• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.172-177
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• 2013

With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.9-15
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• 2008

Unlike robotic systems, humans excel at a variety of tasks by utilizing their intrinsic impedance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulators human''s superior motor skills in contact tasks. This paper develops a novel method for estimating and predicting the human joint impedance using the electromyogram(EMG) signals and limb position measurements. The EMG signal is the summation of MUAPs (motor unit action potentials). Determination of the relationship between the EMG signals and joint stiffness is difficult, due to irregularities and uncertainties of the EMG signals. In this research, an artificial neural network(ANN) model was developed to model the relation between the EMG and joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. The feasibility of the developed model was confirmed by experiments and simulations.

• Archives of Craniofacial Surgery
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• v.13 no.1
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• pp.46-49
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• 2012

Purpose: The forehead flap is the workhorse in nasal reconstruction, which provides a similar skin color, texture, structure, and reliability. There are some disadvantages, including donor site morbidities, 2- or 3-stage operations, and postoperative management after initial flap transfer. Furthermore, there has been little attention to the exposed raw surface wound, after the first stage of an operation. This article describes the authors' modification to overcome this problem, using artificial collagen membrane. Methods: An Artificial collagen membrane is composed of an outer silicone membrane and an inner collagen layer. After a forehead flap elevation, the expected raw surface was covered by an artificial collagen membrane with 5-0 nylon suture. A simple dressing, which had been applied to the site, was changed every 2 or 3 days in an outpatient unit. At 3 weeks postprocedure, a second stage operation was performed. Results: With biosynthetic protection of the raw surface, there were no wound problems, such as infection or flap loss. Thus, the patient was satisfied due to an effortless management of the wound and a reduction in pain. Conclusion: The application of an artificial collagen membrane to the raw under-surface of the flap could be a comfortable and a protective choice for this procedure.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.643-651
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• 2007

The various blood pressure simulators have been proposed to evaluate and improve the performance of the automatic sphygmomanometer. These have some problems such as the deviation of the actual blood pressure waveform, limitation in the blood pressure condition of the simulator, or difficulty in displaying the blood flow. An improved simulator using disturbance observer is proposed to supplement the current problems of the blood pressure simulator. The proposed simulator has an artificial arm model capable of feeding appropriate fluids that can generate the blood pressure waveform to evaluate the automatic sphygmomanometer. A controller was designed and thereafter, simulation was performed to control the output signal with respect to the reference input in the fluid dynamic model using the proposed proportional control valve. To minimize the external fluctuation of pressure applied to the artificial arm, a disturbance observer was designed on the plant. A hybrid controller combined with a proportional controller and feed-forward controller was fabricated after applying a disturbance observer to the control plant. Comparison of the simulations between the conventional proportional controller and the proposed hybrid controller indicated that even though the former showed good control performance without disturbance, it was affected by the disturbance signal induced by the cuff. The latter exhibited an excellent performance under both situations.

• Journal of Korean society of Dental Hygiene
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• v.18 no.3
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• pp.269-280
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• 2018

Objectives: The purpose of this study was to investigate the change in the posture of dental hygiene students and clinical dental hygienists when implementing dental scaling before and after posture correction training using the rapid upper limb assessment (RULA) method and 3D motion analysis. Methods: Thirty-two healthy volunteers performed dental scaling to remove artificial calculus on dental manikin. The movement and angle of the joints were verified by RULA and 3D motion analysis during the procedure. The subjects were also photographed for 1 minute during the procedure for 10 minutes while the calculus was removed. After the removal of the calculus, the subject and the instructor checked the video together. Posture correction training was conducted by the instructor so that the subject could perform the calculus removal operation in the correct posture. Artificial calculus of the adjacent teeth was then removed for the same period of time, and the change in posture was reviewed. Results: The total score of the posture change using RULA was $5.72{\pm}0.58$ before training and $4.31{\pm}0.10$ after training, showing a significant decrease after training (p<0.001), and upper arm, lower arm, wrist position, neck and waist position showed significant decrease after training. The three-dimensional motion analysis showed significant differences according to the criteria measured at all measurement sites except the left shoulder (p<0.05) Conclusions: It was confirmed through RULA and 3D motion analysis that postural correction training using calculus removal images was effective, and that correct postural education is essential to preventing musculoskeletal diseases caused by removal of calculus.

• Journal of Korean Neurosurgical Society
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• v.50 no.3
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• pp.252-255
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• 2011

Authors report a case of a painless acute dissecting aneurysm of the descending aorta in a patient who presented with unexplained hypotension followed by simultaneous paraplegia and right arm monoparesis. To our knowledge, case like this has not been reported previously. Magnetic resonance imaging of the brain and spine revealed hemodynamic cerebral infarction and extensive cord ischemia, respectively. Computerized tomography angiography confirmed a dissecting aneurysm of the descending aorta. The cause of the brain infarction may not have been embolic, but hemodynamic one. Dissection-induced hypotension may have elicited cerebral perfusion insufficiency. The cause of cord ischemia may be embolic or hemodynamic. The dissected aorta was successfully replaced into an artificial patch graft. The arm monoparesis was improved, but the paraplegia was not improved. In rare cases of brain and/or spinal cord infarction caused by painless acute dissecting aneurysm of the aorta, accurate diagnosis is critical because careless thrombolytic therapy can result in life-threatening bleeding.

• Smart Structures and Systems
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• v.20 no.6
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• pp.657-668
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• 2017

In this research, an active vibration suppression of a smart beam having piezoelectric sensor and actuators is investigated by designing separate controllers comprising a linear quadratic regulator and a neural network. Firstly, design of a smart beam which consists of a cantilever aluminum beam with surface bonded piezoelectric patches and a designed mechanism having a micro servomotor with a mass attached arm for obtaining variations in the frequency response function are presented. Secondly, the frequency response functions of the smart beam are investigated experimentally by using different piezoelectric patch combinations and the analytical models of the smart beam around its first resonance frequency region for various servomotor arm angle configurations are obtained. Then, a linear quadratic regulator controller is designed and used to simulate the suppression of free and forced vibrations which are performed both in time and frequency domain. In parallel to simulations, experiments are conducted to observe the closed loop behavior of the smart beam and the results are compared as well. Finally, active vibration suppression of the smart beam is investigated by using a linear controller with a neural network based adaptive element which is designed for the purpose of overcoming the undesired consequences due to variations in the real system.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.1
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• pp.73-80
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• 2000

To determine the exact direction and location of the human joint in motion is crucial in developing a more accurate human model and producing a more fitting artificial joint. There have been several reports on the biomechanical analysis of the joint to determine the anatomy and movement of joints. However, all the previous researches were made in vitro study, that is, they investigated the passive movement of the joint from cadavers and the suggested location of the joint axis was difficult to make practical applications due to the lack of the direction of joint axis. Also, in many biomechanical models, each joint axis is assumed to lie horizontally or vertically to the adjacent links. Such an assumption causes inherent inaccuracy. In this study, the direction and location of the transverse elbow axis was obtained with respect to the global coordinate system whose origin is on the lateral epicondyle of the humerus. The suggested result based on the global coordinate system lying on the external landmark will be helpful to understand the information of the axis and to make an application. From the experiments conducted for five subjects, the direction and location of the elbow transverse joint was determined for each subject by the helical axis method. A statistical validation was also performed to confirm the result. Finally, the result was applied to develop a simple elbow model which is a part of the kinematic arm model. The simple elbow movement model was developed to validate the significance of the result and the kinematic arm model was able to describe the geometry of any complex linkage system. As a result, the errors incurred from the proposed model were significantly reduced when compared to the ones from the previous approach.

• Composites Research
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• v.35 no.5
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• pp.303-308
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• 2022

PIC (Piecewise Integrated Composite) is a new concept for designing a composite structure with mosaically assigning various types of stacking sequences in order to improve mechanical properties of laminated composites. Also, machine learning is a sub-category of artificial intelligence, that refers to the process by which computers develop the ability to continuously learn from and make predictions based on data, then make adjustments without further programming. In the present study, the tapered box beam type PIC robot arm for carrying and transferring wide and thin LCD display was designed based on the machine learning in order to increase structural stiffness. Essential training data were collected from the reference elements, which were intentionally designated elements among finite element models, during preliminary FE analysis. Additionally, triaxiality values for each finite element were obtained for judging the dominant external loading type, such as tensile, compressive or shear. Training and evaluating machine learning model were conducted using the training data and loading types of elements were predicted in case the level accuracy was fulfilled. Three types of stacking sequences, which were to be known as robust toward specific loading types, were mosaically assigned to the PIC robot arm. Henceforth, the bending type FE analysis was carried out and its result claimed that the PIC robot arm showed increased stiffness compared to conventional uni-stacking sequence type composite robot arm.

• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.243-249
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• 2005

The number of people who have lost limbs due to amputation has increased due to various accidents and diseases. Numerous attempts have been made to provide these people with prosthetic devices. These devices are often controlled using myoelectric signals. Although the success of fitting myoelectric signals (EMG) for single device control is apparent, extension of this control to more than one device has been difficult. The lack of success can be attributed to inadequate multifunctional control strategies. Therefore, the objective of this study was to develop multifunctional myoelectric control strategies that can generate a number of output control signals. We demonstrated the feasibility of a neural network classification control method that could generate 12 functions using three EMG channels. The results of evaluating this control strategy suggested that the neural network pattern classification method could be a potential control method to support reliability and convenience in operation. In order to make this artificial neural network control technique a successful control scheme for each amputee who may have different conditions, more investigation of a careful selection of the number of EMG channels, pre-determined contractile motions, and feature values that are estimated from the EMG signals is needed.