• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.09a
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• pp.208-215
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• 2001

The vertical irregularities occurred in the structure may lessen the overall seismic capacity of the structure. Seismic capacity evaluation guidelines (e.g. FEMA 175, ATC-14) propose the criterion for the vertical irregularities of mass, stiffness and strength respectively. But, the criterion seems groundless and leads us to make a true/false decision only. This study is to draw a reasonable basis on which multi-level grading is possible based fur the evaluation of existing buildings. Time history analysis for 3-,5-, and 10-story steel frame structures has been performed using several earthquake data. ANN (Artificial Neural Network) is introduced to find the relative contribution factor of the irregularities along the irregular position. Also, the application system fur the seismic capacity evaluation can be established using the trained ANN.

• Water Engineering Research
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• v.4 no.2
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.60-67
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• 2005

Compared with conventional welding, laser spot welding offers a unique combination of high speed, precision and low heat distortion. This combination of advantages is attractive for manufacturing industries including automotive and electronics companies. In this paper, a real time monitoring scheme fur a pulsed Nd:YAG laser spot welding was suggested. Acoustic emission (AE) signals were collected during welding and analyzed for given process conditions such as laser power and pulse duration. A back propagation artificial neural network, with AE frequency content inputs, was used to predict the weldability of stainless steel sheets.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.945-948
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• 2005

Mobile computing devices are becoming increasingly prevalent in a huge range of physical area, offering a considerable market opportunity. The focus of this paper is on the development of a platform of fault diagnosis system integrating with personal digital assistant (PDA). An improvement of induction machine rotor fault diagnosis based on AI algorithms approach is presented. This network system consists of two parts; condition monitoring and fault diagnosis by using Artificial Intelligence algorithm. LabVIEW allows easy interaction between acquisition instrumentation and operators. Also it can easily integrate AI algorithm. This paper presents a development environment fur intelligent application for PDA. The introduced configuration is a LabVIEW application in PDA module toolkit which is LabVIEW software.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.991-999
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• 2004

Face robots capable of expressing their emotional status, can be adopted as an efficient tool for friendly communication between the human and the machine. In this paper, we present a face robot actuated with artificial muscle based on dielectric elastomer. By exploiting the properties of polymers, it is possible to actuate the covering skin, eyes as well as provide human-like expressivity without employing complicated mechanisms. The robot is driven by seven types of actuator modules such as eye, eyebrow, eyelid, brow, cheek, jaw and neck module corresponding to movements of facial muscles. Although they are only part of the whole set of facial motions, our approach is sufficient to generate six fundamental facial expressions such as surprise, fear, anger, disgust, sadness, and happiness. Each module communicates with the others via CAN communication protocol fur the desired emotional expressions, the facial motions are generated by combining the motions of each actuator module. A prototype of the robot has been developed and several experiments have been conducted to validate its feasibility.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.780-785
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• 2000

The equi-potential lines designed in the electric field are introduced to find the preform shape in axisymmetric hot forging. The equi-potential lines generated between two conductors of different voltages show similar trends of the minimum work paths between the undeformed shape and the deformed shape. Base on this similarity, the equi-potential lines obtained by arrangement of the initial and final shapes are utilized fur the design of preform, and then the artificial neural network is used to find the range of initial volume and potential value of the electric field.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.780-783
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• 2002

This paper proposes an approximate LCA methodology fur the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes Into impact driver (ID) index. The relationship Is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then an artificial neural network model is developed to predict an approximate LCA of grouping products in conceptual design stage. The training is generalized by using identified product attributes for an ID In a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give an approximate LCA results for design concepts.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.443-448
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• 1998

With using artificial neural networks (ANNs), an analytical study related to the heated length effect on critical heat flux(CHF) has been carried out to make an improvement of the CHF prediction accuracy based on local condition correlations or table. It has been carried out to suggest a feasible criterion of the threshold length-to-diameter (L/D) value in which heated length could affect CHF. And within the criterion, a L/D correction factor has been developed through conventional regression. In order to validate the developed L/D correction factor, CHF experiment for various heated lengths have been carried out under low and intermediate pressure conditions. The developed threshold L/D correlation provides a new feasible criterion of L/D threshold value. The developed correction factor gives a reasonable accuracy fur the original database, showing the error of -2.18% for average and 27.75% for RMS, and promising results for new experimental data.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.628-634
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• 2003

Rumbling sound is one of the most important interior sound of a passenger cu. The conventional rumbling noise research was focused on the reduction of the A-weighted sound pressure level. However A-weighted sound pressure level can not give the whole story about the rumbling sound of a passenger car. In this paper, we employed sound metric which is the subjective parameter used in psycoacoustics. According to recent research results, the relation between sound metrics and subjective evaluation is very complex and has nonlinear characteristics. In order to estimate this nonlinear relationship, artificial neural network th[ ory has been applied to derivation of sound quality index fur rumbling sound of a passenger car.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.233-240
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• 2002

In this paper, the effects of vehicle loads on flat slab system are investigated on the basis of the previous studies for beam-girder parking structural system. The influence surfaces of flat slab for typical design section are developed for the purpose of obtaining maximum member forces under vehicle loads. In addition, the equivalent vehicle load factors for flat slab parking structures are suggested using artificial neural network. The network responses are compared with the results by numerical analyses to verify the validation of Levenberg-Marquardt algorithm adopted as training method in this paper. Many parameter studies fur the flat slab structural system show dominant vehicle load effects at the center positive moments in both column and middle strips, like the beam-girder parking structural system.