• Earthquakes and Structures
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• v.23 no.2
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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.

• Transactions of Materials Processing
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• v.12 no.1
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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.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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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.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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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.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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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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• v.14 no.10
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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.

• Journal of Aerospace System Engineering
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• v.15 no.5
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• pp.33-42
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• 2021

Modern aircraft fighters improve the maneuverability and performance with the RSS (Relaxed Static Stability) concept and therefore these aircrafts are susceptible to abrupt pitch-up in the transonic and moderate Angle-of-Attack (AoA) flight region where the shock wave is formed and the mean aerodynamic center is moved forward during deceleration. Also, the modeling of the aircraft flying in this flight region is very difficult due to complex flow filed and unpredictable dynamic characteristics and the model-based control design technique does not fully cover this problem. In this paper, we analyzed the performance of the TPMC (Transonic Pitching Moment Compensation) control based on the model-based IDI (Incremental Dynamic Inversion) and the Hybrid IDI based on the model and sensor based IDI during the SDT (Slow Down Turn) in transonic region. As the result, the Hybrid IDI had quicker response and the same maximum g suppression performance and provided the predictable flying qualities compared to the TPMC control. The Hybrid IDI improved the performance of the Over-G protection controller in the transonic and moderate AoA region

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.10
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• pp.941-950
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• 2011

In this paper, we analysis the end-to-end performance of the incremental cooperative communication with relay selection. In the conventional cooperative scheme, the source(S) broadcasts the signal to the relay(R) and the destination(D) at 1st phase, and the R forwards the signal to the D at 2nd phase. Although this scheme can improve performance and provide diversity gain, it suffers from decreasing spectrum efficiency. In order to overcome this problem, the incremental cooperative model can be used. In this paper, we study two incremental cooperative method : the first uses ARQ with threshold SNR and the second uses HARQ with channel coding. we also evaluated performance of the incremental cooperative communication based on the R arrangement by using both methods.

• Smart Structures and Systems
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• v.26 no.4
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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.