• Title/Summary/Keyword: machine lerning

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Development of Polynomial Based Response Surface Approximations Using Classifier Systems (분류시스템을 이용한 다항식기반 반응표면 근사화 모델링)

  • 이종수
    • Korean Journal of Computational Design and Engineering
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    • v.5 no.2
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    • pp.127-135
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    • 2000
  • Emergent computing paradigms such as genetic algorithms have found increased use in problems in engineering design. These computational tools have been shown to be applicable in the solution of generically difficult design optimization problems characterized by nonconvexities in the design space and the presence of discrete and integer design variables. Another aspect of these computational paradigms that have been lumped under the bread subject category of soft computing, is the domain of artificial intelligence, knowledge-based expert system, and machine learning. The paper explores a machine learning paradigm referred to as teaming classifier systems to construct the high-quality global function approximations between the design variables and a response function for subsequent use in design optimization. A classifier system is a machine teaming system which learns syntactically simple string rules, called classifiers for guiding the system's performance in an arbitrary environment. The capability of a learning classifier system facilitates the adaptive selection of the optimal number of training data according to the noise and multimodality in the design space of interest. The present study used the polynomial based response surface as global function approximation tools and showed its effectiveness in the improvement on the approximation performance.

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Rear-Approaching Vehicle Detection Research using Region of Interesting based on Faster R-CNN (Faster R-CNN 기반의 관심영역 유사도를 이용한 후방 접근차량 검출 연구)

  • Lee, Yeung-Hak;Kim, Joong-Soo;Shim, Jae-Chnag
    • Journal of IKEEE
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    • v.23 no.1
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    • pp.235-241
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    • 2019
  • In this paper, we propose a new algorithm to detect rear-approaching vehicle using the frame similarity of ROI(Region of Interest) based on deep learning algorithm for use in agricultural machinery systems. Since the vehicle detection system for agricultural machinery needs to detect only a vehicle approaching from the rear. we use Faster R-CNN model that shows excellent accuracy rate in deep learning for vehicle detection. And we proposed an algorithm that uses the frame similarity for ROI using constrained conditions. Experimental results show that the proposed method has a detection rate of 99.9% and reduced the false positive values.