• Journal of the Korean Society of Physical Medicine
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• v.11 no.3
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• pp.133-139
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• 2016

PURPOSE: The purpose of this study is to examine the impact of balance training on a three-dimensional balance trainer that provides the up-and-down vertical movement of the knee joint and left-and-right horizontal movement, along with visual feedback on the functional ability of children with spastic cerebral palsy (CPs). METHODS: 8 CPs participated in this study. The experiment was implemented for 40 minutes, three times a week for a total of six weeks. The subjects received general physiotherapy for 15 minutes in each session focused on balance and walking, as based on the neuro-developmental treatment theory. Balance training was performed for 20 minutes on a three-dimensional balance trainer where knee joint movement providing visual feedback is applied. The evaluations were conducted before and after the test, and posture sway was measured using 10 Meter Walking Test (10MWT), Timed Up & Go Test (TUG), and the Good Balance System to evaluate the functional ability and balance of the subjects RESULTS: 10MWT was not statistically significant (p>.05). On the contrary, TUG and postural sway indicate static balance showed a statistically significant decrease (p<.05). In a static balance test using the Good Balance System, the average moving speed statistically significantly decreased in the AP and ML directions (p<.05), and the mean velocity moment also significantly decreased (p<.05). CONCLUSION: These findings suggested that balance training using the three-dimensional balance trainer, with the features of visual feedback and up-and-down knee joint movement effects on increasing dynamic and static balance.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.524-529
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• 2005

In this paper, a novel neural network compensation technique for PD like fuzzy controlled robot manipulators is presented. A standard PD-like fuzzy controller is designed and used as a main controller for controlling robot manipulators. A neural network controller is added to the reference trajectories to modify input error space so that the system is robust to any change in system parameter variations. It forms a neural-fuzzy control structure and used to compensate for nonlinear effects. The ultimate goal is same as that of the neuro-fuzzy control structure, but this proposed technique modifies the input error not the fuzzy rules. The proposed scheme is tested to control the position of the 3 degrees-of-freedom rotary robot manipulator. Performances are compared with that of other neural network control structure known as the feedback error learning structure that compensates at the control input level.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1561-1564
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• 2003

Intelligent Wearable Module is intelligent system that arises when a human is part of the feedback loop of a computational process like a certain control system. Applied system is mobile robot. This paper represents the mobile robot control system remote controlled by Intelligent Wearable Module. So far, owing to the development of 802.l1b technologies, lots of remote control methods through internet have been proposed. To control a mobile robot through internet and guide it under unknown environment. The information about the direction and velocity of the mobile robot feedbacks to the PDA and the PDA send new control method produced from the combination of Neuro and Hierarchical Fuzzy Algorithm

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1992_1993
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• 2009

뇌의 활동전위를 측정하여 원하지 않게 형성된 두뇌의 성향을 뇌파를 조절하여 치료하는 뉴로피드백과 임상적으로 유용하게 사용하고 있고 두뇌의 이상을 일련의 검사를 통해 판단하기 위한 도구인 신경심리검사를 함께 사용하면서 시너지효과를 낼 수 있도록 하는 프로그램을 개발하였다. 신경심리검사는 최초 14개의 컨텐츠로 설계했지만 컨텐츠 추가를 쉽게 할 수 있도록 했고 뉴로피드백을 동시에 할 수 있도록 구성하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.4
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• pp.238-242
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• 2013

We consider the problem of constructing observers for nonlinear systems with unknown inputs. Connectionist networks, also called neural networks, have been broadly applied to solve many different problems since McCulloch and Pitts had shown mathematically their information processing ability in 1943. In this thesis, we present a genetic neuro-control scheme for nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1202-1209
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• 2011

Neuro feedback training using ElectroEncephalo Grams (EEGs) is commonly utilized in the treatment of Alzheimer's disease, and Attention Deficit Hyperactivity Disorder (ADHD). Recently, BCI (Brain-computer Interface) contents have developed, not for the purpose of treatment, but for concentration improvement or brain relaxation training. However, as each user has different wave forms, it is hard to develop contents controlled by such different wave. Therefore, an EEG process that allows the ability to transform the variety of wave forms into one standard signal and use it without taking a user's characteristic of EEG into account, is required. In this paper, a framework that can reduce users' characteristics by normalizing and converting measured EEGs is proposed for contents. This framework also contains the process that controls different brainwave measuring devices. In experiment a handling process applying the proposed framework to the developed BCI contents is introduced.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.90-98
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• 1997

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilzation of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the systems with constrained inputs and/or states. A neural identifier or a genetic algorithm identifier could be utilized for modeling the partially known systems and the unknown systems. The nature of neural networks as a parallel processor would circumvent the problem of "curwe of dimensionality". The control law has been demonstrated for both a torque input motor and a velocity input motor identified by a genetic algorithm called GENOCOPed GENOCOP.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.1005-1007
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• 2014

뇌파를 이용하는 바이오피드백 중 한 형태인 뉴로피드백의 적용의 시도는 ADHD, 뇌손상 등 뇌기능과 관련된 여러 가지 영역에서 다양한 효과가 확인되었고 이미 여러 분야에서 관련 임상 연구들이 계속 발표되고 있다. 본 논문에서는 뉴로피드백 훈련이 집중력을 향상시킬 수 있다는 기존의 연구 결과를 바탕으로 간단한 게임 및 퀴즈 컨텐츠로 구성되어 있는 뉴로피드백 시스템을 설계하였으며 뇌파 측정 단계에서 마크를 해두어 보다 정확한 뇌전도를 이용하여 집중도를 분석하는데 오류율을 줄일 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1715-1717
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• 2015

최근 의학 및 게임 분야에서 뉴로 피드백에 관한 연구들이 활발하다. 뉴로 피드백이란 뇌의 전기적 활동을 뜻하는EEG(Electroencephalogram)을 대상으로 하는 바이오피드백(biofeedback) 시스템의 일종이다. 좌 우뇌의 불균형은 불안과 우울 등의 정서적 장애를 초래한다. 본 논문에서는 좌, 우뇌 활성도를 대칭적으로 향상시켜 뇌기능의 최적화를 위한 시스템을 제안하며 뇌파를 분석하여 정서 상태 파악 후 규칙적 훈련을 통한 우울, 불안 등의 정서적 문제를 개선하는 뉴로 피드백 시스템 개발에 대한 결과를 보고한다. 실험을 통한 시스템의 검증으로 3주간 진행한 훈련 결과를 비교하였으며 그 증가율을 살펴보았다. 1주차와 3주차의 데이터를 비교해본 결과 평균적으로 26.18%의 증가율을 보였고 좌, 우뇌의 활성도의 대칭이 향상된 것을 알 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1706-1708
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• 2013

최근 규칙적인 수면을 이루지 못해 수면장애를 호소하는 사람들이 좌시하기 어려울 정도로 늘어나고 있는 추세이다. 수면장애 치료를 위한 보편적인 방법은 수면제 투여이나, 약물의 지속적인 투여로 인해 약물의 내성 및 의존성 등의 부작용이 일어날 수 있다. 따라서 근래에는 비약물적인 인지행동치료(Cognitive Behavior Therapy)를 통해 환자가 스스로 자신의 상태를 이완시킬 수 있는 훈련을 제공하고 있다. 하지만 치료를 위해서는 병원과 환자 모두 많은 시간과 비용이 든다는 문제점이 있다. 따라서 본 논문에서는 환자 혼자서도 바이오피드백 훈련이 가능하도록 하는 시스템을 제안한다. 이 시스템을 통해 좀 더 효율적인 수면장애 치료를 기대할 수 있다.