• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.632-640
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• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.20 no.1
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• pp.39-45
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• 2014

Background: The purpose of this study is to identify effects of neuro-muscular control training on vastus medialis oblique (VMO) after menisectomy of the knee. Methods: The subjects of this study are women aged 42 and 39 each who did menisectomy. Case 1 was applied quadriceps setting exercise and neuro-muscular contrlol training and case 2 was applied quadriecps setting. Intervention was done 5 times a week for 2 weeks. Measurement of muscle activity on VMO and vastus lateralis (VL) was standardized signals of each muscle to %RVC using surface EMG. Results: On comparison of exercise before and after on VMO and VL, VL activation of case 2 was increased more than case 1. Conclusion: Quadriecps-setting exercise and selective neuro-muscular control training of VMO is effective intervention on VMO activity and muscle activity ratio of VMO to VL.

• Smart Structures and Systems
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• v.7 no.3
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• pp.229-240
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• 2011

Recent developments in Smart Structures with very large scale embedded sensors and actuators have introduced new challenges in terms of data processing and sensor fusion. These smart structures are dynamically classified as a large-scale system with thousands of sensors and actuators that form the musculoskeletal of the structure, analogous to human body. In order to develop structural health monitoring and diagnostics with data provided by thousands of sensors, new sensor informatics has to be developed. The focus of our on-going research is to develop techniques and algorithms that would utilize this musculoskeletal system effectively; thus creating the intelligence for such a large-scale autonomous structure. To achieve this level of intelligence, three major research tasks are being conducted: development of a Bio-Inspired data analysis and information extraction from thousands of sensors; development of an analytical technique for Optimal Sensory System using Structural Observability; and creation of a bio-inspired decision-making and control system. This paper is focused on the results of our effort on the first task, namely development of a Neuro-Morphic Engineering approach, using a neuro-symbolic data manipulation, inspired by the understanding of human information processing architecture, for sensor fusion and structural diagnostics.

• Clinical and Experimental Pediatrics
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• v.55 no.9
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• pp.354-357
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• 2012

Behçet disease (BD) is rare in childhood. We report a 9-year-old boy with neuro-Behçet disease who presented diplopia and weakness on the left side after a cerebral concussion. Brain magnetic resonance imaging (MRI) revealed hyperintensity of the right mesodiencephalic junction on T2-weighted and fluid attenuated inversion recovery images. Prednisolone administration resulted in complete remission and normalization of abnormal MRI finding. Brain MRI is a useful diagnostic tool when the neurological sign is the first symptom of subclinical BD.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.539-541
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• 2006

Biped locomotion is a popular research area in robotics due to the high adaptability of a walking robot in an unstructured environment. When attempting to automate the motion planning process for a biped walking robot, one of the main issues is assurance of dynamic stability of motion. This can be categorized into three general groups: body stability, body path stability, and gait stability. A zero moment point (ZMP), a point where the total forces and moments acting on the robot are zero, is usually employed as a basic component for dynamically stable motion. In this rarer, learning based neuro-fuzzy systems have been developed and applied to model ZMP trajectory of a biped walking robot. As a result, we can provide more improved insight into physical walking mechanisms.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.772-777
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• 1993

This paper presents a neuro-fuzzy modelling method that determines chemicals dosing model based on historical operation data for effective water quality control in water treatment system and calculates automatically the amount of optimum chemicals dosing against the changes of raw water qualities and flow rate. The structure identification in the modelling by means of neuro-fuzzy reasing is performed by Genetic Algorithm(GA) and Complex Method in which the numbers of hidden layer and its hidden nodes, learning rate and connection pattern between input layer and output layer are identified. The learning network is implemented utilizing Back Propagation(BP) algorithm. The effectiveness of the proposed modelling scheme and the feasibility of the acquired neuro-fuzzy network is evaluated through computer simulation for chemicals dosing control in water treatment system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1892-1896
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• 2005

In this paper, an effective method for selecting significant input variables in building ANFIS (Adaptive Neuro-Fuzzy Inference System) for nonlinear system modeling is proposed. Dominant inputs in a nonlinear system identification process are extracted by evaluating the performance index and they are applied to ANFIS. The availability of our proposed model is verified with the Box and Jenkins gas furnace data. The comparisons with other methods are also given in this paper to show our proposed method is superior to other models.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.960-962
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• 1999

The main idea of the proposed neuro-fuzzy system is conditional clustering whose main objective is to develop clusters preserving homogeneity of the clustered patterns with regard to their similarity in the input space as well as their respective values assumed in the output space. In the proposed neuro-fuzzy system, the structure identification is used with conditional fuzzy clustering, the parameter identification carried out by the hybrid learning scheme using back-propagation and total least squares.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.373-375
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• 2005

The purpose of this paper is to develop the Plastic Cortex Stimulator(PCS) for stroke patients using ZigBee technology. The PCS consists of an implantable neuro-stimulator and a user controller, The neuro-stimulator has the stimulus circuit which is the H-bridge circuit to generate a bipolar pulse. The bipolar pulse is known to be effective for stroke recovery. The user controller sends several wave-shape parameters (amplitude, pulse width, cycle, etc.) to the neuro-stimulator for variable stimulation using ZigBee technology. The CC2420 and atmega128L was used to implement ZigBee protocol stack. The wireless control of PCS based on ZigBee can help the tele-rehabilitation of the stroke patients. The most effective pulse shape parameters are being investigated through animal experiments. The bio-compatibility and user-friendly interface are supposed to be handled in further study.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.494-501
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• 2001

In this paper an ANFIS(Adativo Neuro-Fuzzy Inference System)- based fault detection and diagnosis for a closed loop control system is proposed. The proposed diagnostic system contains two ANFIS. One is run as a parallel model within the model in closed loop control(MCL) and the other is run as a series-parallel model within the process in closed loop(PCL) for the generation of relevant symptoms for fault diagnosis. These symptoms are further processed by another classification logic with simple rules and neural network for process and controller fault diagnosis. Experimental results for a DC shunt motor control system illustrate the effectiveness of the proposed diagnostic scheme.