Iliycho Petkov Iliev

Parametric and Nonparametric Empirical Regression Models: Case Study of Copper Bromide Laser Generation

In order to model the output laser power of a copper bromide laser with wavelengths of 510.6 and 578.2 nm we have applied two regression techniques—multiple linear regression and multivariate adaptive regression splines. The models have been constructed on the basis of PCA factors for historical data. The influence of first- and second-order interactions between predictors has been taken into account. The models are easily interpreted and have good prediction power, which is established from the results of their validation. The comparison of the derived models shows that these based on multivariate adaptive regression splines have an advantage over the others. The obtained results allow for the clarification of relationships between laser generation and the observed laser input variables, for better determining their influence on laser generation, in order to improve the experimental setup and laser production technology. They can be useful for evaluation of known experiments as well as for prediction of future experiments. The developed modeling methodology is also applicable for a wide range of similar laser devices—metal vapor lasers and gas lasers.

Statistical Techniques For Examining Copper Bromide Laser Parameters

Factor analysis, regression analysis and other multivariate statistical techniques were applied for examining the copper bromide operational parameters. The study was carried out for a large amount of the experimental data. The approximate degrees of influence of the input laser parameters on the enhancement of the output laser power and the overall laser efficiency were obtained.

Modeling of Output Characteristics of a UV Cu+ Ne-CuBr Laser

This paper examines experiment data for a Ne-CuBr UV copper ion laser excited by longitudinal pulsed discharge emitting in multiline regime. The flexible multivariate adaptive regression splines (MARSs) method has been used to develop nonparametric regression models describing the laser output power and service life of the devices. The models have been constructed as explicit functions of 9 basic input laser characteristics. The obtained models account for local nonlinearities of the relationships within the various multivariate subregions. The built best MARS models account for over 98% of data. The models are used to estimate the investigated output laser characteristics of existing UV lasers. The capabilities for using the models in predicting existing and future experiments have been demonstrated. Specific analyses have been presented comparing the models with actual experiments. The obtained results are applicable for guiding and planning the engineering experiment. The modeling methodology can be applied for a wide range of similar lasers and laser devices.

Statistical Study of the Copper Bromide Laser Efficiency

On the basis of experimental data, the overall efficiency of the copper bromide laser is investigated by means of factor analysis, variances, and other methods. The relative influence of the main laser parameters: inner diameter of the laser tube, electrode separation distance, input electric power and others are estimated with respect to the factors. The obtained results can be applied for different types of optimization in order to improve the output laser characteristics.

Mathematical Modeling Of The Electric Field In Copper Bromide Laser

A two‐dimensional model for evaluating electric field intensity in copper bromide laser, including parabolic differential equation with variable conductivity coefficient in the temporal operator and Laplaces equation is constructed. The problem is discretized by means of the alternating direction implicit difference scheme with weights. Numerical results are presented.

Studying the effect of meteorological factors on the SO2 and PM10 pollution levels with refined versions of the SARIMA model

Numerous time series methods are used in environmental sciences allowing the detailed investigation of air pollution processes. The goal of this study is to present the empirical analysis of various aspects of stochastic modeling and in particular the ARIMA/SARIMA methods. The subject of investigation is air pollution in the town of Kardzhali, Bulgaria with 2 problematic pollutants – sulfur dioxide (SO2) and particulate matter (PM10). Various SARIMA Transfer Function models are built taking into account meteorological factors, data transformations and the use of different horizons selected to predict future levels of concentrations of the pollutants.

Modeling of the Radial Heat Flow and Cooling Processes in a Deep Ultraviolet CuNe-CuBr Laser

An improved theoretical model of the gas temperature profile in the cross-section of an ultraviolet copper ion excited copper bromide laser is developed. The model is based on the solution of the one-dimensional heat conduction equation subject to special nonlinear boundary conditions, describing the heat interaction between the laser tube and its surroundings. It takes into account the nonuniform distribution of the volume power density along with the radius of the laser tube. The problem is reduced to the boundary value problem of the first kind. An explicit solution of this model is obtained. The model is applied for the evaluation of the gas temperature profiles of the laser in the conditions of free and forced air-cooling. Comparison with other simple models assumed constant volume power density is made. In particular, a simple expression for calculating the average gas temperature is found.

Prognosis of the copper bromide laser generation through statistical methods

A new statistical approach to predict the possible increase of the laser generation in copper bromide lasers is presented. The results are based on a big amount of experimental data with wavelengths 510.6 nm and 578.2 nm, obtained in Georgi Nadjakov Institute of Solid State Physics, Bulgarian Academy of Sciences. The data are treated by statistical methods, such as multivariable factor analysis and linear regression modeling. The approximate increase of the output laser power up to 18% is obtained by using six input working parameters. The proposed methodology is applied for prognosticate the experiment and further design of the laser tube.

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

A big amount of experimental data for copper bromide vapor lasers with wavelengths 510.6 nm and 578.2 nm, obtained in Georgi Nadjakov Institute of Solid State Physics, Bulgarian Academy of Sciences are examined. Multidimensional statistical methods, such as factor analysis, and cluster analysis were used for classifying the laser parameters. It is established a good accordance between the results from these techniques. Some solutions for designing and planning the experiment in order to enhance the outgoing laser power generation are discussed.

Application of the Classification and Regression Trees for Modeling the Laser Output Power of a Copper Bromide Vapor Laser

This study examines the available experiment data for a copper bromide vapor laser (CuBr laser), emitting in the visible spectrum at 2 wavelengths—510.6 and 578.2 nm. Laser output power is estimated based on 10 independent input parameters. The CART method is used to build a binary regression tree of solutions with respect to output power. In the case of a linear model, an approximation of 98% has been achieved and 99% for the model of interactions between predictors up to the the second order with an relative error under 5%. The resulting CART tree takes into account which input quantities influence the formation of classification groups and in what manner. This makes it possible to estimate which ones are significant from an engineering point of view for the development and operation of the considered type of lasers, thus assisting in the design and improvement of laser technology.