Energy Science, Engineering and Technology
Typical Scenario Load Identification Based on Feature Fusion and Transfer Learning
The electricity demand is continuously increasing. However various institutions enterprises and individuals exhibit many irregularities in their electricity usage leading to significant wastage of electricity. To achieve effective energy management researchers are attempting to analyze and regulate users' electricity demands by monitoring their load usage through Non-Intrusive Load Monitoring (NILM) technology. The accuracy of load identification in this technology will greatly impact the results of load monitoring. Although there are currently many articles and patents related to NILM they utilize a large amount of computational resources and require high sampling rates from devices yet the results are still unsatisfactory. Therefore it is necessary to improve the accuracy of load identification in data with relatively low sampling frequencies.
To improve the accuracy of load identification with low sampling frequency data this paper proposes a typical scenario load identification method based on feature fusion and transfer learning.
This method adopts the fusion of current and power factor angles to provide abundant identification information for NILM effectively reducing the situation of single-feature overlap of different loads. By inputting the fused feature data into GoogLeNet and utilizing transfer learning for training not only is the accuracy improved but also the training time and the requirement for the sampling rate of training data are greatly reduced. In addition selecting typical scenario loads can monitor loads in a targeted manner reduce the waste of computing resources caused by irrelevant loads and more effectively guide electricity usage strategies.
The proposed load identification method was tested on the low sampling frequency dataset used in this paper. It achieved an overall load identification accuracy of 94.61% across three scenarios improving accuracy by 3% to 7% compared to other models.
The simulation results indicate that this method achieves high load identification accuracy at low sampling frequencies. It also exhibits good generalization ability. This method not only reduces the performance requirements for monitoring equipment but also enhances monitoring efficiency.
Combinatorial Method for Quality Improvement of the Thrust Plate – A Case Study
This research aims to mitigate defects in the turning operation of thrust plates used in fighter jet fuel tank assemblies thereby reducing the rejection rate and improving overall quality. This aligns with the aerospace industry's reliability goals.
The thrust plate is a critical component in fighter jet fuel tank assembly transmitting engine thrust to the airframe. Quality compromises in this component can impair jet performance. It was observed that the thrust plate had a rejection rate of about 2.9% due to various defects. This real-world scenario underscores the importance of our study on the thrust plate and its potential impact on the aerospace industry. The rejection rate underscores its significance and potential for patent by quality improvement in turning of the thrust plate.
The objective is to mitigate turning operation defects on the thrust plate to reduce rejection rates aligning with aerospace industry reliability goals.
Experimentation encompassed four pivotal factors: turning speed feed rate cutting depth and tool inserts implemented through Taguchi's Orthogonal Array technique. Grey Relational Analysis was utilized to optimize parameters in thrust plate turning. Specifically this paper targeted the enhancement of its diameter surface roughness and tool life.
A single coefficient for the multiple responses i.e. grey relational grade has been determined and optimum levels for the parameters have been identified. Confirmation experiments with the optimal factor level combination were carried out on a sample of thrust plates and no rejections were observed.
An experimental design based on Taguchi’s orthogonal array approach was used to conduct the experiments. The Grey Relational Analysis has been applied to analyze the experimental results and optimize the turning operation process parameters for the responses thrust plate diameter tool life and surface roughness. With this the rejection of the thrust plate has been considerably reduced.
Evaluation of the Critical Success Factors for Household Product Sustainability
Sustainability and sustainable development have received growing attention in both industry and academia due to concerns regarding the rapid decrease in natural resources and increase in carbon emissions.
In this study we focus on the determination evaluation and analysis of the critical success factors in product sustainability by specifically focusing on the household goods industry. In the first phase of the study we determine the critical success factors by referring to the existing literature and opinions of the experts who have experience in the household goods industry. Next we use a trapezoidal type-2 fuzzy AHP algorithm to rank the determined criteria and discuss the main findings from a practical point of view.
Computational results bring several important managerial insights. First we observe that all three aspects of sustainability (economic environmental and social) should be considered to ensure product sustainability. Second the analysis reveals that cost (economic) quality (economic) generated waste and emission during the life cycle (environmental) energy and water consumption during the life cycle (environmental) and occupational health and safety (social) are among the highly ranked criteria.
In order to increase product sustainability the companies should determine ways to decrease water usage energy usage carbon emission and waste without neglecting the cost and quality of the product and without ignoring occupational health and safety.
Computational Modeling and Simulation in Biomedical Research
This reference provides a comprehensive overview of computational modelling and simulation for theoretical and practical biomedical research. The book explains basic concepts of computational biology and data modelling for learners and early career researchers.
Chapters cover these topics:
1. An introduction to computational tools in biomedical research
2. Computational analysis of biological data
3. Algorithm development for computational modelling and simulation
4. The roles and application of protein modelling in biomedical research
5. Dynamics of biomolecular ligand recognition
Key features include a simple easy-to-understand presentation detailed explanation of important concepts in computational modeling and simulations and references.
Ultrasound Technology for Fuel Processing
Ultrasound Technology for Fuel Processing is a comprehensive reference guide that explores the application of sonochemistry and ultrasound waves in the intensified processing of fuels. The book focuses on the cavitation phenomenon which generates extreme conditions such as high temperatures and pressures within the cavitation bubbles leading to significant enhancements in chemical reactions and overall process yields. Key features of the book include comprehensive coverage of ultrasound fuel processing with the inclusion of information about several new processing techniques detailed references and a focus on sustainability enhancing petrochemical technologies. Key Topics: - The basics of ultrasound technology including its history acoustic wave origin and process parameters influencing cavitation thresholds. - Green hydrogen production through sonolysis of water and the influence of various parameters on hydrogen yield. - Pre-treatment methods for biofuel production exploring both conventional and novel green methods. - Ultrasound-based techniques to enhance alternative energy production (biocrude biogas and bioethanol). - Biodiesel synthesis using ultrasound-microwave synergy for enhanced processing rates. - Intensified approaches in sonochemistry including the use of cavitation fundamentals of sonochemical reactors and operational guidelines for maximizing biodiesel yields. - Enhanced oil recovery and crude oil upgradation using ultrasound and cavitation techniques focusing on cracking heavy hydrocarbon molecules. - Ultrasound-assisted chemical and bio-desulfurization processes. Ultrasound Technology for Fuel Processing provides an in-depth understanding of the principles and applications of ultrasound in fuel processing offering valuable insights for researchers faculty and professionals in fuel processing technology and related areas in industrial petroleum and chemical engineering.
Application of Fuzzy Neutrosophic Cone in Decision Making
Aims: This article deals with a new decision-making process under a neutrosophic fuzzy environment. First of all we develop various types of neutrosophic set by means of neutrosophic cones. In fact this set has been developed from the general equation of second degree in the field of classical geometry. Considering the neutrosophic components “true membership” the “falsity membership” and the “indeterminacy” as the three variables of three-dimensional rectangular axes we develop various types of cones like structures of the traditional neutrosophic set and hence a new defuzzification method.
Background: Fuzzy set has some limitations in its domain [01] to describe real-life decision-making problems. The problem of difficulties lies in the variation of lower and upper bound and also the single valued logic (membership function only) systems. In reality three valued logics (membership function non-membership function and indeterminacy) have been established in the name of Neutrosophic logic/sets and two valued logics (membership and non-membership functions) have developed in the name of Intuitionistic fuzzy logic/sets. In three valued logic system the concepts of negation are now a growing subject of any group decision making problems. However to draw a clear estimation of a neutrosophic decision has not yet been studied by modern researchers.
Objective: Various kinds of new establishments of the Neutrosophic set have been studied from the algebraic point of view along with some polynomial structures. We have seen that; no finite geometric structures have been developed yet to qualify the real-world problems.
Methods: We consider the three components of a neutrosophic set as the variables of three-dimensional geometry. Since the decisions are compact and constructive we may consider the convex neutrosophic cone for analyzing single/ multiple group decision making problems.
Results: Various definitions are made over the cone- fundamentals using non-standard neutrosophic set in the domain [−11] x [−11] x [−11]. Then we studied the constructions of several expressions/functions of neutrosophic cones such as reciprocal cone and enveloping cone via a novel thinking process. Then using some examples we have developed a new ranking method along with their geometric structures exclusively.
Conclusion: In this changing world the nature of decision-making behaviors is also changing rapidly. So the need of establishing new concepts is an emerging area of research. However more attention is required in discussing such vital issues in near future. The proposed approach may be applied to the decision-making problems of global issues also.
A Game Theory-based Approach to Fuzzy Linear Transportation Problem
Background: Transport models have wide application areas in the real world and play an important role in reducing transportation costs increasing service quality etc. These models may have uncertain transportation costs and supply or demand capacities of the product. Hence it would be effective to model the vagueness of customer demands economic conditions and technical or non-technical uncertainties because of uncontrollable factors. Therefore we focus on developing a mathematical solution approach to the fuzzy transportation problems.
Objective: In this paper an integrated approach is proposed for the solution of the fuzzy linear transportation problem that has fuzzy cost coefficients in the objective function. Since transportation problem is encountered frequently in the national and international environment it is considered that proposing a new solution method to this problem will be useful.
Methods: Fuzzy cost coefficients are taken as trapezoidal fuzzy numbers due to their widespread use in the literature. Firstly the fuzziness is removed by converting the original single objective fuzzy transportation problem into a crisp Multi-Objective Linear Programming Problem (MOLPP). After the classical payoff matrix is constructed ratio matrices are obtained to scale the objectives. Then an approach based on game theory is implemented to solve the MOLPP which is handled as a zero-sum game.
Results: Creating different ratio matrices in the game theory part of the approach can generate compromise solutions for the decision-makers. To demonstrate the effectiveness of the proposed approach two numerical examples from the literature are solved. While the same solution is obtained in one of the examples a different compromise solution set is generated which could be presented to the decision-maker in the other example.
Conclusion: In this paper we developed a novel game theory-based approach to the fuzzy transportation problem. The proposed approach overcomes the non-linear structure due to the uncertainty in the cost coefficients. The greatest advantage of the proposed approach is that it can generate more than one optimal solution for the decision-maker.
FMEA Method Using Spherical Fuzzy Sets for Risk Analysis of the Tech Startup
Introduction: Tech startups are fast-growing businesses that target the demands of the marketplace by developing innovative products services or platforms. Startups ensure socially economically or environmentally more effective alternatives by using or by creating appropriate technologies. Many factors have become prominent regarding the success and sustainability of the product or service offered by the startup: investment experience and education of the team the leadership of the management creativity innovation technological breakthroughs surrounding community future perspective target marketing strategy location and the analysis of the market etc. But since 80% of startups do not survive after five years defining the important risk factors is crucial to develop the right strategies for successful startups. In this study the risk factors have been defined based on the business model which has an important place in the success of the technology startups which use technology intensively. Comprehensive risk analysis on identified factors is presented to identify effective managerial strategies for technology startups to not fail.
Methods: Spherical Fuzzy Failure Mode and Impact Analysis (SFFMEA) was used within the framework of a business model canvas for risk analysis for the failure of technology startup projects. Due to the lack of recorded data for analysis the opinions of field experts were used. While the business model canvas guided the identification of detailed risk factors FMEA enabled the risk analysis of factors that cause startup projects to fail and considering parameters related to the probability of the relevant risk factors their impact on the failure of the project and the detection level of the risk factor. Spherical Fuzzy on the other hand allowed the quantitative inference of FMEA's comprehensive parameter definitions associated with the risk factors through experts. Thus all risk factors that may cause the failure of tech startups were ranked according to their risk priority numbers (RPNs) with the SFFMEA analysis which offers a comprehensive risk analysis.
Results: The findings show that the most important causes of the tech startup’s failure are “non-compliance with existing restrictions” “inappropriate venture capital strategy” and “lack of clustering support”.
Conclusion: These failure modes can be interpreted according to their frequency of encounter potential effects and detectability and can be considered an important finding in the development of appropriate managerial strategies for the mitigation of the risk factors so the startups can survive in their first five years. Also with the proposed risk analysis methodology a comprehensive analysis of any startup project can be performed according to its conditions and characteristics.
Evaluation of Online Grocery Platform Alternatives Using Fuzzy Z-Numbers
Background: Retail management has evolved into a new business model with the development of online shopping habits. There may be significant differences between onsite service and online service in terms of customer expectations.
Introduction: In this study companies providing online grocery services in Turkey are evaluated by examining the services they provide from the perspective of customers. Fuzzy Z numbers which also add the reliability of linguistic assessments to the analysis are used in order to better describe the uncertainty.
Methods: Fuzzy Z-analytic hierarchy method (FZ-AHP) is used to weight the decision criteria and fuzzy Z-Grey relational analysis (FZ-GRA) method is used to find the best online market company.
Results: As a result of the analysis it is revealed that the most important criteria for online grocery shopping are minimum order amount and brand diversity. The results are also compared with ordinary fuzzy methods.
Conclusion: The comparison of the methods used in the study shows that although the ranks of the criteria and alternatives are the same using fuzzy Z linguistic scale results in a wider interval for the weights and the scores of the alternatives which could change the ordering especially in cases where criterion weights or alternative scores are very close to each other.
A Novel Construction Method of (OP) Polynomial and Rational Fuzzy Implications
In this article we develop new constructed methods with specific conditions. The first method is a generalization of convex combination using n fuzzy implications. The second method is a parameterization of Lukasiewicz implication in an Ordering Property (OP) fuzzy implication form. The innovation in this work is the presentation of three new constructed methods of (OP) polynomial and (OP) rational fuzzy implications. We investigate some families of Ordering Property (OP) and Ordering Property (OP) Rational fuzzy implications. To these methods we give some coefficient conditions in order to satisfy basic properties like ordering property (OP) identity property (IP) and contrapositive symmetry (CP).
Background: Fuzzy implication functions are one of the main operations in fuzzy logic. They generalize the classical implication which takes values in the set {0 1} to fuzzy logic where the truth values belong to the unit interval [0 1]. The study of this class of operations has been extensively developed in the literature in the last 30 years from both theoretical and applicational points of view.
Introduction: In this paper we develop five new methods for constructing fuzzy implications with specific properties. The paper starts by presenting the first fuzzy implication construction machine that uses n fuzzy implications with specific conditions. Next we parameterize Lukasiewicz implication and create new families of (OP) polynomial and (OP) rational implications. For each method we investigate which conditions are satisfied and we give some examples.
Methods: The first constructed method uses n fuzzy implications in a linear product representation. The second method is an (OP) polynomial implication a parameterized Lukasiewicz implication. The third method is a rational implication with five parameters. In the fourth method we give a general form in the previous method by changing variables x and y with increasing functions. Finally the last method is another (OP) rational implication with three parameters.
Results: In each method we present the properties that are satisfied. We generalize the (OP) polynomial and rational by replacing the variables with monotonic functions or add powers on them. Finally we generalize and we give examples of new produced fuzzy implications.
Conclusion: As a future work we can create new families of rational implications by changing the polynomials of the numerator and denominator so that they satisfy more properties. Finally the new methods we presented can contribute in the construction of uninorms and copulas under certain conditions.
Multi-Shift Single-Vehicle Routing Problem Under Fuzzy Uncertainty During the COVID-19 Pandemic
Background: This work studies the single vehicle routing problem (VRP) with multi-shift and fuzzy uncertainty. In this case a company perpetually exploits a vehicle to accomplish demand over a scheduling period of several work shifts. In our problem a crew performs maintenance jobs at different locations. The working team operates in different shifts with a maximum duration but recurrently returns to the depot by the end of the shift to avoid overtime.
Methods: The objective is to minimize the number of shifts and the completion time (makespan). In addition we analyze the influence of uncertainty in driving and processing times on the overtime avoidance constraint in shift duration. We develop an Artificial Immune Heuristic to determine optimal solutions considering both makespan and overtime avoidance. We implement a Pareto-based framework to evaluate the impact of uncertainty.
Results: We present several numerical case studies to examine the problem. In particular we analyze different case study scenarios inferred from the environmental changes in travel and processing times observed in the Apulia region (SE Italy) during the COVID-19 lockdown periods that occurred in spring (started on March 9 2020) and autumn (after November 6 2020) of the year 2020.
Conclusion: The work program was revised as soon as the Italian COVID-19 restrictions were implemented in the spring and autumn of 2020 due to the changing environment. Our approach allowed for the rapid release of new robust maintenance programs. Results show significant improvements with the presented approach.
A Fuzzy Multi-Criteria Decision Making Methodology for Job Evaluation
Introduction: In this study the integrated methods Hesitant Fuzzy Analytic Hierarchy Process (HF-AHP) Fuzzy COmplex Proportional Assessment (F-COPRAS) and Fuzzy Technique for Order Performance to Ideal Solution (F-TOPSIS) were used for job evaluation studies in a food company.
Background: There has been a decline in employee performance in the company. Unfair wages and unequal workload were identified as the reasons for the failure. Therefore it has been observed that the staff turnover rate in the company is quite high.
Objective: The objective is to determine a fair wage policy that will increase employee satisfaction by stratifying with job evaluation analysis between positions.
Methods: The experts of Human Resources Department determined eight competency evaluation criteria for job evaluation studies in the proposed approach. Based on their judgments on these criteria the competencies were rated using a linguistic scale and the weighting values were calculated using the HF-AHP method. These values are inputs for the next stage. Employees were ranked using F-COPRAS and F-TOPSIS methods.
Results: This study showed that the integrated method can be an effective alternative solution approach for calculating the weighting values and ranking of competencies in job evaluation studies.
Conclusion: It has been shown that the use of the strata created as a result of this study is a great facilitator in determining employee pay policies.
ACKNOWLEDGEMENTS TO REVIEWERS
Partial Fuzzy Quantifiers and their Computation
Background: In computer science one often needs to deal with undefined values. For example they naturally increase when a mistake such as the square root of a negative number or division by zero occurs. A similar problem occurs in the logical analysis of natural language. For example the expression “Czech president in the 18th century” has no denotation because there was no Czech president before 1918. Such a situation in mathematics is characterized by partial functions i.e. functions that may be undefined for specific arguments.
Methods: In this paper we will extend the theory of intermediate quantifiers (i.e. expressions such as “most almost all many a few” etc.) to deal with partially defined fuzzy sets. First we will extend algebraic operations that are used in fuzzy logic by the additional value “undefined.” Then we will introduce intermediate quantifiers using the former. The theory of intermediate quantifiers has been developed as a special theory of higher-order fuzzy logic.
Results: In this paper we introduce the quantifiers semantically and show how they can be computed. The latter is also demonstrated in three illustrative examples.
Conclusion: The paper contributes to the development of fuzzy quantifier theory and its extension by undefined values and suggests methods for computation of truth values.
Fuzzy Form of Euler Method to Solve Fuzzy Differential Equations
Background: The Euler method is a elementary method to solve fuzzy differential equations numerically. Several authors have explored the Euler method by applying various approaches and derivative concepts.
Methods: This paper proposes a fuzzy form of the Euler method to solve fuzzy initial value problems. Novelty of this approach is that the method developed based on fuzzy arithmetic. The solution by this method is readily available in the form of fuzzy-valued function. The method does not require to re-write fuzzy differential equation into a system of two crisp ordinary differential equations.
Results: The algorithm of proposed method and local error expression are discussed. An illustration and solution of the fuzzy Riccati equation are provided for the applicability of the method.
Conclusion: The proposed Euler method is a natural generalization of crisp Euler method. It is very efficient in solving linear and nonlinear fuzzy differential equations. One of the advantage of this method is that the solution obtained by the method is always a fuzzy-valued function due to well-defined fuzzy arithmetic.
A New Cutset-type Kernelled Possibilistic C-means Clustering Segmentation Algorithm Based on SLIC Super-pixels
Background: The kernelled possibilistic C-means clustering algorithm (KPCM) can effectively cluster hyper-sphere data with noise and outliers by introducing the kernelled method to the possibilistic C-means clustering (PCM) algorithm. However the KPCM still suffers from the same coincident clustering problem as the PCM algorithm due to the lack of between-class relationships.
Introduction: This paper introduces the cut-set theory into the KPCM and proposes a novel cutset-type kernelled possibilistic C-means clustering (C-KPCM) algorithm to solve the coincident clustering problem of the KPCM.
Methods: In the C-KPCM the memberships of some data samples in a cluster core which is generated by the cut-set theory are selected. Then the values of the selected memberships are modified in the iterative process to introduce the between-class relationship in the KPCM. Simultaneously an adaptive method of estimating the cut-set threshold is also given by averaging inter-class distances. Additionally a cutset-type kernelled possibilistic C-means clustering segmentation algorithm based on the SLIC super-pixels (SS-C-KPCM) is also proposed to improve the segmentation quality and efficiency of the color images.
Results: Several experimental results on artificial data sets and image segmentation simulation results prove the excellent performance of the proposed algorithms in this paper.
Conclusion: The proposed C-KPCM can overcome the coincident clustering problem of the KPCM algorithm and the proposed SS-C-KPCM can reduce the misclassification points and improve the color segmentation performance.
A Robust Fuzzy Decision Making on Global Warming
Background: In this article we develop a global warming indicator model under fuzzy system. It is the light of the sun that environmental pollution is responsible for the cause and immediate effect of global warming. Limited amount of oxygen in the air continuous decrease of fresh water volume more especially the amount of drinking water and the rise of temperature of the globe are the major symptoms (variants) of global warming. Thus to capture the facts we need to develop a mathematical model which has not yet been developed by the earlier researchers.
Introduction: An efficient literature survey has been done over the three major parameters of the environment namely oxygen fresh water and surface temperature exclusively. In fact we have accumulated 150 years-data structure of these major components and have analyzed them under fuzzy system.
Methods: First of all we gave few definitions on fuzzy set. Utilizing the data set we have constructed appropriate membership functions of the three major components of the environment. Then applying goal programming problem we have constructed a fuzzy global warming indicator (GWI) model subject to some goal constraints with respective priority vectors (Scenario 1 and Scenario 2). An extension has also been included for multi-valued goal programming problem and numerical illustrations have been done with the help of LINGO software.
Results: Numerical study reveals that the GWI takes maximum and minimum values in a decreasing manner as time increases. It is seen that for scenario 1 the global environmental system will attain its stability after 30 years by degrading 31% of GWI with respect to present base line. For scenario 2 after the same time the global environmental system will attain its stability quite slowly by degrading 28% of GWI with respect to present base line.
Conclusion: Here we have studied a mathematical model of global warming first time using fuzzy system. No other mathematical models have been found existing in the literature. Thus the basic novelty lies in a robust decision-making approach which showed the expected time of extinction of major species in this world. However extensive study on data analytics over major environmental components can tell the stability of the global warming indicator and hence the future fate of the globe also.
Industry 4.0 Roadmapping: A Fuzzy Linguistic Approach
Background: The industry 4.0 transition is becoming crucial for organizations. The literature reviewed showed that whilst there are many studies on industry 4.0 assessment that help organizations to evaluate their current state limited studies exist for road-mapping activities.
Objective: The main aim of this study is to construct a model that leads organizations to their fourth industrial revolution transition. Companies especially small and medium-sized ones (SMEs) need clear agile and efficient road maps because of their limited resources. Lack of a procedure that guides organizations in the right way is the motivation of this study.
Methods: A linguistic fuzzy inference system is used in this study. Concepts are determined and relations between concepts with if-then rules have been constructed according to the expert opinion. MATLAB R2015a is used for the inference system.
Results: An exemplary case is considered and the results show that the inference system can provide company-specific roadmaps. To which extend an industry 4.0 concept should be taken into account for a company can be seen with the proposed method.
Conclusion: The proposed method showed that specific and agile roadmaps could be obtained. Because of the dependency of expert opinion for the fuzzy rule base different methods for obtaining rules and relations may be a future research direction.
Fabrics Recommendation for Fashion Design by Using Fuzzy Logic and Rough Sets
Background: Fabric is one of the keys and vital design factors in fashion design. However the selection of relevant fabrics is rather complex for designers and managers due to the complexity of criteria at different levels.
Introduction: In this paper we propose a new fabric recommendation model in order to quickly realize fabric selection from non-technical fashion features only and predict fashion features from any fabric’s technical parameters. This approach is extremely significant for fashion designers who do not completely master fabric technical details. It is also very useful for fabric developers who have no knowledge on fashion markets and fashion consumers.
Methods: The proposed fabric recommendation model has been built by exploiting designers’ professional knowledge and consumers’ preferences. Concretely we first use fuzzy sets for formalizing and interpreting measured technical parameters and linguistic sensory properties of fabrics and then model the relation between the technical parameters and sensory properties by using rough sets. Next we model the relation between fashion themes and sensory properties using fuzzy relations. By combining these two models we establish a hybrid model characterizing the relation between fashion themes and technical parameters.
Results: The proposed model has been validated through a real fabric recommendation case for designer’s specific requirements. We can find that the proposed model is efficient since the averaged value of prediction errors is 8.57% which does not exceed 10% (generally considered as an allowable range of human perception error).
Conclusion: The proposed model will constitute one important component for establishing an intelligent recommender system for garment design enabling to support innovations in textile/apparel industry in terms of mass customization and e-shopping.