Absolute Mean Square Error Research Articles

Vehicle Tire Traction Torque Estimation Using a Dual Extended Kalman Filter

Abstract Vehicle tire traction torque, heavily dependent on vehicle speed and tire stiffness, is critical for improving vehicle traction performance. However, due to the limitation of existing technology and sensor cost, it becomes rather expensive to accurately measure the vehicle tire traction torque and/or other vehicle variables directly. This paper proposes to estimate the tire traction torque by estimating vehicle speed (vehicle state) and tire stiffness simultaneously based on a few available low-cost measurements from any production vehicle. Specifically, the tire and full vehicle dynamics are considered to form a unified traction torque estimation model under various vehicle operational conditions. Estimation of vehicle speed and tire stiffness is formulated into a dual-estimation problem of system states and parameters. A recursive real-time implementable solution for the dual-estimation problem is realized with the help of dual extended Kalman filter (DEKF) algorithm. The effectiveness of the proposed algorithm under different vehicle operating conditions is validated by comparing the estimated results with directly measured ones as well as those from existing estimation approaches. It is found that for a 4-wheel driving vehicle, under clutch overtaken condition, for the best case, the absolute mean square error (ASME) improves by around 20 Nm, and the relative mean square error (RMSE) reduces 12%; and under clutch slip condition, the absolute mean square error improves by around 40 Nm, and the RMSE reduces 6%.

Forecasting of Drought: A Case Study of Water-Stressed Region of Pakistan

Demand for water resources has increased dramatically due to the global increase in consumption of water, which has resulted in water depletion. Additionally, global climate change has further resulted as an impediment to human survival. Moreover, Pakistan is among the countries that have already crossed the water scarcity line, experiencing drought in the water-stressed Thar desert. Drought mitigation actions can be effectively achieved by forecasting techniques. This research describes the application of a linear stochastic model, i.e., Autoregressive Integrated Moving Average (ARIMA), to predict the drought pattern. The Standardized Precipitation Evapotranspiration Index (SPEI) is calculated to develop ARIMA models to forecast drought in a hyper-arid environment. In this study, drought forecast is demonstrated by results achieved from ARIMA models for various time periods. Result shows that the values of p, d, and q (non-seasonal model parameter) and P, D, and Q (seasonal model parameter) for the same SPEI period in the proposed models are analogous where “p” is the order of autoregressive lags, q is the order of moving average lags and d is the order of integration. Additionally, these parameters show the strong likeness for Moving Average (M.A) and Autoregressive (A.R) parameter values. From the various developed models for the Thar region, it has been concluded that the model (0,1,0)(1,0,2) is the best ARIMA model at 24 SPEI and could be considered as a generalized model. In the (0,1,0) model, the A.R term is 0, the difference/order of integration is 1 and the moving average is 0, and in the model (1,0,2) whose A.R has the 1st lag, the difference/order of integration is 0 and the moving average has 2 lags. Larger values for R2 greater than 0.9 and smaller values of Mean Error (ME), Mean Absolute Error (MAE), Mean Percentile Error (MPE), Mean Absolute Percentile Error (MAPE), and Mean Absolute Square Error (MASE) provide the acceptance of the generalized model. Consequently, this research suggests that drought forecasting can be effectively fulfilled by using ARIMA models, which can be assist policy planners of water resources to place safeguards keeping in view the future severity of the drought.

THE FORECASTING OF MONTHLY INFLATION IN MALANG CITY USING AN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE

Abstract: Malang is known as a student city since there are a lot of schools and universities that can be found in Malang Indonesia. Malang is also an attractive tourist place with many tourist attractions in the city of Malang. Public transportation in the city of Malang is also very varied, ranging from conventional and based online. Access to the city of Malang is varied, namely trains, buses, and planes. Thus economic growth in the city of Malang is getting better, this can be seen from the economic activity in the increasingly crowded city of Malang. A good economy is usually followed by stable inflation. For this reason, it is necessary to examine how the monthly inflation rate in Malang city. This study aims to forecast inflation in the coming periods using the Autoregressive Integrated Moving Average (ARIMA) model. Secondary monthly inflation data is obtained from BPS Malang. From this research, the ARIMA model (2,0,3) is obtained. The accuracy model is used in this research namely root means square error (RMSE), mean absolute error (MAE), and mean absolute square error (MASE). The accuracy value is RMSE equal 0.2645467, MAE equal 0.2013898, and MASE equal 0.6047399. Keywords: Monthly inflation forecasting, BPS Malang city, ARIMA model.

Penerapan Metode Peramalan untuk Identifikasi Permintaan Konsumen

The forecast model is done using data from several years before, with the involvement of time parameters in the forecast process is usable for the company to made an effective and efficient planning. Forecasting has an important role because the company requires short-term, medium-term and long-term estimates for each management. For short-term estimates, a company requires personnel, production and transportation scheduling, which is part of the process of scheduling and estimating consumer demand. In this study the results of three forecasting methods were compared, there is simple average, naïve and seasonal naive on demand data of PT SUPER SUKSES NIAGA to be further these three method measured its forecast accuracy using the value of MASE (Mean Absolute Square Error). From the results of data pre-processing consumers whose high value of demand are PT. DIESELINDO, PT. DUTA, PT. HEXINDO and PT. PANATAMA. The results of forecasting shown that the method that has the smallest MASE value is the simple moving average method.

Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing

In this paper, two models of forecasting are used the Box-Jenkins procedure employing the SARIMA and the Holt-Winters triple exponential smoothing. Published Consumer Price Index Data from Kenya National Bureau of Statistics (KNBS) for the period November 2011 to October 2016 was used. This paper we equate the forecasted values of both the models and we choose the best model based on the least mean Absolute square error (MASE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The three step model building for Box-Jenkins was first employed, followed by the Hold-Winters triple exponential smoothing. The study found the SARIMA Model was a better model than the Holt-winters triple exponential smoothing as per the obtained results using MASE, MAE and MAPE.

Augmenting Photometric Stereo with Coaxial Illumination under Water

In this paper, two coaxial light sources are added to four light sources based on Photometric Stereo (PMS), and we put forward a kind of gradient fast algorithm. The simulation and underwater experiments show that the proposed algorithm can eliminate the influence of the shadow in four light photometric stereo algorithm and also reduce Mean Square absolute Error (MSE) and Relative Mean Square Error (RMSE) of underwater target 3D reconstruction, and can get underwater target albedo. This algorithm can be widely used in underwater target fast 3D reconstruction.

Performance Comparison between Orthogonal, Bi-Orthogonal and Semi- Orthogonal Wavelets

The main work in the wavelet analysis is to find a good wavelet basis to perform an optimal decomposition. The goal of the proposed study is to obtain a basis function that can give optimal information from PQ signal. The study presents the wavelet basis to obtain the reconstruction and decomposition filter coefficients for orthogonal, bi-orthogonal and semi-orthogonal wavelet basis. In this study, the task is to choose better wavelet basis which has been used for PQ signal compression or decomposition among orthogonal, bi-orthogonal and semi-orthogonal wavelet basis. Certain criterion have been adopted to decide the best basis for the decomposition of the PQ signal which are as energy compaction ratio (ECR), absolute mean square error (AMSE), percent residual difference (PRD) and peak signal to noise ratio (PSNR). Numbers of experiments have been performed on real time PQ signal. The comparisons have been made in tabular form to choose the best wavelet basis.

Design of nonrecursive filters satisfying arbitrary magnitude and phase specifications using a least-squares approach

A method is described which can be used to design nonrecursive filters satisfying prescribed magnitude and phase specifications. The method is based on formulating the absolute mean-square error between the frequency response of the practical filter and the desired response as a quadratic function. The coefficients of the filters are obtained by solving a set of linear equations. It is shown that our method, in general, has an order of magnitude lower computational complexity than the eigenfilter method. For the design of allpass filters, in particular, the computational complexity is three orders of magnitude lower than the eigenfilter method. In addition, our method yields a lower mean-square error. >

Least-squares design of higher order nonrecursive differentiators

A method is described that can be used to design non-recursive linear-phase higher order differentiators that can perform differentiation over any frequency range. The method is based on formulating the absolute mean-square error between the amplitude responses of the practical and ideal differentiator as a quadratic function. The coefficients of the differentiators are obtained by solving a set of linear equations. This method leads to a lower mean-square error and is computationally more efficient than both the eigenfilter method and the method based on the Remez exchange algorithm. Design of differentiators based on minimization of the relative mean-square error is also carried out. Finally, the method is extended to the design of frequency selective higher order differentiators. >