본 연구의 목표는 쌀가루의 물리화학적 특성을 분석하여 쌀면을 제조하는데 있어 최적 품종을 결정하는 것이다. 쌀 면 제조에 사용된 쌀 품종은 아밀로오스 함량이 다른 품종인 밀양 278호, 한아름 2호, 백진주를 사용하였다. 본 연구 에서는 쌀을 건식 제분하여 100-150 mesh 체를 통과한 쌀가루를 쌀면 제조에 이용하였다. 쌀가루의 물리화학적 특성 은 호화특성, thermo-mechanical 방법을 통해 분석하였으며, 쌀가루의 아밀로오스 함량에 따라 호화특성 및 thermomechanical 특성에서 차이를 나타냈다. 품종별 호화 개시 온도는 밀양 278호, 한아름 2호, 백진주 각각 70.0, 62.7, 63.0 ℃이었으며, 고아밀로스 함량을 갖는 쌀가루 품종인 밀양 278호는 높은 호화 온도를 나타내며, 호화 시 점도의 특성 또한 다른 품종과 차이를 보였다. 제조된 쌀면의 특성은 면의 질감분석, 조리손실률, 국물의 탁도, 단면사진을 통해 분 석하였다. 밀양 278호, 한아름, 백진주로 제조한 쌀 건면의 경도는 각각 4.55, 2.24, 1.89 N의 값을 나타내었다. 면을 삶았을 경우에 백진주는 면이 형성되지 않고 잘게 끊어져 면의 질감을 측정을 할 수 없었으며, 밀양 278호와 한아름 2호 쌀면의 Rmax 값은 각각 0.86, 0.46 N을 나타내었다. 밀양 278호 품종의 경우 다른 두 품종에 비해 호화 시 높은 점도를 나타내고, 낮은 조리 손실률 등 제면 가공적성에 적합할 것으로 판단된다.

Estimation approaches for casual relation model with high-order factors have strict restrictions or limits. In the case of ML (Maximum Likelihood), a strong assumption which data must show a normal distribution is required and factors of exponentiation is impossible due to the uncertainty of factors. To overcome this limitation many PLS (Partial Least Squares) approaches are introduced to estimate the structural equation model including high-order factors. However, it is possible to yield biased estimates if there are some differences in the number of measurement variables connected to each latent variable. In addition, any approach does not exist to deal with general cases not having any measurement variable of high-order factors. This study compare several approaches including the repeated measures approach which are used to estimate the casual relation model including high-order factors by using PLS (Partial Least Squares), and suggest the best estimation approach. In other words, the study proposes the best approach through the research on the existing studies related to the casual relation model including high-order factors by using PLS and approach comparison using a virtual model.

In ubiquitous computing, shared environments adjust themselves so that all users in the environments are satisfied as possible. Inevitably, some of users sacrifice their satisfactions while the shared environments maximize the sum of all users’ satisfactions. In our previous work, we have proposed social welfare functions to avoid a situation which some users in the system face the worst setting of environments. In this work, we consider a more direct approach which is a preference based clustering to handle this issue. In this approach, first, we categorize all users into several subgroups in which users have similar tastes to environmental parameters based on their preference information. Second, we assign the subgroups into different time or space of the shared environments. Finally, each shared environments can be adjusted to maximize satisfactions of each subgroup and consequently the optimal of overall system can be achieved. We demonstrate the effectiveness of our approach with a numerical analysis.