• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.11
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• pp.2058-2065
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• 2010

This paper presents the sensor location and step estimation algorithm for personal navigation system (PNS). PNS has the disadvantage in that the position of the sensor must be fixed on a human body. Three-axis acceleration sensor is used to solve the disadvantage and to consider the real situation. We simplify the measurement data by using the band pass filter, witch It has the advantage in the detection of characteristic point. Through the detected characteristic points, it is possible to setup the parameter for the pattern detection. Depending on the sensor location, the parameters have the different type of noise covariance. Particularly, when the position of the sensor is changed, the impulse noise shows up. Considering the noise, we apply the recursive least square algorithm using the variable forgetting factors, which can classify the sensor location based on the estimated parameters. We performed the experiment for the verification of the proposed algorithm in the various environments. Through the experimental results, the effectiveness of the proposed method is verified.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1167-1172
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• 2015

This paper describes the implementation of wearable AHRS for an electromagnetic motion capture system that can trace and analyze human motion on the principal nine axes of inertial sensors. The module provides a three-dimensional (3D) attitude and heading angles combining MEMS gyroscopes, accelerometers, and magnetometers based on the extended Kalman filter, and transmits the motion data to the 3D simulation via Wi-Fi to realize the unrestrained movement in open spaces. In particular, the accelerometer in AHRS is supposed to measure only the acceleration of gravity, but when a sensor moves with an external linear acceleration, the estimated linear acceleration could compensate the accelerometer data in order to improve the precision of measuring gravity direction. In addition, when an AHRS is attached in an arbitrary position of the human body, the compensation of the axis of rotation could improve the accuracy of the motion capture system.

• Journal of Practical Engineering Education
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• v.10 no.2
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• pp.119-124
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• 2018

Educational equipment that enables the user or device to recognize the indoor position by using the acceleration and angular velocity of the IMU (Inertial Measurement Unit) sensor is introduced. With this educational equipment, various position recognition and tracking algorithms can be learned and creative engineering design works can be realized. The data value of the IMU sensor is transmitted to the MCU (microcontroller unit) through $I^2C$ (Inter-Integrated Circuit), and the indoor position recognition algorithm is applied by processing the data value through the filter and numerical method. It is then designed to use wireless communication to send and receive processed values and to be recognized by the user. As an example using this equipament, the case of "Implementation and recognition of virtual position using computation of moving direction and distance using IMU sensor" is introduced, and various creative engineering design application is discussed.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1046-1048
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• 2003

Recently, various researches in respect of the positioning technologies using satellites and the other sensors have made location-based services (LBS) more common and accurate. Consequently, concern about position information has been increasing. However, since these positioning systems only focus on user's position, it is difficult to know the user's attitude or detailed behaviors at the specific position. It is worthy to study on how to acquire such human attitude or behavior, because those information is useful to know the context of the user. In this paper, the sensor unit consisting of three dimensional accelerometer was attached to human body, and autonomously measured the perpendicular acceleration of ordinary human behaviors including activity modes such as walking, running, and transportation mode using transportation such as a train, a bus, and an elevator. Subsequently, using the classified measurement results, the method to identify the human activity modes was proposed.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1733-1734
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• 2008

We develope a riding robot system named as "RideBot" for health-care and entertainments. An algorithm for estimating riding position and volition is proposed by using bio-signals. We analyze the riding position and volition in real-horse riding environments and build up the database. With this database and sensor informations, standard positions are made. For the volition estimation, we use the acceleration and deceleration sensor information and bridle information for direction change. We propose a hybrid control algorithm in which discrete-state and continuous-state controls are combined. The efficiency of the proposed algorithm is evaluated thru various experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.651-656
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• 2014

This paper presents a compensation method for an accelerometer to measure acceleration data accurately when a robot manipulator moves slowly. Although the accelerometer works fine under the fast movement of a robot manipulator, low cost accelerometers provide relatively inaccurate acceleration data under slow movements. In order to correct the error of the sensor data in the slow motion, correction factors are obtained experimentally. Then those corrected data are used for the disturbance observer. Experimental studies of the position control of a robot manipulator are conducted by applying the DOB (Disturbance Observer) control using corrected acceleration data.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.5
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• pp.591-598
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• 2011

Tip position control of a flexible cantilever is difficult due to the non-minimum phase dynamics that result from the finite propagating speed of a mechanical wave along the cantilever. In this paper, we propose a method for the tip position control using a light and cheap accelerometer that does not bring any significant change to the dynamics of the cantilever system. The linear system identification model of the flexible cantilever is obtained with measurements by a laser displacement sensor. A Kalman estimator is designed with this model and calculates the estimated tip position with the acceleration data of the accelerometer that is attached on the tip of the cantilever. To verify reliability of the estimator, the estimated tip position is used to the feedback control system that uses a fuzzy logic controller. The control results are compared with those of the fuzzy control system where the real tip position is measured by a laser displacement sensor. Also, the performance of the estimator with the accelerometer is presented and discussed.

• Journal of Sensor Science and Technology
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• v.27 no.3
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• pp.165-169
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• 2018

A single-axis Cubli frame realized simply with an IMU sensor and DC motor is presented herein. To maintain the balance on the Cubli frame, an LQR controller based on a Lagrangian derivation of the dynamics was designed, which utilized the state variables of the frame angle and its angular acceleration, as well as the wheel angle and its angular acceleration. The designed LQR controller showed a settling time balancing capability of approximately two seconds and 40% of the maximum overshoot in Matlab/Simulink simulations. Our experimental results of the fabricated Cubli frame matched with the simulation results. It maintained balancing at the reference position even though an initial offset as well as external disturbance during the balancing was applied.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.31-36
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• 2009

This paper presents a novel scalar adaptive filter, which is reformulated by additional acceleration term. The filter continuously estimates three different kinds of covariance such as the measurement noise covariance, the velocity error covariance and the acceleration error covariance. For estimating three covariances, we use the innovation method for the measurement noise covariance and the least square method for other covariances. In order to verify the proposed filter performance compared with the conventional scalar adaptive filter, we make indoor experimental environment similar to outdoor test using the ultrasonic sensors instead of GPS. Experimental results show that the proposed filter has better position accuracy than the traditional scalar adaptive filter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.394-397
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• 2012

Wireless sensor network is configured to be able to collect information collected from the various sensors, real-world environment wireless network. In this paper, the information collected from the wireless sensor networks in the real world using AR (Augmented Reality) data by projecting, in a variety of ways, the information collected from the wireless sensor network is utilized, which more intuitive is intended to provide to users. In this paper, stored in the DB table and the information collected from the wireless sensor network, wireless LAN (Access Point) that are placed in the sensor field and the terminal attached to the geomagnetic sensor, terminal position, after correction using acceleration sensors and augmented reality the aim is to visualize the data stored in the DB table using.