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The paper makes further research on estimating the parameters of the asymmetrical model describing effect of nutrient base on present literature, and provides a new method for estimating parameters that this method combines one-dimension search of optimization theory and multiple linear regression. The method not only overcomes the faultiness of estimation methods in existing literature, but also...
Gesture input interfaces for mobile devices with a touch screen have become widespread. Although gesture interfaces in common use are limited to the small screens of these mobile devices, pointing interfaces for large screens using handheld devices or attachments are common. However, these devices increase the user's cognitive load because they are unable to cancel the effect of spatial cognition...
This paper investigates the use of multi-linear regression models (MLRMs) and machine learning techniques for online voltage stability margin prediction. The methodology relies upon the relationship between system wide reactive power reserves and voltage stability margin. A comprehensive voltage stability assessment considering an extensive contingency list and several load increase directions is...
Prediction of village electrical load is very important to manage village electrical load efficiently. Support vector regression (SVR) is a new learning algorithm based on statistical learning theory, which has a good time-series forecasting ability. As the choice of the best parameters of support vector regression is an important problem for support vector regression, and this problem will directly...
A machine vision system is used to estimate surface roughness of machined surface after grinding, milling and shaping. The estimated roughness is compared with stylus measurements. The estimation is usually done by linear regression which suffers from outliers and hence the estimation is not robust. In this paper we compare redescending and monotone type of robust M estimation for estimation of surface...
This paper presents computational intelligence techniques for software cost estimation. We proposed a new recurrent architecture for genetic programming (GP) in the process. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are implemented. We also performed GP based feature selection. The efficacy of these techniques viz multiple linear regression, polynomial...
Optical coherence tomography (OCT) is an emerging medical imaging technology able to detect tissue microstructure in vivo and in situ. However, many changes, associated with diseases such as cancer, result in cellular and sub-cellular variations which are very important for the diagnosis but are below the resolution limit of OCT. Since the spectrum of scattered light is structure-dependent, the spectral...
Managing power consumption in a System-On-Chip (SoC) design is becoming increasingly important. SoCs generally consist of various co-processors. Accurate power estimation of these co-processors at the highest possible abstraction level helps in performing early power-aware design tradeoffs. This paper presents a methodology to create abstract statistical power models for hardware co-processors and...
The identification of corporation accounting frauds is a major difficulty in financial study. This article establishes an identifying model of financial frauds of Chinese listed firms by adopting Logistic regression analysis. Then it defines public companies whose accounting statements are rejected or denied by register accountant in Shanghai & Shenzhen security market as " corporation with...
We show how to solve two problems of optimal linear estimation from a finite set of phase data. Clock noise is modeled as a stochastic process with stationary dth increments. The covariance properties of such a process are contained in the generalized autocovariance function (GACV). We set up two principles for optimal estimation; these principles lead to a set of linear equations for the regression...
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