• Title/Summary/Keyword: gradient algorithm

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Hovering Control of 1-Axial Drone with Reinforcement Learning (강화학습을 이용한 1축 드론 수평 제어)

  • Lee, Taewoo;Ryu, Jinhoo;Park, Heemin
    • Journal of Korea Multimedia Society
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    • v.21 no.2
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    • pp.250-260
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    • 2018
  • In order to control the quadcopter using reinforcement learning, hovering of 1-axial drones prototype is implemented through reinforcement learning. A complementary filter is used to measure the correct angle, and the range of angles is from -180 degrees to +180 degrees using modified complementary filter. The policy gradient method is used together with the REINFORCE algorithm for reinforcement learning. The prototype learned in this way confirmed the difference in performance depending on the length of the episode.

Context-free multiple-object segmentation using attention operator based on modified generalized symmetry transform (일반화 대칭변환을 변형한 관심 연산자에 의한 사전 정보없는 다중 물체 분할)

  • 구태모;전준형;최흥문
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.34C no.4
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    • pp.36-44
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    • 1997
  • An efficient context-free multiple-object segmentation using attention operator based on modified generalized symmetry transform is proposed and implemented by modifying a radial basis function network. By using the difference of intensity gradient, instead of te intensity gradient itself, in generalized symmetry tranform so as to make the attention operator to preserve the edges of the objects shape, an efficient context-free multiple-object segementation is proposed in which no a priori shape informtion on the objects is requried. The attention operator is implemented by using a modified radial basis function network which can reflect symmetry, and by using te edge pyramid of the input image, both of the local and the global symmetry of the objects are reflected simultaneously to make the multiple-object with different sizes be segmented with a singel fixed-size $n\timesm$ can be done with O(n) complexity. The simulaton results show that the proposed algorithm can efficiently be used in context-free multiple-object segmentation even for the low contrast IR images as well as for the images from the camera.

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BACKPROPAGATION BASED ON THE CONJUGATE GRADIENT METHOD WITH THE LINEAR SEARCH BY ORDER STATISTICS AND GOLDEN SECTION

  • Choe, Sang-Woong;Lee, Jin-Choon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.107-112
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    • 1998
  • In this paper, we propose a new paradigm (NEW_BP) to be capable of overcoming limitations of the traditional backpropagation(OLD_BP). NEW_BP is based on the method of conjugate gradients with the normalized direction vectors and computes step size through the linear search which may be characterized by order statistics and golden section. Simulation results showed that NEW_BP was definitely superior to both the stochastic OLD_BP and the deterministic OLD_BP in terms of accuracy and rate of convergence and might sumount the problem of local minima. Furthermore, they confirmed us that stagnant phenomenon of training in OLD_BP resulted from the limitations of its algorithm in itself and that unessential approaches would never cured it of this phenomenon.

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Development of Directed Diffusion Algorithm with Enhanced Performance (향상된 성능을 갖는 Directed Diffusion 알고리즘의 개발)

  • Kim Si-Hwan;Han Yun-Jong;Kim Sung-Ho
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2005.11a
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    • pp.527-530
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    • 2005
  • 센서 네트워크는 다수의 센서 노드들이 싱크(sink) 노드와 데이터 중심(Data centric) 기반으로 통신을 하게 되는데 이때 사용되는 라우팅 알고리즘 중 한 가지가 Directed Diffusion이다. Directed Diffusion 은 싱크(sink)의 named data 질의 방송(diffuse)에 기반을 둔 라우팅 프로토콜(protocol)로 다수의 소스 노드와 다수의 싱크 노드의 상황에서도 효율적으로 동작한다는 점과 각각의 질의에 의한 라우팅 경로 중에 aggregation 과 caching을 수행할 수 있다는 장점을 갖는다. 그러나 강화된 gradient 패스를 얻기 위해 요구되는 부담이 크다는 단점을 갖는다. 따라서 본 연구에서는 interest 패킷에 hop-count를 도입하여 gradient가 과다하게 설정되는 것을 제한함으로써 에너지 사용 효율을 높일 수 있는 개선된 Directed Diffusion 알고리즘을 제시한다. 또한 시뮬레이션을 통해 제안된 알고리즘의 유용성 확인을 확인하고자 한다.

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A real-time path planning method for efficient movement of a mobile robot (자율이동로봇의 효과적인 이동을 위한 실시간 경로생성 방법)

  • Sa, In-Kyu;Ahn, Ho-Seok;Lee, Hyung-Kyu;Choi, Jin-Young
    • Proceedings of the KIEE Conference
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    • 2008.10b
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    • pp.331-332
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    • 2008
  • A real-time path planning of mobile robots is a broad topic, covering a large spectrum of different technologies and applications. Briefly a path planning is designated moving technique from current pose to desired pose. It is remarkably easy to handle for human, not for robot. It is difficult that a robot recognizes surround to get a current pose and to avoid an obstacles. In this paper covers kinematics, path planning for efficient movements of a mobile robot. Kinematics of mobile robot which is suggested in this paper is exploited to create reliable and suitable motions. In addition, Gradient method is a algorithm which can guarantee for real-time path planning.

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Path Planning based on Ray-casting in Indoor Environments for Safe Navigation of a Mobile Robot (이동로봇의 안전한 주행을 위한 광선투사법 기반의 실내 경로계획)

  • Kim, Jong-Won;Song, Jae-Bok
    • The Journal of Korea Robotics Society
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    • v.5 no.4
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    • pp.302-308
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    • 2010
  • A gradient method can provide a global optimal path in indoor environments. However, the optimal path can be often generated in narrow areas despite a sufficient wide area which lead to safe navigation. This paper presents a novel approach to path planning for safe navigation of a mobile robot. The proposed algorithm extracts empty regions using a ray-casting method and then generates temporary waypoints in wider regions in order to reach the goal fast and safely. The experimental results show that the proposed method can generate paths in the wide regions in most cases and the robot can reach the goal safely at high speeds.

A Novel Method for a Reliable Classifier using Gradients

  • Han, Euihwan;Cha, Hyungtai
    • IEIE Transactions on Smart Processing and Computing
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    • v.6 no.1
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    • pp.18-20
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    • 2017
  • In this paper, we propose a new classification method to complement a $na{\ddot{i}}ve$ Bayesian classifier. This classifier assumes data distribution to be Gaussian, finds the discriminant function, and derives the decision curve. However, this method does not investigate finding the decision curve in much detail, and there are some minor problems that arise in finding an accurate discriminant function. Our findings also show that this method could produce errors when finding the decision curve. The aim of this study has therefore been to investigate existing problems and suggest a more reliable classification method. To do this, we utilize the gradient to find the decision curve. We then compare/analyze our algorithm with the $na{\ddot{i}}ve$ Bayesian method. Performance evaluation indicates that the average accuracy of our classification method is about 10% higher than $na{\ddot{i}}ve$ Bayes.

A Study on the Contour-Preserving Image Filtering for Noise Removal (잡음 제거를 위한 윤곽선 보존 기법에 관한 연구)

  • Yoo, Choong-Woong;Ryu, Dae-Hyun;Bae, Kang-Yeul
    • Journal of the Korean Institute of Telematics and Electronics T
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    • v.36T no.4
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    • pp.24-29
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    • 1999
  • In this paper, a simple contour-preserving filtering algorithm is proposed. The goal of the contour-preserving filtering method is to remove noise ad granularity as the preprocessing for the image segmentation procedure. Our method finds edge map and separates the image into the edge region and the non-edge region using this edge map. For the non-edge region, typical smoothing filters could be used to remove the noise and the small areas during the segmentation procedure. The result of simulation shows that our method is slightly better than the typical methods such as the median filtering and gradient inverse weighted filtering in the point of view of analysis of variance (ANOVA).

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Hybrid Atmospheric Compensation in Free-Space Optical Communication

  • Wang, Tingting;Zhao, Xiaohui
    • Journal of the Optical Society of Korea
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    • v.20 no.1
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    • pp.13-21
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    • 2016
  • Since the direct-gradient (DG) method uses the Shack-Hartmann wave front sensor (SH-WFS), based on the phase-conjugation principle, for atmospheric compensation in free-space optical (FSO) communication, it cannot effectively correct high-order aberrations. While the stochastic parallel gradient descent (SPGD) can compensate the distorted wave front, it requires more calculations, which is sometimes undesirable for an FSO system. A hybrid compensation (HC) method is proposed by properly using the DG method and SPGD algorithm to improve the performance of FSO communication. Simulations show that this method can well compensate wave-front aberrations and upgrade the coupling efficiency with few computations, preferable correction results, and rapid convergence rate.

An Identification Technique Based on Adaptive Radial Basis Function Network for an Electronic Odor Sensing System

  • Byun, Hyung-Gi
    • Journal of Sensor Science and Technology
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    • v.20 no.3
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    • pp.151-155
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    • 2011
  • A variety of pattern recognition algorithms including neural networks may be applicable to the identification of odors. In this paper, an identification technique for an electronic odor sensing system applicable to wound state monitoring is presented. The performance of the radial basis function(RBF) network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those parameters are initialized by an ill-conditioned genetic fuzzy c-means algorithm, and the distribution of input patterns in the very first stage, the stochastic gradient(SG), is adapted. The adaptive RBF network with singular value decomposition(SVD), which provides additional adaptation capabilities to the RBF network, is used to process data from array-based gas sensors for early detection of wound infection in burn patients. The primary results indicate that infected patients can be distinguished from uninfected patients.