Analysis of experimental data on internal waves with statistical method

Abstract

PurposeThis study seeks to develop a systematic means of identifying regression models using a complex regression model with a statistical method.Design/methodology/approachAs a widely adopted statistical scheme for analyzing multifactor data, regression analysis provides a conceptually simple algorithm for examining functional relationships among variables. This investigation assesses the proposed relationship using a sample of data in regression analysis and then estimates the fit using statistics. Furthermore, several algorithms and added variable plots are presented to obtain an appropriate regression model and the relationship between response variables y, p and explanatory variables x0,x1,x2, … ,xp.FindingsThe proposed statistical scheme is demonstrated by the analysis of experimental data on internal waves, in which the results can well illustrate what has been investigated in laboratory experiment and may be applicable to the naturally occurring reflection of internal waves from sloping bottoms.Practical implicationsIn previous studies, field observations of internal waves were carried out. Owing to the limit of stationary measurement in situ, sufficient experiment data were not easily obtained. On the other hand, data collected by laboratory experiments express more information on wave mechanism, such as energy dissipation, mixing efficiency, and stratification thickness in a stratified layer fluid system. In present study, the authors supply valuable experiment data and analytic method, which will be a great contribution to the geophysical fluid dynamics. The results illustrate well what has been investigated in laboratory experiment and may be applicable to the naturally occurring reflection of internal waves from sloping bottoms.Originality/valueMore recently, it has been proposed that internal wave mixing may contribute significantly to internal mixing in the ocean and hence has an important influence on world climatic changes. Based on the statistical algorithm and regression model, the reduction in internal wave energy can be predicted on a sloping bottom due to frictional effect. Since, interaction between internal waves and uniform slopes has occurred in an estuary, a lake or in the ocean, the results available in this paper would benefit future study on internal wave hydrodynamics.

Using discriminant analysis to determine the breaking criterion for an ISW propagating over a ridge

“…They made field measurements of sea surface wind, sea state and vertical temperature profiles which were used for analyzing IW generation and for generating synthetic aperture radar (SAR) imaging conditions. Soon after this, the effects of weighted parameters on the amplitude and reflection of energy-based ISWs from uniform slopes in a two layered fluid system were investigated [8]. The results of that study were quite consistent with other experimental results, and are applicable to the naturally occurring reflection of ISWs from sloping bottoms.…”

Retracted: Computer‐aided ergonomics and visualization for improving 3D display techniques

“…There has been increasing interest in computer-aided ergonomics and its applications in recent years, such as in the fields of intelligent robots, artificial intelligence, 3D display technology, and so on (Chen, 2009a(Chen, , 2009b(Chen, , 2010a(Chen, , 2010bChen and Chen 2010;Chen et al, 2007aChen et al, , 2007bChen et al, , 2007cChen et al, , 2009aChen et al, , 2009bChen et al, , 2010aChen et al, , 2010bChen et al, , 2010cChen, 2011aChen, , 2011bChen, , 2011cChen, , 2011d. In this study, we use Flash to design a simple game and use 3D red-blue filter glasses to test the effects.…”

Retracted: A hybrid artificial intelligence sales‐forecasting system in the convenience store industry

“…Currently, many studies are being presented, and it would not be a problem for the ECFM. There has been increasing interest in computer‐aided ergonomics and its applications in recent years (Chen, 2009; 2010; Chen, Chen, Yang, & Chen, 2007; Chen, Shih, & Chou, 2010; Lee, Chen, Kuo, & Sui, 2010; Lee, Chen, & Wu, 2010; Lee, Chen, Chen, & Chen, 2010). The ECFM is different from the fuzzy model, which needs to decide the fuzzy set and fuzzy membership function subjectively, although it needs to consider the linguistic factors, which cannot be described quantitatively.…”