• Journal of Sensor Science and Technology
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• v.21 no.2
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• pp.127-134
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• 2012

Stroke patients must do the rehabilitation exercise to recover their fingers' function using a rehabilitation robot. But the rehabilitation robots mostly have not the force sensors to control the applied force to each finger. Thus, in this paper, the development of a force sensor for thumb rehabilitation robot and four two-axis force sensors for four-finger rehabilitation robot were developed. The force sensor and four two-axis force sensors could be used to measure the applied force to each finger, and the forces could be used to control the applied forces to each sensor in rehabilitation exercise using in the rehabilitation robot. The developed sensors have non-linearlity error of less than 0.05 %, repeatability error of less than 0.03 %, and the interference error of two-axis force sensor is less than 0.2 %.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.42-46
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• 2021

In this paper, we describe the development of a force sensor to measure the tool gripping force of the BT50 spindle. The force sensor for a BT50 must be installed inside the gripping force tester; hence, it must be of an appropriate size and have a rated capacity suitable for measuring the gripping force. So, the structure of the force sensor for BT50 was modeled, the size of the sensing part was determined by structural analysis, and the force sensor was manufactured by attaching a strain gauge. The characteristic test results of the manufactured force sensor, indicated that the nonlinearity error, hysteresis error, and reproducibility errors were each within 0.91%, Therefore it was determined that the manufactured force sensor can be used for checking the spindle tool gripping force.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.2
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• pp.211-217
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• 2010

It is important to measure the mechanical properties of nanostructures because they are required to determine the lifetime and reliability of nanodevices developed for various fields. In this study, tensile tests for a multi-walled carbon nanotube (MWCNT) and a ZnO nanorod were performed in a scanning electron microscope (SEM). The force sensor was a cantilever type and was mounted in front of a nanomanipulator placed in the chamber. The nanomanipulator was controlled using a joystick and personal computer. The nanostructures dispersed on the cut area of a transmission electron microscope (TEM) grid were gripped with the force sensor by exposing an electron beam in the SEM; the tensile tests were the performed. The in situ tensile loads of the nanostructure were obtained. After the tensile test, the cross-sectional areas of the nanostructures were observed by TEM and SEM. Based on the TEM and SEM results, the elastic modulus of the MWCNT and ZnO nanorod were calculated to be 0.98 TPa and 55.85 GPa, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.221-228
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• 2022

With the application of artificial intelligence technology, robots can provide efficient services in real life. Unlike industrial manipulators that do simple repetitive work, this study presented design methods of 6 degree of freedom robot arm and intelligent object search and movement methods for use alone or in collaboration with no place restrictions in the service robot field and verified performance. Using a depth camera and deep learning in the ROS environment of the embedded board included in the robot arm, the robot arm detects objects and moves to the object area through inverse kinematics analysis. In addition, when contacting an object, it was possible to accurately hold and move the object through the analysis of the force sensor value. To verify the performance of the manufactured robot arm, experiments were conducted on accurate positioning of objects through deep learning and image processing, motor control, and object separation, and finally robot arm was tested to separate various cups commonly used in cafes to check whether they actually operate.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.3 no.1
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• pp.7-13
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• 2009

Recently, the interest on walking assistances in order to assist aged people has increased due to the increase of the aged. However, most walking aid systems have a weakness for a slope because they don't have power. So, they have a weak point which makes users difficult to move when they are weak in the legs. That is why the interest on walking assistances with power has increased. The use of the walking aid systems should be easy because most users are old people. Thus, we produce module to grasp walking intent of users by using various sensors such as potentiometer, FSR(Force Sensing Resistance) Sensor and Stretch Sensor and calculate the response time to the module. Firstly, the response time of handlebar which is a kind of potentiometer is 420ms and Resilience of it is 140ms. Secondly, the response time of handlebar which use FSR Sensor is 320ms and Resilience of it is 220ms. Finally, the response time of the Stretch Sensor is 160ms and Resilience of it is 140ms. The performance of Stretch Sensor is the best among the three kind of sensors.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.187.2-187.2
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1061-1062
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• 2011

• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.910-917
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• 2007

Of the possible interactions between human and robot, touch is an important means of providing human beings with emotional relief. However, most previous studies have focused on interactions based on voice and images. In this paper. a method of recognizing human touching behaviors is proposed for developing a robot that can naturally interact with humans through touch. In this method, the recognition process is divided into pre-process and recognition Phases. In the Pre-Process Phase, recognizable characteristics are calculated from the data generated by the touch detector which was fabricated using force sensors. The force sensor used an FSR (force sensing register). The recognition phase classifies human touching behaviors using a multi-layer perceptron which is a neural network model. Experimental data was generated by six men employing three types of human touching behaviors including 'hitting', 'stroking' and 'tickling'. As the experimental result of a recognizer being generated for each user and being evaluated as cross-validation, the average recognition rate was 82.9% while the result of a single recognizer for all users showed a 74.5% average recognition rate.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.1
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• pp.64-70
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• 1999

When manipulator moves the objects, the object position error can be occurred because of acceleration or negative acceleration according to the direction. So we make manipulator working path for establishing optimal gripper force control preventing occurrence of object position error. And we attached the tactile sensor on the gripper of manipulator which gives us very specific information between manipulator and object. Reasoning of continuous tactile image data, manipulator can sense rotation and slippage and change the grasping force that corrects calculated grasping force and compensation can be possible of the object position error. We use the FSR(Force Sensing Resistor)sensor which consists of 22 by 22 taxels and continuous taxel number is used for filtering and using the moment method for sensing algorithm in our experiment.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.1061-1064
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• 2015

본 논문은 근전도 센서가 탑재된 손목착용형 웨어러블 디바이스를 통하여 스마트폰의 입력방식을 개선시켜 사용자와 스마트폰간의 인터렉션을 보다 발전시킨 시스템을 다룬다. 이 시스템은 사용자가 손목에 착용한 웨어러블 디바이스에 탑재된 근전도센서를 통하여 손의 고유근의 근전도를 측정함으로서 사용자의 엄지와 검지에 가해지는 힘을 탐지하고 이를 바탕으로 사용자가 스마트폰을 터치했을 경우 해당 터치행위가 사용자가 스마트폰을 의도적으로 힘을 가하여 누른 행위인지에 대한 여부를 판단할 수 있다. 결과적으로 본 논문은 스마트폰과 사용자와의 인터렉션에 있어 근전도 센서를 탑재한 웨어러블 디바이스를 활용하여 사용자의 의도를 보다 효과적으로 스마트폰 제어에 반영시킬 수 있는 행위 인지(Activity Recognition) 터치 인터페이스 시스템을 제안한다.