• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.331-333
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• 1994

A learning method for induction motor trajectory using neuro-fuzzy networks (NFN) based on fusion of fuzzy logic theory and neural networks is proposed. The premise and consequent parameters of the NFN affecting the controllers performances are modified during the learning stages by the proposed learning method to implement an optimal controller only with pre-determined target trajectory and the least amount of knowledge about an induction motor. The induction motor position control system is simulated to verify the effectiveness of the learned NF controller(NFC). The simulation results shows that the proposed learning method has good dynamic performance and small steady state error.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.65-68
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• 1995

This paper describes trajectory generation of a riobot arm by self-organizing neural networks. These neural networks are based on competitive learning without a teacher and this algorithm which is suitable for problems in which solutions as teaching signal cannot be defined-e.g. inverse dynamics analysis-is adopted to the trajectory generation problem of a robot arm. Utility of unsupervised learning algorithm is confirmed by applying the approximated solution of each joint calculated through learning to an actual robot arm in giving the experiment of tracking for reference trajectory.

• Journal of IKEEE
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• v.25 no.4
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• pp.766-769
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• 2021

In order to perform tasks such as design, control, optimization, and prediction of flight vehicle trajectories based on machine learning techniques including deep learning, a certain amount of flight vehicle trajectory data is required. However, there are cases in which it is difficult to secure more than a certain amount of flight vehicle trajectory data for various reasons. In such cases, synthetic data generation could be one way to make machine learning possible. In this paper, to explore this possibility, we generated and evaluated synthetic flight vehicle trajectory data using time-series generative adversarial neural network. In addition, various ablation studies (comparative experiments) were performed to explore the possibility of using synthetic data in the aircraft trajectory prediction task. The experimental results presented in this paper are expected to be of practical help to researchers who want to conduct research on the possibility of using synthetic data in the generation of synthetic flight vehicle trajectory data and the work related to flight vehicle trajectories.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.627-634
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• 1989

A collision-free trajectory planning algorithm using the iterative learning concept is proposed for dual robot arms in a 3-D workspace to accurately follow their specified paths with constant velocities. Specifically, a collision-free trajectory minimizing the trajectory error is obtained first by employing the linear programming technique. Then the total operating time is iteratively adjusted based on the maximum trajectory error of the previous iteration so that the collision-free trajectory has no deviation from the specified path and also the operating time is near-minimal.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.6
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• pp.62-70
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• 2003

This paper deals with a approximation and tracking control of hydraulic servo system using a real time recurrent neural networks (RTRN) with 2-dimensional iterative learning rule. And it was driven that 2-dimensional iterative learning rule in discrete time. In order to control the trajectory of position, two RTRN with same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm is able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RTRN was very effective to control trajectory tracking of electro-hydraulic servo system.

• Transactions of The Korea Fluid Power Systems Society
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• v.1 no.1
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• pp.1-9
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• 2004

This paper addresses an approximation and tracking control of recurrent neural networks(RNN) using two-dimensional iterative learning algorithm for an electro-hydraulic servo system. And two dimensional learning rule is driven in the discrete system which consists of nonlinear output function and linear input. In order to control the trajectory of position, two RNN's with the same network architecture were used. Simulation results show that two RNN's using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RNN was very effective to control trajectory tracking of electro-hydraulic servo system.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.386-388
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• 1996

This paper presents the learning controller for robot manipulators to track the desired trajectory exactly. The learning controller, based on the Lyapunov theory, consists of a fixed PD action and a repetitive action for the purpose of feedforward compensation which is adjusted utilizing a linear combination of the velocity and position errors. The learning controller Is often used In case of the desired trajectories are periodic tasks, and has advantage that it periodically converges to zero even if we don't know the exact dynamic parameters. In this paper, we show that the position and velocity errors of robot manipulators converge to zero sa time goes infinite for the input is periodic function and show a good trajectory tracking performance In the cartesian space.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.1
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• pp.69-77
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• 1991

A collision-free trajectory planning algorithm using an iterative learning concept is proposed for dual robot arms in a 3-D common workspace to accurately follow their specified paths with constant velocities. Specifically, a collision-free trajectory minimizing the trajectory error is obtained first by employing the linear programming technique. Then the total operating time is iteratively adjusted based on the maximum trajectory error of the previous iteration so that the collision-free trajectory has no deviation from the specified path and also that the operating time is near-minimal. To show the validity of the proposed algorithm, a numerical example is presented based on two planar robots.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.230-235
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• 1992

We propose a method of generating data to train a neural network controller. The data can be prepared directly by an iterative learning technique which repeatedly adjusts the control input to improve the tracking quality of the desired trajectory. Instead of storing control input data in memory as in iterative learning control, the neural network stores the mapping between the control input and the desired output. We apply this concept to the trajectory control of a two link robot manipulator with a feedforward neural network controller and a feedback linear controller. Simulation results show good generalization of the neural network controller.