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윤한수(Hansoo Yun),이현소(Hyunso Lee),최성진(Sungjin Chol),이승희(Seunghee Lee),이창선(Changsun Lee),이강식(Kangsik Lee) 한국추진공학회 2022 한국추진공학회 학술대회논문집 Vol.2022 No.5
국내에서는 나로호, 누리호 등 중형발사체의 개발이 이루어졌으나, 소형발사체는 개발이 시작되는 단계이다. 소형발사체에 적용되는 에비오닉스 시스템의 기능 요구사항은 중형발사체 시스템과 동일하나, 소형발사체의 특성에 따라 중량 절감 및 저비용화 개발이 필수적인 요소이다. 이러한 소형발사체 에비오닉스 시스템의 요구사항을 충족하기 위해 공통 플랫폼 기반 모듈화 설계 및 조합을 통해 장비를 구성함에 따라 소형, 경량화가 가능하며, 다양한 발사체 환경에 적용하기 위해 네트워크 기반의 시스템 설계를 적용하여 유연성을 확보하도록 설계하였다. 또한 발사체의 안전을 담당하는 비행 안전 시스템은 자율 비행 안전 시스템을 적용하여 지상시스템 간소화 및 운영인원의 감축을 통해 운영비용을 절감할 수 있도록 설계함으로서, 소형발사체의 경제적 발사 서비스가 가능한 에비오닉스 시스템을 설계하였다. In Korea, the development of medium-lift launch vehicle was carried out the development of Naro and Nuri, But the development of small launch vehicle is now in the beginning stage. The avionics system of small launch vehicle is not much different from the functions required by medium-lift launch vehicle, but the economical launch service of small launch vehicle requires development by significantly reducing the cost and weight of avionics system compared to medium-lift launch vehicle. To apply the requirement of the avionics system of small launch vehicle, the hardware is designed to develop equipment through a combination of small and lightweight functional modules and to provide flexibility through network-based system design for application to multiple projectiles. The flight safety system, which is responsible for the safety of launch vehicle, is designed to reduce operating costs by reducing ground system and operating personnel by applying an autonomous flight safety system, and thus designed and avionics system capable of economic launch of small launch vehicle.
Hansoo Lee,Sungshin Kim 한국지능시스템학회 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.1
Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.
Lee, Hansoo,Kim, Sungshin Korean Institute of Intelligent Systems 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.1
Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.