A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account

Varying Spread Fuzzy Regression for Affective Quality Estimation

Design of preferred products requires affective quality information which relates to human emotional satisfaction. However, it is expensive and time consuming to conduct a full survey to investigate affective qualities regarding all objective features of a product. Therefore, developing a prediction model is essential in order to understand affective qualities on a product. This paper proposes a novel fuzzy regression method in order to predict affective quality and estimate fuzziness in human assessment, when objective features are given. The proposed fuzzy regression also improves on traditional fuzzy regression that simulate only a single characteristic with the resulting limitation that the amount of fuzziness is linear correlated with the independent and dependent variables. The proposed method uses a varying spread to simulate nonlinear and nonsymmetrical fuzziness caused by affective quality assessment. The effectiveness of the proposed method is evaluated by two very different case studies, affective design of an electric iron and image quality assessment, which involve different amounts of data, varying fuzziness, and discrete and continuous data. The results obtained by the proposed method are compared with those obtained by the state of art and the recently developed fuzzy regression methods. The results show that the proposed method can generate better prediction models in terms of three fuzzy criteria, which address both predictions of magnitudes and fuzziness

Affective design using machine learning : a survey and its prospect of conjoining big data

Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today’s competitive markets. Consumers not only need to be solely satisfied with the functional requirements of a product, and they are also concerned with the affective needs and aesthetic appreciation of the product. A product with good affective design excites consumer emotional feelings so as to buy the product. However, affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This article presents a survey of commonly used machine learning approaches for affective design when two data streams namely traditional survey data and modern big data are used. A classification of machine learning technologies is first provided which is developed using traditional survey data for affective design. The limitations and advantages of each machine learning technology are also discussed and we summarize the uses of machine learning technologies for affective design. This review article is useful for those who use machine learning technologies for affective design. The limitations of using traditional survey data are then discussed which is time consuming to collect and cannot fully cover all the affective domains for product development. Nowadays, big data related to affective design can be captured from social media. The prospects and challenges in using big data are discussed so as to enhance affective design, in which very limited research has so far been attempted. This article provides guidelines for researchers who are interested in exploring big data and machine learning technologies for affective design

Metaheuristic Design Patterns: New Perspectives for Larger-Scale Search Architectures

Design patterns capture the essentials of recurring best practice in an abstract form. Their merits are well established in domains as diverse as architecture and software development. They offer significant benefits, not least a common conceptual vocabulary for designers, enabling greater communication of high-level concerns and increased software reuse. Inspired by the success of software design patterns, this chapter seeks to promote the merits of a pattern-based method to the development of metaheuristic search software components. To achieve this, a catalog of patterns is presented, organized into the families of structural, behavioral, methodological and component-based patterns. As an alternative to the increasing specialization associated with individual metaheuristic search components, the authors encourage computer scientists to embrace the ‘cross cutting' benefits of a pattern-based perspective to optimization algorithms. Some ways in which the patterns might form the basis of further larger-scale metaheuristic component design automation are also discussed

Tradition and Innovation in Construction Project Management

This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

A fuzzy front end model for concurrent specification in new product development

This research reports on the development of a new model for an early design stage in new product development (NPD) programmes called the Fuzzy Front End (FFE). The new FFE model aims at overcoming two kinds of limitations identified in previous FFE models. The first limitation concerns current trends in FFE model improvement including the need for a data-driven model, and to address agile development, incremental and radical NPDs, balanced explicitness and responsiveness characteristics, and balanced procedural and performative structures. The second limitation concerns deficiencies in the performance structure and operating mechanism regarding contextual performance and concurrent collaboration. This means that performances in the FFE do not systematically link with each other, either in a single functional domain or multidimensionally across diverse functional domains, but instead exist independently. A pragmatic-prescriptive model has been functionally embodied by analysing real-world FFE scenarios using inductive reasoning. The model is data-driven with a performative structure wherein parameters can interlock for contextual performance and concurrent collaboration throughout the entire FFE process. With this interlocking structure, once an initial parameter is produced, all remaining parameters considered from both perspectives can be obtained successively. This model allows performers to explicitly understand the purpose and roles of parameters and their relationships from both perspectives when processing parameters. The model thus leads to more agile FFE execution by reducing the iterative work needed to correct defective parameters which have not been handled with contextual performance and concurrent collaboration in mind but instead exist independently. A theoretical-descriptive model, produced by validating the developed pragmatic-prescriptive model, using deductive reasoning, consists of mathematical formulas, providing the underlying concept of an overall FFE as well as that of its parts. Consequently, the pragmatic-prescriptive model can serve as functional performance guidance, while the theoretical-descriptive model can serve as conceptual performance guidance when employing the pragmatic-prescriptive model.Open Acces

Quantitative Techniques in Participatory Forest Management

Forest management has evolved from a mercantilist view to a multi-functional one that integrates economic, social, and ecological aspects. However, the issue of sustainability is not yet resolved. Quantitative Techniques in Participatory Forest Management brings together global research in three areas of application: inventory of the forest variables that determine the main environmental indices, description and design of new environmental indices, and the application of sustainability indices for regional implementations. All these quantitative techniques create the basis for the development of scientific methodologies of participatory sustainable forest management

Quantitative Techniques in Participatory Forest Management

Forest management has evolved from a mercantilist view to a multi-functional one that integrates economic, social, and ecological aspects. However, the issue of sustainability is not yet resolved. Quantitative Techniques in Participatory Forest Management brings together global research in three areas of application: inventory of the forest variables that determine the main environmental indices, description and design of new environmental indices, and the application of sustainability indices for regional implementations. All these quantitative techniques create the basis for the development of scientific methodologies of participatory sustainable forest management

Operational progression of digital soil assessment for agricultural growth in Tasmania, Australia

Tasmania, Australia, is currently undergoing a period of agricultural expansion through the development of new irrigation schemes across the State, primarily to stimulate the economy and ensure future food security. ‘Operational Progression of Digital Soil Assessment (DSA) for Agricultural Growth in Tasmania, Australia’ presents the adaptation and operationalisation of quantitative approaches for regional land evaluation within these schemes, specifically applied Digital Soil Mapping (DSM) to inform a land suitability evaluation for 20 different agricultural crops, and ultimately a spatial indication of the State’s agricultural versatility and capital. DSM had not previously been applied or tested in Tasmania; the research examines and validates DSM approaches with respect to the State’s unique and complex soils and biophysical interactions with climate and terrain, and how these apply to various agricultural land uses. The thesis is a major contribution to the methodology and development of one of the first major operational DSA programs in Australia, and forms a framework for this type of DSM approach to be used in future operational land evaluation elsewhere