• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.377-382
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• 2004

This paper presents a novel optimization method for the air- and gas-supplying network comprised of several air compression systems and air and gas streams in an industrial chemical plant. The optimization is based on the hybrid model developed by Han and $Han^1$ for predicting the power consumption of a compression system. A constrained optimization problem was formulated to minimize the total electric power consumption of all the compression systems in the air- and gas-supplying network under various operating constraints and was solved using a successive quadratic optimization algorithm. The optimization approach was applied to an industrial terephthalic acid manufacturing plant to achieve about 10% reduction in the total electric power consumption under varying ambient conditions.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.5
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• pp.147-153
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• 2002

In this paper, a substrate network model to be used with BSIM3 MOSFET model for submicron MOSFETs in giga hertz frequencies and its direct parameter extraction with physically meaningful values are proposed. The proposed substrate network model includes a conventional resistance and single inductance originated from ring-type substrate contacts around active devices. Model parameters are extracted from S-parameter data measured from common-bulk configured MOS transistors with floating gate and use where needed without any optimization process. The proposed modeling technique has been applied to various-sized MOS transistors. The substrate model has been validated for frequency up to 300Hz.

• Journal of Environmental Impact Assessment
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• v.19 no.3
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• pp.307-314
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• 2010

Brebbia's model has been analyzed to develop the appropriate waterworks management system in Korea, and compared with the conventional models such as EPANET, WaterCad, and InfoWorks. The hydraulic theory of the models was analyzed. Each model's numerical techniques, required parameters, input data and operational methodologies, restrictions, practical applicability and other aspects were investigated. In order to check the validity of Brebbia model, the comparative analysis with EPANET, WaterCAD, and InfoWorks models was performed for linear and nonlinear cases. To find out advantages and disadvantages of each model, the modeling was performed for a simple network and for more complicated A city waterworks system, and the three models applicability was examined. Finally, optimal modeling technique and a model suitable for the use in Korea was suggested, and the problems related to present projects of waterworks management system in Korea were analyzed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.4
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• pp.89-96
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• 2002

This paper aims to develop an intelligent model for predicting top-bead width for the robotic GMA(Gas Metal Arc) welding process using BP(Back-propagation) neural network and multiple regression analysis. Firstly, based on experimental data, the basic factors affecting top-bead width are identified. Then BP neural network model and multiple regression models of top-bead width are established. The modeling methods and procedure are explained. The developed models are then verified by data obtained from the additional experiment and the predictive behaviors of the two kind of models are compared and analysed. Finally the modeling methods, predictive behaviors md the advantages of each models are discussed.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.170-176
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• 2004

MR dampers show highly nonlinear and histeretic dynamic behavior. Therefore, for a vehicle dynamic simulation with MR dampers, this dynamic characteristics should be accurately reflected in the damper model. In this paper, an artificial neural network technique was developed for modeling MR dampers. This MR damper model was successfully verified through a random input forcing test. This MR damper model can be used for semi-active suspension vehicle dynamics and control simulations with practical accuracy.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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• pp.95-103
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• 2003

Driving safety of a vehicle is largely influenced by the damper and the tire. Developed in this research is a fault diagnosis algorithm for the two components so that the driver can be promptly informed when fault occurs in one or both of them. To this end, the damper and the tire were modeled using the neural network from their experimental data, and fault diagnosis was made using frequency responses of the damping force and the dynamic wheel force. The algorithm was tested via experiments, and it demonstrated successful diagnostic performance under various driving conditions.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.243-245
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• 2005

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. In this paper, a model-based method using neural network modeling of induction noter in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals to continuous spectra so that the neural network model can be trained with vibration spectra. And the faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results.

• Korean Journal of Computational Design and Engineering
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• v.4 no.3
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• pp.238-246
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• 1999

The modeling method to support product development processes (PDP) must have certain characteristics including the ability to represent multiple viewpoints of the product development and integrate with currently available analysis and design methods based on CE concept. This paper describes the reference model to support multiple viewpoints (PPO: Products, Processes, and Organizations/Resources viewpoints) of the product development processes, from which each model (Products model, Processes model, and Organizations/Resources model) can be extracted, as well as produces PPO data schema. This reference model has associative relationships among the products, processes, and organizations/resources. To allow the extensibility to support design evolution, we propose structured dat representation methods using semantic network, which can be constructed through first-order logic. The product development processes is so represented by specifying entities and semantic relationships among them hat he appropriate information can be accessed and all of the relevant attributes about the entities can be retrieved simultaneously.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.273-276
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• 2004

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.363-370
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• 2002

A substrate network model characterizing substrate effect of submicron MOS transistors for RF operation and its parameter extraction with physically meaningful values are presented. The proposed substrate network model includes a single resistance and inductance originated from ring-type substrate contacts around active devices. Model parameters are extracted from S-parameter data measured from common-bulk configured MOS transistors with floating gate and use where needed with out any optimization. The proposed modeling technique has been applied to various-sized MOS transistors. Excellent agreement the measurement data and the simulation results using extracted substrate network model up to 30GHz.