• Journal of Communications and Networks
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• v.9 no.3
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• pp.254-264
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• 2007

A promising approach to improve the performance of mobile location system is the use of antenna arrays in both transmitter and receiver sides. Using advanced array signal processing techniques, such multiple-input multiple-output (MIMO) communication systems can offer more mobile location information by exploiting the spatial properties of the multipath channel. In this paper, we propose a novel approach to determine the position of mobile terminal based on estimated multipath signal parameters using only one base station in MIMO communication systems. This approach intends to minimize the error occurring from the estimation of multiple paths and gives an optimal estimation of the position of mobile terminal by simultaneously calculating a set of nonlinear location equations. This solution breaks the bottleneck of conventional mobile location systems which have to require multilateration of at least three base stations.

• Journal of Radiation Protection and Research
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• v.16 no.1
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• pp.53-72
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• 1991

An integrated model is developed in this paper for the performance assessment of high level radioactive waste repository. This integrated model consists of two simple mathematical models. One is a multiple-barrier failure model of the repository system based on constant failure rates which provides source terms to biosphere. The other is a biosphere model which has multiple pathways for radionuclides to reach to human. For the parametric uncertainty and sensitivity analysis for the risk assessment of high level radioactive waste repository, Latin hypercube sampling and rank correlation techniques are applied to this model. The former is cost-effective for large computer programs because it gives smaller error in estimating output distribution even with smaller number of runs compared to crude Monte Carlo technique. The latter is good for generating dependence structure among samples of input parameters. It is also used to find out the most sensitive, or important, parameter groups among given input parameters. The methodology of the mathematical modelling with statistical analysis will provide useful insights to the decision-making of radioactive waste repository selection and future researches related to uncertain and sensitive input parameters.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.264-268
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• 2011

This paper presents a Takagi-Sugeno (T-S) type Fuzzy Logic Controller (FLC) with only 3 rules. The choice of parameters of FLC is very difficult job on design FLC. Therefore, the choice of appropriate linguistic variable is an important part of the design of fuzzy controller. However, since fuzzy controller is nonlinear, it is difficult to analyze mathematically the affection of the linguistic variable. So this choice is depend on the expert's experience and trial and error method. In this paper, we propose the method to choose the consequence linear equation's parameter of T-S type FLC. The parameters of consequence linear equations of FLC are tuned according to the system error that is the input of FLC. The full equation of T-S type FLC is presented and using this equation, the relation between output and parameters can represented. The parameters are tuned with gradient algorithm. The parameters are changed depending on output. The simulation results demonstrate the usefulness of this T-S type 3 rule fuzzy controller.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.123-128
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• 1995

A classical PID controller is designed by applying the GA (Genetic Algorithm) which searches the optimal parameters through three major operators of reproduction, crossover and mutation under the given constraints. The GA could minimize the designer's interference and the whole design process could easily be automated. In contrast with other traditional PID design methods which allows for the system output responses only, the design with the GA can take account of the magnitude or the rate of change of control input together with the output responses, which reflects the more realistic situations. Compared with other PIDs designed by the traditional methods such as Ziegler and analytic, the PID by the GA shows the superior response characteristics to those of others with the least control input energy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1096-1103
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• 2015

Electromagnetic levitation system(EMLS) is one of the well known nonlinear systems due to its high degree of nonlinearities. Moreover, when there are uncertain parameters in EMLS, it is not easy to have an accurate control of EMLS. In this paper, we first apply a standard input-output feedback linearzing controller to EMLS and investigate the possible control error caused by uncertain coil resistor. Then, as a remedy, we design and apply a robust controller using Lyapunov redesign technique to deal with this uncertain coil resistor in the system. The validity of our robust controller is verified via system analysis and experimental results.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.717-719
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• 1996

An artificial neural network(ANN) modeling is presented using the Input-output power characteristics of SMES. When using the ANN which functions as a model-free system, network construction and determination of learning parameters are carefully chosen to represent the complicated nonlinear input-output relation from the black-boxed SMES system. The proposed ANN-based SMES model is applied to analyse the power system stability and the simulation results provide the property of this approach.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.121-127
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• 1997

Multilayer feedforward networks may be applied to identify the deterministic relationship between input and output data. When the results from the network require a high level of assurance, consideration of the stochastic relationship between the input and output data may be very important. Variance is one of the effective parameters to deal with the stochastic relationship. This paper presents a new algroithm for a multilayer feedforward network to learn the variance of dispersed data without preliminary calculation of variance. In this paper, the network with this learning algorithm is named as a variance learning neural network(VALEAN). Computer simulation examples are utilized for the demonstration and the evaluation of VALEAN.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.238-242
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• 2004

The paper describes an automatic frequency control current-fed inverter for forging applications. The IGBT in series with diodes as its switching devices in the inverter circuit which is of full-bridge type. The operating frequency is automatically tracked to maintain a small constant leading phase angle when load parameters change. The load voltage is controlled to protect the switches. The output power can be adjusted by varying the input current from phase controlled rectifiers which is a part of current source. The system has been operated at 15-17 kHz. The output power transferred to the load is 1,595 watts. It can heat the steel work pieces with 15 mm diameter and 120 mm long from room temperature to approximately 1100 $^{\circ}C$ within 20 seconds with 0.97 leading power factor on the input side.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.144-155
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• 2004

This paper addresses the issue of position control of a chain of multiple mass-spring-damper (CMMSD) units which can be found in many physical systems. The dynamic model of a CMMSD system with any degrees of freedom is expressed in a closed-form for the convenience of the controller design. Backstepping and model reference adaptive control (MRAC) approaches are then used to develop two adaptive output feedback controllers to control the position of a CMMSD system. The proposed controllers rely on the measurements of the input (force) and the output (position of the mass unit at the end of the chain) of the system without the knowledge of its parameters and internal states. Simulations are used to verify the effectiveness of the controllers

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.