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김경모(Kim Kyeong-Mo) 한국토목섬유학회 2007 한국토목섬유학회 학술발표회 Vol.2007 No.11
Since the reinforced earth retaining wall was introduced in Korea in the late of 1970's, it was wide spread from middle of 1980's. And, in the middle of 1990's, the segmental retaining wall which was somewhat different from previous system and used the modular block and geogrid type geosynthetic reinforcement was introduced. But, yet we have no unified design guidelines for the reinforced earth retaining walls, and many different design methods were used in each system. In this paper, we attempt to compare the design methods which is used in the design of reinforced earth retaining walls presently. As a result, it was founded that the use of different method resulted in different outcomes in the analysis or design of reinforced earth retaining walls. When the wall have a sloped fill its top, It was NCMA method that the largest value of tensile forces of reinforcements was provided 10 the lower elevation in comparison with other methods. The tensile forces of reinforcements 10 the upper part of the wall was smaller than compaction induced tensile forces. As a consequences, when the reinforced earth retaining wall was designed or analysed, which method is used may be important. Therefore, the design method have to be carefully selected.
김경모(Kyeong-Mo Kim),김영윤(Young-Yoon Kim) 한국토목섬유학회 2012 한국토목섬유학회 학술발표회 Vol.2012 No.4
Generally, the reinforced earth retaining wall can tolerate much larger settlements than conventional RC walls. The type of facing influences settlement tolerances. In general, concrete-faced reinforced earth structures using discrete articulating panels can accommodate maximum longitudinal differential settlements of about 1/100. Walls with drycast modular block facing should be limited to settlements of 1/200. For walls with relatively flexible facings, the limiting differential settlement may be larger than 1/50. For wrapped faced walls, the facing -whether geotextile, geogrid, or wire mesh -can be attached drycast modular block or prefabricated facing panels after construction of the wall. This multi-staging facing approach adds cost but is advantageous where significant settlement is anticipated. In this case study, an example of reinforced earth wall constructed on soft ground using staged construction method is shown.
딥러닝을 이용한 공압형 인공근육의 모델링 및 정밀제어에 대한 연구
이재승(Jae Seung Lee),김경모(Kyeong Mo Kim),강봉수(Bong Soo Kang) 대한기계학회 2021 大韓機械學會論文集A Vol.45 No.1
본 연구에서는 특성데이타를 가지고 딥러닝 기법을 이용하여 수학적 모델링이 어려운 공압형 인공근육의 동특성을 정확하게 모델링하였다. 제안된 신경망 모델은 학습이 완료된 가중치로 구동력을 빠른 시간에 계산하므로 모델에 기초한 피드포오드 제어기법에 활용이 가능하다. 그리고 신경망 모델을 실시간 제어에 적용하기 위해서 딥러닝이 수행되는 파이썬 프로그램을 서버로 기계시스템의 운동제어를 수행하는 랩뷰 프로그램을 클라이언트로 사용하는 분산된 제어기법을 제시하였다. 실제 공압형 인공근육으로 구동되는 동적시스템에 제안된 제어기법을 적용하여 우수한 궤적추종성능을 보였다. In this study, the dynamic behaviors of pneumatic artificial muscles, which cannot be easily mathematically modeled were accurately predicted using a deep learning technique with characteristic data. As the proposed neural network model calculates the driving force with the weights of the neural network trained off-line, it can be applied to a model-based feedforward control scheme in real-time. In addition, a distributed control architecture, where a Python program for deep learning had the role of a server and a LabVIEW program for motion control had the role of a client, was implemented to a dynamic system driven by a single pneumatic artificial muscle. Experimental results showed that the proposed server/client control architecture with the deep learning model could yield a higher performance in trajectory following.