Taguchi method is one of the most popular approaches for design optimization such that performance characteristics become robust to uncontrollable noise variables. However, most previous Taguchi method applications have addressed a single-characteristic problem. Problems with multiple characteristics are more common in practice. The multi-criteria decision making(MCDM) problem is to select the optimal one among multiple alternatives by integrating a number of criteria that may conflict with each other. Representative MCDM methods include TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey Relational Analysis), PCA(Principal Component Analysis), fuzzy logic system, and so on. Therefore, numerous approaches have been conducted to deal with the multi-characteristic design problem by combining original Taguchi method and MCDM methods. In the MCDM problem, multiple criteria generally have different measurement units, which means that there may be a large difference in the physical value of the criteria and ultimately makes it difficult to integrate the measurements for the criteria. Therefore, the normalization technique is usually utilized to convert different units of criteria into one identical unit. There are four normalization techniques commonly used in MCDM problems, including vector normalization, linear scale transformation( max-min, max, or sum). However, the normalization techniques have several shortcomings and do not adequately incorporate the practical matters. For example, if certain alternative has maximum value of data for certain criterion, this alternative is considered as the solution in original process. However, if the maximum value of data does not satisfy the required degree of fulfillment of designer or customer, the alternative may not be considered as the solution. To solve this problem, this paper employs the desirability function that has been proposed in our previous research. The desirability function uses upper limit and lower limit in normalization process. The threshold points for establishing upper or lower limits let us know what degree of fulfillment of designer or customer is. This paper proposes a new design optimization technique for multi-characteristic design problem by integrating the Taguchi method and our desirability functions. Finally, the proposed technique is able to obtain the optimal solution that is robust to multi-characteristic performances.

In contemporary global warfare, the significance and imperative of air transportation have been steadily growing. The Republic of Korea Air Force currently operates only light and medium-sized military cargo planes, but does not have a heavy one. The current air transportation capability is limited to meet various present and future air transport needs due to lack of performance such as payload, range, cruise speed and altitude. The problem of population cliffs and lack of airplane parking space must also be addressed. These problems can be solved through the introduction of heavy cargo planes. Until now, most studies on the need of heavy cargo plane and increasing air transport capability have focused on the necessity. Some of them suggested specific quantity and model but have not provided scientific evidence. In this study, the appropriate ratio of heavy cargo plane suitable for the Korea's national power was calculated using principal component analysis and cluster analysis. In addition, an optimization model was established to maximize air transport capability considering realistic constraints. Finally we analyze the results of optimization model and compare two alternatives for force structure.

In supply chain, most partners except the top level suppliers have inbound and outbound logistics. For example, toll manufacturing companies get unprocessed materials from a requesting company and send the processed materials back to the company after toll processing. Accordingly, those companies have inbound and outbound transportation costs in their total logistics costs. For many cases, the company may make the schedule of distributions by considering only the due delivery dates. However, the inbound and outbound transportation costs could significantly affect the total logistics costs. Thus, this paper considers the inbound and outbound transportation costs to find the optimal distribution plans. In addition, we have considered the inventory holding costs as well with transportation costs. From the experimental results, we have provided the optimal strategies for the distributions of replenishment as well as deliveries.

Effects-Based Operations (EBO) refers to a process for achieving strategic goals by focusing on effects rather than attrition-based destruction. For a successful implementation of EBO, identifying key nodes in an adversary network is crucial in the process of EBO. In this study, we suggest a network-based approach that combines network centrality and optimization to select the most influential nodes. First, we analyze the adversary’s network structure to identify the node influence using degree and betweenness centrality. Degree centrality refers to the extent of direct links of a node to other nodes, and betweenness centrality refers to the extent to which a node lies between the paths connecting other nodes of a network together. Based on the centrality results, we then suggest an optimization model in which we minimize the sum of the main effects of the adversary by identifying the most influential nodes under the dynamic nature of the adversary network structure. Our results show that key node identification based on our optimization model outperforms simple centrality-based node identification in terms of decreasing the entire network value. We expect that these results can provide insight not only to military field for selecting key targets, but also to other multidisciplinary areas in identifying key nodes when they are interacting to each other in a network.

인공 고관절 치환술에 사용되는 금속 삽입물은 크기와 성분에 따라 주변 조직과 크고 작은 자화율의 차이를 일으켜 다양한 금속 인공물의 원인이 되며, 영상에 진단적 가치를 떨어뜨린다. 수신대역폭을 높이는 것은 인공물 감소에 효과가 있으나, 높은 수신대역폭은 획득 영상의 신호대잡음비를 감소시키는 단점이 있어 일정 수치 이상으로는 적용 하기에는 어려움이 있다. 딥러닝 알고리즘은 영상의 신호대잡음비를 높이고 전체 영상에서 균일하게 배경 잡음을 제거하는 데 매우 효과적이다. 이에 본 연구에서는 금속 인공물 감소를 위해 기존에 높은 수신대역폭을 이용하는 MARS(metal artifact reduction sequence) 프로토콜과 더욱 높은 수신대역폭을 설정한 프로토콜(Ultra MARS) 을 획득한 후 딥러닝을 이용하여 딥러닝 Ultra MARS로 변환한 후에 금속 인공물의 차이를 비교하였다. 딥러닝 적 용 후 Ultra MARS에서 적용 전 또는 기존의 MARS 기법보다 인공물의 크기가 작게 측정이 되었다. 또한, 인공물의 전체적인 SSIM(structural similarity index measure)에서도 기존의 MARS 기법보다 전체면적이 작게 측정되었 다. 더 나아가 SSIM의 결과 딥러닝 적용 전후의 구조적 유사성 역시 유사하게 나왔다. 딥러닝 알고리즘을 기존에 인공물을 줄이기 위해 사용하는 MARS와 같은 기법에서도 월등하게 높은 수치를 사용하는 강조영상을 획득 가능하 며 영상의 인공물도 줄이며, 영상의 대조도 또한 유지되는 영상을 제공할 수 있을 것으로 사료된다.

자기공명(magnetic resonance, MR)영상에서 주로 발생하는 Rician 노이즈는 영상의 화질을 저하하는 주요 요소 중의 하나이다. 본 연구에서는 노이즈 제거에 효율적이라고 잘 알려진 총변이(total variation, TV) 알고리즘을 모 델링하여 Rician 노이즈 레벨에 따른 파라미터를 최적화하고자 한다. 시스템은 8채널 기반의 3.0 T 장치를 활용하였 고 물 팬텀 영상을 획득하여 각각 Rician 노이즈를 0.05, 0.10, 0.15, 그리고 0.20 값을 부가하였다. TV 알고리즘 은 Rudin-Osher-Fatemi 모델을 기반으로 모델링하였고 최적화를 수행하기 위하여 반복수 파라미터를 조정하여 획득된 영상에 적용하였다. 결과적으로 Rician 노이즈 레벨을 0.05, 0.10, 0.15, 그리고 0.20을 사용하였을 때 각 각 30, 40, 80, 그리고 120 반복수를 기반으로 한 TV 노이즈 알고리즘에서 가장 우수한 신호 대 잡음비(signal to noise ratio, SNR)와 대조도 대 잡음비(contrast to noise ratio, CNR) 결괏값이 도출되었다. 또한, 최적화된 반복수를 적용한 TV 알고리즘을 사용한 MR 영상에서 기존의 위너 및 중간값 필터를 사용하였을 때 비하여 SNR과 CNR 모두 우수한 값을 획득할 수 있었다. 특히 기본적으로 획득된 MR 영상보다 최적화된 TV 알고리즘을 적용한 영상의 평균 SNR과 CNR은 각각 3.11 및 3.31배 향상됨이 증명되었다. 결론적으로, 노이즈 제거 효율이 우수한 TV 알고리즘의 최적화된 파라미터를 활용한다면 MR 영상에서의 활용 가능성이 클 것으로 기대한다.

압축센스(Compressed SENSE) 기법은 검사 시간을 획기적으로 단축할 수 있으나, 시간 단축을 위한 기법적용 시 가속계수를 증가시키면 인공물의 발생이 영상에서 증가하는 문제점이 있다. 이에 인공물이 발생하지 않으면서 검사 시간을 최대한 단축할 수 있는 최적의 압축센스 가속계수를 제시하고자 하였다. 연구 방법은 인공물이 발생하지 않는 가속계수 1.0을 기준으로 0.5 간격씩 5.0까지 무릎관절 자기공명영상의 팬텀 실험과 임상실험 영상을 획득한 후, 방사선사 10명이 5점 척도로 영상을 평가하여 유의한 차이가 있는지 판단하였다. 연구 결과 T1 강조영상과 T2 강조 영상 모두 팬텀 실험은 가속계수 2.0 이하로 하였을 때 임상실험은 3.0 이하로 하였을 때 기준이 되는 1.0 영상과 차이가 없었다. 결론적으로 무릎관절 자기공명영상 검사 시 인공물이 발생하지 않으며 검사 시간을 최대로 단축할 수 있는 최적의 압축센스 가속계수는 팬텀 실험의 경우 2.0, 임상실험의 경우 3.0이 적정하리라 판단된다.

Protein is an essential nutrient for humans to sustain life, but it is predicted that it will be challenging to secure protein through the traditional livestock industry in the future. Microalgae has high future value as an alternative protein food source due to resource utilization and sustainability advantages. In order to increase productivity, the culture conditions of microalgae, Chlorella vulgaris, Dunaliella salina, and Scenedesmus obliquus were examined in this study. The optimal culture conditions of C. vulgaris were mixotrophic culture, 25oC culture temperature, 7.0 initial pH, 10% initial inoculation, stirring culture, 3000 Lux light intensity, and 24L:0D light/dark cycle period with red LED. For D. salina, the optimal culture conditions were mixotrophic culture, 20oC culture temperature, 8.0 initial pH, 10% initial inoculation, stirring culture, 6000 Lux light intensity, and 12L:12D light/dark cycle period with white LED. For S. obliquus, the optimal culture conditions were mixotrophic culture, 30oC culture temperature, 8.0 initial pH, 10% initial inoculation, stirring culture, 4500 Lux light intensity, and 14L:10D light/dark cycle period with fluorescent light. These findings can be used as important information for increasing the production of microalgae as an alternative protein material resource in the future.