• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.90-98
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• 2015

In this paper, an advanced teaching-learning based optimization(TLBO) method for the magnetic levitation controller of Maglev transportation system is proposed to optimize the control performances. An attraction-type levitation system is intrinsically unstable and requires a delicate control. It is difficult to completely satisfy the desired performance through the methods using conventional methods and intelligent optimizations. In the paper, we use TLBO and clonal selection algorithm to choose the optimal control parameters for the magnetic levitation controller. To verify the proposed algorithm, we compare control performances of the proposed method with the genetic algorithm and the particle swarm optimization. The simulation results show that the proposed method is more effective than conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.69-74
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• 2009

In this paper, we propose a method of ontology-based control of autonomous robots. Advancing one step further from using ontology as a hierarchical storage of information, the proposed method shows how to control robots through ontology inference. That is, the information on obstacles detected by robots is represented as an ontology, and robots' action planning and control are performed according to robots' surroundings through ontology inference. We make a differentially driven robot and illustrate the effectiveness of the proposed method via the experiment of the robot's navigation in real environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.365-380
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• 2022

The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1091-1096
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• 1989

All mechanical systems have saturation nonlinearity in actuators or in final control elements. When controllers have integral action, reset windup can cause instability as well as make the system performance unsatisfactory. In this study, an intelligent limiter which needs no tuning of parameters is tested with the PDF controller used for control of a second order plant. This paper presents analysis of the stability of the system using the describing function method and the Nyquist stability theorem. The improvement of the system performance by the limiter is illustrated by computer simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.172-175
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• 1996

In the paper is proposed a hierarchical self-learning fuzzy controller for balancing and position control of an circular inverted pendulum system. To stabilize the pendulum at a specified position, the hierarchical fuzzy controller consists of a supervisory controller, a self-learning fuzzy controller, and a forced disturbance generator. Simulation example shows the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.1-6
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• 2015

In this paper, a fuzzy control method is used for balancing a single-wheel robot. A single-wheel robot controlled by the PD control method becomes easily unstable since the flywheel tends to lean against one direction. In the previous research, we have used the gain scheduling method. To remedy this problem, in this paper, a fuzzy compensation technique is proposed to compensate for the balancing angle. The fuzzy control method compensates offset values at the balancing angle to prevent the gimbal from falling against one direction. Experimental studies of the balancing control performance of a single-wheel mobile robot validate the proposed control method.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.601-611
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• 2005

In this paper, an active vibration control of a tensioned, elastic, axially moving string is investigated. The dynamics of the translating string are described with a non-linear partial differential equation coupled with an ordinary differential equation. A right boundary control to suppress the transverse vibrations of the translating continuum is proposed. The control law is derived via the Lyapunov second method. The exponential stability of the closed-loop system is verified. The effectiveness of the proposed control law is simulated.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.342-347
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• 2003

In this paper, an active vibration control of a translating tensioned string with the use of an electro-hydraulic servo mechanism at the right boundary is investigated. The dynamics of the moving strip is modeled as a string with tension by using Hamilton’s principle for the systems with changing mass. The control objective is to suppress the transverse vibrations of the strip via boundary control. A right boundary control law in the form of current input to the servo valve based upon the Lyapunov’s second method is derived. It is revealed that a time-varying boundary force and a suitable passive damping at the right boundary can successfully suppress the transverse vibrations. The exponential stability of the closed loop system is proved. The effectiveness of the control laws proposed is demonstrated via simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2502-2507
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• 2003

In this paper, a crack sealing robot is developed. The crack sealing robot is built to detect, track, and seal the crack on the pavement. The sealing robot is required to brush all dirt in the crack out for preparing a better sealing job. Camera calibration has been done to get accurate crack position. In order to perform a cleaning job, the explicit force control method is used to regulate a specified desired force in order to maintain constant contact with the ground. Experimental studies of force tracking control are conducted under unknown environment stiffness and location. Crack tracking control is performed. Force tracking results are excellent and the robot finds and tracks the crack very well.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2735-2737
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• 2000

In this paper, we deal with modeling and analysis for mobile manipulator systems. In order to avoid the difficulties occurring due to slippage or unevenness of the terrain, we propose the utilization of minimum actuators. In this case, the resulting systems typically possess kinematic redundancies which can be beneficially employed for correcting the position error. A simple PD control method along with kinematic redundancy is employed to recover position errors for trajectory control in task space. Several primary and secondary criteria utilizing kinematic redundancy of the mobile manipulator system are tested through graphic animation.