Sum Of Relative Errors Research Articles

Integration of statistical forest reflectance model and Sentinel-2 MSI images into a continuous forest inventory system

Spectral signatures of forest stands in Sentinel-2 MSI spectral bands are simulated with the statistical forest reflectance (SFRM) model and compared to the spectral signatures measured in spectral images at ten study sites in Estonia. As an overall measure of the agreement between simulated and measured spectral signatures we used the total error calculated as the sum of relative errors over spectral bands B2 to B11 of Sentinel-2. The distribution of the total error has strongly positive skewness at all study sites and all types of forests (broadleaf, pine and spruce forests). The right tail of the distribution is low. The stands of high value of the total error far right in the tail of the distribution may have some errors in their inventory data, or the inventory data are outdated. Pertinent stands should have priority in their in situ checking process. The SFRM model is a simple and reliable tool for the validity checking of forest inventory data, using routinely collected forest inventory data and operational satellite information of moderate spatial resolution. The model is simple and computationally efficient. Preparing input data for the model is a simple query in the forest inventory database. The suggested procedure can be incorporated into the automated systems of continuous forest inventory. 
 
 Keywords: Forest inventory, Sentinel-2 MSI images, Statistical forest reflectance model

An Improved Teaching-Learning Based Optimization Algorithm and Its Application to Aero-Engine Start Model Adaptation

The modeling of the engine starting process is vital to ensure the successful start of the engine. However, the engine starting process is very complicated and challenging to model. To optimize the start model performance, an improved teaching-learning based optimization (ITLBO) algorithm is proposed. In ITLBO, a collective lesson preparation phase is increased to enhance the teaching ability of the teacher. The random learning phase is replaced by S-shape group learning, and students learn from the top students of their groups. Also the deterministic sampling selection phase is introduced to ITLBO, and the students with higher evaluation have more possibility to advance in class. The improved algorithm is tested on 18 benchmark functions. The results indicate that the proposed ITLBO algorithm performs much better in terms of convergence speed and accuracy than standard TLBO. When applied to the model adaptation of the turbofan engine starting process, ITLBO is used to optimize the speed line of the rotation components gradually from the lower speed line to the idle speed line. The weighted sum of relative errors between the model outputs and the start test data is taken as the fitness function. After adaptation, the maximum relative errors of model outputs to start test data are significantly decreased, which shows the effectiveness of the ITLBO in model adaption.

Approach for designing and modelling of nanoscale DG MOSFET devices using Kriging metamodelling technique

In this study, the authors focus mainly on the investigation of Kriging interpolation method to elaborate surrogate models of the nanoscale double‐gate metal oxide silicon field effect transistors (DG MOSFET) analogue/RF performance under critical operational conditions. The elaboration of such models is made possible through the generation of computer experiments using ATLAS‐2D simulator, where the numerical simulations or experimental measurements, account for the accurate behaviour of the device including the ageing phenomena, short channel and quantum confinement effects. The validity of the obtained Kriging models is tested by comparing the predicted responses of the device with their numerical counterpart in terms of some statistical criteria namely the sum of relative errors, the mean percentage of absolute errors and the correlation coefficient. It is also shown that the obtained Kriging interpolation models are precise enough to be used as objective functions in the context of a genetic algorithm optimisation with the aim of improving the device analogue/RF performance in terms of transconductance and cut‐off frequency parameters. Therefore, this study may provide more insights regarding the investigation of surrogate modelling tools in the field of deep nanoscale devices especially with the intractable mission of developing physical based models at this scale for nanoelectronic simulators.