• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.734-739
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• 1988

For the trajectory control of dynamic systems with unidentified parameters a second-order iterative learning control method is presented. In contrast to other known methods, the proposed learning control scheme can utilize more than one error history contained in the trajectories generated at prior iterations. A convergency proof is given and it is also shown that the convergence speed can be improved in compared to conventional methods. Examples are provided to show effectiveness of the algorithm, and, via simulation, it is demonstrated that the method yields a good performance even in the presence of distubances.

• Phonetics and Speech Sciences
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• v.3 no.1
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• pp.23-34
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• 2011

Many Korean children have difficulty learning English vowels. The gestures inside the oral and pharyngeal cavities are hard to control when they cannot see the gestures and the target vowel system is quite different from that of Korean. This study attempts to collect children's acoustic data of twelve English vowels published by Hillenbrand et al. (1995) online and to examine the acoustic features of English vowels for phoneticians and English teachers. The author used Praat to obtain the data systematically at six equidistant timepoints over the vowel segment avoiding any obvious errors. Results show inherent acoustic properties for vowels from the children's distribution of vowel duration, f0 and intensity values. Second, children's gestures for each vowel coincide with the regression analysis of all formant values at different timepoints regardless of the vocal fold and tract difference. Third, locus points appear higher than those of American males and females. Their gestures along the timepoints display almost similar patterns. From the results the author concludes that vowel formant trajectories provide useful and important information on dynamic articulatory gestures, which may be applicable to Korean children's education and correction of English vowels. Further studies on the developmental study of vowel formants and pitch values are desirable.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.358-364
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• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.410-414
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• 1996

A visual servoing algorithm is proposed for a robot with a camera in hand. Specifically, novel image features are suggested by employing a viewing model of perspective projection to estimate relative pitching and yawing angles between the object and the camera. To compensate dynamic characteristics of the robot, desired feature trajectories for the learning of visually guided line-of-sight robot motion are obtained by measuring features by the camera in hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a, commercially provided function of linear motion. And then, control actions of the camera are approximately found by fuzzy-neural networks to follow such desired feature trajectories. To show the validity of proposed algorithm, some experimental results are illustrated, where a four axis SCARA robot with a B/W CCD camera is used.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.177-182
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• 1997

A visual servoing method is proposed for a robot with a camera in hand. Specifically, vanishing point features are suggested by employing a viewing model of perspective projection to calculate the relative rolling, pitching and yawing angles between the object and the camera. To compensate dynamic characteristics of the robot, desired feature trajectories for the learning of visually guided line-of-sight robot motion are obtained by measuring features by the camera in hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a commercially provided function of linear motion. And then, control actions of the camera are approximately found by fuzzy-neural networks to follow such desired feature trajectories. To show the validity of proposed algorithm, some experimental results are illustrated, where a four axis SCARA robot with a B/W CCD camera is used.

• Phonetics and Speech Sciences
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• v.5 no.2
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• pp.71-79
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• 2013

This study provided longitudinal examination of the Chinese learners' acquisition of the Korean vowels. Specifically the author examined whether Korean monophthongs are acquired rapidly in early stages of learning (Flege, Munro and Skelton, 1992; Munro and Derwing, 2008) or they develop rather gradually in proportion to the learners' experience (Byee, 2001; Ellis, 2006). This study collected the Korean vowel production by 23 Chinese learners for a year, and then analysed F1 and F2 of each Korean vowel. The results showed that 1) Most of the second language (L2) vowels were rapidly improved during the first six or nine months of Korean learning before reaching the constant stage; and 2) The exact acquisition trajectories varied across the seven vowels. Specifically the vowels which were acquired in the early stage of learning were /i, e, ɨ/ for F1 and /ʌ, e, o, u/ for F2. Thus this study supports the hypothesis of Flege et al. (1992) and Munro and Derwing (2008) except the fact that each vowel showed the different learning route.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.474-474
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• 2000

This paper shows that a system with two-link arm can obtain hand reaching movement to a target object projected on a visual sensor by reinforcement learning using a layered neural network. The reinforcement signal, which is an only signal from the environment, is given to the system only when the hand reaches the target object. The neural network computes two joint torques from visual sensory signals, joint angles, and joint angular velocities considering the urn dynamics. It is known that the trajectory of the voluntary movement o( human hand reaching is almost straight, and the hand velocity changes like bell-shape. Although there are some exceptions, the properties of the trajectories obtained by the reinforcement learning are somewhat similar to the experimental result of the human hand reaching movement.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.2
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• pp.76-80
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• 2005

In this paper, a Direct Learning Control (DLC) method is proposed for linear feedback systems to improve the tracking performance when the task of the control system is repetitive. DLC can generate the desired control input directly from the previously learned control inputs corresponding to other output trajectories. It is assumed that all the desired output functions given to the system have some relations called proportionality and it is shown by mathematical analysis that DLC can be utilized to genera additional control efforts for the perfect tracking. To show the validity and tracking performance of the proposed method, some simulations are performed for the tracking control of a linear system with a PI controller.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.178-186
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• 1998

In this paper, a robust.adaptive learning control scheme is presented for precise trajectory tracking of rigid mobile robots. In the proposed controller, a set of desired trajectories is defined and used in constructing the control input and learning rules which constitute the main part of the proposed controller. Stable operating characteristics such as precise trajectory tracking, parameter estimation, disturbance suppression, etc., are shown thorugh experiments and computer simulations.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.386-388
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• 1996

This paper presents the learning controller for robot manipulators to track the desired trajectory exactly. The learning controller, based on the Lyapunov theory, consists of a fixed PD action and a repetitive action for the purpose of feedforward compensation which is adjusted utilizing a linear combination of the velocity and position errors. The learning controller Is often used In case of the desired trajectories are periodic tasks, and has advantage that it periodically converges to zero even if we don't know the exact dynamic parameters. In this paper, we show that the position and velocity errors of robot manipulators converge to zero sa time goes infinite for the input is periodic function and show a good trajectory tracking performance In the cartesian space.