본 연구는 주변 환경의 차이에 따른 화분매개곤충의 유입 특성을 파악하기 위하여 국립수목원 내 진화속을걷 는정원과 부추속전문전시원에 식재된 울릉산마늘의 화분매개곤충을 조사하였다. 2023년 5월 22일부터 6월 2일 까지 꽃이 70% 이상 개화하였을 때 포충망을 활용하여 8일간 곤충을 채집하였고, 각 전시원 별 식생(피도), 기후 (온도·습도·조도)를 조사하였다. 조사 결과 진화속을걷는정원에서 피도 60% 온도 26.4℃, 습도 31.5%, 조도 40953.6lx, 화분매개곤충 20과 450개체, 부추속전문전시원은 피도 90%, 온도 25.6℃, 습도 31.6%, 조도 6387lx, 화분매개곤충 15과 196개체로 나타났다. 온도와 조도가 상대적으로 높은 진화속을걷는정원이 채집된 곤충의 다양성과 방문 빈도가 높았다. 시간대별 곤충의 방문 빈도를 비교해본 결과 온도와 조도는 개체수가 증가할 때 같이 증가하는 경향을 보였으며, 습도는 반대의 경향을 보였다.

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

The nerippe fritillary butterfly, Argynnis nerippe , is listed as an endangered species in Korea. Establishment of effective conservation strategies can be aided by the development and application of molecular markers that can be used to investigate the population genetics of the butterfly. Therefore, in this study, we identified ten microsatellite markers specific to A. nerippe using the Next-Seq 500 platform, and applied these markers to investigate the characteristics of five South Korean butterfly populations. Genotyping of 48 A. nerippe individuals from five localities showed that at each locus the number of alleles ranged from 4 to 14, and that the observed and expected heterozygosities were 0.324–0.863 and 0.138–0.985, respectively. Significant deviation from the Hardy–Weinberg equilibrium was not observed at any locus. Population structure analysis indicated that there are two genetic groups in Korea, but no population-based gene pool assignments were found. Analysis of FST, RST, and a principal coordinates analysis suggested that the Gureopdo and Yaecheon populations were isolated from other populations. Genetic isolation of the Gureopdo population may be a consequence of unequal population change between Gureopdo and inland populations and to the offshore habitat of Gureopdo. Genetic isolation of the Yaecheon population may be a consequence either of the southernmost location of the population or of the limited sample size available. Further studies with increased sample sizes will be necessary to draw robust conclusions on population isolation and to devise conservation strategies.

The Acoptolabrus changeonleei Ishikawa et Kim, 1983 (Coleoptera: Carabidae), has been listed as an endangered insect in South Korea. The complete mitochondrial genome of the species was 16,831 bp with a typical set of genes (13 protein-coding genes [PCGs], 2 rRNA genes, and 22 tRNA genes) and one non-coding region, with the arrangement identical to that observed in most insect genomes. Phylogenetic analyses with concatenated sequences of the 13 PCGs and 2 rRNA genes, using the Bayesian inference (BI) and maximum-likelihood (ML) methods, placed A. changeonleei as a sister to the within-subfamilial species Damaster mirabilissimus in Carabinae, with the highest nodal support by both analyses.

항공기 연료셀은 추락 상황에서 승무원의 생존성과 직결되는 중요 구성품으로 회전익 항공기에 적용되고 있는 내충격성 연료셀은 추락시 승무원의 생존성 향상에 큰 역할을 하고 있다. 미육군은 항공기가 처할수 있는 다양한 상황에서 연료셀이제 기능을 발휘할 수 있도록 1960년대 초부터 MIL-DTL-27422 이라는 연료셀 개발규격을 제정하여 현재까지 적용해 오고있다. 해당 개발규격에 규정된 시험 중에서 충돌충격시험은 연료셀의 내충격 성능을 검증하는 시험으로써, 해당 시험을 통과하는 연료셀은 생존가능 충돌환경에서 화재가 발생하지 않아 승무원의 생존성이 대폭 향상될 수 있음을 의미한다. 그러나 충돌충격시험은 작용하는 하중 수준이 너무 높기 때문에 실패 위험성이 가장 큰 시험이기도 하다. 연료셀이 해당 시험을 통과하지 못하는 경우에는 재시험을 위한 비용과 준비기간이 상당히 소요되어 항공기 개발일정에 심각한 지장을 초래할 가능성도 높다. 따라서, 연료셀 설계 초기부터 내충격성능 만족여부에 대한 예측을 위해 충돌충격시험의 수치해석을 통한 실물시험에서의 실패 가능성을 최소화해야 한다는 필요성이 제기되어 왔다. 본 연구에서는 충돌모사 프로그램인 LS-DYNA에서 지원하는 유체-구조 연성해석 방법인 SPH 방법을 사용하여 연료셀 충돌충격시험 수치 모사를 수행하였다. 수치해석 조건으로 MIL-DTL-27422에서 요구하는 시험조건을 고려하였고, 실물 연료셀의 시편시험을 통해 확보한 물성데이타를 해석에 반영하였다. 그 결과로 연료셀 자체의 응력수준을 평가하고 취약부위에 대한 고찰을 수행하였다.