Pursuit of methodology for data input related to taste in design: Using eye tracking technology

Being knowledgeable and using the knowledge accurately is the basis for all development. Knowledge sheds light for the designers on the topics of correct decision making and early intervention while assessing the success of current products. The aesthetic value of a product is the fundamental point of communication between the designer and the user. Therefore, with the aim of accessing the hidden aesthetic taste in the users via the user, eye tracking technology was used as a tool since the sight has a great influence on one’s internal world. Within this scope, it was decided to work on a static product group, and 40 armchair variations were created as a sample. The eye movements on armchairs with variable stylistic features and the taste evaluations of 60 participants were monitored via the three different stages of the experiments. In line with the main objective of the study, significant and guiding outputs were obtained. The results showed that the taste related to the product can be understood via the eye with viewing metrics such as time to first fixation, first fixation duration, fixation count and duration, visit count and duration, and that the knowledge of view could affect the design decisions. On the other hand, a road map and a procedure were created for the purposes of accessing the implicit taste information for the design practice through using the eye tracking technology.     

Trust-based consensus reaching process for product design decision-making with heterogeneous information

Pursuing a consensual result is vital for selecting optimum product design schemes as it helps eliminate preference conflicts in product design decision-making (PDDM). As a dynamic and iterative activity, the consensus reaching process (CRP) of PDDM always involves heterogeneous, vague, and inconsistent information, which makes it challenging to adjust decision-makers’ judgement to achieve an acceptable consensus level. To address this issue, triangular fuzzy numbers were introduced to depict decision-makers’ heterogeneous judgement. The indicator weights were determined by integrating the fixed weight obtained from the interval analytic hierarchy process (IAHP) and the variable weight acquired using the maximizing deviation method. Decision-makers’ weights were identified through a combination of the uncertainty degree measured by fuzzy entropy and the consistency degree solved by a distance minimizing model of the PDDM matrix. A dynamic CRP for PDDM was proposed by adjusting decision-makers’ judgement based on their trust relationships and updating the PDDM matrix in an evolutionary manner. A case study is conducted to verify the feasibility and effectiveness of the proposed method.     

关于空调器(A.H.U)选型技术参数的见解

本文针对一些工程设计和招标文件中存在的空调器选型技术参数的误解问题,提出并分析了"热湿比ε"下的关键参数"送风控制点"S是空调设计的最基本要素,对于正确选择空调器的风量、冷量、系统控制具有重要的意义.     

软件项目管理的知识语义模型

探讨了构建一个基于软件项目管理的知识语义模型，它是软件项目管理系统的核心，包含过程模型、资源模型和产品模型。使用这个模型去开发一个软件项目管理系统，以帮助管理员去控制软件开发过程。     

Design and Analysis of Storing Experiments: A Case Study

In order to achieve robust and stable food products of desired quality and characteristics, all stages of the food production process, including storage conditions, should be considered during product development projects. This article describes a multistage production development project on low-fat mayonnaise, where experimental design was used to set up a pilot plant study involving ingredient factors, processing factors, and storage factors and their effect on mayonnaise viscosity. The article discusses three alternative empirical modeling approaches to analyze the data—namely, a simple graphics approach, a robustness approach, and a mixed models approach—considering their multistratum (split-plot) structure and repeated measurements of each subsample. In the case study, all information relevant for business decisions was obtained through the combination of graphical analysis and the robustness approach. This information could also be extracted by a practitioner, while the mixed model analysis clearly requires a graduate statistician. In order to obtain valid and useful information for the practitioner in an efficient way, the authors believe that, usually, the first two approaches would be sufficient. The more complex mixed model strategy might be advisable if a deeper understanding is required or desired.     

Development of a product configuration system with an ontology-based approach

Product configuration is a crucial means to implement the mass customization paradigm by assembling a set of customizable components to satisfy both customers’ needs and technical constraints. With the aim of enabling efficient and effective development of product configuration systems by reusing configuration knowledge, an ontology-based approach to modeling product configuration knowledge is presented in this paper. The ontology-based product configuration models are hierarchically organized. At the lower level, a configuration meta-model is defined. Based on this meta-model, domain-specific configuration knowledge can be derived by reusing or inheriting the classes or relations in the meta-model. Configuration models are formalized using OWL (Ontology Web Language), an ontology representation language developed by W3C. As a result, configuration models have well-defined semantics due to the logic semantics of OWL, making it possible to automatically detect inconsistencies of configuration knowledge bases. Furthermore, configuration constraints are represented in SWRL, a rule language based on OWL. Finally, actual configuration processes are carried out using JESS, a rule engine for the Java platform, by mapping OWL-based configuration facts and SWRL-based configuration constraints into JESS facts and JESS rules, respectively. The proposed methodology is illustrated with an example for configuring the ranger drilling machine.     

Development of a distributed collaborative design framework within peer-to-peer environment

Distributed collaborative design and manufacture enables manufacturing organizations to maintain competitiveness in the fiercely competitive global industry. This requires that the distributed system not only maintains data consistency across globally-distributed locations seamlessly, but also allows team members to access the storage system and computing resources transparently and securely. Current grid applications mainly based on client-server architecture are inflexible and rigid for fast changing collaborations among manufacturers, especially for small and medium enterprises. In this paper, a distributed collaborative design framework is presented with a hybrid of grid and peer-to-peer technology. In order to access computational resources for design, analysis and process simulation, a meta-scheduler is designed and implemented. It helps in resource discovery and optimal utilization of resources. A test bed is established, based on the framework proposed to demonstrate a distributed collaborative design and manufacturing environment.     

The canonical expression of the drama product manufacturing processes

As the broadcast industry is evolving toward IT-based facilities, the production workflows and their associated production metadata should similarly take advantage of IT commodities. This paper presents a manufacturing system for the production of drama television and motion picture programmes, constructed using IT-based technologies in a file-based media environment. This drama production facility implements a production workflow based on common industrial manufacturing processes and extensively models the individual aspects of the drama production process. We aim to show that the different processes contained in this manufacturing workflow can be expressed in terms of elementary building blocks, the canonical processes of media production. By identifying recurring and canonical functionality, process implementations can be simplified and input and output from different processes can be coordinated for better integration with external systems.     

Optimal platform investment for product family design

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.     

A cooperative coevolutionary algorithm for design of platform-based mass customized products

As a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution.