• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.4_1
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• pp.753-758
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• 2024

Trajectory tracking of the AMR robot is one research for the AMR robot navigation. For the control system of the Autonomous mobile robot(AMR) being in non-honolomic system and the complex relations among the control parameters, it is d ifficult to solve the problem based on traditional mathematics model. In this paper, we presents a simple and effective way of implementing an adaptive tracking controller based on the PID for AMR robot trajectory tracking. The method uses a non-linear model of AMR robot kinematics and thus allows an accurate prediction of the future trajectories. The proposed controller has a parallel structure that consists of PID controller with a fixed gain. The control law is constructed on the basis of Lyapunov stability theory. Computer simulation for a differentially driven non-holonomic AMR robot is carried out in the velocity and orientation tracking control of the non-holonomic AMR. The simulation results of wheel type AMR robot platform show that the proposed controller is more robust than the conventional back-stepping controller to show the effectiveness of the proposed algorithm.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.191-200
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• 1993

This study introduced speical characteristics of an epidemiologic study on blood pressure and compared several statistical methods for evaluating the tracking phenomenon of blood pressure for Korean children. While correlation coefficients adjusted for measurement error are commonly used for the evaluation of tracking, it is hard to interpretate the results when correlation functions for lag-difference are not monotonous. McMahan defined a tracking as maintenance of relative rank over time and calculated tracking index usng growth curve model. The tracking index in McMahan's model is complicate to calculate, and it is hard to determine the degree of growth curve parameter. Blomqvist showed the relationship between the rate of change and the initial value. This concept could be extended for the evaluation of tracking. However, it is not so easy to interpretate the estimates in his model when those are non-positive.

• Smart Structures and Systems
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• v.24 no.2
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.4
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• pp.257-261
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• 2001

Nonlinear pulse width modulation (PWM) controlled system is considered to achieve control performance of thruster controlled spacecraft. The actual PWM controlled motions occur, very closely, around the average model trajectory. Furthermore nonlinear PWM controller design can be directly applied to thruster controlled spacecraft to determine thruster on-time. Sliding mode control for attitude tracking of three-axis thruster-controlled spacecraft is presented. Simulation results are shown which use modified Rodrigues parameters and sliding mode control law to achieve attitude tracking of a three-axis spacecraft with thrusters.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.159-167
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• 1999

A digital tracking controller is proposed for a precise positioning control under a large repetitive and/or non repetitive disturbances. The proposed control system. Numerical Examples are illustrated for a precise head positioning of optical disk drives regardless of a torque disturbance and/or output disturbance.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.267-273
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• 1998

This paper introduces an adaptive algorithm determining the measurement-track association problem in multi-target tracking(MTT). We model the target and measurement relationships with mean field theory and then define a MAP estimate for the optimal association. Based on this model, we introduce an energy function defined over the measurement space, that incorporates the natural constraints for target tracking. To find the minimizer of the energy function, we derived a new adaptive algorithm by introducing the Lagrange multipliers and local dual theory. Through the experiments, we show that this algorithm is stable and works well in general environments. Also the advantages of the new algorithm over other algorithms are discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.711-719
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• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.

• Proceedings of the IEEK Conference
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• 2000.06a
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• pp.209-212
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• 2000

In this paper, effect of imperfect power control on performance of a pseudonoise (PN) code tracking loop is analyzed and simulated for a direct-sequence/code-division multiple access (DS/CDMA) system. The multipath fading channel is modeled as a two-ray Rayleigh fading model. Power control error is modeled as a log-normally distributed random variable. The tracking performance of DLL (delay-locked-loop) is evaluated in terms of tracking jitter and mean-time-to-lose-lock (MTLL). From the simulation results, it is shown that the PN tracking performance is very sensitive to the power control error.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.108-113
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• 1991

In this paper, an improved method for tracking maneuvering target is proposed. The proposed tracking filter is constructed by combining the input estimation approach with the variable dimension filtering approach. In this approach, the filter also provides the estimated time instant at which target starts maneuver, when the target maneuver is detected. Using this estimated maneuvering time, the maneuver input is estimated and the tracking system changes to the maneuver model. Simulations are performed to demonstrate the efficiency of the proposed tracking filter.