Fast Diagnosis of Volatile Organic Compounds with a Temperature-Modulated Chemoresistor

2011 ◽  
Vol 495 ◽  
pp. 310-313 ◽  
Author(s):  
Amir Amini ◽  
Seyed Mohsen Hosseini-Golgoo
Keyword(s):  
Cross Validation ◽  
Operating Temperature ◽  
Moving Average ◽  
Feature Space ◽  
Autoregressive Moving Average ◽  
Temperature Modulation ◽  
Voltage Waveform ◽  
Volatile Organic ◽  
Gas Identification ◽  
Armax Model

Virtual arrays formed by operating temperature modulation of a commercial non selective chemoresistor have been utilized for gas identification. Here, we are reporting the details of a refined system which distinctly classifies methanol, ethanol, 1-butanol, acetone and hydrogen contaminations in a wide concentration range. A staircase voltage waveform of 5 plateaus is applied to the sensor’s microheater and gas recognition is achieved in 25 s. Sensor’s output is modeled by an “autoregressive moving average with exogenous variables” (ARMAX) model. The modeling parameters obtained for an unknown analyte are utilized as the components of its feature vectors which afford its classification in a feature space. Cross-validation in the 5 to 100 ppm concentration range for H2, and 200 to 2000 ppm for the other analytes examined, resulted in an overall classification success rate of 100%.

scholarly journals Forecasting hydrologic parameters using linear and nonlinear stochastic models

2019 ◽  
Vol 11 (4) ◽  
pp. 1284-1301
Author(s):  
Hamed Nozari ◽  
Fateme Tavakoli
Keyword(s):  
Support Vector Machine ◽  
Moving Average ◽  
Sustainable Use ◽  
Rain Gauge ◽  
Support Vector ◽  
Autoregressive Moving Average ◽  
Monthly Precipitation ◽  
Precipitation Prediction ◽  
Armax Model ◽  
Monthly Discharge

Abstract One of the most important bases in the management of catchments and sustainable use of water resources is the prediction of hydrological parameters. In this study, support vector machine (SVM), support vector machine combined with wavelet transform (W-SVM), autoregressive moving average with exogenous variable (ARMAX) model, and autoregressive integrated moving average (ARIMA) models were used to predict monthly values of precipitation, discharge, and evaporation. For this purpose, the monthly time series of rain-gauge, hydrometric, and evaporation-gauge stations located in the catchment area of Hamedan during a 25-year period (1991–2015) were used. Out of this statistical period, 17 years (1991–2007), 4 years (2008–2011), and 4 years (2012–2015) were used for training, calibration, and validation of the models, respectively. The results showed that the ARIMA, SVM, ARMAX, and W-SVM ranked from first to fourth in the monthly precipitation prediction and SVM, ARIMA, ARMAX, and W-SVM were ranked from first to fourth in the monthly discharge and monthly evaporation prediction. It can be said that the SVM has fewer adjustable parameters than other models. Thus, the model is able to predict hydrological changes with greater ease and in less time, because of which it is preferred to other methods.

Recursive Identification of the Dynamic Behavior in a Distillation Column by Means of Autoregressive Models

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Lakhdar Aggoune ◽  
Yahya Chetouani ◽  
Hammoud Radjeai
Keyword(s):  
Performance Measures ◽  
Distillation Column ◽  
Moving Average ◽  
Information Criterion ◽  
Recursive Identification ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Recursive Algorithms ◽  
Optimal Model ◽  
Armax Model

In this study, an Autoregressive with eXogenous input (ARX) model and an Autoregressive Moving Average with eXogenous input (ARMAX) model are developed to predict the overhead temperature of a distillation column. The model parameters are estimated using the recursive algorithms. In order to select an optimal model for the process, different performance measures, such as Aikeke's Information Criterion (AIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE), are calculated.

Assessing the Effects of Terrorism on Tourism by Use of Time Series Methods

2003 ◽  
Vol 9 (2) ◽  
pp. 179-190 ◽  
Author(s):  
Brian W. Sloboda
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Moving Average ◽  
Single Equation ◽  
Autoregressive Moving Average ◽  
Short Term ◽  
Explanatory Variables ◽  
Use Of Time ◽  
Dependent Variables ◽  
Armax Model

This paper presents an assessment of the effects of terrorism on tourism by using time series methods, namely the ARMAX (autoregressive moving average with explanatory variables) model. This is a single-equation approach, which has the ability to provide impact analysis easily. The use of the ARMAX model allows for the general shape of the lag distribution of the impacts of the explanatory variables based on the ratio of lag polynomials for the independent and dependent variables. The ARMAX models, like the ARIMA models, provide for a short-term assessment of terrorist incidents on tourism.

Input Excitation Analysis for Black-Box Quadrotor Model System Identification

2020 ◽  
Author(s):  
John Angarita ◽  
Daniel Doyle ◽  
Gustavo Gargioni ◽  
Jonathan Black
Keyword(s):  
System Identification ◽  
Dynamic Models ◽  
Moving Average ◽  
Black Box ◽  
Autoregressive Moving Average ◽  
Nonlinear Responses ◽  
Box Models ◽  
Armax Model ◽  
Quadrotor System ◽  
Error Method

Abstract System identification provides a process to develop different dynamic models of varying structures based on user-defined requirements. For a quadrotor, system identification has been primarily in the field of off-white and grey-box models, but black-box models have the advantage of incorporating nonlinear aero-dynamic effects while also maintaining performance. For the identification, both a chirp and Hebert-Mackin parameter identification method waveform are used as inputs to maximize excitation while minimizing nonlinear responses. The quadrotor structure is defined by the an autoregressive with exogenous input (ARX) model, an autoregressive-moving-average (ARMAX) model, and a Box-Jenkins (BJ) models and then identified with the prediction error method. The black-box method shows that it maintains identification performance while improving upon the flexibility of different cases and ease of implementation.

Selective Hydrogen Detection in a Highly Contaminated Background Using only a Single Generic Metal Oxide Gas Sensor

2013 ◽  
Vol 543 ◽  
pp. 109-112
Author(s):  
Amir Amini ◽  
Khachik Babaians ◽  
Mohsen Gharesi
Keyword(s):  
Metal Oxide ◽  
Gas Sensor ◽  
Operating Temperature ◽  
Response Patterns ◽  
Voltage Waveform ◽  
Common Pattern ◽  
Temperature Changes ◽  
Temperature Jumps ◽  
Volatile Organic ◽  
Metal Oxide Gas Sensor

Detection of highly ppm range hydrogen concentration in atmospheres contaminated with various volatile organic compounds is in demand for numerous applications. Different devices and techniques have been applied for the problems which are mostly based on utilization of hydrogen permeable membranes. Here, we have used a single generic metal oxide gas sensor for this task. No filter or membrane is utilized. The operating temperature of the sensor is modulated with a voltage waveform specifically designed for producing step-like temperature changes on the oxide pallet. By applying four different step-like temperature jumps, each of 1s duration, the sensor produces response patterns which are processed with common pattern recognition techniques. The technique was examined by its practical use for the ~10 ppm (volume) hydrogen measurement in a background containing ~1000 ppm ethanol. The analysis takes only 4s, and the obtained patterns are reproducible.

scholarly journals PERAMALAN KUNJUNGAN WISATAWAN MENGGUNAKAN MODEL ARMAX DENGAN NILAI KURS DAN EKSPOR-IMPOR SEBAGAI FAKTOR EKSOGEN

2014 ◽  
Vol 3 (4) ◽  
pp. 138
Author(s):  
PUTU IKA OKTIYARI LAKSMI ◽  
KOMANG DHARMAWAN ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Foreign Currency ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Mathematical Form ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Armax Model ◽  
Past Data ◽  
Chinese Yuan

Forecasting is science to estimate occurrence of the future. This matter can be conducted by entangling intake of past data and place to the next period with a mathematical form. This research aims to estimate the number of foreign tourists visiting Bali models using autoregressive moving average exogenous (ARMAX). The data used in this study is the number of tourists in Australia and the number of tourists in the RRC as a variable Y, and foreign currency exchange rate AUD, Chinese Yuan, and Export Import as the X factor from the period July 2009 to July 2014. In the analysis can be obtained in the best ARMAX models of the number of tourists in Australia is ARMAX(1,2,2) and the best model of the number of tourists in the RRC does not exist because the data for the ARMAX model parameters tourists no significant RRC.

An Ionic-Polymer-Metal-Composite Electrical Model With a Linear Time-Variant Method

2013 ◽  
Author(s):  
Yi-chu Chang ◽  
Won-jong Kim
Keyword(s):  
Water Distribution ◽  
Linear Time ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Metal Composite ◽  
Ionic Polymer Metal Composite ◽  
Electrical Model ◽  
Ionic Polymer ◽  
Polymer Metal ◽  
Armax Model

Ionic polymer metal composite (IPMC), categorized as an ionic electroactive polymer (EAP), can exhibit conspicuous deflection with low external voltages (∼5 V). This material has been commonly applied in robotic artificial muscles since reported in 1992 because it can be fabricated in various sizes and shapes. Researchers developed numerous IPMC models according to its deflection in response to the corresponding input stimulation. In this paper, an IPMC strip is modeled (1) as a cantilever beam with a loading distribution on the surface, and (2) with system identification tools, such as an autoregressive with exogenous (ARX)/autoregressive moving average with exogenous (ARMAX) model and an output-error (OE) model. Nevertheless, the loading distribution is non-uniform due to the imperfect surface conductivity. Finally, a novel linear time-variant (LTV) modeling method is introduced and applied to an IPMC electrical model on the basis of the internal environment such as surface resistance, thickness, and water distribution related to the unique working principle of IPMC. A comparison between the simulated and the experimental deflections demonstrates the benefits and accuracy of the LTV electrical model.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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