• Journal of Biosystems Engineering
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• v.33 no.3
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• pp.186-195
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• 2008

In this paper, a prototype tele-operative system with a mobile base was developed in order to automate cultivation of house melon. A man-machine interactive hybrid decision-making system via tele-operative task interface was proposed to overcome limitations of computer image recognition. Identifying house melon including position data from the field image was critical to automate cultivation. And it was not simple especially when melon is covered partly by leaves and stems. The developed system was composed of 5 major modules: (a) main remote monitoring and task control module, (b) wireless remote image acquisition and data transmission module, (c) three-wheel mobile base mounted with a 4 dof articulated type robot manipulator (d) exchangeable modular type end tools, and (e) melon storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. Once task was selected from the task control and monitoring module, the analog signal of the color image of the field was captured and transmitted to the host computer using R.F. module by wireless. A sequence of algorithms to identify location and size of a melon was performed based on the local image processing. Laboratory experiment showed the developed prototype system showed the practical feasibility of automating various cultivating tasks of house melon.

• Physical Therapy Rehabilitation Science
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• v.8 no.2
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• pp.93-98
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• 2019

Objective: The purpose of this study was to investigate the effect of robot arm reach training on upper extremity functional movement in chronic stroke survivors. Design: One group pretest-posttest design. Methods: Thirteen chronic stroke survivors participated in this study. Robot arm reach training was performed with a Whole Arm Manipulator (WAM) and a 120-inch projective display to provide visual and auditory feedback. During the robotic arm reach training, WAM provided gravity compensation and assist-as-needed (AAN) force according to the robot control mode. When a participant could not move the arm toward the target for more than 2 seconds, WAM provided AAN force to reach the desired targets. All patients participated in the training for 40 minutes per day, 3 times a week, for 4 weeks. Main outcome measures were the Fugl-Meyer Assessment (FMA), Action Research Arm Test (ARAT) and Box and Block Test (BBT) to assess upper extremity functional movement. Results: After 4 weeks, significant improvement was observed in upper extremity functional movement (FMA: 42.15 to 46.23, BBT: 12.23 to 14.00, p<0.05). In the subscore analysis of the FMA upper extremity motor function domains, significant improvement was observed in upper extremity and coordination/speed units (p<0.05). However, there were no significant differences in the ARAT. Conclusions: This study showed the positive effects of robot arm reach training on upper extremity functional movement in chronic stroke survivors. In particular, we confirmed that robot arm reach training could have a positive influence by leading to improvement of motor recovery of the proximal upper extremity.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.8
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• pp.1203-1211
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• 2009

The dynamics of underwater vehicles can be greatly influenced by the dynamics of the vehicle thrusters. The control of the state of the hovering or very slow motion of the underwater vehicle is most important for automatic docking or control of the manipulator of the vehicle. The dynamics of the thruster based on the electric motor is nonlinear and has uncertain parameters. Since the dynamics of the vehicle coupled with the dynamics of the thruster is nonlinear and has uncertain parameters, a robust control is very effective for a desired motion tracking of the uncertain and nonlinear vehicle. In this paper a study was performed on the robust control scheme of the very slow motion or hovering motion of the underwater vehicle actuated by the electric motor. Also, a concurrent control on the state of the vehicle with nonlinearity and uncertain parameters was performed. A sliding mode control algorithm out of robust controllers was designed and applied, which compensates the nonlinear forces and uncertain parameters of the vehicle and actuator. Through a computer simulation, the proposed control scheme was compared with a linear PD controller and its superior performance was validated.