• Title/Summary/Keyword: self-adaptive

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A Study on Improved Denoising Algorithm for Edge Preservation in AWGN Environments (AWGN환경에서 에지보호를 위한 개선된 잡음제거 알고리즘에 관한 연구)

  • Yinyu, Gao;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.8
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    • pp.1773-1778
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    • 2012
  • Nowadays, the high quality of image is required with the demand for digital image processing devices is rapidly increasing. But image always damaged by many kinds of noises and it is necessary to remove noise and the denoising becomes one of the most important fields. In many cases image is corrupted by AWGN(additive white Gaussian noise). In this paper, we proposed an improved denoising algorithm with edge preservation. The proposed algorithm averages values processed by spatial weighted filter and self adaptive weighted filter. Then we add the value which is computed by the equation considering variance of mask and the estimated noise variance. Through the experience, the proposed filter performs well on noise suppression and edge preservation properties and improves the image visual quality.

Analysis of Humor in the Picture Books of Mo Willems (모 윌렘스의 그림책에 나타난 유머 분석)

  • Kang, Eun-Jin
    • The Journal of the Korea Contents Association
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    • v.14 no.10
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    • pp.374-384
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    • 2014
  • Humor is an essential ability in young children's life. That is why humor has relation with cognitive development, and humor is the effective factor of attachment, social relationship, and positive self-concept for young children. For this study, the picture books written and illustrated by Mo Willems, Caldetcott Honor Medal winner, were chosen and analyzed. Mo Willems' five winning works were analyzed according to the five elements of humor from Bergson's comic theory. As results, the picture books of Mo Willems include the elements of humor, such as shape, movement, situation and language, and personality. This results suggest that the "adaptive" humor in picture books of Mo Willems should have an educational power to develop a sense of humor to young children, parents, and teachers.

A Study on the Evaluation Method of ACC Test Using Monocular Camera (단안카메라를 활용한 ACC 시험평가 방법에 관한 연구)

  • Kim, Bong-Ju;Lee, Seon-Bong
    • Journal of Auto-vehicle Safety Association
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    • v.12 no.3
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    • pp.43-51
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    • 2020
  • Currently, the second level of the six stages of self-driving technology, as defined by SAE, is commercialized, and the third level is preparing for commercialization. The purpose of ACC is to be evaluated as a system useful for preventing and preventing accidents by minimizing driver fatigue through longitudinal speed control and relative distance control of the vehicle. In this regard, for the study of safety assessment methods in the practical environment of ACC. Distance measurement method using monocular camera and data acquisition equipment such as DGPS are utilized. Based on the evaluation scenario considering the domestic road environment proposed by the preceding study, the relative distance obtained from equipment such as DPGS and the relative distance using a monocular camera in the actual test is verified by comparing and analyzing the safety assessment. The comparison by scenario results showed a minimum error rate of 3.83% in Scenario 1 and a maximum of 14.61% in Scenario 6. The cause of the maximum error is that the lane recognition is not accurate in the camera image and irregular operation conditions such as rushing in or exiting the surrounding area from the walkway. It is expected that safety evaluation using a monocular camera will be possible for other ADAS systems in the future.

Vibration Control a Flexible Single Link Robot Manipulator Using Neural Networks (신경회로망을 이용한 유연성 단일 링크 로봇 매니퓰레이터의 진동제어)

  • 탁한호;이상배
    • Journal of the Korean Institute of Navigation
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    • v.21 no.3
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    • pp.55-66
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    • 1997
  • In this paper, applications of neural networks to vibration control of flexible single link robot manipulator are ocnsidered. The architecture of neural networks is a hidden layer, which is comprised of self-recurrent one. Tow neural networks are utilized in a control system ; one as an identifier is called neuro identifier and the othe ra s a controller is called neuro controller. The neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by dynamic error-backpropagation algorithm(DEA). To guarantee concegence and to get faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. When a flexible manipulator is ratated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlle dinsuch as way, that the motor is rotated by a specified angle. while simulataneously stabilizing vibration of the flexible manipulators so that it is arrested as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large body motions, as well as the flexural vibrations. Therefore, dynamic models for a flexible single link manipulator is derived, and LQR controller and nerual networks controller are composed. The effectiveness of the proposed nerual networks control system is confirmed by experiments.

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Time-varying modal parameters identification of large flexible spacecraft using a recursive algorithm

  • Ni, Zhiyu;Wu, Zhigang;Wu, Shunan
    • International Journal of Aeronautical and Space Sciences
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    • v.17 no.2
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    • pp.184-194
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    • 2016
  • In existing identification methods for on-orbit spacecraft, such as eigensystem realization algorithm (ERA) and subspace method identification (SMI), singular value decomposition (SVD) is used frequently to estimate the modal parameters. However, these identification methods are often used to process the linear time-invariant system, and there is a lower computation efficiency using the SVD when the system order of spacecraft is high. In this study, to improve the computational efficiency in identifying time-varying modal parameters of large spacecraft, a faster recursive algorithm called fast approximated power iteration (FAPI) is employed. This approach avoids the SVD and can be provided as an alternative spacecraft identification method, and the latest modal parameters obtained can be applied for updating the controller parameters timely (e.g. the self-adaptive control problem). In numerical simulations, two large flexible spacecraft models, the Engineering Test Satellite-VIII (ETS-VIII) and Soil Moisture Active/Passive (SMAP) satellite, are established. The identification results show that this recursive algorithm can obtain the time-varying modal parameters, and the computation time is reduced significantly.

Dual NLMS Type Feedback Interference Cancellation Method in RF Repeater System (무선 중계기에서의 Dual NLMS 방식 궤한 간섭 제거 방법)

  • Park, Won-Jin;Park, Yong-Seo;Hong, Een-Kee
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.2A
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    • pp.91-99
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    • 2011
  • Several repeater systems are used to enhance the cell coverage to location such as shadow and rural areas in mobile systems. But the general RF repeater solutions are not suitable for high power outdoor environment because it has the weakness such as self oscillation problem With adoption of a adaptive digital filter technology, feedback interference cancellation repeater prevents oscillation by detecting and canceling the unwanted feedback signal between transmission and receiver antenna. In this paper, dual NLMS based interference cancellation method is proposed and the step size adaptation can be implemented by the estimation of the feedback channel Doppler frequency characteristics. The performance of the proposed algorithm is quantified via analysis and simulation for the static and multipath fading feedback channels.

An adaptive nonlocal filtering for low-dose CT in both image and projection domains

  • Wang, Yingmei;Fu, Shujun;Li, Wanlong;Zhang, Caiming
    • Journal of Computational Design and Engineering
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    • v.2 no.2
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    • pp.113-118
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    • 2015
  • An important problem in low-dose CT is the image quality degradation caused by photon starvation. There are a lot of algorithms in sinogram domain or image domain to solve this problem. In view of strong self-similarity contained in the special sinusoid-like strip data in the sinogram space, we propose a novel non-local filtering, whose average weights are related to both the image FBP (filtered backprojection) reconstructed from restored sinogram data and the image directly FBP reconstructed from noisy sinogram data. In the process of sinogram restoration, we apply a non-local method with smoothness parameters adjusted adaptively to the variance of noisy sinogram data, which makes the method much effective for noise reduction in sinogram domain. Simulation experiments show that our proposed method by filtering in both image and projection domains has a better performance in noise reduction and details preservation in reconstructed images.

Structure optimization of neural network using co-evolution (공진화를 이용한 신경회로망의 구조 최적화)

  • 전효병;김대준;심귀보
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.4
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    • pp.67-75
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    • 1998
  • In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

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Evaluation of Immediate Effects of an Electrical Massager on Stress Relaxation Using the Heart Rate Variability(HRV) (심박변위도를 이용한 전동 안마기의 단기적 스트레스 완화 효과 평가)

  • Kim, Yong-Dae;Chang, Yun-Seung;Choi, Dong-Hyuk;Lee, Hyun-Ju;Tae, Ki-Sik
    • Journal of the Korean Society for Precision Engineering
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    • v.27 no.6
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    • pp.75-81
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    • 2010
  • The purpose of this study was to investigate the short-term effects of electrical massager on stress-related parameters including heart rate variability (HRV), heart rate (HR) using the photoplethysmogram (PPG) signal with motion artifact correction. Twenty healthy subjects were randomly allocated to receive a 15-min section of three types ((1) resting mode (control group), (2) light massage mode, (3) strong massage mode). Results indicated that self-report, VAS (Visual Analog Scale) significantly decreased for two massage modes after massage except control group. In strong massage mode, it was associated with significant increases in HF, but significant decreases in LF and LH/HF ratio compared with the light massage mode. For all outcomes, similar changes were not observed in the control group. Also, the result founded that mean HR of all groups decrease. We conclude that electrical massager reduces perceived stress and improves adaptive autonomic response to stress in healthy adults.

A Study on Traffic Prediction Algorithm for Proactive Self-Adaptive System in Road Network (선행적 자가적응형 시스템을 위한 도로 교통량 예측 알고리즘에 관한 연구)

  • Jeong, Hohyeon;Kim, Misoo;Jeong, Jaehoon (Paul);Lee, Eunseok
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.10a
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    • pp.983-986
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    • 2015
  • 물리적, 논리적 공간에서 다양한 오브젝트들이 상호작용할 수 있게 되고, 오브젝트에 탑재되는 소프트웨어가 고도화 됨에 따라 엔지니어가 관리 가능한 수준의 시스템 제어가 힘들어지고 있다. 이런 복잡한 시스템의 자율적인 관리를 위해 다양한 상황에 대응 가능한 자가적응성이 요구된다. 자가적응형 소프트웨어는 대상 시스템의 목표나 QoS를 만족할 수 있도록 런타임에 스스로를 변화 시킬 수 있는 능력을 가진 소프트웨어이다. 이러한 소프트웨어는 고도화된 시스템의 관리에 있어서 엔지니어의 부담을 경감시킬수 있다. 본 논문에서 제안하는 선행적 자가적응형 시스템은 도로망과 같은 주기적 특성을 가진 시스템에서 시스템이 직면하는 상황을 사전에 예측하여 미리 대응할 수 있는 시스템이다. 이는 기존에 반응적으로 대응했던 시스템들이 적용한 정책의 효과를 보기까지 낭비되는 시간을 고려하여 해당 지연시간동안에 시스템의 목표나 QoS가 하락하는 상황을 미연에 방지할 수 있다. 본 시스템의 적용분야로 지능형교통체계를 사용하였으며, 도로망 전체에서 정체 발생빈도와 평균 이동속도 그리고 단위길이당 운행시간을 평가항목으로 사용하고, 대상 도로망 전체적인 최적화를 목표로 한다.