In this study, the evaluation items related to the effectiveness evaluation of the LVC (Live, Virtual, Constructive) training system of the Air Force were derived and the weights of each item were analyzed. The LVC training system evaluation items for AHP (Analytic Hierarchy Process) analysis were divided into three layers, and according to the level, 3 items were derived at level 1, 11 items at level 2, and 33 items at level 3. For weight analysis of evaluation items, an AHP-based pairwise comparison questionnaire was conducted for Air Force experts related to the LVC training system. As a result of the survey, related items such as (1) Achievement of education and training goals (53.8%), (1.2) Large-scale mission and operational performance (25.5%), and (1.2.1) Teamwork among training participants (19.4%) was highly rated. Also, it was confirmed that the weights of evaluation items were not different for each expert group, that is, the priority for importance was evaluated in the same order between the policy department and the working department. Through these analysis results, it will be possible to use them as evaluation criteria for new LVC-related projects of the Air Force and selection of introduction systems.

There has been considerable recent interest in deep learning techniques for structural analysis and design. However, despite newer algorithms and more precise methods have been developed in the field of computer science, the recent effective deep learning techniques have not been applied to the damage detection topics. In this study, we have explored the structural damage detection method of truss structures using the state-of-the-art deep learning techniques. The deep neural networks are used to train knowledge of the patterns in the response of the undamaged and the damaged structures. A 31-bar planar truss are considered to show the capabilities of the deep learning techniques for identifying the single or multiple-structural damage. The frequency responses and the elasticity moduli of individual elements are used as input and output datasets, respectively. In all considered cases, the neural network can assess damage conditions with very good accuracy.

텐세그리티 구조물은 인장력을 받는 연속된 케이블 안에 압축력을 받는 스트럿이 결합된 형태로 구성된다. 텐세그리티 구조물은 자기 응력 상태를 갖는 프리스트레스 핀 접합 구조물에 속한다. 텐세그리티 구조물 설계의 핵심은 평형 배열상태를 구하는 일명 형상탐색 과정이다. 본 논문에서는 세 가지의 효과적인 텐세그리티 구조물의 형상탐색 기법을 제안하였다. 형상탐색과정을 수행하면 평형상태의 내력 밀도와 그에 대응하는 위상을 얻을 수 있다. 이 때 평형상태를 형성하는 적절한 내력밀도 값을 얻기 위해 유전자 알고리즘을 결합한 내력밀도법이 사용되었다. 수치해석 예제를 통해 제안 알고리즘의 효율성을 입증하였다.

Various saponins are present in soybean seed, and they can be divided into three groups, group A, B and E saponin, in which the aglycone are soyasapogenol A, B and E, respectively. Group A saponins are supposed to be responsible for the undesirable bitter and astringent taste, and Group B saponins including 2,3-dihydro-2,5-dihydroxy-6-methyl- 4H-pyran-4-one (DDMP)-conjugated soyasaponins have the health-contributing activities. This study was conducted to investigate the saponin composition and content of 69 Korean cultivars. Thin-layer chromatography (TLC) and High-performance liquid chromatography (HPLC) were used to analyze the composition and content of soyasaponins. The composition of saponins in the seed hypocotyl of 69 Korean cultivars were divided into three groups, Aa, Ab and AaAb types. The numbers of Aa, Ab and AaAb types were 10, 36 and 23, respectively. The contents of group A saponin (Aa, Ab and Ac), group B saponin (non-DDMP, Ba and Bb) and DDMP saponin (αg and βg) ranged 21.7 ~ 57.0mg/g, 1.4 ~ 5.1mg/g and 9.8 ~ 27.4mg/g, respectively. A total of saponin content of seed hypocotyl in 69 Korean cultivars ranged from 43.8mg/g to 85.3mg/g with an average of 59.1mg/g.

Energy calibration is important to identify accurate neutron capture resonance energy in the neutron TOF (Time-of-Flight) experiment. In present study, the accurate neutron capture resonance energies of natural Sm were measuredby using a 46-MeV electron linear accelerator (linac) at the Research Reactor Institute, Kyoto University(KURRI). The BGO spectrometer were adopted for measurement the prompt capture gamma-ray of the sample. To obtain energy calibration curve, resonance energy of a gold sample used as standard resonance energy Mughabghab’s data (From neutron resonance parameters data). Previous data (by Mughabghab) of natural Sm sample have been compared with the present result.