COMPUTER MAPPING OF ALBERTA SOIL TEST DATA

1970 ◽  
Vol 50 (1) ◽  
pp. 1-7 ◽  
Author(s):  
D. R. CAMERON ◽  
J. A. TOOGOOD
Keyword(s):  
Test Data ◽  
Moving Average ◽  
Mapping Function ◽  
Available Phosphorus ◽  
Soil Test ◽  
Nitrogen Levels ◽  
Ph Values ◽  
Soil Zone ◽  
Weighted Moving Average ◽  
Phosphorus Levels

A circular mapping function based on the principle of a weighted moving average was applied in a computer program to the available Alberta soil test data, and nutrient levels of soils in the province were plotted and contoured. The finished maps displayed clearly marked trends. Variation in nitrate-nitrogen levels for samples from fallow and cropped land was readily seen, but correlation with soil zone was not well marked. Available phosphorus levels were relatively higher on the Solonetzic soils in central Alberta. Exchangeable potassium levels appeared to be closely related to the soil zones of Alberta. The mapped pH values presented the least variable picture. The electronic computer was essential for the mapping of data as reported in this study, and future applications of the computer to other soil data appear to be unlimited.

scholarly journals Dietary metabolizable energy, digestible lysine, available phosphorus levels and exogenous enzymes affect broiler chicken performance

animal ◽  
2021 ◽  
Vol 15 (5) ◽  
pp. 100206
Author(s):  
P. Cozannet ◽  
R. Davin ◽  
M. Jlali ◽  
J. Jachacz ◽  
A. Preynat ◽  
...  
Keyword(s):  
Broiler Chicken ◽  
Metabolizable Energy ◽  
Available Phosphorus ◽  
Exogenous Enzymes ◽  
Phosphorus Levels

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

2020 ◽  
pp. 1-21
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Moving Average ◽  
Integrated System ◽  
Measurement Noise ◽  
Noise Estimation ◽  
Doppler Velocity ◽  
Forgetting Factor ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

Simple Multivariate Conditional Covariance Dynamics Using Hyperbolically Weighted Moving Averages

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hiroyuki Kawakatsu
Keyword(s):  
Moving Average ◽  
Curse Of Dimensionality ◽  
Moving Averages ◽  
Conditional Covariance ◽  
Arch Models ◽  
Empirical Application ◽  
Proposed Model ◽  
Out Of Sample ◽  
Weight Model ◽  
Weighted Moving Average

AbstractThis paper considers a class of multivariate ARCH models with scalar weights. A new specification with hyperbolic weighted moving average (HWMA) is proposed as an analogue of the EWMA model. Despite the restrictive dynamics of a scalar weight model, the proposed model has a number of advantages that can deal with the curse of dimensionality. The empirical application illustrates that the (pseudo) out-of-sample multistep forecasts can be surprisingly more accurate than those from the DCC model.

Sign in / Sign up

Export Citation Format

Share Document