• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.472-478
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• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The error back-propagation method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

• Journal of KIISE:Information Networking
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• v.32 no.1
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• pp.1-11
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• 2005

Internet has become an essential part of our daily lives. Many of the day to day activities can already be carried out over Internet, and its convenience has greatly increased the number of Internet users. Hut as Internet gains its popularity, the illicit incidents over Internet has also proliferated. The intruder trace-back technology is the one that enables real time tracking the position of the hacker who attempts to invade the system through the various bypass routes. In this paper, the RTS algorithm which is the TCP connection trace-back system using the watermarking technology on Internet is proposed. Furthermore, the trace-bark elements are modeled by analyzing the Proposed trace-back algorithm, and the results of the simulation under the virtual topology network using ns-2, the network simulation tool are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1073-1078
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• 2014

In this paper, an effective repeat-back jamming (RBJ) mitigation scheme known assuccessive repeat-back jamming cancellation (SRC) is proposed for the utilization of the successive interference cancellation (SIC) algorithm which is used to mitigate the near-far effect and the multiple-access interference for code division multiple-access communication systems. The proposed scheme uses a combined pseudo-random noise (C-PRN) signal from the estimated major parameters of RBJ signals. To evaluate the performance of the proposed scheme, the root mean squared (RMS) code tracking errors are shown according to the standard deviation of the parameter estimation errors of an RBJ signal, and using the well-known major parameters estimation schemes with a C-PRN signal through Monte-Carlo simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.370-374
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• 1999

The proposed algorithm used the Hybrid teaming rule in the input and hidden layer, and Back-Propagation teaming rule in the hidden and output layer. From the results of simulation of tracking control with one link manipulator as a plant, we verify the usefulness of the proposed control method to compare with common direct adaptive neural network control method; proposed hybrid teaming rule showed faster loaming time faster settling time than the direct adaptive neural network using Back-propagation algorithm. Usefulness of the proposed control method is that it is faster the learning time and settling time than common direct adaptive neural network control method.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.2
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• pp.111-124
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• 1996

The main purpose of this study is to develop an algorithm to solve the scheduling problems of FMS using Petri-net is well suited to model the dynamics of FMS and Petri-net is an ideal tool to formulate scheduling problems with routing flexibility and shared resources. By using the marking of Petri-net, We can model features of discrete even system, such as concurrency, asynchronous, conflict and non-determinism. The proposed algorithm in this paper can handle back-tracking using the marking of Petri-net. The results of the experiment show that marking is one of the best ways that describe exactly movement of the discrete event system. To show the effectiveness of the algorithm suggested here, we compare it with L1 algorithm using the Petri-net through the test on randomly generated test problems.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.169-171
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• 2006

This paper studies the step motors moved up-down and fore-back direction and deals with a position control and tracking the trajectory of the robot. For simulation and experiment, we demonstrate core part with usefulness and effectiveness.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.155-162
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• 2003

We propose a new technique far real-tine controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Caussian function as a unit function in the fuzzy neural network. and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-foray. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1697-1700
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• 2003

We propose a new technique for real-time controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.312-318
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• 2003

We propose a new technique for the cruise control system design of a mobile robot with three drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized teaming architecture. It is proposed a learning controller consisting of too neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by three independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.207-212
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• 2004

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.