Patterns

The results of studying the phase composition of advanced calcium phosphates Ca10(PO4)6(OH)2, β-Ca3(PO4)2, α-Ca3(PO4)2, CaHPO4 · 2H2O, Ca8(HPO4)2(PO4)4 · 5H2O using an x-ray diffractometer with a curved position-sensitive detector are presented. Optimal experimental conditions (angular positions of the x-ray tube and detector, size of the slits, exposure time) were determined with allowance for possible formation of the impurity phases during synthesis. The construction features of diffractometers with a position-sensitive detector affecting the profile characteristics of x-ray diffraction peaks are considered. The composition for calibration of the diffractometer (a mixture of sodium acetate and yttrium oxide) was determined. Theoretical x-ray diffraction patterns for corresponding calcium phosphates are constructed on the basis of the literature data. These x-ray diffraction patterns were used to determine the phase composition of the advanced calcium phosphates. The features of advanced calcium phosphates, which should be taken into account during the phase analysis, are indicated. The powder of high-temperature form of tricalcium phosphate strongly adsorbs water from the environment. A strong texture is observed on the x-ray diffraction spectra of dicalcium phosphate dihydrate. A rather specific x-ray diffraction pattern of octacalcium phosphate pentahydrate revealed the only one strong peak at small angles. In all cases, significant deviations are observed for the recorded angular positions and relative intensity of the diffraction peaks. The results of the study of experimentally obtained mixtures of calcium phosphate are presented. It is shown that the graphic comparison of experimental x-ray diffraction spectra and pre-recorded spectra of the reference calcium phosphates and possible impurity phases is the most effective method. In this case, there is no need for calibration. When using this method, the total time for analysis of one sample is no more than 10 min.

Q-learning System Based on Cooperative Least Squares Support Vector Machine

2009 ◽  
Vol 35 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Xue-Song WANG ◽  
Xi-Lan TIAN ◽  
Yu-Hu CHENG ◽  
Jian-Qiang YI
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Learning System ◽  
Support Vector ◽  
Q Learning

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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