scholarly journals Pemisahan Anomali Regional-Residual pada Metode Gravitasi Menggunakan Metode Moving Average, Polynomial dan Inversion

2016 ◽  
Vol 3 (01) ◽  
pp. 10 ◽  
Author(s):  
Jarot Purnomo ◽  
Sorja Koesuma ◽  
Mohtar Yunianto
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Continuation Method ◽  
Inversion Method ◽  
Polynomial Method ◽  
Minimum Error ◽  
Gravity Method ◽  
Upward Continuation ◽  
And Inversion ◽  
Separation Results

<span>It has been done a research about separation of regional-residual anomaly in Gravity method. <span>This research compares the result of three methods i.e. moving average method, polynomial <span>method, and inversion method. The computer program is created using a computer programming <span>Matlab 7. From three methods that have been made, the separation results are compared with<br /><span>results of separation by using Upward Continuation method. From the results of these <span>comparisons will be available an excellent program of regional-residual anomali separation. The <span>results show that in polynomial method of the order 4 obtained similar contour to the separation <span>by Upward Continuation Software. So that the output of this separation will be treated again <span>with Grav2DC software. The output of this software is the density of rock Grav2DC of the study<br /><span>area. Processing results obtained the minimum error of 1.85% for the separation by polynomial <span>method, while for the method of Upward Continuation obtained minimum error of 2.22%. The <span>results obtained show that the separation of regional-residual anomali by polynomial method is <span>similar to separation by Upward Continuation method.</span></span></span></span></span></span></span></span></span></span></span></span><br /></span>

scholarly journals Comparative Results of Regional and Residual Anomalies with the Upward Continuation, Moving Average, and Polynomial Methods for Magnetic Data

2020 ◽  
Vol 2 (2) ◽  
pp. 90-93
Author(s):  
Luvera Deva Intan Indrawati ◽  
Rina Dwi Indriana ◽  
Irham Nurwidyanto
Keyword(s):  
Moving Average ◽  
Continuation Method ◽  
Second Order ◽  
Magnetic Data ◽  
Third Order ◽  
First Order ◽  
Upward Continuation ◽  
Order Polynomial ◽  
Matlab Programming ◽  
Polynomial Methods

Geophysics programing of regional and residual anomaly separation on Magnetic data has been carried out with the results compared with the upward continuation method in the OasisMontaj software. Separation of anomalies with moving average and polynomial methods is processed using Matlab programming. The orders used in the polynomial method are first-order, second-order and third-order. Comparison is done by calculating the match value. The chosen matching method is autocorrelation. Correlation of residual magnetic anomalies resulting from upward continuation (Magpick) to moving averages, 1st-order polynomials, 2nd-order polynomials and 3rd-order polynomials. Correlation values obtained for the moving average method are 0.9604, first order polynomial 0.9072, 2nd order polynomial 0.9482 and third order polynomial 0.6057. The moving average and second order polynomial methods can be used as a substitute method if we do not use the upward continuation method.

scholarly journals Aerosol microphysical retrievals from Precision Filter Radiometer direct solar radiation measurements and comparison with AERONET

2014 ◽  
Vol 7 (1) ◽  
pp. 99-130 ◽  
Author(s):  
S. Kazadzis ◽  
I. Veselovskii ◽  
V. Amiridis ◽  
J. Gröbner ◽  
A. Suvorina ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Volume Concentration ◽  
Effective Radius ◽  
Optical Data ◽  
Inversion Method ◽  
High Temporal Resolution ◽  
Random Errors ◽  
Percentage Difference ◽  
Filter Radiometer ◽  
And Inversion

Abstract. Synchronized sun-photometric measurements from the AERONET-CIMEL and GAW-PFR aerosol networks are used to compare retrievals of the aerosol optical depth, effective radius and volume concentration during a high temporal resolution measurement campaign at the Athens site in the Mediterranean Basin from 14–22 July 2009. During this period, direct sun AOD retrievals from both instruments exhibited small differences in the range 0.01–0.02 despite the presence of a strong dust event. In addition to AERONET-CIMEL inversion data, an independent inversion method was applied that involves expanding the particle size distribution in terms of measurement kernels so as to estimate bulk particle parameters from a linear-estimated combination of the input optical data. AOD measurements obtained from both CIMEL and PFR instruments using this method also showed reasonable agreement. For low aerosol loads (AOD < 0.2), measurements of the effective radius by the PFR were found to be −20% to +30% different from CIMEL values for both direct sun data and inversion data. At higher loads (AOD > 0.4), measurements of the effective radius by the PFR are consistently 20% lower than CIMEL for both direct sun and inversion data. Volume concentrations at low aerosol loads from the PFR are up to 80% higher than the CIMEL for direct sun data, but inversion data suggests that volume concentrations from the PFR are up to 20% lower than the CIMEL under these same conditions. At higher loads, the percentage difference in volume concentrations from the PFR and CIMEL is systematically negative with inversion data predicting differences 30% lower than those obtained from direct sun data. An assessment of the effect of errors in the AOD retrieval on the estimation of PFR bulk parameters was made using Monte Carlo simulations and demonstrated that it is possible to estimate the effective radius with an uncertainty below 60% and the volume concentration with an uncertainty below 65% even when AOD < 0.2 and when the input errors are as high as 10%. Highlights – A comparison of high temporal resolution synchronous CIMEL and PFR direct sun AOD measurement retrievals – Calculation of bulk aerosol microphysics parameters using a linear estimation inversion technique – A comparison of retrieved aerosol volume concentrations and effective radii from CIMEL and PFR inversions – An analysis of the sensitivity of PFR retrievals to random errors on the optical input data

scholarly journals Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

2018 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
M. Tirtana Siregar ◽  
S. Pandiangan ◽  
Dian Anwar
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Production Capacity ◽  
Mean Absolute Percentage Error ◽  
Programming Method ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Forecasting Method ◽  
Planning Production

The objectives of this research is to determine the amount of production planning capacity sow talc products in the future utilizing previous data from january to december in year 2017. This researched considered three forecasting method, there are Weight Moving Average (WMA), Moving Average (MA), and Exponential Smoothing (ES). After calculating the methods, then measuring the error value using a control chart of 3 (three) of these methods. After find the best forecasting method, then do linear programming method to obtain the exact amount of production in further. Based on the data calculated, the method of Average Moving has a size of error value of Mean Absolute Percentage Error of 0.09 or 9%, Weight Moving Average has a size error of Mean Absolute Percentage Error of 0.09 or 9% and with Exponential Method Smoothing has an error value of Mean Absolute Percentage Error of 0.12 or 12%. Moving Average and Weight Moving Average have the same MAPE amount but Weight Moving Average has the smallest amount Mean Absolute Deviation compared to other method which is 262.497 kg. Based on the result, The Weight Moving Average method is the best method as reference for utilizing in demand forecasting next year, because it has the smallest error size and has a Tracking Signal&nbsp; not exceed the maximum or minimum control limit is &le; 4. Moreover, after obtained Weight Moving Average method is the best method, then is determine value of planning production capacity in next year using linier programming method. Based on the linier programming calculation, the maximum amount of production in next year by considering the forecasting of raw materials, production volume, material composition, and production time obtained in one (1) working day is 11,217,379 pcs / year, or 934,781 pcs / month of finished product. This paper recommends the company to evaluate the demand forecasting in order to achieve higher business growth.

scholarly journals Post-Exercise Assessment of Cardiac Repolarization Alternans in Patients With Coronary Artery Disease Using the Modified Moving Average Method

2009 ◽  
Vol 53 (13) ◽  
pp. 1130-1137 ◽  
Author(s):  
Michael P. Slawnych ◽  
Tuomo Nieminen ◽  
Mika Kähönen ◽  
Katherine M. Kavanagh ◽  
Terho Lehtimäki ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Average Method ◽  
Moving Average ◽  
Cardiac Repolarization ◽  
Post Exercise ◽  
Artery Disease
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