About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN

Cognitive Designers Activity Study, Formalization, Modelling, and Computation

This study aims to explore how designers mentally categorise design information during the early sketching performed in the generative phase. An action research approach is particularly appropriate for identifying the various sorts of design information and the cognitive operations involved in this phase. Thus, we conducted a protocol study with eight product designers based on a descriptive model derived from cognitive psychological memory theories. Subsequent protocol analysis yielded a cognitive model depicting the mental categorisation of design information processing performed by designers. This cognitive model included a structure for design information (high, middle, and low levels) and linked cognitive operations (association and transformation). Finally, this paper concludes by discussing directions for future research on the development of new computational tools for designers

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Kansei engineering with online review mining methodology for robust service design

Kansei Engineering (KE) has shown its prominent applicability in service design and development, focusing on translating and interpreting customers’ emotional needs (Kansei) into service characteristics. It is critical and promising as the services sector has grown faster than the manufacturing sector in developing economies in the past three decades. It accounted for an average of 55% of GDP in some developing economies. KE’s flexibility in collaborating with other methods and covering various service settings shows its unique superiority. However, there is criticism of the collected Kansei’s validity and the proposed solution’s robustness. It might be potentially caused by the dynamics of customer emotional needs and various service settings. As a result, Kansei is found to be somewhat fuzzy, unclear, and ambiguous. Hence, a more structured KE methodology incorporating the Kansei text mining process for robust service design is proposed. Kansei text mining approach will extract and summarize service attributes and their corresponding affective responses based on the online product descriptions and customer reviews. The Taguchi method will support the robustness of the proposed improvement strategy. An empirical study of a zoo as a tourism attraction service and its practical implication is discussed and validated in the proposed integrative framework

The use of software systems to implement Case-Based Reasoning enabled intelligent components for architectural briefing and design

This thesis describes the development of a prototype Case-Based Reasoning (CBR) enabled intelligent component system, called Architectural General Object System (ARGOS), to facilitate the storage of design information in lightweight cases that can be used on the desktop computer over the total life of the facility. It uses CBR techniques combined with Microsoft ActiveX controls (object technology) to provide a useful autonomous component to implement some of the software requirements of such a system within the context of the global design and construction environment. These technologies ensure a platform independent environment and integration into the Internet. The use of XML (Extensible Mark-up Language) as a design language is explored to facilitate the storage of design data in a persistent and neutral manner independent from the software that originally created it. This ensures a long data life and the enables different actors over the life cycle of a facility to use their own relevant software to process the design information. During the development of AEDES (Architectural Evaluation and Design System), the research team realised that the problem of structuring design knowledge in such a way to support relevant software systems across the life cycle of a facility is far more complex than originally anticipated. Although there are many similarities between the construction and the manufacturing industries, there are also significant and problematic differences. Architectural design tasks take place in an open world where the reasoner's knowledge is incomplete or inconsistent. Due to this the focus in computer-aided architectural design research has shifted back and forth from attempts to totally automate the entire design process to its partial support through drafting tools. In an attempt to overcome some of the enormous complexities, that researchers struggled with over the past 35 years, a prototype intelligent autonomous design component ARGOS is developed in this research. It is clear that automated design methods are not tractable and it is therefore more worthwhile to pursue the creation of a neutral design language and the creation of intelligent and flexible design tools to manipulate these design fragments. An in-depth study is made of various important out-of-industry manufacturing techniques, CBR and object technology and to establish clearly what the desirable characteristics of ARGOS should be. An important requirement is that ARGOS should be generic and non-prescriptive and should work in a Microsoft Windows compliant environment. A solution without the use of CAD is proposed that ensure a generic solution that could add value to many different construction industry actors in many different environments. More recently attempts are being made to introduce post-modern Artificial Intelligence (AI) into design and architecture. Despite all these efforts it is clear that architectural briefing and design has not reached the status of a science and it is unlikely ever to. This is confirmed by recent breakthroughs in the field of Artificial Intelligence (AI) and Knowledge Management that provide deeper insights into the cognitive processes of the designer. This study indicates that XML is a viable means of expressing design knowledge and a feasible alternative for the complex Building Product Models currently proposed whilst at the same time supporting operations in the Internet environment. Design information and the ability to retrieve it is now more important than the software application that originally created it. The autonomous intelligent component ARGOS provides a method to encapsulate design knowledge at both tacit and explicit cognitive levels whilst at the same time providing global communication in a convenient desktop environment. ARGOS is designed in a parametric way that supports any design process that requires positional, volumetric and spatial relationship analysis in both 2D and 3D. Multiple autonomous copies can be placed in a container environment such as Excel. Any process written in any computer language that supports the use of ActiveX controls can be used to manipulate the ARGOS instances.Dissertation (Ph.D. (Applied Sciences))--University of Pretoria, 2000.Architectureunrestricte

Improving Recall of Browsing Sets in Image Retrieval from a Semiotics Perspective

The purpose of dissertation is to utilize connotative messages for enhancing image retrieval and browsing. By adopting semiotics as a theoretical tool, this study explores problems of image retrieval and proposes an image retrieval model. The semiotics approach conceptually demonstrates that: 1) a fundamental reason for the dissonance between retrieved images and user needs is representation of connotative messages, and 2) the image retrieval model which makes use of denotative index terms is able to facilitate users to browse connotatively related images effectively even when the users' needs are potentially expressed in the form of denotative query. Two experiments are performed for verifying the semiotic-based image retrieval model and evaluating the effectiveness of the model. As data sources, 5,199 records are collected from Artefacts Canada: Humanities by Canadian Heritage Information Network, and the candidate terms of connotation and denotation are extracted from Art & Architecture Thesaurus. The first experiment, by applying term association measures, verifies that the connotative messages of an image can be derived from denotative messages of the image. The second experiment reveals that the association thesaurus which is constructed based on the associations between connotation and denotation facilitates assigning connotative terms to image documents. In addition, the result of relevant judgments presents that the association thesaurus improves the relative recall of retrieved image documents as well as the relative recall of browsing sets. This study concludes that the association thesaurus indicating associations between connotation and denotation is able to improve the accessibility of the connotative messages. The results of the study are hoped to contribute to the conceptual knowledge of image retrieval by providing understandings of connotative messages within an image and to the practical design of image retrieval system by proposing an association thesaurus which can supplement the limitations of the current content-based image retrieval systems (CBIR)

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

Image manipulation and user-supplied index terms.

This study investigates the relationships between the use of a zoom tool, the terms they supply to describe the image, and the type of image being viewed. Participants were assigned to two groups, one with access to the tool and one without, and were asked to supply terms to describe forty images, divided into four categories: landscape, portrait, news, and cityscape. The terms provided by participants were categorized according to models proposed in earlier image studies. Findings of the study suggest that there was not a significant difference in the number of terms supplied in relation to access to the tool, but a large variety in use of the tool was demonstrated by the participants. The study shows that there are differences in the level of meaning of the terms supplied in some of the models. The type of image being viewed was related to the number of zooms and relationships between the type of image and the number of terms supplied as well as their level of meaning in the various models from previous studies exist. The results of this study provide further insight into how people think about images and how the manipulation of those images may affect the terms they assign to describe images. The inclusion of these tools in search and retrieval scenarios may affect the outcome of the process and the more collection managers know about how people interact with images will improve their ability to provide access to the growing amount of pictorial information

Automated mood boards - Ontology-based semantic image retrieval

The main goal of this research is to support concept designers’ search for inspirational and meaningful images in developing mood boards. Finding the right images has become a well-known challenge as the amount of images stored and shared on the Internet and elsewhere keeps increasing steadily and rapidly. The development of image retrieval technologies, which collect, store and pre-process image information to return relevant images instantly in response to users’ needs, have achieved great progress in the last decade. However, the keyword-based content description and query processing techniques for Image Retrieval (IR) currently used have their limitations. Most of these techniques are adapted from the Information Retrieval research, and therefore provide limited capabilities to grasp and exploit conceptualisations due to their inability to handle ambiguity, synonymy, and semantic constraints. Conceptual search (i.e. searching by meaning rather than literal strings) aims to solve the limitations of the keyword-based models. Starting from this point, this thesis investigates the existing IR models, which are oriented to the exploitation of domain knowledge in support of semantic search capabilities, with a focus on the use of lexical ontologies to improve the semantic perspective. It introduces a technique for extracting semantic DNA (SDNA) from textual image annotations and constructing semantic image signatures. The semantic signatures are called semantic chromosomes; they contain semantic information related to the images. Central to the method of constructing semantic signatures is the concept disambiguation technique developed, which identifies the most relevant SDNA by measuring the semantic importance of each word/phrase in the image annotation. In addition, a conceptual model of an ontology-based system for generating visual mood boards is proposed. The proposed model, which is adapted from the Vector Space Model, exploits the use of semantic chromosomes in semantic indexing and assessing the semantic similarity of images within a collection