• The Journal of Korean Physical Therapy
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• v.29 no.2
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• pp.74-79
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• 2017

Purpose: The purpose of this study was to compare the effects of the FES-gait with augmented feedback training to the FES alone on the gait and functional performance in individuals with chronic stroke. Methods: This study used a pretest and posttest randomized control design. The subjects who signed the agreement were randomly divided into 12 experimental groups and 12 control groups. The experimental groups performed two types of augmented feedback training (knowledge of performance and knowledge of results) together with FES, and the control group performed FES on the TA and GM without augmented feedback and then walked for 30 minutes for 40 meters. Both the experimental groups and the control groups received training five times a week for four weeks. Results: The groups that received the FES with augmented feedback training significantly showed a greater improvement in single limb support (SLS) and gait velocity than the groups that received FES alone. In addition, timed up and go (TUG) test and six minute walk test (6MWT) showed a significant improvement in the groups that received FES with augmented feedback compared to the groups that received FES alone. Conclusion: Compared with the existing FES gait training, augmented feedback showed improvements in gait parameters, walking ability, and dynamic balance. The augmented feedback will be an important method that can provide motivation for motor learning to stroke patients.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.141-144
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• 2001

In this paper, we proposed a neural network controller with a fuzzy compensator which compensate a output of neural network controller. Even if learn by neural network controller, it can occur a bad results from disturbance or load variations. So in order to adjust above case. we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning an inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. In order to confirm a performance of the proposed controller, we implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• PNF and Movement
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• v.16 no.1
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• pp.105-113
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• 2018

Purpose: The aim of this study was to explore the effects of dynamic balance training on pain, physical function, and dynamic balance in individuals with knee osteoarthritis. Methods: Fourteen patients with knee osteoarthritis participated in this study. The patients were randomly assigned to two groups: an experimental group (n=7) or a control group (n=7). All the patients took part in a lower extremity strength program for 30 min. In addition, the experimental group participated in a 30-min dynamic balance program. Both groups performed the program five times a week for 3 weeks. Outcomes, including the numeric rating scale (NRS), Western Ontario and MacMaster Universities Arthritis Index (WOMAC), and Community Balance and Mobility Scale (CB&M), were measured at baseline and after 3 weeks. Results: Both groups showed pre-to-post intervention improvements on all outcome measures (p<0.05). The experimental group showed a significant improvement in WOMAC (p = 0.00; Z = -2.82) and CB&M (p = 0.03; Z = -2.20) scores after the intervention as compared with those of the control group. Conclusion: The results revealed that dynamic balance training improved physical function, as well as balance ability, in patients with knee osteoarthritis as compared with that of a control group with no balance training.

• Physical Therapy Korea
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• v.9 no.4
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• pp.13-35
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• 2002

Lesch-Nyhan 증후근(LNS)은 hypoxanthine guanine phosphoribosyle transferase(HGPRT) 효소를 암호화 하는 X 염색체가 불완전해서 일어나는 유전적인 추제외로계(또는 기저핵)의 드문 병변이다. 출생시 LNS 유아는 정상적인 운동발달이 관찰되어진다. LNS에게서 현저하게 진단적인 특징으로 보여지는 운동심리적 행동인 self-mutilating 행위는 4살 이후에나 나타난다. LNS 아이들은 오히려 초기에 Rett's 증후근, 뇌성마비, 자폐, 다운증후근과 유사한 운동행위를 보인다. 그래서 LNS 아이들은 앞에 기술한 신경학적 장애로 오진을 받을 수가 있다. 오진으로 인해 초기에 적절한 치료를 받지 못한다면 LNS는 결과적으로 합병증(신장부전)과 self-mutilating 행위로 인하여 치명적일 수가 있다. 그러므로, 이 연구의 목적은 LNS 평가 동안 더 나은 진단을 하도록 하기 위하여 LNS와 관련된 기능부전에 대한 지식을 임상가들에게 제공하고자 함이었다. 연구 대상자는 10살인 2명의 쌍둥이 남아이었으며 실험은 뻗기 과제 수행(reaching task)시 움직임 특성을 보기 위하여 운동형상학적과 비디오 분석을 사용하였다. 기술통계로 분석 결과 움직임 시간과 단위가 증가됨을 보였고 사지의 분절적 움직임이 협응되지 않음을 보였다. ballistic과 jerky 움직임 양상은 dysmetric과 비긴장성 운동 행위에서 우세하였다. LNS은 추체로계 운동 장애 (과근긴장도나 저긴장도) 와 추체외로계의 운동 장애(dystonia와 choreoathetosis)의 혼합된 형태를 보였다. 결론으로 이 연구는 운동발달 장애를 가진 아이들을 치료하고자 할 때 임상가들한테 LNS 아이들의 움직임 장애의 다른 진단적 특징을 알아야 한다는 것을 제시하고자 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.46-50
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• 1997

In fuzzy logic control, static fuzzy rules cannot cope with significant changes of parameters of plants or environment. To solve this prohlem, self-organizing fuzzy control. neural-network-hased fuzzy logic control and so on have heen introduced so far. However, dynamically changed fuzzy rules of these schemes may make a fuzzy logic controller Fall into dangerous situations because the changed fuzzy rules may he incomplete or inconsistent. This paper proposes a new adaptive filzzy logic control scheme using a predictivc neural network. Although some parameters of a controlled plant or environment are changed, proposed fuzzy logic controller changes its decision outputs adaptively and robustly using unchanged initial fuzzy rules and the predictive errors generated hy the predictive neural network by on-line learning. Experimental results with a D<' servo-motor position control problem show that propnsed cnntrol scheme is very useful in the viewpoint of adaptability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.289-292
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• 2005

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose an adaptive PID controller based on a gradient descent learning. This algorithm has a simple structure like conventional PID controller and robustness to system parameters variation. To verify performances of the proposed adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.701-704
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• 2005

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But They are difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PID controller based on a cell-mediated immune response and a gradient descent learning. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.225-227
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed artificial intelligent-PI(AIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.420-423
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Journal of Power Electronics
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• v.7 no.2
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• pp.159-173
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• 2007

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.