• Journal of Sensor Science and Technology
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• v.17 no.4
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• pp.295-302
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• 2008

$TiO_2,\;ZrO_2$, and $SiO_2$ were added in the concentration of 1 - 3 wt.% to improve long-term stability for the $SnO2$ thick film gas sensor. Short-term sensor resistances up to 90 h were measured to investigate the stabilization time of initial resistance in air. Long-term resistance drifts in air and in gas to 5000 ppm methane for the sensors annealed at $750^{\circ}C$ for 1 h and continuously heated at an operating temperature of $400^{\circ}C$ were also measured up to 90 days at an interval of 1 day. The long-term drifts in methane sensitivity for the three metal oxide-doped $SnO2$ sensors are closely related to methane sensitivity level, catalytic activity, and long-term drift in sensor resistance in air. Those stabilities are mainly discussed in terms of oxidation state and catalytic activity.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.186-191
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• 2018

As influence of the 4th industry is growing with development of information society more electronic devices and sensor are used in the field. As this is the case, importance of signal processing during data transfer is rising Furthermore, the need for technology to remove noise caused by various reasons and to stabilize sensor output is growing as well. This research suggests digital filter algorithm that efficiently remove noise by stabilizing output of accelerating sensor. The standard value of this algorithm is calculated by applying Gaussian coefficient. To maintain its feature, final output is obtained by subtracting weight depending on variance from standard value For its evaluation, it is compared with other protocols and its function is checked through output features.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.600-603
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• 2004

UAV (Unmanned Aerial Vehicle) is an aerial vehicle that can accomplish the mission without pilot. UAV was developed for a military purpose such as a reconnaissance in an early stage. Nowadays usage of UAV expands into a various field of civil industry such as a drawing a map, broadcasting, observation of environment. These UAV, need vision system to offer accurate information to person who manages on ground and to control the UAV itself. Especially LOS(Line-of-Sight) system wants to precisely control direction of system which wants to tracking object using vision sensor like an CCD camera, so it is very important in vision system. In this paper, we propose a method to recognize object from image which is acquired from camera mounted on gimbals and offer information of displacement between center of monitor and center of object.

• The Journal of Korea Robotics Society
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• v.6 no.1
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• pp.18-26
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• 2011

This paper presents a method, Exposure Controlled Temporal Filtering (ECF), applied to visual motion tracking, that can cancel the temporal aliasing of periodic vibrations of cameras and fluctuations in illumination through the control of exposure time. We first present a theoretical analysis of the exposure induced image time integration process and how it samples sensor impingent light that is periodically fluctuating. Based on this analysis we develop a simple method to cancel high frequency vibrations that are temporally aliased onto sampled image sequences and thus to subsequent motion tracking measurements. Simulations and experiments using the 'Center of Gravity' and Normalized Cross-Correlation motion tracking methods were performed on a microscopic motion tracking system to validate the analytical predictions.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.129-137
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• 2008

Hull-Mounted Sonar(HMS) has been the main equipment to detect and track underwater threats like torpedoes and enemy submarines. The HMS has short warming-up time and is employable independently with sea-state and weather condition. But these bad environmental condition and ship maneuvering make ship's roll and pitch. Ship's roll and pitch make unstability of sensor position, then cause degradation of the HMS performance. In this paper, we will show how the unstability influences the HMS performance, propose the 'Beam Stabilization Beamforming Technique' to overcome these phenomenon. And present the effectiveness of proposed technique by comparing with conventional beamforming result.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.221-225
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• 1998

This paper is to present a method for recognizing an image of a tracking object by processing the image from a camera, whose attitude is controlled in inertial space with inertial co-ordinate system. In order to recognize an object, a pseudo-random M-array is attached on the object and it is observed by the camera which is controlled on inertial coordinate basis by inertial stabilization unit. When the attitude of the camera is changed, the observed image of M-array is transformed by use of affine transformation to the image in inertial coordinate system. Taking the cross-correlation function between the affine-transformed image and the original image, we can recognize the object. As parameters of the attitude of the camera, we used the azimuth angle of camera, which is de-fected by gyroscope of an inertial sensor, and elevation an91e of camera which is calculated from the gravitational acceleration detected by servo accelerometer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.627-630
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• 2009

The combination of the marine use various multi sensor surveillance system technology with the development of servo motion control algorithm and gyro sensor in six freedom motion is implemented to analyze the movement response. The stabilization of the motion control is developed and Nano driving Precision Pan-Tilt/Gimbal system is obtained from the security positioning cameras with ultra high speed device is used to carry out the exact behavior of the device. The exact behavior will be used to make a essential equipment. Finally the development of the Nano Driving Multi Sensor, Nano of Surveillance System Driving Precision Pan-Tilt/Gimbal optimal design and production, 3-aix Gyro Sensor based with Servo Motion Control algorithm development, Image trace video software and hardware tracking the development is organized and discuss in details. The development of the equipment and the system integration are fully experimented and verified.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.8
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• pp.703-710
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• 2016

In robotics research, accurate estimation of current robot position is important to achieve motion control of a robot. In this research, we focus on a sensor fusion method to provide improved position estimation for a wheeled mobile robot, considering two different sensor measurements. In this case, we fuse camera-based vision and encode-based odometry data using Kalman filter techniques to improve the position estimation of the robot. An external camera-based vision system provides global position coordinates (x, y) for the mobile robot in an indoor environment. An internal encoder-based odometry provides linear and angular velocities of the robot. We then use the position data estimated by the Kalman filter as inputs to the motion controller, which significantly improves performance of the motion controller. Finally, we experimentally verify the performance of the proposed sensor fused position estimation and motion controller using an actual mobile robot system. In our experiments, we also compare the Kalman filter-based sensor fused estimation with two different single sensor-based estimations (vision-based and odometry-based).

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.95-102
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• 2016

Geomagnetic sensors are widely utilized for sensing heading direction of quadrotors. However, measurement from a geomagnetic sensor is easily corrupted by environmental magnetic field interference and roll/pitch directional motion. In this paper, a measurement method of a quadrotor heading direction is proposed for application to yaw attitude control. In order to eliminate roll/pitch directional motion effect, the geomagnetic sensor data is compensated using the roll/pitch angles measured for stabilization control. In addition, yaw-directional angular velocity data from a gyroscope sensor is fused with the geomagnetic sensor data using a complementary filter which is a simple and intuitive sensor fusion method. The proposed method is applied to experiments, and the results are presented to prove validity and effectiveness of the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.569-577
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• 2015

The navigation algorithm developed based on the extended Kalman filter (EKF) sometimes diverges when the linearity between the measurements and the states is not preserved. In this study, new update conditions together with two conditions from previous study for gravity gradient referenced navigation (GGRN) were deduced for the filter performance. Also, the effect of each update conditions was evaluated imposing the various magnitudes of the database (DB) and the sensor errors. In case the DB and the sensor errors were supposed to 0.1 Eo and 0.01 Eo, the navigation performance was improved in the eight trajectories by using part of gravity gradient components that independently estimate states located within trust boundary. When applying only the components showing larger variation, around 200% of improvement was found. Even the DB and sensor error were supposed to 3 Eo, six update conditions improved performance in at least seven trajectories. More than five trajectories generated better results with 5 Eo error of the DB and the sensor. Especially, two update conditions successfully control divergence, and bounded the navigation error to the 1/10 level. However, these update conditions could not be generalized for all trajectories so that it is recommended to apply update conditions at the stage of planning, or as an index of precision of GGRN when combine with various types of geophysical data and algorithm.