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비최소 위상 특성을 갖는 유도탄의 기준 모델 적응 제어
송찬호(Chanho Song),김승환(Seung-Hwan Kim) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
In this paper, a model reference adaptive control algotithm is applied to the design of the normal acceleration controller for missiles with nonminimun-phase characteristics. The method used in this paper is due to Ohkubo. In this scheme, a feedforward compensator is designed first so that the extended system becomes minimun-phase and after that an adaptive control algorithms is designed for the extended system. The feedforward compensator is obtained by solving the robust stabilization problem. It is shown that the performance of the designed controller is satisfied via computer simulation.
송찬호(Chanho Song),김윤식(Yoon Sik Kim) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
In this paper, we present a method to design a coupled autopilot for SIT missiles which have severe aerodynamic cross-coupling. The aerodynamic model is derived in the meneuver plane and, based on that model, an autopilot scheduled by the normal acceleration and the estimated bank angle is designed. Bank angle is obtained by a simple estimator. With the proposed autopilot, it is shown by computer simulations that induced moments are properly compensated and the performance is supiorior to the conventional autopilot.
송찬호(Chanho Song),황동환(Donghwan Hwang),김신혜(Shinhye Kim),윤혜성(Hyeseung Yoon),위은정(Eunjung Wie),정의성(Euisung Jung) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
Cephalometric analysis is an essential step in evaluating facial structure relationships and orthognathic surgical planning. In general, anatomical landmark localization within cephalometric analysis has been a time-consuming task that requires surgeons expert knowledge. With advancements in computer vision, research has conducted towards automatic anatomical landmark localization. This study aims to compare the performance of 3D convolutional neural network (CNN) for cephalometric landmark localization. We compare five previously proposed CNN models in terms of their ability to perform cephalometric landmark regression. An open-dataset was used to construct the learning dataset for model training and evaluation. Performance metrics were calculated to assess the accuracy of each model. We expect that this comparative analysis of 3D CNN for cephalometric landmark localization has the potential to facilitate automatic cephalometric analysis.