• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.215-219
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• 2002

인터넷의 급속한 성장으로 e비즈니스의 인터넷 사용이 증대되었다. 인터넷 환경에서는 새로운 인터넷 사용자라는 소비자를 대상으로 인터넷 소비자 행동에 관한 연구가 중요한 분야로 자리잡게 되었다. 인터넷상에서의 소비자 행동을 설명하기 위해 온라인 인지절차 (Cognitive process)에 관한 연구로, 웹 사이트에 대한 소비자의 태도에 미치는 영향을 밝히는 연구들이 수행되었다. 웹 사이트에 대한 소비자의 태도에 따른 개인화된 마케팅을 위해서는 웹사이트를 소비자의 특성을 고려해서 개인화된 웹사이트를 운영해야 한다. 개인의 정보 시스템 사용에 대한 설명을 위하여 많은 모형들이 개발되어 왔다. 기술 수용 모형(Technology Acceptance Model: TAM)은 개인의 정보 시스템 수용에 영향을 미치는 요소를 설명하기 위하여 가장 폭 넓게 사용되고 있는 모형이다. TRA 모형에 따르면, 개인의 사회적 행위는 그 행위의 결과에 대한 신념에 의해 영향을 받는다고 할 수 있다. 본 연구에서는 고객의 신념을 신뢰 (Trust), 유용성 (Usefulness), 사용의 편의성 (Ease of Use), 위험 (Risk), 보안통제 (Security control)로 분류하고, 고객의 실제 사용 (Usage)을 인터넷 뱅킹 환경에서 측정하여 고객세분화에 적용하였다. 세분화된 고객집단을 분류하기 위해서 인공신경망, 판별 분석 기법을 적용하여 웹 사이트에서 사용할 수 있는 개인화 모형을 개발하였다.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.286-290
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• 2011

This paper introduces the optimum macro-siting of a potential site for an offshore wind farm around Jeju Island using the RDAPS sea wind model. The statistical model was developed by analyzing the sea wind data from RDAPS model, and the meso-scale digital wind map was prepared. To develop the high resolution spatial calibration model, Artificial Neural Network(ANN) models were used to construct the wind and bathymetric maps. Accuracy and consistency of wind/bathymetric spatial calibration models were obtained using analysis of variance. The optimization problem was defined to maximize the energy density satisfying the criteria of maximum water depth and maximum distance from the coastline. The candidate site was selected through Genetic Algorithm(GA). From the results, it is possible to predict roughly a candidate site location for the installation of the offshore wind jam, and to evaluate the wind resources of the proposed site.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.159-164
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• 2005

In a computing environment connected pan-based computers and a server by Internet, This paper describes a language learning system evaluating the quality of a handwriting string. For the purpose of the system, this paper explains how to retrieve reference data from a database, how to evaluate the quality of a handwriting string using global and local features. The Proposed system can evaluate the qualify of a handwriting string as well as a handwriting character. The qualify can be computed in the case of different language between reference and input. Therefore, we expect that the system is very useful not only for training on handwriting but also learning a language.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.4
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• pp.82-90
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• 2009

Pneumatic cylinder is one of the low cost actuation sources which have been applied in industrial and prosthetic application since it has a high power/weight ratio, a high-tension force and a long durability However, the control problems of pneumatic systems, oscillatory motion and compliance, have prevented their widespread use in advanced robotics. To overcome these shortcomings, a number of newer pneumatic actuators have been developed such as McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. In this paper, one solution for position control of a robot arm, which is driven by two pneumatic artificial muscles, is presented. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external load of the robot arm. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is proposed in this paper. This estimates the external load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external working loads.

• Transactions of Materials Processing
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• v.29 no.4
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Transactions of Materials Processing
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• v.29 no.5
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• pp.272-281
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• 2020

As the thickness of mobile communication devices is getting thinner, the size of the internal parts is also getting smaller. Among them, the battery case requires a high-level deep drawing technique because it has a rectangular shape with a large aspect ratio. In this study, the initial blank shape was optimized to minimize earing in a multi-stage deep drawing process using an artificial neural network(ANN). There has been no reported case of applying artificial neural network technology to the initial blank optimal design for a square case with large aspect ratio. The training data for ANN were obtained though simulation, and the model reliability was verified by performing comparative study with regression model using random sample test and goodness-of-fit test. Finally, the optimal design of the initial blank shape was performed through the verified ANN model.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.10
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• pp.923-929
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• 2004

In this research, Forced Vibration Test(FVT) on a cable stayed bridge was conducted to examine the validity of the frequency domain pattern recognition method using signal anomaly index and artificial neuralnetwork. 7he considering structure, Samchunpo Bridge, located in Sachun-Shi, Kyungsangnam-Do, is a cable stayed bridge with the 436 meter span. The excitation force was induced by a sudden braking of a fully loaded truck. and vortical acceleration signals were acquired at 14 points. The initial 2-dimensional FE-model was developed from the design documents to prepare the training sets for the artificial neural network, and then the model calibration was performed with the field test data. As a result of the model calibration, we obtained the FFT spectrums from the model simulation, which was similar to those from the vibration test. These tests and the simulation data will be used for the structural identification using arbitrarily added masses to the bridge.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.11
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• pp.990-996
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• 2008

Nowadays, the trend to a lightweight design accelerates the use of advanced high strength steel (AHSS) in automotive industry. Springback phenomena is a hot issue in the sheet metal forming, especially bending process using AHSS. Several analytical methods for that have been proposed in recent years. Each of method has their advantages and disadvantages. There are only a few optimal solutions which can minimize the two objectives simultaneously. In this study, an effective method optimized the multi objective value. The method by the design of experiments(DOE) and artificial neural network(ANN) was presented to compensate springback of bending parts. This method was applied to L and V bending process. The effective method could be optimized to multiple object. It was confirmed that the proposed method was more efficient than traditional manual FEA procedure and the trial and error approach for springback compensation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.619-623
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• 2003

In this research, Forced Vibration Test(FVT) on a cable stayed bridge was conducted to examine the validity of the frequency domain pattern recognition method using signal anomaly index and artificial neural network. The considering structure, Samchunpo Bridge, located in Sachun-Shi, Kyungsangnam-Do, is a cable stayed bridge with the 436 meter span. The excitation force was induced by a sudden braking of a fully loaded truck, and vertical acceleration signals were acquired at 14 points. The initial 2-dimensional FE-model was developed from the design documents to prepare the training sets for the artificial neural network, and then the model calibration was performed with the field test data. As a result of the model calibration, we obtained the FFT spectrums from the model simulation, which was similar to those from the vibration test. These tests and the simulation data will be used fur the structural identification using arbitrarily added masses to the bridge.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.952-957
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• 2006

The present study applied wavelet transform and artificial neural networks (ANNs) for the prediction of daily TOC data. TOC data were transformed into denoised data by the wavelet transform and the noise-reduced data were used for the prediction model by artificial neural networks. For the application of wavelet transform, Daubechies wavelet of order 10 ('db10') was used as a basis function and decomposed the TOC data up to fifth level with five detail components and one approximation component. ANNs were calibrated with the input data of the segregated TOC data corresponding to the details from second to fifth level and the approximation. Consequently, the ANNs model for the prediction of daily TOC data showed the best result when it had seventeen hidden nodes in its layer.