• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.401-411
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• 2003

Tube expanding process is for combining a heat-sink plate with hair-pins (Cu-tube) through plastic deformation. The two parts, i. e. , heat-sink plate and hair-pins are they components of a heat-exchanger for an air conditioner. This paper presents the analysis and dynamic modeling of a high-speed tube expander which integrates transfer of parts, fixing of parts, and tube expanding into one process. The 3-dimensional modeling of all the parts for the tube-expander was constructed using CATIA$\circledR$. then the CATIA$\circledR$ models are transferred into visuaINastran$\circledR$ to execute the 3-dimensional animation for checking prescribed cycle-time. The technique presented in this paper has been shown to be effective as a priori tool for verifying the design of a high-speed tube expander.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.587-591
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• 1996

Usually triangular patches are used to transfer geometric shape in Rpaid Prototyping CAM system. STL, a list of triangles, is de facto in RP industry. Because STL has no topology data, it can cause errornous results. So, STL should be verified before using. After adding support structures to anchor the part to the platform and to prevent sagging or distortion, slicing and layer by layer manufacturing process are done. But triangular patch is surface model and cannot provide dufficient information on geometry in the above processes. So, geometric modeling is necessary in verifying STL, adding support structures, and slicing. It is natural that triangle based modeling is the best when traingular patches are used as input. Considering support structures, solid and faces coexist in RP process. Therefore non-manifold modeler is required. In this study, triangle based non-manifold geometric modeling is proposed for RP system consitent with STL input.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.141-144
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• 1997

This paper presents design automation system using API and parametric modeling of solid modeler, which is applied on axial fans for cooling towers. The design data including chord length and twist angle according to the fan length are given by design program, and API functions are applied to automate the modeling and assembly process of fan blade. The boss to connect fan and motor is designed with parametric design function provided by UG so as to be flexibly changed by the value of design parameters. The process of generating 2-D drafting for parts and an assembly is also automated. With developed system, the modeling time is reduced to 5 minutes even with unskilled operators.

• Biotechnology and Bioprocess Engineering:BBE
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• v.11 no.1
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• pp.67-71
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• 2006

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.555-561
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• 2009

The established mathematical modeling methods have limitation to know the hydraulic characteristics at the wastewater treatment plant which are complex and nonlinear systems. So, an artificial neural network (ANN) model based on hydraulic characteristics is applied for modeling wastewater quality of a full-scale wastewater treatment plant using DNR (Daewoo nutrient removal) process. ANN was trained using data which are influents (TSS, BOD, COD, TN, TP) and effluents (COD, TN, TP) components in a year, and predicted the effluent results based on the training. To raise the efficiency of prediction, inputs of ANN are added the influent and effluent information that are in yesterday and the day before yesterday. The results of training data tend to have high accuracy between real value and predicted value, but test data tend to have lower accuracy. However, the more hydraulic characteristics are considered, the results become more accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.473-476
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• 2001

최근 기존 정보시스템에 고객이나 사용자의 다양한 요구사항이나 기업의 환경 변화를 반영하여 새로운 정보시스템으로 재구축하는 경향을 보이고 있다. 이를 통해 기업들에서 경쟁 우위를 선점함으로써 보다 우세한 경쟁력을 갖출 수 있다. 본 논문에서는 정보시스템 재구축을 위한 프로세스 모델링(Process Modeling)과 데이터 모델링(Data Modeling)을 체계적으로 수행하는 방안을 제시하고, 이를 실제로 적용한 사례를 보여준다. 정보시스템의 전체적인 관점에서의 요구사항 및 기존 정보시스템의 미비사항을 분석하여 정보화 대상을 추출하는 프로세스 모델 분석(Process Model Analysis) 단계와 정보화 대상을 개념 모델로 전환하는 논리 데이터 모델링(Logical Data Modeling) 단계, 실제 컴퓨터에 저장하여 사용하는 물리 데이터 모델링(Physical Data Modeling) 단계로 구성된다.

• Environmental Engineering Research
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• v.10 no.2
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• pp.88-103
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• 2005

In this paper, data-driven modeling and multiresolution analysis (MRA) are applied for a full-scale wastewater treatment plant (WWTP). The proposed method is based on modeling by partial least squares (PLS) and multiscale monitoring by a generic dissimilarity measure (GDM), which is suitable for nonstationary and non-normal process monitoring such as a biological process. Case study in an industrial plant showed that the PLS model could give good modeling performance and analyze the dynamics of a complex plant and MRA was useful to detect and isolate various faults due to its multiscale nature. The proposed method enables us to show the underlying phenomena as well as to filter out unwanted and disturbing phenomena.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.340-345
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• 1996

Because of limited resources of time and information processing capability during abnormal situation, diagnosis is difficult tasks in nuclear power plant (NPP) operators. Moreover since minimizing of adverse consequences according to process abnormalities is vital for the safety of NPP, introducing of diagnosis support systems have particularly emphasized. However, considerable works to develop effective diagnostic support system are not sufficiently fulfilled because of the complexity of NPP is one of the major problems. To cope with this complexity, a lot of model-based diagnosis support systems have considered and implemented worldwide. In this paper, as a prior step to development of model-based diagnosis support systems, primary side of pressurized water reactor is functionally modeled by multilevel flow modeling (MFM) concept. MFM is suitable for complex system modeling and for diagnosis of abnormalities. Furthermore, knowledge-based diagnosis process, of NPP operator could be supported because this diagnosis strategy can represent operator's one.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.163-164
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• 2023

The active promotion of construction technology application, aimed at cost reduction, construction time shortening, and performance enhancement during the implementation of Value Engineering (VE) in design, is underway. However, in the process of deriving VE ideas, it is common to simply reuse VE ideas from similar past construction projects, and the application of construction technology is often insufficient. Therefore, in this study, we intend to establish a database linking construction technology information with Building Information Modeling (BIM) models to activate the application of construction technology in the process of deriving VE ideas.