• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.3
• /
• pp.535-541
• /
• 2006

This paper proposes a robust method to find corresponding points for un-calibrated stereo images by using a classical method based on the epipolar constraints and motion flows. If we detect matching for the only epipolar geometry, the problem is very high. Therefore, in order to nod an initial set of matches, we use the correlation technique and then exploit motion vectors to remove mismatches among matching candidates. Then, the epipolar geometry can be accurately estimated using a veil adapted criterion and computed the fundamental matrix. The proposed algorithm has been widely tested and works remarkably well in various scenes, evenly, with many repetitive patterns. The results show that the proposed algorithm is better than the conventional.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2001.06a
• /
• pp.99-102
• /
• 2001

컴퓨터 기술의 향상과 인터넷의 보급화로 인하여 가상환경의 구현에 대한 관심도 높아지고 있으며 이에 따른 여러 기술들이 제안되고 있다. 본 논문은 간단한 영상취득장치로 얻은 몇 장의 영상으로 영상 기반 환경을 자동으로 생성하는 방법에 대해 논한다. 특히, 취득한 영상간의 카메라 회전 성분에 강건한 기준 특징 벡터 필터링 방법을 제안하며 실험을 통해 그 유용성을 검증한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.44 no.4
• /
• pp.324-332
• /
• 2016

In this paper, the terminal homing loop with a IIR-type strapdown seeker and a roll rate gyroscope is proposed. Basically, the proposed homing loop is based on the look-angle-control guidance. Since the range of the seeker is strictly limited, the missile is delivered to a point to lock the target on the seeker via non-guided flight during the midcourse guidance. The non-standard firing table is developed to compensate the wind and the target movement. To secure the delay margin is very important to prevent the instability of the homing loop when the time delay of the seeker is included. To validate the proposed homing loop, the 6-DOF nonlinear simulation is performed, and the Monte-Carlo simulation is also done for checking the robustness for the various kinds of uncertainty.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.3
• /
• pp.175-189
• /
• 2023

Fault detection and diagnosis (FDI) algorithms are actively being researched for ensuring the integrity and reliability of environment perception sensors in autonomous vehicles. In this paper, a fault detection algorithm based on a multi-sensor perception system composed of radar, camera, and lidar is proposed to guarantee the safety of an autonomous vehicle's perception system. The algorithm utilizes reference generation filters and residual generation filters based on finite impulse response (FIR) filter estimates. By analyzing the residuals generated from the filtered sensor observations and the estimated state errors of individual objects, the algorithm detects faults in the environment perception sensors. The proposed algorithm was evaluated by comparing its performance with a Kalman filter-based algorithm through numerical simulations in a virtual environment. This research could help to ensure the safety and reliability of autonomous vehicles and to enhance the integrity of their environment perception sensors.

• Journal of Internet Computing and Services
• /
• v.1 no.1
• /
• pp.63-72
• /
• 2000

Data mining means a process of nontrivial extraction of hidden knowledge or potentially useful information from data in large databases. Data mining algorithm is a multi-disciplinary field of research; machine learning, statistics, and computer science all make a contribution. Different classification schemes can be used to categorize data mining methods based on the kinds of tasks to be implemented and the kinds of application classes to be utilized, and classification has been identified as an important task in the emerging field of data mining. Since classification is the basic element of human's way of thinking, it is a well-studied problem in a wide varietyof application. In this paper, we propose a classifier system based on genetic algorithm with robust property, and the proposed system is evaluated by applying it to nDmC problem related to classification task in data mining.

• Journal of Navigation and Port Research
• /
• v.47 no.4
• /
• pp.256-270
• /
• 2023

In this study, a sliding mode (SM) controller for dynamic positioning (DP) was specifically designed for a turret connection operation of a ship or an offshore structure in which an arbitrary point on the structure could be controlled as the motion center instead of the center of mass. The SM controller allows control of the arbitrary point and provides capability to manage uncertainties in the dynamics of ships and offshore structures, external forces caused by unknown changing marine environments, and transient performance of DP systems. The Jacobian matrix included in kinematic equations of the controlled object was modified to design the SM controller to control based on an arbitrary point of ships or offshore structures. To ensure robustness of the controller, the Lyapunov stability theory was applied in the design of the SM controller. In general, for robustness in DP control, gain scheduling based on a proportional-derivative (PD) control algorithm is employed. However, finding appropriate gains for gain scheduling complicates the application of DP systems. Therefore, in this study, the SM control algorithm was considered to mitigate the complexity of the DP controller for ships and offshore structures. To validate the proposed SM control algorithm, time-domain simulations were conducted and utilized to evaluate the performance of the control algorithm. The effectiveness of the proposed SM controller was assessed by comparing simulation results with results of a conventional PD control algorithm applied in DP control.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.6C
• /
• pp.815-823
• /
• 2004

A hue-based attention operator and a combinational integral projection function(CIPF) are proposed to extract the normalized regions of face and facial features robustly against illumination variation. The face candidate regions are efficiently detected by using skin color filter, and the eyes are located accurately nil robustly against illumination variation by applying the proposed hue- and symmetry-based attention operator to the face candidate regions. And the faces are confirmed by verifying the eyes with the color-based eye variance filter. The proposed CIPF, which combines the weighted hue and intensity, is applied to detect the accurate vertical locations of the eyebrows and the mouth under illumination variations and the existence of mustache. The global face and its local feature regions are exactly located and normalized based on these accurate geometrical information. Experimental results on the AR face database［8］ show that the proposed eye detection method yields better detection rate by about 39.3% than the conventional gray GST-based method. As a result, the normalized facial features can be extracted robustly and consistently based on the exact eye location under illumination variations.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.40 no.4
• /
• pp.343-352
• /
• 2016

A multi-rotor vehicle is an unmanned vehicle consisting of multiple rotors. A multi-rotor vehicle can be categorized as tri-, quad-, hexa-, and octo-rotor depending on the number of the rotors. Multi-rotor vehicles have many advantages due to their agile flight capabilities such as the ability for vertical take-off, landing and hovering. Thus, they can be widely used for various applications including surveillance and monitoring in urban areas. Since multi-rotors are subject to uncertain environments and disturbances, it is required to implement robust attitude stabilization and flight control techniques to compensate for this uncertainty. In this research, an advanced nonlinear control algorithm, i.e. sliding mode control, was implemented. Flight experiments were carried out using an onboard flight control computer and various real-time autonomous attitude adjustments. The feasibility and robustness for flying in uncertain environments were also verified through real-time tests based on disturbances to the multi-rotor vehicle.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.38 no.2
• /
• pp.107-114
• /
• 2014

This paper proposes a common rail pressure control algorithm for passenger car diesel engines. For handling the parameter-varying characteristics of common rail systems, the quantitative feedback theory (QFT) is applied to the design of a robust rail pressure control algorithm. The driving current of the pressure control valve and the common rail pressure are used as the input/output variables for the common rail system model. The model parameter uncertainty ranges are identified through experiments. Rail pressure controller requirements in terms of tracking performance, robust stability, and disturbance rejection are defined on a Nichols chart, and these requirements are fulfilled by designing a compensator and a prefilter in the QFT framework. The proposed common rail pressure control algorithm is validated through engine experiments. The experimental results show that the proposed rail pressure controller has a good degree of consistency under various operating conditions, and it successfully satisfies the requirements for reference tracking and disturbance rejection.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.6
• /
• pp.107-116
• /
• 2015

In compliance with digital device growth, the proliferation of high-tech computers, the availability of high quality and inexpensive video cameras, the demands for automated video analysis is increasing, especially in field of intelligent monitor system, video compression and robot vision. That is why object tracking of computer vision comes into the spotlight. Tracking is the process of locating a moving object over time using a camera. The consideration of object's scale, rotation and shape deformation is the most important thing in robust object tracking. In this paper, we propose a robust object tracking scheme using Random Forest. Specifically, an object detection scheme based on region covariance and ZNCC(zeros mean normalized cross correlation) is adopted for estimating accurate object location. Next, the detected region will be divided into five regions for random forest-based learning. The five regions are verified by random forest. The verified regions are put into the model pool. Finally, the input model is updated for the object location correction when the region does not contain the object. The experiments shows that the proposed method produces better accurate performance with respect to object location than the existing methods.