=Paper=
{{Paper
|id=Vol-2533/invited5
|storemode=property
|title=Information Technology of Forming the Quality of Art and Technical Design of Books
|pdfUrl=https://ceur-ws.org/Vol-2533/invited5.pdf
|volume=Vol-2533
|authors=Oksana Sichevska,Vsevolod Senkivskyy,Sergii Babichev,Orest Khamula
|dblpUrl=https://dblp.org/rec/conf/dcsmart/SichevskaSBK19
}}
==Information Technology of Forming the Quality of Art and Technical Design of Books==
https://ceur-ws.org/Vol-2533/invited5.pdf
Information Technology of Forming the Quality of Art
and Technical Design of Books
Oksana Sichevska1[0000-0002-1239-206X], Vsevolod Senkivskyy1[0000-0002-4510-540X],
Sergii Babichev1,2[0000-0001-6797-1467], Orest Khamula1[0000-0003-0926-9156]
1Ukrainian Academy of Printing, Lviv, Ukraine
mag_oks@ukr.net, senk.vm@gmail.com, khamula@gmail.com
2Jan Evangelista Purkyne University in Usti nad Labem, Usti nad Labem, Czech Republic
sergii.babichev@ujep.cz
Abstract. The paper presents a structural and functional model of information
technology for forming the quality of art and technical design of book at the
stage of prepress preparing. The basis of the proposed technology is a fuzzy
knowledge base which allows us to build the relationships between physically
separated variables during the process of art and technical design of books by
the use of both the means of mathematical theory of fuzzy sets and
distinguished factors for prepress preparation of books. A fuzzy knowledge
matrix for linguistic variables H – "quality of art design of books" which
contain the linguistic variables G – "quality of art decoration" and R – "quality
of technical design" have been formed and presented as the tables. The systems
of fuzzy logical equations which determine the procedures of obtaining the
weights of membership functions for a set of linguistic terms are developed and
calculated. The defuzzification procedure has been performed for all linguistic
variables considering the table of the membership functions normalized values
in the three points of the universal set division. It has been proposed the
quantitative indicator of the level of the predicted quality of the printing process
by the implementation of defuzzification procedure of the output fuzzy set using
the mass center method. The numerical value of the level of quality of art and
technical design of books H = 52,5% has been obtained during the simulation
process.
Keywords: integral quality index, fuzzy knowledge base, linguistic variable, art
and technical design of books, prepress preparing
1 Introduction
During a lot of time, from the beginning of creation to the present time, the book has
undergone a long stage of its identity forming. Considering the stages of becoming a
book, we can select several important processes which form its quality. For example,
the perception of content is determined by the design of the publication or the visual
factors which come into at the stage of transforming the author's work into a print
edition. The process of art and technical design belongs to the stage of prepress
preparation of publications [1]. Research in this subject area allows us to present the
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)
2019 DCSMart Workshop.
publishing process in the form of information-technological system with the use of a
system approach to improving the functioning of the publishing house for purpose of
quality execution of a complex of publishing procedures.
Investigation concerning formation of quality of technical design of books can be
performed based on the applying of mathematical theory of fuzzy sets within the
framework of fuzzy logic inference system [2] using the modern tools and techniques
of scientific research, as well as processes and factors that have different dimensions
(numerical, conditional, qualitative). Nowadays, there are a lot of works which are
devoted to the issues of fuzzy logic inference systems implementation for various
purposes in different fields of research [3-17]. However, it should be noted that the
authors' research are mainly focused on small dimension data processing in order to
create fuzzy logic inference system for direction and control the appropriate object
functioning. Implementation of the fuzzy logic inference system for evaluation of the
prepress process quality create the conditions for complex estimation of various
factors influence on integral parameter, which determines the quality of the book
prepress preparing. The application of the fuzzy logic theory to the evaluation of the
prepress quality assumes the following: formation of a set of linguistic variables (LV)
that determine the quality of the prepress process; formalization of input parameters
into linguistic variables, i.e. identification of recommended ranges of values of
universal term sets and linguistic terms of art and technical design of book
publications; creation a model of integral quality index formation which include both
calculation of membership functions (MF) of linguistic variables and formation of
rules base agreed between input variables and output parameter.
The issues devoted to prepress of books based on fuzzy logic technique are
considered in [18–22]. So, in [18,19] the authors considered the process of realization
of mounting descents. The paper [20] presents the results of research concerning
formation of quality of designing and structuring of publications. Issues concerning
formation integral prediction of the book publication quality are considered in [21].
The paper [23] presents the result of the research concerning quality assurance of
publishing and printing processes. However, it should be noted that despite of
achievement in this subject area, there are some unsolved tasks. For example, the
prognostic evaluation of the quality of the investigated process considering a unified
methodological basis, whose main components are the methods based on the theory of
hierarchical multilevel systems, simulation techniques, study of operations and fuzzy
sets, remains insufficiently studied nowadays.
The aim of the paper is development of information technology of forming the
quality of art and technical design of book publications, the basis of which are both a
fuzzy knowledge base and a system of fuzzy logical equations, which are the basis to
determine an integral indicator of the quality level of art and technical design of
publications.
2 Formal Problem Statement
Fig. 1 presents the structure block-chart of fuzzy inference process.
Fig. 1. Structural block-chart of fuzzy inference process
Its implementation assumes the following steps:
• formation of a set of input variables and output parameter;
• formalization of both the input variables and output parameter. Formation of a
basic term-set of the input variables and output parameter with corresponding
membership functions for each of the terms;
• formation of a set of fuzzy rules agreed with the input variables and output
parameter;
• creation of the fuzzy logic equations for fuzzy inference process
implementation;
• development of the method for defuzzification process implementation.
3 Formation of Fuzzy Knowledge Base and Fuzzy Logical
Equations
A fuzzy knowledge base (FKB) is a set of fuzzy rules "if-then" which determine the
relationship between the inputs and outputs of the investigated object and realize the
investigated relationships between natural language factors using fuzzy set theory and
linguistic variables. This allows us to build relationships between physically separated
quantities during the process of art and technical design of books. A logical
continuation of the formed fuzzy knowledge base is creation of a system of fuzzy
logic equations which determine the procedures of obtaining the weights of the
membership function for the set of linguistic terms of the integral quality index of the
corresponding process and, as the result, its calculation.
The FKB is formed considering the expert assessments concerning influence of
factors to the process of the book design. Moreover, the fuzzy knowledge base
determines the algorithm of predicted quality formation based on combinations of
values of linguistic terms (LT). Thus, the FKB can be represented as the set H –
“quality of art and technical design of book editions” with linguistic terms LT – “Low,
Medium, High”. The next step is formation of the linguistic variables (LV) sets: G –
“quality of the art design” and R – “quality of the technical design” with LT – “Low,
Medium, High”.
Considering the models of integral quality index formation [24], we can form the
FKB as a set of fuzzy rules. This fuzzy knowledge base is presented in table 1.
Table 1. Knowledge base for language variable H
Quality of art design Quality of technical design Quality of art and technical
(G) (R) design (H)
Low Low
Low
Low Medium
Medium Low
Medium
Medium Medium
High Medium
High
High High
The fuzzy knowledge base, presented in table 1, allows us to form the fuzzy logic
equations in order to calculate the values of membership functions of terms which
determine an integral index of the publication design process quality. These equations
are presented below:
• for term “Low”:
Low ( H ) = ( Low (G) Low ( R)) ( Low (G) Med ( R)) ;
• for term “Medium”:
Med ( H ) = ( Med (G) Low ( R)) ( Med (G) Med ( R)) ;
• for term “High”:
( ) (
Hg ( H ) = Hg (G) Med ( R) Hg (G) Hg ( R) . )
The fuzzy knowledge base for the next level, for both the G and R linguistic
variables are presented in table 2 and table 3. The fuzzy logic equations which are
formed based on both the table 2 and table 3 are presented below. These equations
allow us to calculate the membership functions for linguistic variables G and R
respectively.
Table 2. Knowledge base for language variable G.
Graphical illustration Complexity of the edition Layout of the edition Quality of art
methods (g1) structure (g2) (g3) design (G)
Line-art graphics (l_art) Complicated (compl) Simple
Low
Continuous-tone (ct) Simple Simple
Line-art graphics (l_art) Simple Complicated (compl)
Medium
Continuous-tone (ct) Complicated (compl) Simple
Art graphics (art) Simple Complicated (compl)
High
Continuous-tone (ct) Complicated (compl) Complex
Linguistic equations for determine the values of the membership functions for G
variable:
• for term “Low”:
(
Low (G ) = l _ art ( g1 ) compl( g 2 ) simple ( g 3 ) )
(
ct ( g1 ) simple ( g 2 ) simple ( g 3 ) ) ;
• for term “Medium”:
(
Med (G ) = l _ art ( g1 ) simple ( g 2 ) compl( g 3 ) )
(
ct ( g1 ) compl( g 2 ) simple ( g 3 ) ) ;
• for term “High”:
(
High (G ) = art ( g1 ) complex ( g 2 ) compl( g 3 ) )
(
ct ( g1 ) compl( g 2 ) complex ( g 3 ) ) .
Table 3. Knowledge base for language variable R.
Number of corrections Quality of technical
Editing (r2) Text information (r3)
and colour tests (r1) design (R)
A little Literary Large
Low
A little Literary Medium
Medium Artistic Large
Medium
Medium Literary Medium
Large Technical Medium
High
Large Artistic A little
Linguistic equations for determine the values of the membership functions for R
variable:
• for term “Low”:
(
Low ( R) = little (r1 ) literary(r2 ) large (r3 ) )
(
little (r1 ) literary (r2 ) medium (r3 ) ) ;
• for term “Medium”:
(
Med ( R) = medium(r1 ) artistic(r2 ) large (r3 ) )
(
medium(r1 ) literary(r2 ) medium (r3 ) ) ;
• for term “High”:
(
High ( R) = l arg e (r1 ) technical(r2 ) medium (r3 ) )
(
l arg e (r1 ) artistic(r2 ) little(r3 ) ) .
At the next step, we form the fuzzy sets of the linguistic variable H "quality of art
and technical design" with appropriate membership functions for the terms "low",
"medium" and "high" in accordance with the following equation:
( H ) Med ( H ) High ( H )
H (G, R) = Low , , (1)
1 2 3
where 1, 2 , 3 are the quantitative values of the H linguistic variable relative to the
appropriate terms.
4 Implementation of Defuzzification Process
Defuzzification process is performed based on hereinbefore described expert
knowledge base, knowledge base of the linguistic variables and fuzzy set (1). Table 4
contains normalized values of the membership functions in three points of the
universal set A for all linguistic variables.
Table 4. Membership functions of term-sets
Membership functions of term-set U(g1) Membership functions of term-set U(r1)
(Graphical illustration methods) (Number of corrections and colour tests)
ai , у.о 1 5 9 ai , у.о. 1 5 9
l _ art (ai ) 1 0,77 0,11 little (ai ) 1 0,77 0,11
ct (ai ) 0,11 1 0,11 medium(ai ) 0,11 1 0,11
art (ai ) 0,11 0,33 1 l arg e (ai ) 0,11 0,77 1
Membership functions of term-set U(g2) Membership functions of term-set U(r2)
(Complexity of the edition structure) (Editing)
ai , у.о 1 5 9 ai , у.о. 1 2 3
simple (ai ) 1 0,77 0,11 literary (ai ) 1 0,77 0,11
compl(ai ) 0,11 1 0,11 artistic (ai ) 0,11 1 0,11
complex(ai ) technical (ai )
0,11 0,66 1 0,11 0,45 1
Membership functions of term-set U(g3) Membership functions of term-set U(r3)
(Layout of the edition) (Text information)
ai , у.о 1 5 9 ai , % 10 42,5 95
simple (ai ) 1 0,66 0,11 little (ai ) 1 0,55 0,11
compl(ai ) 0,11 1 0,11 medium(ai ) 0,11 1 0,11
complex(ai ) 0,11 0,55 1 l arg e (ai ) 0,11 0,66 1
Below, we present the fuzzy logic equations which allows us to determine the
membership functions quantitative values for linguistic variables G "quality of art
design" and R "quality of technical design" based on the data presented in the table 4.
Calculation of linguistic variable G values:
• for term “Low”:
low (G) = (0,77 1 0,66) (1 0,77 0,66) = 0,66
• for term “Medium”:
med (G) = (0,77 0,77 0,66) (0,77 0,77 1) = 0,77
• for term “High”:
high (G) = (0,33 0,66 1) (1 1 0,55) = 0,55
Calculation of linguistic variable R values:
• for term “Low”:
low ( R) = (0,77 0,77 0,66) (0,77 0,77 1) = 0,77
• for term “Medium”:
med ( R) = (1 1 0,66) (1 0,77 1) = 0,77
• for term “High”:
high ( R) = (0,77 0,45 1) (0,77 1 0,55) = 0,55
At the next step, we calculate the values of the membership functions for linguistic
variable H "quality of art and technical design" considering the obtained membership
functions values for the variables G and R, which were calculated before:
• for term “Low”:
low ( H ) = (0,66 0,77) (0,66 0,77) = 0,66
• for term “Medium”:
med ( H ) = (0,77 0,77) (0,77 0,77) = 0,77
• for term “High”:
high ( H ) = (0,55 0,77) (0,55 0,55) = 0,55
Finally, we perform the defuzzification of the fuzzy set (1) using center mass
technique. The result of this step implementation is determination of quantitative
value of the quality index of book editions prepress preparing:
m
H −H
H + (i − 1) m − 1 i ( H )
H = i =1
(2)
m
i ( H )
i =1
where H, H are the minimum and the maximum values of the linguistic variable H
"quality of art and technical design" respectively; m is the number of linguistic terms.
Let, variables in the formula (2) take the following values: m = 3;
1 ( H ) = low ( H ) , 2 ( H ) = med ( H ) , 3 ( H ) = high ( H ) ; lower and upper bounds
for linguistic variable H are follows: H = 10% , H = 100% . The calculation is carried
out in three points of the range: 10, 55, 100. In this case, the value the quality level of
art and technical design of the books is calculated as follows:
10 0,66 + 55 0,77 + 100 0,55
H= = 52,5% (3)
0,66 + 0,77 + 0,55
Hereinbefore presented results of the research have allowed us to propose the
information technology of forming the quality of art and technical design of the book
editions.
5 Structural and Functional Model of Information Technology of
Forming the Quality of Art and Technical Design of Books
Fig. 2 presents the structure block-chart of the information technology of forming the
quality of art and technical design of the book editions. Its implementation assumes
the following stages:
Stage 1. Forming the classification model of prepress preparing of the book
editions.
1.1. Formation of general principles of the publishing process and the importance
levels of its components.
1.2. Describing the processes of prepress preparation of the books as a component
of the research subject.
1.3. Classification and formation of processes, procedures and factors of influence
to the stage of the book editions prepress preparing with the use of Delphi's expert
method.
1.4. Creating the classification model of prepress preparing the book editions.
Stage 2. Creating a model of priority influence of factors to the quality of art and
technical design of the book editions.
2.1. Formation of semantic network of influence of the factors to the quality of art
and technical design of the book editions with the use of the logic of predicates.
Creation of hierarchical trees of linkages between factors taking into account the
influence of both types: direct and indirect ones.
2.2. Estimation of factor weights (based on the semantic network) by ranking
method. This step allows us to determine the hidden relationships between the factors.
Creation of a multilevel model of weight values of the factors of quality of art and
technical design of the book editions.
1.1. Formation of general principles of the publishing
process and the importance levels of its components
Stage 1
1.2. Describing the processes of prepress preparation of the
Forming the books as a component of the research subject
classification model of
1.3. Classification and formation of processes, procedures and
prepress preparing of the factors of influence to the stage of the book editions prepress
book editions preparing with the use of Delphi's expert method
1.4. Creating the classification model of prepress preparing
the book editions
2.1. Formation of semantic network of influence of the
factors to the quality of art and technical design of the book
editions with the use of the logic of predicates
Stage 2
2.2. Estimation of the factor weights by ranking method
Creating a model of
priority influence of
factors to the quality of 2.3. Evaluation of the priority of the factors influence using
the method of hierarchy analysis
art and technical design of
the book editions 2.4. Analysing the results of the use of both the ranking and
analysis of hierarchy methods
2.5. Synthesis of model of priority influence of the factors
to quality of art and technical design of book editions
3.1. Forming alternatives and selection of the optimal
Stage 3 variant for quality assurance of the process using decision
Solving the problems of making theory
choosing alternatives of
3.2. Forming alternatives and selection of the optimal
art and technical design variants based on a fuzzy preference ratio
of the books
3.3. Analysis of the alternatives of the book editions design
obtained by both the decision-making theory and fuzzy
preference ratio
4.1. Forming the fuzzy logic system components in order to
predict the quality of art and technical design of the books
4.2. Creating the models to form the integral quality index
Stage 4 of the book editions design
Predicting the quality of
4.3. Creating and calculation of the MF for the LV
art and technical design
of book editions 4.4. Designing a fuzzy knowledge base and a system of
fuzzy logic equations of the book editions design
4.5. Defuzzification process implementation
4.6. Calculation of quantitative index to evaluate the degree
of predicted quality of book editions design
Fig. 2. Structural and functional model of information technology of forming the quality of art
and technical design of book editions
2.3. Evaluation of the priority of the factors influence (based on the semantic
network of factors) using the method of hierarchy analysis by creation of a binary
dependences matrix and a reachability matrix between the factors. Implementation of
an iterative algorithm for the formation of importance levels using the reachability
matrix. Creation of the iterative tables for purpose of formation of the factor levels.
Creation of a model of priority influence of the factors to the studied process quality.
2.4. Analysing the results of the use of both the ranking and analysis of hierarchy
methods and determination of the priority for the best method.
2.5. Synthesis of model of priority influence of the factors to quality of art and
technical design of book editions. Creation of a matrix of pairwise comparisons by
comparison analysis of the factors based on the scale of the relative importance of the
objects. Processing the matrix and obtaining specified weighted values of the factors -
the basis of an optimized multilevel model of ensuring the quality of the books
design.
Stage 3. Solving the problems of choosing alternatives of art and technical design
of the books.
3.1. Forming alternatives and selection of the optimal variant for quality assurance
of the process using decision making theory. Solving the problem of multicriterial
optimization for the mutually undominant factors which make up the Pareto set.
Evaluating the alternatives using importance degree of the selected factors. Creating a
matrix of pairwise comparisons for factors of Pareto set, normalizing the components
of its main vector. Determination of the utility function maximum value.
3.2. Forming alternative variants of art and technical design of book editions
considering the factors-linguistic variables of the Pareto set on the basis of fuzzy
relations of their advantages in the alternatives and calculated values of the
convolutions membership functions. Determination of the optimal variant based on
the alternative selection algorithm in the following sequence: establishing a
preference ratio for each of the factors relative to the set of the alternatives; creation
of the relations matrix; creation of the relationships convolution; determination of the
undominant alternatives set; calculation of the convolution membership function
values for appropriate alternatives; finding the convolution intersection and
appropriate membership function.
3.3. Analysis of the alternatives of the book editions design obtained by both the
decision-making theory and fuzzy preference ratio.
Stage 4. Predicting the quality of art and technical design of book editions.
4.1. Forming the fuzzy logic system components in order to predict the quality of
art and technical design of the book editions.
4.2. Creating the models to form the integral quality index. Setting the term sets of
values for the linguistic variables with corresponding values variation ranges.
4.3. Creating and calculation of the linguistic variables membership functions.
Determination of the membership functions quantitative values for the used terms.
4.4. Designing a fuzzy knowledge base and a system of fuzzy logic equations of
the book editions design. Creation of the knowledge matrices for the appropriate
linguistic variables. Formation of the fuzzy logic equations. Determination of the
membership functions values for the set of terms.
4.5. Defuzzification process implementation. Formation of the normalized values
of membership functions in the points of division of the universal set for all linguistic
variables. Calculation of fuzzy logical equations for appropriate terms.
4.6. Calculation of quantitative index to evaluate the degree of predicted quality of
book editions design.
6 Conclusions
This paper reflects the result of the research concerning the formation of the quality of
art and technical design of book editions. At the first step, we have created the
classification model of prepress preparing book editions. Then, we carried out based
on this model formalized mapping and description of relationships between factors
using semantic networks and predicate logic, which allowing us further research with
the use of both the hierarchy theory and fuzzy logic. Multilevel models of priority
influence of separated factors to the quality of art and technical design of book
editions based on the calculation and ordering of their weight values by the method of
hierarchies ranking and analysis were synthesized and optimized. This fact has
allowed us to design alternatives and calculations of optimal variants of the book
editions process preparing. As the result of the research, we have proposed a
multilevel hierarchical model which contains the algorithm of forming the quality of
art and technical design of the book editions.
The decision concerning evaluation of the quality of art and technical design of
the book editions was taken based on fuzzy logic theory. Implementation of this
technique assumes design of the fuzzy knowledge base which allows us to form
relationships between physically separated quantitative variables during the process of
art and technical design of book editions. The fuzzy knowledge matrices for the
corresponding linguistic variables are designed during the simulation process. The
systems of fuzzy logical equations that determine the procedures of obtaining the
weights of the membership functions for the set of linguistic terms of the integral
quality index of the investigated process quality have been development and
calculated.
Defuzzification process was performed based on the normalized values of the
membership functions in three points of division of the universal set A for all
linguistic variables. The numeric value H = 52,5% of quality of the art and technical
design of the book edition was obtained as the simulation result. The maximum value
of this index was 100%. The conducted research allows us to obtain the predicted
value of the quality index of the process of art and technical design of a book edition
at the initial stage of its design.
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