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Quality Assessment of Speckle Patterns by Estimating RMSE
The primary objective of this proceeding is to properly assess the quality of speckle patterns utilized in digital image correlation (DIC). Since a good pattern should associate with a small measurement error, the overall error can be used to assess the quality of a speckle pattern.Yong Su, Qingchuan Zhang
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RMSE POWER COEFFICIENT OF WIND TURBINES WITH INVERSE TOQUE-SPEED CONTROL
ShodhKosh: Journal of Visual and Performing ArtsThe Blade Element Momentum (BEM) method stands out among various turbine modelling techniques due to its integration of airfoil aerodynamics with momentum theory, allowing detailed force calculations on blade elements. While traditional methods like BEM and Wake Models are efficient for initial turbine design, advancements in computational power have ...
Rishikesh Choudhary +2 more
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RMSE comparison of path loss models for UHF/VHF bands in India
2014 IEEE REGION 10 SYMPOSIUM, 2014This paper describes a study on path loss variation in UHF/VHF bands in India from a root mean square error perspective. The aim of this study is to compare existing propagation path loss models in various parts of India. We calculate average root mean square error (RMSE) between measured path loss and those predicted by the existing path loss models ...
Bolli Sridhar, Mohammed Zafar Ali Khan
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Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE
Remote Sensing of Environment, 2003Abstract Multiple endmember spectral mixture analysis (MESMA) models mixed spectra as a linear combination of endmembers that are allowed to vary in number and type on a per pixel basis. For modeling an image using MESMA, a parsimonious set of endmembers is desirable for computational efficiency and operational simplicity.
Philip E. Dennison, Dar A. Roberts
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Multi-Layered Cardiac Arrhythmia Detection Using RMSE Method
2023 IEEE International Conference on Contemporary Computing and Communications (InC4), 2023A Raja +3 more
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Canopy�K-means Combined Collaborative Filtering Using RMSE-minimization
2022 IEEE International Conference on Big Data and Smart Computing (BigComp), 2022Sao-I Kuan +3 more
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Approach Advancing Stock Market Forecasting with Joint RMSE Loss LSTM-CNN Model
Fluctuation and Noise LettersThe intricacies and dynamism of financial markets pose challenges to models seeking to comprehensively capture the multitude of factors influencing stock price movements. As such, there remains room for improvement in forecasting accuracy. In response, we introduce a novel approach that unifies the Root Mean Square Error (RMSE), loss functions of Long
Mungara Kiran Kumar +5 more
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RMSE Reduction for GMM Estimators of Linear Time Series Models [PDF]
, 2000 In this paper we analyze GMM estimators for time series models as advocated by Hayashi and Sims, and Hansen and Singleton. It is well known that these estimators achieve efficiency bounds if the number of lagged observations in the instrument set goes to infinity. A new version of the GMM estimator based on kernel weighted moment conditions is proposed.
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MAE and RMSE Analysis of K-means Predictive Algorithm for Photovoltaic Generation
2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022Paula Zenni Lodetti +4 more
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Friction estimation – optimization of sensor configuration with respect to RMSE and costs
2014The accuracy of friction estimation depends on the sensors used. Furthermore, the costs of sensors have to be considered during system design.
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