• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.4
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• pp.447-459
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• 2013

Many problems need to be solved before vision systems can actually be applied in industry, such as the precision of the kinematics model of the robot control algorithm based on visual information, active compensation of the camera's focal length and orientation during the movement of the robot, and understanding the mapping of the physical 3-D space into 2-D camera coordinates. An algorithm is proposed to enable robot to move actively even if the relative positions between the camera and the robot is unknown. To solve the correction problem, this study proposes vision system model with six camera parameters. To develop the robot vision control algorithm, the N-R and EKF methods are applied to the vision system model. Finally, the position accuracy and processing time of the two algorithms developed based based on the EKF and the N-R methods are compared experimentally by making the robot perform slender bar placement task.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.87-94
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• 2017

Many industrial accidents have occurred continuously in the manufacturing industries, construction industries, and service industries of Korea. Fatal accidents have occurred most frequently in the construction industries of Korea. Especially, the trend analysis of the accident rate and fatal accident rate is very important in order to prevent industrial accidents in the construction industries systematically. This paper considers forecasting of the accident rate and fatal accident rate with static and dynamic time series analysis methods in the construction industries. Therefore, this paper describes the optimal accident rate and fatal accident rate by minimization of the sum of square errors (SSE) among regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM), and kalman filtering model (KFM) with existing accident data in construction industries. In this paper, microsoft foundation class (MFC) soft of Visual Studio 2008 was used to predict the accident rate and fatal accident rate. Zero Accident Program developed in this paper is defined as the predicted accident rate and fatal accident rate, the zero accident target time, and the zero accident time based on the achievement probability calculated rationally and practically. The minimum value for minimizing SSE in the construction industries was found in 0.1666 and 1.4579 in the accident rate and fatal accident rate, respectively. Accordingly, RAM and ARIMA model are ideally applied in the accident rate and fatal accident rate, respectively. Finally, the trend analysis of this paper provides decisive information in order to prevent industrial accidents in construction industries very systematically.

• Korean Business Review
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• v.19 no.1
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• pp.81-92
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• 2006

This study investigates the relation between institutional investor's net purchase and the volatility of KOSPI. Some portion of volatility in stock prices comes from noise trading of irrational traders. Observed volatility may be defined as the sum of the portion caused by information arrival, fundamental volatility, and the portion caused by noise trading, transitory volatility. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. Most studies investigates the effect on the observed volatility. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Estimation results show that institutional investor's net purchase was not significantly related to all kinds of volatility(observed volatility, fundamental volatility and transitory volatility). This means that institutional investor's net purchase did not increase noise trading.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.25-33
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• 2010

In this paper, we propose a simple and efficient algorithm for head pose estimation using a single camera. First, four subimages are obtained from the camera image for face feature extraction. These subimages are used as feature templates. The templates are then tracked by Kalman filtering, and camera projective matrix is computed by the projective mapping between the templates and their coordinate in the 3D coordinate system. And the user's face pose is estimated from the projective mapping between the user's face and image plane. The accuracy and the robustness of our technique is verified on the experimental results of several real video sequences.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.169-175
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• 2004

This paper develops a dynamic travel time prediction model for ATIS in a national highway. While there have been many research on travel time prediction, none of them is for national highway in Korea. The study uses AVI data installed on the national highway No.1 with 10km interval for travel time prediction model, and probe vehicle data for evaluating the model. The study area has many access points, so there are many outlying observations in the raw AVI data. Therefore, this study uses the algorithm proposed by the author for removing the outliers, and then Kalman filtering algorithm is applied for the travel time prediction. The prediction model is performed for 5, 10, 15 and 30 minute-aggregating interval and the results are $0.061{\sim}0.066$ for 5, 10 and 15 interval and 0.078 for 30 minute one with a little low performance as MAREs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.648-656
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• 2001

In this paper, we propose a new feature-based vehicle tracking system using trajectory matching for intelligent traffic surveillance. The proposed system consists of three parts: feature extraction, feature tracking, and feature grouping using trajectory matching. For feature extraction and feature tracking, features of vehicles are selected based on the measure of cornerness and are tracked using linear Kalman filtering. We then group features from the same vehicle in the grouping step. We suggest a new grouping algorithm using the spatial information of features and trajectory matching to solve the over-grouping Problems of the feature-based tracking method. Finally, our proposed tracking system demonstrates good performance for typical traffic scenes with partial occlusion and neighboring conditions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.10
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• pp.51-59
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• 2003

A method of autonomous update is presented for onboard orbit propagator. On board propagator is an alternative means that could be used for navigation purpose in case of CPS receiver's failure. Although the ground station is not a able to upload a new propagator, the onboard propagator must be maintained most up-to-date. For this, a filtering technique is proposed wherein GPS data are effectively used to continuously update the on board propagator which was uploaded previously. Even if the ground station has generated the on board propagator based on the wrong information, the onboard propagator with updating scheme can automatically correct the errors in the coefficients of residual reconstruction function. Several scenarios were used to show the validity of the scheme for updating the onboard propagator using KOMPSAT-1 orbit data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1B
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• pp.86-97
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• 2009

RTLS is a system for automatically locating and tracking people and objects. The TDOA-based RTLS determines the location of the tag by calculating the time differences of a signal received from the tag. In TDOA-based RTLS, time synchronization is essential to calculate the time difference between readers. This paper presents a precision time synchronization method for TDOA-based RTLS over IEEE 802.15.4. In order to achieve precision time synchronization in IEEE 802.15.4 radio, we analyzed the error factors of delay and jitter. We also deal with the implementation of hardware assisted time stamping and the Kalman filtering method to minimize the error factors. In addition, this paper described the experiments and performance evaluation of the proposed precision time synchronization method in IEEE 802.15.4 radio. The results show that the nodes in a network can maintain their clocks to within 10 nanoseconds offset from the reference clock.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.597-606
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• 2010

This paper describes a real-time camera tracking framework specifically designed to track a monocular camera in an AR workspace. Typically, the Kalman filter is often employed for the camera tracking. In general, however, tracking performances of conventional methods are seriously affected by unpredictable situations such as ambiguity in feature detection, occlusion of features and rapid camera shake. In this paper, a recursive Bayesian sampling framework which is also known as the particle filter is adopted for the camera pose estimation. In our system, the camera state is estimated on the basis of the Gaussian distribution without employing additional uncertainty model and sample weight computation. In addition, the camera state is directly computed based on new sample particles which are distributed according to the true posterior of system state. In order to verify the proposed system, we conduct several experiments for unstable situations in the desktop AR environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.168-172
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• 2005

An autopilot system of a ship is very important for a safe and convenient navigation, which is realized with getting an azimuth data from a gyrocompass, a magnetic compass and a GPS(Global Positioning System) compass. Magnetic compass an azimuth error is generated by a vessel magnetism material such as steels. The magnetic pole is detected by the magnetic field sensor, it does not coincide with the true north, therefore, the detected azimuth data can not but accompany error. In this paper, in order to detect the minimum change of azimuth data which generates errors of azimuth information, a search algorithm using the Kalman Filtering method is utilized. The digital electronic compass is designed with the integration algorithm using the merits of an earth's magnetic field sensor and a GPS receiver.