• Title/Summary/Keyword: Neural Network Modeling

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Moving Target Tracking and Recognition for Location Based Surveillance Service (위치기반 감시 서비스를 위한 이동 객체 추적 및 인식)

  • Kim, Hyun;Park, Chan-Ho;Woo, Jong-Woo;Doo, Seok-Bae
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.1211-1212
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    • 2008
  • In this paper, we propose image process modeling as a part of location based surveillance system for unauthorized target recognition and tracking in harbor, airport, military zone. For this, we compress and store background image in lower resolution and perform object extraction and motion tracking by using sobel edge detection and difference picture method between real images and a background image. In addition to, we use Independent Component Analysis Neural Network for moving target recognition. Experiments are performed for object extraction and tracking of moving targets on road by using static camera in 20m height building and it shows the robust results.

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Analysis of High Speed Linear Motor Feed System Characteristics (리니어모터 응용 고속 이송시스템 특성분석에 관한 연구)

  • 유송민
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2000.11a
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    • pp.993-996
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    • 2000
  • A brushless linear motor is suitable for a high-accuracy servo mechanism. It is also suitable for operation with higher speed and precision. Since it does not involve some sort of mechanical coupling, linear driving force can be applied directly. Basic models including magetomotive force and electromotive forces are introduced and simplified. Both conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Several system operational characteristics have been observed. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.

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Quantitative Structure Activity Relationship Prediction of Oral Bioavailabilities Using Support Vector Machine

  • Fatemi, Mohammad Hossein;Fadaei, Fatemeh
    • Journal of the Korean Chemical Society
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    • v.58 no.6
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    • pp.543-552
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    • 2014
  • A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

Development of High Speed Feed System using Linear Motor (리니어모터 응용 고속이송계 제어기술 개발)

  • 유송민
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2000.05a
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    • pp.973-976
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    • 2000
  • A brushless linear motor is suitalbe fur a high-accuracy servo mechanism. It is also suitable for operation with higher speed and precision. Since it does not involve some sort of mechanical coupling, linear driving force can be applied directly. Basic models including magetomotive farce and electromotive forces are introduced and simplified. Both conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.

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Environmental Consciousness Data Modeling by Association Rules

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • 한국데이터정보과학회:학술대회논문집
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    • 2004.10a
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    • pp.115-124
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    • 2004
  • Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.

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Characterization of Linear Motor Feed System with AE and Acceleration Signal (AE 및 가속도 신호를 이용한 리니어 모터 이송시스템의 특성분석)

  • 유송민
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2000.10a
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    • pp.299-303
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    • 2000
  • A brushless linear motor is suitable for operation with higher speed and precision. Since it does not involve mechanical coupling, linear driving force can be applied directly. Conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Several system operational characteristics have been observed. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.

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Neural Network Modeling for Color Reproduction on Scanner (원색 재현을 위한 스캐너의 신경회로망 모델링)

  • 김홍기;강병호;윤창락;김진서;한규서;조맹섭
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
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    • 1998.04a
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    • pp.135-140
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    • 1998
  • 본 논문에서는 신경회로망에서 가장 널리 쓰이고 있는 오차 역 전파 알고리즘(Error Back-propagation) 을 사용하여 스캐너를 모델링함으로써 스캐너의 원색 재현을 위한 방법을 제시하였다. 이것은 스캐너의 하드웨어적 특성을 고려, 입력된 영상의 원색과 출력물의 색과 일치시키는 방법이다. 우선, 오차 역전파 알고리즘에 대하여 학습 규칙을 살펴보고 학습을 위한 데이터를 추출하기 위해 고르게 분포된 색 샘플들을 계측기로 측정하여 칼라 공간에서의 X, Y, Z 값을 얻어낸다. 그 중에서 표본 샘플을 추출한다. 그리고 이를 스캐너로 스캐닝하여 얻은 R, G, B값을 오차 역전파 알고리즘의 입력값으로, 목표값은 X, Y, Z값을 사용하여 학습시킨다. 학습하는 동안 샘플 색상의 수와 중간층의 수, 노드의 수를 변화시킴으로써 최적의 결과를 얻도록 실험하였다. 결론에서는 서로간의 결과를 분석한다.

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FMFNN Modeling of the Tire Characteristics for Ground Vehicle Control (차량 제어를 위한 타이어 특성의 퍼지 소속 함수 신경망 모델링)

  • 박명관;서일홍
    • Journal of the Korean Institute of Intelligent Systems
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    • v.6 no.2
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    • pp.57-71
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    • 1996
  • 차량 모델 비선형성의 주된 요인중 하나는 타이어의 비선형성이라고 할 수 있다. 타이어 모델도 간편화하기 위해 선형화된 타이어 모델을 적용할 경우에 저속 주행 또는 고속 주행이라고도 조향각이 적을 때는 문제가 없지만, 급격한 가감속과 과도한 조향각을 주었을 때는 타이어 미끄럼 각(Tire Slip Angle)이 급격히 변화되므로 선형화 된 타이어 모텔을 적용하지 못하게 된다. 그러므로 타이어와 지면 사이의 물리적 현상을 자세히 표현할 수 있는 비선형 타이어 모델을 적용하지 못하게 된다. 그러므로 타이어와 지면 사이의 물리적 현상을 자세히 표현할 수 있는 비선형 타이어 모델이 요구되어진다. 실험적 모델은 실제 차량의 실험 데이터를 바탕으로 커브 피팅(Curve Fitting)하여 타이어의 동특성을 표현하도록 모델링 하므로서 모델의 정확도를 높일 수 있는 반면 요구하는 계수들이 많아지게 되어 계산량이 증가되는 단점이 있다. 기존의 타이어 모델 연구 결과에 대해 분석하고, 관측 자료들을 바탕으로 FMFNN(Fuzzy Membership Function based Neural Network)을 이용한 함수 근사화로서 타이어 횡축력과 종축력의 모델링 방법을 제안하였다.

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A Path tracking algorithm and a VRML image overlay method (VRML과 영상오버레이를 이용한 로봇의 경로추적)

  • Sohn, Eun-Ho;Zhang, Yuanliang;Kim, Young-Chul;Chong, Kil-To
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.907-908
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    • 2006
  • We describe a method for localizing a mobile robot in its working environment using a vision system and Virtual Reality Modeling Language (VRML). The robot identifies landmarks in the environment, using image processing and neural network pattern matching techniques, and then its performs self-positioning with a vision system based on a well-known localization algorithm. After the self-positioning procedure, the 2-D scene of the vision is overlaid with the VRML scene. This paper describes how to realize the self-positioning, and shows the overlap between the 2-D and VRML scenes. The method successfully defines a robot's path.

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Neural Network Modeling of Actinometric Optical Emission Spectroscopy Information for Mo nitoring Plasma Process (플라즈마 공정 감시를 위한 Actinometric 광방사분광기 정보의 신경망 모델링)

  • Kwon, Sang-Hee;Bo, Kwang;Lee, Kyu-Sang;Uh, Hyung-Soo;Kim, Byung-Whan
    • Proceedings of the KIEE Conference
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    • 2007.10a
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    • pp.177-178
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    • 2007
  • 플라즈마 공정은 집적회로 제작을 위한 미세 박막의 증착과 패턴닝에 핵심적으로 이용되고 있다. 본 연구에서는 플라즈마공정감시와 제어에 응용될 수 있는 모델을 제안한다. 본 모델은 광방사분광기 (Optical emission spectroscopy-OES)정보와 역전파 신경망을 이용해서 개발하였다. 제안된 기법은 Oxide 식각공정에서 수집한 데이터에 적용하였으며, 체계적인 모델링을 위해 공정데이터는 통계적 실험계획법을 적용하여 수집되었다. Raw OES 정보대신, Actinometric OES 정보를 이용하였으며, 신경망의 예측성능은 유전자 알고리즘을 이용해서 증진시켰다. OES의 차수를 줄이기 위해 주인자 분석 (Principal Component Analysis-PCA)을 세 종류의 분산(100, 99, 98%)에 대해서 적용하였다. 최적화한 모델의 예측에러는 323 $\AA/min$이었다. 이전에 PCA를 적용하고 은닉층 뉴런의 함수로 최적화한 모델의 예측에러는 570 $\AA/min$이었으며, 개발된 모델은 이에 비해 43% 증진된 예측 성능을 보이고 있다.

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