• 한국전문물리치료학회지
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• 제2권2호
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• pp.73-84
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• 1995

Spasticity has been defined as a motor disorder characterised by a velocity-dependent increase in tonic stretch reflexes with exaggerated tendon jerks resulting in hyperexcitability of the stretch reflexes as one component of the upper motor neuron syndrome. Weakness and loss of dexterity, however, are considered to be more disabling to the patient than changes in muscle tone. The discussion includes the important role that alterations in the physiology of motor units, notably changes in firing rates and muscle fiber atrophy, play in the manifestation of muscle weakness. This paper considers both the neural and mechanical components of spasticity and discusses, in terms of clinical intervention, the implications arising from recent research. Investigations suggest that the resistance to passive movement in individuals with spasticity is due not only to neural mechanisms but also to changes in mechanical properties of muscle. The emphasis is on training the individual to gain control over the muscles required for different tasks, and on preventing secondary and adaptive soft tissue changes and ineffective adaptive motor behaviours.

• 전기학회논문지P
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• 제58권3호
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

• Journal of Korean Neurosurgical Society
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• 제47권3호
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• pp.191-198
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• 2010

Objective : This study is planned to determine the neurocognitive difficulties of hydrocephalic adults. Methods : The research group contained healthy adults (control group, n : 15), and hydrocephalic adults (n : 15). Hydrocephalic group consisted of patients with idiopathic aquaduct stenosis and post-meningitis hydrocephalus. All patients were followed with shunted hydrocephalus and not gone to shunt revision during last two years. They were chosen from either asymptomatic or had only minor symptoms without motor and sensorineural deficit. A neuropsychological test battery (Raven Standart Progressive Matrices, Bender-Gestalt Test, Cancellation Test, Clock Drawing Test, Facial Recognition Test, Line Orientation Test, Serial Digit Learning Test, Stroop Color Word Interference Test-TBAG Form, Verbal Fluency Test, Verbal Fluency Test, Visual-Aural Digit Span Test-B) was applied to all groups. Results : Neuropsychological assessment of hydrocephalic patients demonstrated that they had poor performance on visual, semantic and working memory, visuoconstructive and frontal functions, reading, attention, motor coordination and executive function of parietal lobe which related with complex and perseverative behaviour. Eventually, these patients had significant impairment on the neurocognitive functions of their frontal, parietal and temporal lobes. On the other hand, the statistical analyses performed on demographic data showed that the aetiology of the hydrocephalus, age, sex and localization of the shunt (frontal or posterior parietal) did not affect the test results. Conclusion : This prospective study showed that adult patients with hydrocephalus have serious neuropsychological problems which might be directly caused by the hydrocephalus; and these problems may cause serious adaptive difficulties in their social, cultural, behavioral and academic life.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.408-411
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• 2009

In this paper, conventional PI, fuzzy neural network(FNN) and adaptive teaming mechanism(ALM)-FNN for rotor field oriented controlled(RFOC) induction motor are studied comparatively. The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. Comparative study of PI, FNN and ALM-FNN are carried out from various aspects which is dynamic performance, steady-state accuracy, parameter robustness and complementation etc. To have a clear view of the three techniques, a RFOC system based on a three level neutral point clamped inverter-fed induction motor drive is established in this paper. Each of the three control technique: PI, FNN and ALM-FNN, are used in the outer loops for rotor speed. The merit and drawbacks of each method are summarized in the conclusion part, which may a guideline for industry application.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.