Erdoğan Köse

Modelling of colour perception of different age groups using artificial neural networks

Colour is a subjective term which changes according to each persons view. We should define colour physically to work with it in terms of printing or any other professional study. However, enabling the observation of colour in terms of sales and marketing and product design and development is as important as product and its packaging. The perception of colour and tendencies to colours will be different for people of different age groups and socio-economical levels. In this study, different colour samples will be shown to four different groups of primary school, university, midage and elderly people and to two different categories, namely men and women. The aim of the study is to determine which colour is more perceived and adopted by which group and which category using artificial neural network (ANN). In the direction of these determinations it has been studied with graphical and statistical illustrations how different age groups prefer colours, how much they are able to recognise primary and secondary colours and to what extent they are able to perceive them correctly.

The investigation of effects of digital proofing systems used in colour management on print quality with neural networks

The aim of this study is to assist users and to determine how digital proofing systems used in colour management effect print qualities. Also the main theme of this study is to determine the effects of digital proofing systems used in colour management on print quality by artificial neural network (ANN). The R^2 values are obtained 0.99702 and 0.99688 for training data as matte and cuated papers, respectively. Similarly, these values for testing data are 0.994707 and 0.99629, respectively. The ANN approach shows greater accuracy for evaluating colour management. Based on the outputs of the study, ANN model can be used to estimate the effects of digital proofing systems used in colour management on print quality with highly confidence.

Modelling of color perception of different eye colors using artificial neural networks

Color is a subjective term that changes according to each person’s view. We should define color physically to work with it in terms of printing or any other professional study. However, enabling the observation of color in terms of sales, marketing, product design, and development is as important as product and its packaging. The perception of color and tendencies to colors will be different for people of different eye colors. In this study, different color samples will be shown to five different eye colors of hazel, green, blue, brown, and black eyes both of men and women. The aim of the study is to determine which eye color is more perceived and adopted by which eye color using artificial neural network (ANN) for between 6 and 17 ages. By considering these determinations, it has been studied with graphical and statistical illustrations how different eye color groups prefer colors, how much they are able to recognize primary and secondary colors, and to what extent various eye colors are able to perceive RGB and CMY colors correctly.

Modelling of time related drying changes on matte coated paper with artificial neural networks

In this study, the determinability of time related colour changes in prints made using ink that dries on matte coated paper with the offset printing technique and infrared method, has been investigated with artifical neural networks after analysis of the experimental and numerical methods. In the experimental part a ECI 2002 CMYK test scale is printed on a 135g/m^2 matte coated. The prints obtained were measured with a spectrophotometer at certain intervals and by converting the reflection data obtained into the CIELAB colour description spectrum, time related changes in printed colours have been determined. In the empirical study however, colour changes in prints made on matte coated paper using inks that dry with the infrared method are modelled numerically. The average deviation has been found to be 0.4%. As a result, artificial neural networks can be used with the aim to digitally calculate time related colour changes in the offset printing system.