• Journal of the Korean Society for Precision Engineering
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• v.25 no.2
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• pp.140-147
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• 2008

This paper describes a Neural Network based PD control scheme for motion control of pneumatic servo cylinder. Pneumatic systems have inherent nonlinearities such as compressibility of air and nonlinear frictions present in cylinder. The conventional linear controller is limited in some applications where the affection of nonlinear factor is dominant. A self-excited oscillation method is applied to derive the dynamic design parameters of linear model. Based on the parameters thus identified, a PD feedback compensator is designed first and then a neural network is incorporated. The experiments of a trajectory tracking control using the proposed control scheme are performed and a significant reduction in tracking error is achieved by comparing with those of a PD control.

• Proceedings of the KIEE Conference
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• 1997.07e
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• pp.1813-1815
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• 1997

In this study, in order to develop outdoor insulating materials, SIN(simultaneous interpenetrating polymer network) was introduced to Epoxy resin and the environment resistance was investigated. Six kinds of specimen were manufacture by filler($SiO_2$) content. SEM was untilized in order to confirm their network structure changes. Also, tracking test, UV test and acid rain test were carried out investigate the environment resistance characteristic. Therefore it was confirmed that simultaneous interpenetrating polymer network specimens were more excellent than single network structure specimens. But, acid rain almost never changed resistance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.5
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• pp.603-609
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• 2004

This paper deals with the issue of Neural Network-based control for a rodless pneumatic cylinder system which is utilized for a pneumatic XY-plotter. In order to identify the system design parameters, the open loop response of a pneumatic rodless cylinder controlled by a pneumatic servovalve is investigated by applying a self-excited oscillation method. Based on the system design parameters, the PD feedback compensator is designed and then Neural Network is incorporated with it. The experiment of a trajectory tracking control using a PD-NN has been performed and proved its excellent performance by comparing with that of a PD feedback compensator.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.515-526
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• 2003

Tutorial contents of kinematics and dynamics of a wheeled drive mobile robot are presented. Based on the dynamic model, simulation studies of position tracking of a mobile robot are performed. The control structure of several position control algorithms using visual feedback are proposed and their performances are compared. In order to compensate for uncertainties from unknown dynamics and ignored dynamic effects such as slip conditions, neural network based position control schemes are proposed. Experiments are conducted and the results show the performance of the vision based neural network control scheme fumed out to be the best among several proposed schemes.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.88-95
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• 2005

This paper presents a design scheme of model reference adaptive control incorporating a Neural Network for a pneumatic servo system. The parameters of discrete-time model of plant are estimated by using the recursive least square method. Neural Network is utilized in order to compensate the nonlinear nature of plant such as compressibility of air and frictions present in cylinder. The experiment of a trajectory tracking control using the proposed control scheme has been performed and its effectiveness has been proved by comparing with the results of a model reference adaptive control.

• Journal of the Korean Society of Safety
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• v.15 no.3
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• pp.115-123
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• 2000

Pneumatic actuators are widely used in a variety of hazardous working environments. Any process that involves pneumatic actuation is also recognized as "eco-friendly". In most cases, applications of pneumatic actuators require only point-to-point control. In recent years, research efforts have been directed toward achieving precise position tracking control. In this study, a tracking position control method is proposed and experimentally evaluated for a linear positioning system. The positioning system is composed of a pneumatic actuator and a 3-port proportional valve. The proposed controller has an inner pressure control loop and an outer position control loop. A PID controller with feedback linearization is used in the pressure control loop to nullify the nonlinearity arising from the compressibility of the air. The position controller is also a PID controller augmented with the friction compensation by a neural network. Experimental results indicate that the proposed controller significantly improves the tracking performance.rformance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1771-1779
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• 2016

In these days, the world is getting connected to the internet like a sophisticated net, such an environment gives a suitable environment for cyber attackers, so-called cyber-terrorists. As a result, a number of cyber attacks has significantly increased and researches to find cyber attack traffics in the field of network monitoring has also been proceeding. But cyber attack traffics have been appearing in new forms in every attack making it harder to monitor. This paper suggests a method of tracking down cyber attack traffic sources by defining relational information flow of traffic data from highest cascaded and grouped relational flow. The result of applying this cyber attack source tracking method to real cyber attack traffic, was found to be reliable with quality results.