• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.286-290
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• 2000

Optimal neuro-control algorithm is extended to the control of a multi-degree-of-freedom structure. An active mass driver(AMD) system on the top roof is used as an exciter. The control signals are made by a multi-layer perceptron(MLP) which is trained by minimizing a sub-optimal performance index. The performance index is a function of both the output responses and the control signals. Structure having nonlinear hysteretic behavior is also trained and controlled by using proposed control algorithm. In training neuro-controller, emulator neural network is not used. Instead, sensitivity-test data are used. Therefore, only one neural network is used for the control system. Both the time delay effect and the dynamics of hydraulic actuator are included in the simulation. Example shows that optimal neuro-control algorithm can be applicable to the multi-degree of freedom structures.

• Tuberculosis and Respiratory Diseases
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• v.53 no.6
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• pp.595-611
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• 2002

Akt/PKB protein kinase B, ALI acute lung injury, ARDS acute respiratory distress syndrome, CREB C-AMP response element binding protein, ERK extracelluar signal-related kinase, fMLP fMet-Leu-Phe, G-CSF granulocyte colony-stimulating factor, IL interleukin, ILK integrin-linked kinase, JNK Jun N-terminal kinase, LPS lipopolysaccharide, MAP mitogen-activated protein, MEK MAP/ERK kinase, MIP-2 macrophage inflammatory protein-2, MMP matrix metalloproteinase, MPO myeloperoxidase, NADPH nicotinamide adenine dinucleotide phosphate, NE neutrophil elastase, NF-kB nuclear factor-kappa B, NOS nitric oxide synthase, p38 MAPK p38 mitogen activated protein kinase, PAF platelet activating factor, PAKs P21-activated kinases, PMN polymorphonuclear leukocytes, PI3-K phosphatidylinositol 3-kinase, PyK proline-rich tyrosine kinase, ROS reactive oxygen species, TNF-${\alpha}$ tumor necrosis factor-a.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1871-1874
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• 2003

Knowledge-based numeric open caption recognition is proposed that can recognize numeric captions generated by character generator (CG) and automatically superimpose a modified caption using the recognized text only when a valid numeric caption appears in the aimed specific region of a live sportscast scene produced by other broadcasting stations. in the proposed method, mesh features are extracted from an enhanced binary image as feature vectors, then a valuable information is recovered from a numeric image by perceiving the character using a multiplayer perceptron (MLP) network. The result is verified using knowledge-based hie set designed for a more stable and reliable output and then the modified information is displayed on a screen by CG. MLB Eye Caption based on the proposed algorithm has already been used for regular Major League Base-ball (MLB) programs broadcast five over a Korean nationwide TV network and has produced a favorable response from Korean viewer.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.23-30
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• 1997

In this paper, a decision feedback recurrent neural equalization (DFRNE) scheme is proposed for adaptive equalization problems. The proposed equalizer models a nonlinear infinite impulse response (IIR) filter. The modified Real-Time recurrent Learning Algorithm (RTRL) is used to train the DFRNE. The DFRNE is applied to both linear channels with only intersymbol interference and nonlinear channels for digital video cassette recording (DVCR) system. And the performance of the DFRNE is compared to those of the conventional equalizaion schemes, such as a linear equalizer, a decision feedback equalizer, and neural equalizers based on multi-layer perceptron (MLP), in view of both bit error rate performance and mean squared error (MSE) convergence. It is shown that the DFRNE with a reasonable size not only gives improvement of compensating for the channel introduced distortions, but also makes the MSE converge fast and stable.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.671-674
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• 1997

The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FIT, DWT(Discrete Wavelet Transform), and Fisher's criterion are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 10-class power quality disturbances are also provided.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.2
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• pp.22-30
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• 2003

Cutting force signals are very useful to evaluate the cutting state, but many disturbing factors are occurring during cutting. For the reliability of the analysis, selecting pure cutting force signals from the original ones is needed. In the current study, using the ICA(Independent Component Analysis) effective cutting force components are seperated from the original signals. And using this, as input data of MLP(Multi-Layer Perception) cutting forces are predicted Experimental results are then compared with the predicted ones to verify the validation of the proposed model.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.345-351
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• 2004

This Paper describes a nonlinear adaptive noise filter using neural network for digital controller system. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental reaults show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary input is divided by unit and each divided part is processed for very short time than all the processed data are unified to whole data.

• The korean journal of orthodontics
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• v.22 no.1
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• pp.169-177
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• 1992

This investigation was carried out in order to find out changes in head postures and in craniofacial morphology, in relation to the inclination of cervical column. For this study 85 subjects, consisting 39 males and 49 females, between the ages of 7 to 24 years old were chosen, and following results were observed after analysing the correlation coefficients between each structures. 1. No definite relationships were observed between the inclination of cervical column (CVT/HOR) and the inclination of anterior cranial base (SN/VER) 2. No definite relationships were observed between the inclination of mandibular ramus (RL/HOR) and the inclination of mandibular inclination (MLP/VER). 3. In subjects with anteriorly inclined cervical column, increase in mandibular plane inclinations (ML/VER) were observed. 4. No definite relationships were observed between the inclination of cervical column (CVT/HOR) and changes in palatal plane (NL/VER).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.456-459
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• 2001

In this paper, a wavelet neural network for adaptive control was proposed. The structure of this network is similar to that of the multilayer perceptron(MLP), except that here the sigmoid functions are replated by mother wavelet function in the hidden units. The simulation result showed the effectiveness of using the wavelet neural network structure in the adaptive control of one-link manipulator.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.359-364
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• 2008

인터넷 중독이란 인터넷의 게임, 음란물, 커뮤니티, 쇼핑 등을 무절제하게 과다하게 사용하므로 자율적인 통제가 불가능한 증세를 의미하며 최근 성인은 물론이고 청소년 계층의 인터넷 중독 인구가 증가되고 있다. 기존 인터넷 진단 도구는 설문조사를 이용한 것으로 설문 응답자의 주관적 판단이나 고의적인 거짓 등으로 응답오차가 생기며, 이로 인한 진단 결과 및 분석 결과의 신뢰성이 낮다. 본 연구에서는 사용자의 인터넷 사용 데이터를 바탕으로 인터넷 중독을 진단하는 데이터 마이닝을 이용한 인터넷 중독 진단기법을 개발하고, 시뮬레이션 데이터를 이용하여 성능평가를 수행하였다. 성능평가 결과 인터넷 중독 분류 및 중독 점수 예측 모두 MLP를 이용한 경우가 우수한 성능을 보였다.