• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1574-1580
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• 2008

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the Plants of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• School Mathematics
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• v.5 no.4
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• pp.477-506
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• 2003

In this paper, we analyze nine-grade students' conceptions of parameters, their relation to unknowns and variables and the process of understanding of letters in problem solving of equations and functions. The roles of letters become different according to the letters-used contexts and the meaning of letters Is changed in the process of being used. But, students do not understand the meaning of letters correctly, especially that of parameter. As a result, students operate letters in algebraic expressions according to the syntax without understanding the distinction between the roles. Therefore, the parameter of learning should focus on the dynamic change of roles and the flexible thinking of using letters. We develop a self-regulation model based on the monitoring working question in teaching-learning situations. We expect that this model helps students understand concepts of letters that enable to construct meaning in a concrete context.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1007-1013
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• 2012

Modeling of photovoltaic power systems is significant to analytically predict its dynamics in practical applications. This paper presents a novel modeling algorithm of such system by using probability and statistic theories. We first establish a linear model basically composed of Fourier parameter sets for mapping the input/output variable of photovoltaic systems. The proposed model includes solar irradiation and ambient temperature of photovoltaic modules as an input vector and the inverter power output is estimated sequentially. We deal with these measurements as random variables and derive a parameter learning algorithm of the model in terms of statistics. Our learning algorithm requires computation of an expectation and joint expectation against solar irradiation and ambient temperature, which are analytically solved from the integral calculus. For testing the proposed modeling algorithm, we utilize realistic measurement data sets obtained from the Seokwang Solar power plant in Youngcheon, Korea. We demonstrate reliability and superiority of the proposed photovoltaic system model by observing error signals between a practical system output and its estimation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.540-543
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• 1997

In this paper, a precise trajectory tracking method for mobile robot using a vision system is presented. In solving the problem of precise trajectory tracking, a hierarchical control structure is used which is composed of the path planer, vision system, and dynamic controller. When designing the dynamic controller, non-ideal conditions such as parameter variation, frictional force, and external disturbance are considered. The proposed controller can learn bounded control input for repetitive or periodic dynamics compensation which provides robust and adaptive learning capability. Moreover, the usage of vision system makes mobile robot compensate the cumulative location error which exists when relative sensor like encoder is used to locate the position of mobile robot. The effectiveness of the proposed control scheme is shown through computer simulation.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.10
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• pp.1128-1136
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• 2012

In this paper, the states and parameters in a dynamic system are estimated by applying an Unscented Kalman Filter (UKF). The UKF is widely used in various fields such as sensor fusion, trajectory estimation, and learning of Neural Network weights. These estimations are necessary and important in determining the stability of a mobile system, monitoring, and predictions. However, conventional approaches are difficult to estimate based on the experimental data, due to properties of non-linearity and measurement noises. Therefore, in this paper, UKF is applied in estimating the states and parameters needed. An experimental dynamic system has been set up for obtaining data and the experimental data is collected for parameter estimation. The measurement noises are primarily reduced by applying the Low Pass Filter (LPF). Given the simulation results, the estimated error rate is 39 percent more efficient than the results obtained using the Least Square Method (LSM). Secondly, the estimated parameters have an average convergence period of four seconds.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.178-186
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• 1998

In this paper, a robust.adaptive learning control scheme is presented for precise trajectory tracking of rigid mobile robots. In the proposed controller, a set of desired trajectories is defined and used in constructing the control input and learning rules which constitute the main part of the proposed controller. Stable operating characteristics such as precise trajectory tracking, parameter estimation, disturbance suppression, etc., are shown thorugh experiments and computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1816-1819
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• 1997

Using the vision system, robotic tasks in unstructured environments can be accompished, which reduces greatly the cost and steup time for the robotic system to fit to he well-defined and structured working environments. This paper proposes a dynamic control scheme for robot manipulator with eye-in-hand camera configuration. To perfom the tasks defined in the image plane, the camera motion Jacobian (image Jacobian) matrix is used to transform the camera motion to the objection position change. In addition, the dynamic learning controller is designed to improve the tracking performance of robotic system. the proposed control scheme is implemented for tasks of tracking moving objects and shown to outperform the conventional visual servo system in convergence and robustness to parameter uncertainty, disturbances, low sampling rate, etc.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.675-679
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• 2001

In this paper, we present a new competitive learning algorithm called Dynamic Competitive Learning (DCL). DCL is a supervised learning method that dynamically generates output neurons and initializes automatically the weight vectors from training patterns. It introduces a new parameter called LOG (Limit of Grade) to decide whether an output neuron is created or not. If the class of at least one among the LOG number of nearest output neurons is the same as the class of the present training pattern, then DCL adjusts the weight vector associated with the output neuron to learn the pattern. If the classes of all the nearest output neurons are different from the class of the training pattern, a new output neuron is created and the given training pattern is used to initialize the weight vector of the created neuron. The proposed method is significantly different from the previous competitive learning algorithms in the point that the selected neuron for learning is not limited only to the winner and the output neurons are dynamically generated during the learning process. In addition, the proposed algorithm has a small number of parameters, which are easy to be determined and applied to real-world problems. Experimental results for pattern recognition of remote sensing data and handwritten numeral data indicate the superiority of DCL in comparison to the conventional competitive learning methods.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.55-62
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• 1999

In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust ard accurate control of auto-equipnent systems which disturbance, parameter alteration of system, uncertainty ard so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transfonnations in the manirclator of auto-equipnent systems is developed ard the example that DNP can be used is explained The architocture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simllations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.he DNP.