scholarly journals Design and Analysis of a Radio-Frequency Moisture Sensor for Grain Based on the Difference Method

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 708
Author(s):  
Zhongxu Chen ◽  
Wenfu Wu ◽  
Jianpeng Dou ◽  
Zhe Liu ◽  
Kai Chen ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Difference Method ◽  
Gas Flow ◽  
Average Error ◽  
Moisture Sensor ◽  
High Frequency Structure Simulator ◽  
Grain Moisture ◽  
Micro Gas Flow ◽  
The Difference ◽  
Difference Operation

Grain moisture is one of the key indexes of grain quality, and acquiring an accurate moisture value is critical for grain storage security. However, the sensors used in the traditional methods for testing grain moisture are based on capacitance, microwave, or radio-frequency methods and still exhibit low accuracy and instability because they are susceptible to the temperature, moisture, and micro gas flow of the air in the granary. In this study, we employed a new design for a radio-frequency moisture sensor for grain. The structure of the sensor is based on the difference method and consists of two parallel probe units. These units are at different distances to the tested grain, resulting in different sensitivities in the moisture measurements. Through a phase difference operation on the test signals, the disturbance variable was reduced. The specific size of the two parallel probes was confirmed by calculation and simulation using High Frequency Structure Simulator (HFSS) software. The simulated and measured parameters of a prototype sensor agreed well. The linear relationship yielded a correlation coefficient of 0.9904, and the average error of the moisture testing was within ±0.3% under the conditions where the VSWR (voltage standing wave ratio) value and return losses were 1.5896 and −20 dB, respectively, at a measured central frequency of 100 MHz. The results indicate that the performance of the sensor was excellent.

Two-Phase Flows in Mini-/Micro-Gas Flow Channels of PEM Fuel Cells

2013 ◽  
Author(s):  
Sung Chan Cho ◽  
Yun Wang
Keyword(s):  
Pressure Drop ◽  
Gas Flow ◽  
Fluid Model ◽  
Pem Fuel Cells ◽  
Two Phase ◽  
Micro Gas Flow ◽  
Two Fluid Model ◽  
The Impact ◽  
Two Fluid ◽  
Phase Pressure

In this paper, two-phase flow dynamics in a micro channel with various wall conditions are both experimentally and theoretically investigated. Annulus, wavy and slug flow patterns are observed and location of liquid phase on different wall condition is visualized. The impact of flow structure on two-phase pressure drop is explained. Two-phase pressure drop is compared to a two-fluid model with relative permeability correlation. Optimization of correlation is conducted for each experimental case and theoretical solution for the flows in a circular channel is developed for annulus flow pattern showing a good match with experimental data in homogeneous channel case.

scholarly journals The search of the anisotropy of the primary cosmic radiation by the difference method

2017 ◽  
Vol 145 ◽  
pp. 03003
Author(s):  
Victor Pavlyuchenko ◽  
Romen Martirosov ◽  
Natalia Nikolskaya ◽  
Anatoly Erlykin
Keyword(s):  
Difference Method ◽  
Cosmic Radiation ◽  
Primary Cosmic Radiation ◽  
The Difference

Micro-Gas Flow Induced Stochastic Resonance of a Nonlinear Nanomechanical Resonator

2021 ◽  
Vol 38 (2) ◽  
pp. 020502
Author(s):  
Shaochun Lin ◽  
Tian Tian ◽  
Peiran Yin ◽  
Pu Huang ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Stochastic Resonance ◽  
Gas Flow ◽  
Nanomechanical Resonator ◽  
Micro Gas Flow

Study and Simulate the Air Pollution Based on the Finite Difference Method

2012 ◽  
Vol 518-523 ◽  
pp. 2820-2824
Author(s):  
Yi Ni Guo ◽  
Yan Zhang ◽  
Jian Wang ◽  
Ye Huang
Keyword(s):  
Finite Element Method ◽  
Air Pollution ◽  
Finite Difference ◽  
Finite Difference Method ◽  
Difference Method ◽  
The Finite Element Method ◽  
Liquid Diffusion ◽  
Continuous Problems ◽  
The Finite Difference Method ◽  
The Difference

The finite difference method that is the finite element method is used to solve the plane continuous problems. In this article, the theory and method of the finite difference method, as well as the application on the boundary problem are introduced. By analyzing the potential flew field equation and liquid diffusion equation, they are discreted using the difference method and the numerical analysis under certain boundary condition is conducted. In air pollution, the smoke in the diffusion is typical planar continuous problems. In this paper, the finite difference method is used to analyse and simulate the spread of the smoke.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

scholarly journals Urban Change Detection in TerraSAR Image Using the Difference Method and SAR Coherence Coefficient

2018 ◽  
Vol 11 (3) ◽  
pp. 18-23 ◽  
Author(s):  
Zhang Xuedong ◽  
◽  
Liu Wenxi ◽  
He Shuguang ◽  
◽  
...  
Keyword(s):  
Change Detection ◽  
Difference Method ◽  
Urban Change ◽  
The Difference

scholarly journals Potential of seasonal hydrological forecasting of monthly run-off volumes for the Rhone and Arve Rivers from April to July

2018 ◽  
Author(s):  
Oriane Etter ◽  
Frédéric Jordan ◽  
Anton J. Schleiss
Keyword(s):  
Confidence Interval ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Average Error ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Rhône River ◽  
Run Off ◽  
Rhone River ◽  
The Difference

Abstract. In a context where water management is becoming increasingly important, reliable seasonal forecasting of discharge in rivers is crucial for making decisions several months in advance. This paper explores the potential of seasonal forecasting of run-off volumes produced by ensemble streamflow forecasting using past climatology and comparing it to the more commonly used average of past discharge measurements. The seasonal forecast was obtained for the Arve and Rhone rivers by simulation using the Routing System model for lead times of 30, 90 and 120 days. The initialization was performed on a validated simulation of 12 and 16 years for the Arve and Rhone rivers, respectively, obtained through long-term calibration. The performance was assessed by indicators called accuracy and thinness. The normalized mean average error (NMAE) was used to compare the performance of the seasonal forecast with the average of the past measurements. After a bias correction of the seasonal forecast of the Rhone River with the observed run-off volumes during the different lead times, the correlation of the median forecast with the measurements (accuracy) was larger than 0.55 for all lead times from April to July. The Arve River's accuracy was improved by disregarding the year 2007 member, leading to the floods of the 3rd and 9th of July, for lead times of 90 and 120 days. This resulting in the period of April to July having correlation accuracies higher than 0.5. For both rivers, the 80 % confidence interval of the seasonal forecast was relatively thin compared to the measurements (thinness) for the months of April to July. The NMAE was used to validate the range of validity of the forecast. The correction of the forecast resulted in more months being favorable for seasonal forecasting for the Rhone River. The post-processing on the Arve River decreased the difference between the measurement and the forecast (NMAE). Further investigation should concentrate on dividing the meteorological datasets to produce a strong median forecast and confidence interval

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