Statistical analysis of the CuBr laser efficiency improvement

Iliev, I.; Gocheva-Ilieva, Snezhana; Astadjov, D.N.; Denev, Nikolay Petrov; Sabotinov, Nikola V.

doi:10.1016/j.optlastec.2007.09.009

Cited by 13 publications

(6 citation statements)

References 12 publications

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“…It total, the data from more than 300 experiments from literature have been observed (see [4][5][6] and quoted there references). Many carefully randomized samples were examined in order to cover the strong requirements of the used statistical techniques.…”

Section: Description Of Data and Some Preliminary Resultsmentioning

confidence: 99%

“…In recent papers [4][5][6] we have explored a different approach -a phenomelogical independent study, based on the experimental data. To this end a big amount of data for copper bromide laser with wave lengths 510.6 nm and 578.2 nm, obtained in Metal Vapour Lasers Department Georgi Nadjakov Institute of Sohd State Physics, Bulgarian Academy of Sciences -Sofia in the recent decades were examined (see [4][5][6] and quoted there literature). Different statistical methods, such as multidimensional factor analysis, principal components analysis, multiple regression and others were applied for studying the influence of ten basic input laser parameters on the output laser power and laser efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Application of the Multidimensional Statistical Techniques for Exploring Copper Bromide Vapor Laser

Gocheva-Ilieva¹,

Iliev²

2008

AIP Conference Proceedings

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Section: Description Of Data and Some Preliminary Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Application of the Multidimensional Statistical Techniques for Exploring Copper Bromide Vapor Laser

Gocheva-Ilieva¹,

Iliev²

2008

AIP Conference Proceedings

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“…The model is significant at level Sig.=0.000 and the coefficients at level Sig.=0.013. (11) The basic statistics of model (10)-(11) are given in Table 2. This model is statistically significant at level Sig.=0.000 and the coefficients at level Sig.…”

Section: E Ffmentioning

confidence: 99%

“…In [7,10,11] factor, cluster and multivariate regression analyses were applied for investigation of copper bromide laser data. Principal component regression and nonparametric models of laser output power using factor variables for the case of copper bromide laser were constructed and analyzed in [7,12].…”

Section: Introductionmentioning

confidence: 99%

Application of MARS for modeling local nonlinear relationships in data on ultraviolet copper ion lasers

Gocheva-Ilieva¹,

Voynikova²,

Iliev³

2012

AIP Conference Proceedings

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Articles you may be interested inExtended temperature tuning of an ultraviolet diode laser for trapping and cooling single Yb + ions Rev. Sci. Instrum. 81, 053110 (2010); 10.1063/1.3386580Three-dimensional simulation of laser-produced plasma for extreme ultraviolet lithography applications Abstract. When analyzing experiment data, the main statistical procedure is finding relationships in the data and developing models to describe them. Modeling is a powerful means of estimation, simulation, and prediction of the experiment and an indispensable tool in practical engineering. This paper considers the use of the multivariate adaptive regression splines (MARS) method to describe local relationships in the experiment data for several types of metal vapor lasers. The investigated data has been collected from patented UV ion lasers developed at the Bulgarian Academy of Sciences in the last ten years. A short description of MARS is provided. Specific MARS models have been developed describing the efficiency of an ultraviolet ion copper bromide vapor laser dependent on the change of 9 main input physical laser characteristics. The models are of linear and nonlinear type. It has been established that the best statistical indices and model fit are those of nonlinear models which contain second order local terms. The models account for about 95% of the data. The models are used to analyze the local behavior of the efficiency of various devices from the types of lasers under investigation. Graphs illustrating the established relationships are provided. The ability to predict future experiments is demonstrated.

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“…The aim of the study was to measure the individual influence of each factor on performance change in the mass of the fruit and dry matter content in the tomato collection being evaluated through factor analysis by the method of Principal Component Analysis (PCA) [1,2,4].…”

Section: Introductionmentioning

confidence: 99%

Principal component analysis of a canning determinate tomato collection in the IPGR, Sadovo – Bulgaria

Krasteva

Velcheva

Mokreva

2012

AGR

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SummaryThe success of a tomato breeding programme largely depends on the study of initial material and symptoms studied as well as manifestations of dependence between them. The study was conducted during the period 2008-2011 in the IPGR, Bulgaria. The objects of the study were 37 canning tomato accessions with an oval shape of the fruit. The collection included 11 local and 26 accessions of foreign origin. Using factor analysis by applying the PCA methodology, a relationship was established between 15 morphological and biochemical parameters of the accessions studied and their effects on indicators of fruit weight and dry matter content. The investigated parameters were grouped into four general factors affecting the mass change by 73.4%. Applied to dry matter content, the same method identifies four factors with a total impact of 71.7% on this trait.

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Statistical analysis of the CuBr laser efficiency improvement

Cited by 13 publications

References 12 publications

On the Application of the Multidimensional Statistical Techniques for Exploring Copper Bromide Vapor Laser

On the Application of the Multidimensional Statistical Techniques for Exploring Copper Bromide Vapor Laser

Application of MARS for modeling local nonlinear relationships in data on ultraviolet copper ion lasers

Principal component analysis of a canning determinate tomato collection in the IPGR, Sadovo – Bulgaria

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