• Earthquakes and Structures
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• 제23권2호
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• pp.197-206
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• 2022

Recently, steel-timber hybrid buildings have become prevalent worldwide because several advantages of both steel and timber structures are maintained in the hybrid system. In Taiwan, seismic design specification related to steel-timber hybrid buildings remains void. In this study, the ductility capacity of steel-timber hybrid buildings in Taiwanese seismic design specification is first proposed and evaluated using nonlinear incremental dynamic analysis (IDA). Three non-linear structural models, 12-story, 8-story, and 6-story steel-timer hybrid buildings were constructed using OpenSees. In each model, Douglas-fir was adopted to assemble the upper 4 stories as a timber structure while a conventional steel moment-resisting frame was designated in the lower part of the model. FEMA P-695 methodology was employed to perform IDAs considering 44 earthquakes to assess if the ductility capacity of steel-timber hybrid building is appropriate. The analytical results indicate that the current ductility capacity of steel moment-resisting frames can be directly applied to steel-timber hybrid buildings if the drift ratio of each story under the seismic design force for buildings in Taiwan is less than 0.3%. As a result, engineers are able to design a steel-timber hybrid building straightforwardly by following current design specification. Otherwise, the ductility capacity of steel-timber hybrid buildings must be modified which depends on further studies in the future.

• 소성∙가공
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• 제12권1호
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• pp.49-59
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• 2003

To reduce the cost of finite element analyses for sheet forming, a 3D hybrid membrane/shell method has been developed to study the springback of anisotropic sheet metals. In the hybrid method, the bending strains and stresses were analytically calculated as post-processing, using incremental shapes of the sheet obtained previously from the membrane finite element analysis. To calculate springback, a shell finite element model was used to unload the final shape of the sheet obtained from the membrane code and the stresses and strains that were calculated analytically. For verification, the hybrid method was applied to predict the springback of a 2036-T4 aluminum square blank formed into a cylindrical cup. The springback predictions obtained with the hybrid method was in good agreement with results obtained using a full shell model to simulate both loading and unloading and the experimentally measured data. The CPU time saving with the hybrid method, over the full shell model, was 75% for the punch stretching problem.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1999년도 춘계학술대회논문집
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• pp.62-65
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• 1999

To reduce the cost of finite element analyses for sheet forming a 3D hybrid membrance/sheel method has been developed to study the springback of anisotropic sheet metals. in the hybrid method the bending strains and stresses were analytically calculated as post-processing using incremental shapes of the sheet obtained previously from the membrane finite element analysis. To calculate springback a shell finite element model was used to unload the final shape of the sheet obtained from the membran code and the stresses and strains that were calculated analytically. For verification the hybrid method was applied to predict the springback of a 2036-T4 aluminum square blank formed into a cylindrical cup. the springback predictions obtained with the hybrid method was in good agreement with results obtained using a full shell model to simulateboth loading an unloading and the experimentally measured data. The CPU time saving with the hybrid method over the full shell model was 75% for the punch stretching problem.

• 한국정밀공학회지
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• 제19권11호
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• 제어로봇시스템학회논문지
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• 제9권6호
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• pp.442-447
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• 2003

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

• 한국정밀공학회지
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• 제20권5호
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• 항공우주시스템공학회지
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• 제15권5호
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• pp.33-42
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• 2021

현대 전투기는 정안정성 완화 개념을 적용하여 기동성과 성능을 향상시키는데, 천음속 비행영역에서는 충격파 형성과 더불어 감속기동 중 발생하는 공력중심 전방이동 현상에 의해 갑작스런 기수 들림이 발생하는 경향을 갖는다. 또한 천음속 중간 받음각 비행영역은 항공기 모델링이 어려워 모델 기반의 제어 방식은 이 문제를 해결하는데 한계를 갖는다. 이번 논문에서는 초음속 경전투기 모델을 이용하여 천음속 영역에서 감속선회 기동 중 모델 기반 증분형 동적 모델역변환 방식의 천음속 피칭모멘트 보상 제어(TPMC)와 모델과 센서를 기반으로 하는 Hybrid 증분형 동적모델 역변환(IDI) 제어의 성능을 분석하였다. 분석 결과, Hybrid 증분형 동적모델 역변환 제어는 천음속 피칭모멘트 보상 제어에 비해 빠른 초기 반응과 동등한 최대 수직가속도 제한 성능을 가지면서 조종사가 예측 가능한 비행성을 제공하여 천음속 중간 받음각 비행영역에서 하중제한 초과 방지 제어기의 성능을 크게 개선하였다.

• 한국전자파학회논문지
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• 제22권10호
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• pp.941-950
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• 2011

본 논문에서는 중계기 선택 기법이 적용된 증분 협력 통신의 단-대-단 성능을 분석한다. 일반적인 협력 통신은 1 phase에 송신단(S)에서 전송하는 신호를 수신단(D)이 한 번 수신하고, 2 phase에 중계기(R)로부터 S에서 전송한 신호를 재 전송받게 된다. 이러한 과정을 통해 D는 다이버시티 이득을 얻음으로써 수신 성능이 향상되지만, 두 번에 걸친 전송은 스펙트럼 효율성을 감소시킨다. 하지만 증분형 중계 기법을 적용한 협력 통신에서 D는 1 phase에 수신한 신호만으로 복호에 성공할 수 있다고 판단되면, 두 번째 전송을 생략함으로써 이러한 단점을 보완할 수 있다. 증분형 중계 기법에서 D는 ACK/NACK 메시지를 전송하는 ARQ(Automatic Repeat reQuest) 기법을 이용한다. 본 논문에서는 D가 첫 번째 시간 슬롯에 수신한 신호를 복호할 수 있는지를 판단하기 위해 임계 SNR을 이용한 ARQ 기법과 채널 부호화를 이용한 HARQ(Hybrid Automatic Repeat reQuest) 기법과 같이 두 가지 방법을 고려한 시스템에 참여하는 중계기 배치에 따른 성능을 분석하고, 중계기 배치가 성능에 미치는 영향에 대해 연구한다.

• Smart Structures and Systems
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• 제26권4호
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• pp.495-506
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• 2020

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.