• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.18 no.4
• /
• pp.458-464
• /
• 2008

The structural health monitoring has been gaining more importance in civil engineering areas such as earthquake and wind engineering. The use of health monitoring system can also provide tools for the validation of structural analytical model. However, only few structures such as historical buildings and some important long bridges have been instrumented with structural monitoring system due to high cost of installation, long and complicated installation of system wires. In this paper, the structural monitoring system based on cheap and wireless monitoring system is investigated. The use of advanced technology of micro-electro-mechanical system(MEMS) and wireless communication can reduce system cost and simplify the installation. Further the application of wireless MEMS system can provide enhanced system functionality and due to low noise densities. Identification results are compared to ones using data measured from traditional accelerometers and results indicate that the system identification using wireless MEMS system estimates system parameters accurately.

• Proceedings of the KSR Conference
• /
• 2007.05a
• /
• pp.1008-1015
• /
• 2007

The primary purpose of the safety management is to prevent the loss of lives or physical damages arising from potential hazards in the railroad signaling system. Since such potential hazards may occur at any time during the system life cycle from design and development to maintenance, safety management activities have to be continuously taken in the course of the system life cycle. The identification of potential hazards is the early step of the safety management. However, such identification activities have to be continued during the system life cycle. Further, they have to be closely linked with system functions to prevent functional problems. This study provides a systematic approach to identification of potential hazards for their tracking and management during the system life cycle to assure the identification and definition of the most appropriate hazards.

• Journal of the Korean Society for Precision Engineering
• /
• v.10 no.1
• /
• pp.142-146
• /
• 1993

The vibrations of the main spindle of the M/C tools are most important in the consideration of the dymamic performance of the M/C tools. In order to resolve and predict the dynamic behaviour of the main syindle it is necessary to have the mathematical model of the system. The system identification is the procedure to provide us with the mathematical model of the procedure of the system identification of the main spindle of the M/C tools to predict the dynamic behaviour of the machine and further to have the basis for the design of M/C tools.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.4 no.2
• /
• pp.21-28
• /
• 2008

There are some problems on the system that uses a password comprising a digital signature to identify the secret key owner under the public key infrastructure. For example, the password can be difficult to remember or easy to be disclosure, and users should make more complex password to protect it. A number of studies have been proceeded in order to overcome these defects using the fingerprint identification technologies, but they need to change the current standard of public key infrastructure. On the suggested private key escrow system, the private key can be withdrawn only through the enrollment and identification of a fingerprint template after it is saved to a reliable third system. Therefore, this new private key escrow system can remove previous inconveniences of managingthe private key on current public key infrastructure, and it exhibited superior results in terms of the evaluation items when compared with the integrated method of the existing fingerprint identification and public key infrastructure.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.45 no.4
• /
• pp.574-581
• /
• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Networks(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism. Each local network learns only data in a subregion. This paper also discusses neural network as identifier of nonlinear dynamical systems. The structure of nonlinear system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems. (author). 13 refs., 7 figs., 2 tabs.

• Journal of Power Electronics
• /
• v.17 no.6
• /
• pp.1545-1552
• /
• 2017

Identifying the load and mutual inductance is essential for improving the power transfer capability and power transfer efficiency of Inductive Power Transfer (IPT) systems. In this paper, a steady-state load and mutual inductance identification method focusing on series-parallel compensated IPT systems is proposed. The identification model is established according to the steady-state characteristics of the system. Furthermore, two sets of identification results are obtained, and then they are analyzed in detail to eliminate the untrue one. In addition, the identification method can be achieved without extra circuits so that it does not increase the complexity of the system or the control difficulty. Finally, the feasibility of the proposed method has been verified by simulation and experimental results.

• Structural Engineering and Mechanics
• /
• v.78 no.3
• /
• pp.369-378
• /
• 2021

This paper reviewed a few output-only system identification algorithms and identified the shortcomings of those popular blind source separation methods. To address the issues such as less sensors than the targeted modal modes (under-determinate problem), repeated natural frequencies as well as systems with complex mode shapes, this paper proposed a complex wavelet modified second order blind identification method (CWMSOBI) by transforming the time domain problem into time-frequency domain. The wavelet coefficients with different dominant frequencies can be used to address the under-determinate problem, while complex mode shapes are addressed by introducing the complex wavelet transformation. Numerical simulations with both high and low signal-to-noise ratios validate that CWMSOBI can overcome the above-mentioned issues while obtaining more accurate identified results than other blind identification methods.

• Journal of Korean Port Research
• /
• v.14 no.2
• /
• pp.155-163
• /
• 2000

A discrete system has interpreted by using the network model, and PERT network is one of these methods. For the purpose of analysing the real system, it is necessary to measure the parameter of the real system. And system identification problem is to assume the parameter of a real system when we get to know the system model, the input data and output data. System identification method has been only developed to a system of which a structure has expressed a differential equation or a polynomial expression. But it has been scarcely developed yet in that case of network model. The aim of this paper is to examine a changes when new system is introduced to the present system. The changes are as follows : how the present system will be changed, when the changes will be happened. In this paper, genetic algorithm is used to assume the parameter.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 1999.10a
• /
• pp.101-108
• /
• 1999

A discrete system has interpreted by using the network model, and PERT network is one of these methods. For the purpose of analysing the real system. it is necessary to measure the parameter of the real system. And system identification problem is to assume the parameter of a real system when we get to know the system model, the input data and output data. System identification method has been only developed to a system of which a structure has expressed a differential equation or a polynomial expression. But it has been scarcely developed yet in that case of network model. The aim of this paper is to examine a changes when new system isn introduced to the present system, The changes are as follows: how the present system will be changed, when the changes will be happened. In this paper, genetic algorithm is used to assume the parameter.

• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.280-284
• /
• 1998

Many classes of nonlinear systems can be represented by Volterra kernel expansion. Therefore, identification of Volterra kernels of nonlinear system is an important task for obtaining the nonlinear characteristics of the nonlinear system. Although one of the authors has recently proposed a new method for obtaining the Volterra kernels of a nonlinear system by use of M-sequence and correlation technique, our mettled of nonlinear system identification is limited to Wiener-type nonlinear system and we can not apply this method to the identification of Hammerstein-type nonlinear system. This paper describes a new mettled for obtaining Volterra kernels of Hammerstein nonlinear system by adding a linear element in front of tile Hammerstein system. First we calculate the linear element of Hammerstein system by use of conventional correlation method. Secondly, we put a linear element in front of Hammerstein system. Then the total system becomes Wiener-type nonlinear system. Therefore we can use our method on Volterra kernel identification by use of M-sequence. Thus we get the coefficients of the approximation polynomial of nonlinear element of Hammerstein system. From the results of simulation, a good agreement with theoretical considerations is obtained, showing a wide applicability of our method.