• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.1325-1328
• /
• 2007

When we visualize the sound field radiated from a spherical sound source, spherical acoustic holography is proper among acoustic holography methods. However, there are measurement errors due to sensor position mismatch, sensor mismatch, directivity of sensor, and background noise. These errors are amplified if one predicts the pressures close to the sources: backward prediction. The goal of this paper is to quantitatively examine the effects of the error due to sensor position mismatch on acoustic pressure estimation. This paper deals with the cases of which the measurement deviations are distributed irregularly on the hologram plane. In such cases, one can assume that the measurement is a sample of many measurement events, and the cause of the measurement error is white noise on the hologram plane. Then the bias and random error are derived mathematically. In the results, it is found that the random error is important in the backward prediction. The relationship between the random error amplification ratio and the measurement parameters is derived quantitatively in terms of their energies.

• Journal of KSNVE
• /
• v.8 no.6
• /
• pp.1023-1029
• /
• 1998

When one attempts to construct a hologram. one finds that there are many sources of measurement errors. These errors are even amplified if one predicts the pressures close to the sources. The pressure estimation errors depend on the following parameters: the measurement spacing on the hologram plane. the prediction spacing on the prediction plane. and the distance between the hologram and the prediction plane. This raper analyzes quantitatively the errors when these are distributed irregularly on the hologram plane The sensor mismatch and inaccurate measurement location. position mismatch. are mainly addressed. In these cases. one can assume that the measurement is a sample of many measurement events. The bias and random error are derived theoretically. Then the relationship between the random error amplification ratio and the parameters mentioned above is examined quantitatively in terms of energy.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.1
• /
• pp.99-107
• /
• 2014

In this paper, a landmark based localization system using a Kinect sensor is proposed and evaluated with the implemented system for precise and autonomous navigation of low cost robots. The proposed localization method finds the positions of landmark on the image plane and the depth value using color and depth images. The coordinates transforms are defined using the depth value. Using coordinate transformation, the position in the image plane is transformed to the position in the body frame. The ranges between the landmarks and the Kinect sensor are the norm of the landmark positions in body frame. The Kinect sensor position is computed using the tri-lateral whose inputs are the ranges and the known landmark positions. In addition, a new matching method using the pin hole model is proposed to reduce the mismatch between depth and color images. Furthermore, a height error compensation method using the relationship between the body frame and real world coordinates is proposed to reduce the effect of wrong leveling. The error analysis are also given to find out the effect of focal length, principal point and depth value to the range. The experiments using 2D bar code with the implemented system show that the position with less than 3cm error is obtained in enclosed space($3,500mm{\times}3,000mm{\times}2,500mm$).

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.9
• /
• pp.716-722
• /
• 2016

Coordinate estimation is an essential function for autonomous navigation of a mobile robot. The optical mouse sensor is convenient and cost-effective for the coordinate estimation problem. It is possible to overcome the position estimation error caused by the slip and the model mismatch of robot's motion equation using the optical mouse sensor. One of the simple methods for the position estimation using the optical mouse sensor is integration of the velocity data from the sensor with time. However, the unavoidable noise in the sensor data may deteriorate the position estimation in case of the simple integration method. In general, a mobile robot has ready-to-use motion information from the encoder sensors of driving motors. By combining the velocity data from the optical mouse sensor and the motion information of a mobile robot, it is possible to improve the coordinate estimation performance. In this paper, a coordinate estimation algorithm for an autonomous mobile robot is presented based on the well-known Kalman filter that is useful to combine the different types of sensors. Computer simulation results show the performance of the proposed localization algorithm for several types of trajectories in comparison with the simple integration method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.11
• /
• pp.1645-1650
• /
• 2015

This paper presents the 3-dimensional electromagnetic field analysis method and correction of sensor distortion that is used by a motor speed sensor. The magnetic sensors are being expanded due to lower price than the other speed sensors such as resolver and encoder. Magnetic sensor generates sine and cosine waves when the motor rotates. However, the sine and cosine signals are distorted due to magnetic noise, which makes the angle error of the sensor, generated near by the Hall element. This paper defines an optimal design variables by using the Taguchi method to minimize output distortion of the magnetic sensor and permanent magnet. To enhance reliability of the magnetic position sensor from sensitivity error, assembly amplitude mismatch and the electrical angle, 3-Dimensional electromagnetic finite element method and correction algorithm errors were performed in due of the magnetic sensor in order to improve the quality of the initial production model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.05a
• /
• pp.1197-1202
• /
• 2001

Near-field acoustic holography is a powerful tool to visualize sound sources. This method requires pressure measurement at many points for a good hologram. Thus one has to measure carefully so that errors due to the uncertainty of position, sensor mismatch, and so on are reduced. A method to solve this problem is to use a well-designed measurement system. This paper introduces a sound visualization system at center for noise and vibration control (NOVIC), KAIST, and addresses the advantages in terms of the error reduction. The system consists of array microphones, array jigs, a system to control the position and the velocity of the jigs, a data acquisition system, and a monitoring system. This paper also shows some sound visualization results when the system is applied to a speaker and a computer. The results verifies that the sound visualization system is useful for identifying sound sources.