가상현실과 현실 세계를 융합한 공간으로서 가상의 세계 안에서 다양 한 상호작용과 경험을 제공하는 메타버스 서비스는 사용자들이 자아일치 성을 공유하고 강화하는 공간을 형성해줄 수 있다. 본 연구에서는 메타 버스 서비스 특성이 사용자의 재이용의도에 미치는 영향과, 이 관계를 매개하는 자아일치성의 효과를 검증하였다. 메타버스 서비스를 이용해 본 경험이 있는 성인 남녀를 대상으로 수집한 245개 설문자료를 분석한 결과에 따르면 메타버스 서비스 특성 중 실재감과 매개변수인 자아일치 성은 고객의 메타버스 재이용의도에 긍정적인 영향을 미치는 것으로 나 타났다. 또한 매개효과분석 결과에 따르면 자아일치성은 메타버스 서비 스 특성 중 실재감과 재이용의도 간의 관계를 부분매개하고, 유용성과 재이용의도 간의 관계, 상호작용성과 재이용의도 간의 관계를 완전매개 하는 것으로 나타났다. 연구 결과는 변화된 환경과 소비자 특성으로 인 해 전통적 브랜드 마케팅 방식의 한계에 직면하고 있는 기업들에게 메타 버스를 활용한 마케팅 전략 도출에 도움이 되는 유용한 정보를 제공하고 있다.
Quality function deployment (QFD) is a widely adopted customer-oriented product development methodology by translating customer requirements (CRs) into technical attributes (TAs), and subsequently into parts characteristics, process plans, and manufacturing operations. A main activity in QFD planning process is the determination of the target levels of TAs of a product so as to achieve a high level of customer satisfaction using the data or information included in the houses of quality (HoQ). Gathering the information or data for a HoQ may involve various inputs in the form of linguistic data which are inherently vague, or human perception, judgement and evaluation for the information and data. This research focuses on how to deal with this kind of impreciseness in QFD optimization. In this paper, it is assumed as more realistic situation that the values of TAs are taken as discrete, which means each TA has a few alternatives, as well as the customer satisfaction level acquired by each alternative of TAs and related cost are determined based on subjective or imprecise information and/or data. To handle these imprecise information and/or data, an approach using some basic definitions of fuzzy sets and the signed distance method for ranking fuzzy numbers is proposed. An example of a washing machine under two-segment market is provided for illustrating the proposed approach, and in this example, the difference between the optimal solution from the fuzzy model and that from the crisp model is compared as well as the advantage of using the fuzzy model is drawn.
사용후핵연료 파이로프로세싱에서 발생하는 방사성폐기물의 양을 최소화하기 위해서는 방사성 핵종 함유 염폐기물을 효과 적으로 처리할 수 있는 기술개발이 필요하다. 이를 위해 탄산화물(Li2CO3, K2CO3)을 이용한 반응증류공정에서 LiCl-KCl 공융 염 내 NdCl3의 분리특성을 관찰하였다. HSC-Chemistry 프로그램을 이용한 탄산화물과 NdCl3의 반응모델결과에서 NdCl3는 탄산화물의 주입조건 및 온도변화에 따라 산염화물(NdOCl) 또는 산화물(Nd2O3) 형태로 전환됨이 확인되었으며, 탄산화물 의 주입조건에 따른 LiCl-KCl-NdCl3계의 반응증류시험에서 반응모델결과와 유사한 경향을 확인하였다. 이 결과들을 이용하 여 LiCl-KCl 공융염 내 NdCl3를 고화가 용이한 산화물 형태로 분리하기 위한 공정조건을 도출하였다.
Quality function deployment (QFD) is a useful method in product design and development to maximize customer satisfaction. In the QFD, the technical attributes (TAs) affecting the product performance are identified, and product performance is improved to optimize customer requirements (CRs). For product development, determining the optimal levels of TAs is crucial during QFD optimization. Many optimization methods have been proposed to obtain the optimal levels of TAs in QFD. In these studies, the levels of TAs are assumed to be continuous while they are often taken as discrete in real world application. Another assumption in QFD optimization is that the requirements of the heterogeneous customers can be generalized and hence only one house of quality (HoQ) is used to connect with CRs. However, customers often have various requirements and preferences on a product. Therefore, a product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. To overcome these problems, this paper proposes an optimization approach to find the optimal set of TAs under multi-segment market. Dynamic Programming (DP) methodology is developed to maximize the overall customer satisfaction for the market considering the weights of importance of different segments. Finally, a case study is provided for illustrating the proposed optimization approach.
Platform-based product family design is recognized as an effective method to satisfy the mass customization which is a current market trend. In order to design platform-based product family successfully, it is the key work to define a good product platform, which is to identify the common modules that will be shared among the product family. In this paper the clustering analysis using dendrogram is proposed to capture the common modules of the platform. The clustering variables regarding both marketing and engineering sides are derived from the view point of top-down product development. A case study of a cordless drill/drive product family is presented to illustrate the feasibility and validity of the overall procedure developed in this research.
The Age-adjusted effective Modulus Method(AEMM) is one of the methods adopted for the construction stage analysis of concrete structures. The AEMM uses the aging factor to consider the effects of the varying concrete stress. In the aspects of computation time and the accuracy of the results, the AEMM is considered as one of most appropriate methods for construction stage analysis of tall building structures. Previous researches proposed appropriate values of the aging factor in the forms of graphs or using very simple equations. In this paper, an equation for estimating the aging factor as a function of rebar ratio in the section, compressive strength of concrete, notional member dimension, and age of concrete at the load application. The validity of aging factor proposed in this paper were examined by the comparison with the results of step-by step method.
Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical prece- dence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.