• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2314-2316
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• 2000

In the construction of successful fuzzy models and/or controllers for nonlinear systems, identification of a good fuzzy Neural inference system is an important yet difficult problem, which is traditionally accomplished by trial and error process. In this paper, we propose a systematic identification procedure for complex multi-input single- output nonlinear systems with DNA coding method.DNA coding method is optimization algorithm based on biological DNA as are conventional genetic algothms (GAs). We also propose a new coding method for applying the DNA coding method to the identification of fuzzy Neural models. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2687-2689
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• 2000

In many cases the systems are so complex that it is not possible to obtain reasonable models using physical insight. Also a model based on physical insight contains a number of unknown parameters even if the structure is derived from physical laws. These problems can be solved by system identification. In this paper, Arago's disk system which has both stable and unstable regions is selected as an example for identification and a state-space model is identified using tailor-made model structure of this system. In stable region, a state-space model of Arago's disk system is identified through open loop experiment and a state-space model of unstable region is identified through closed loop experiment after using fuzzy controller to stabilize unstable system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1996년도 봄 학술발표회 논문집
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• pp.119-126
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• 1996

In this paper, a method of substructural identification is presented for the estimation of localized structural parameters. for this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for the substructure to process the measurement data impaired by noises. The sequential prediction error method is used fer the estimation of unknown localized parameters. Using the substructural method, the number of unknown parameters can be reduced and the convergence and accuracy of estimation can be improved. For some substructures, the effect of the input excitation is expressed in terms of the responses at the inferences with the main structure, and substructural identification may be carried out without measuring the actual input excitation to the whole structure. Example analysis is carried out for idealized structural models of a multistory building and a truss bridge. The results indicate that the present method is effective and efficient for local damage estimation of complex structures.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.335-339
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• 1998

This paper presents a closed loop identification algorithm in time domain. This algorithm can be used for identification of unstable system and for model validation of system which is difficult to derive analytical model. In time domain, projection filter, which projects a finite number of input output data of a system into its current space, is used to relate the state space model with a finite difference model. Then recursive relations between the Markov parameters and the ARX model coefficients are derived to identify the system, controller and Kalman filter Markov parameters recursively, which are finally used to identify the system, controller and Kalman filter gains. The NASA LAMSTF is used to validate the algorithms developed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.235-237
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• 1993

Semiconductor devices generate harmonics which induced bad effects against power distribution systems. To surpress harmonics, the filter design and the identification of harmonic load sources are needed. In this paper, artificial neural networks are used to identify the nonlinear relationship between harmonic loads and harmonic currents that vary at times. To find the best adequate network for solving this identification problem, we compared with recognition rates of neural networks by changing hidden layer neuron number.

• 전기학회논문지
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• 제66권2호
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• pp.425-430
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• 2017

This paper presents a novel method for identification of FSK modulated radar signal. Three features which measure the number of frequency tones, the regularity of the frequency shifting, and the diversity of power spectrum of detected radar signal, are introduced. A Two-step combined maximum likelihood classifier was used to identify the details of the detected FSK signal; the modulation order and the use of Costas code. We attempted to divide FSK signal into binary FSK, ternary FSK, 8-ary FSK, and FSK with Costas code of length 7. The simulation results indicated that the proposed methods achieves an averaged identification accuracy was 99.93% at a signal-to-noise of 0 dB.

• Journal of Information Processing Systems
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• 제13권1호
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• pp.141-151
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• 2017

Effective identification of wireless channel in different scenarios or regions can solve the problems of multipath interference in process of wireless communication. In this paper, different characteristics of wireless channel are extracted based on the arrival time and received signal strength, such as the number of multipath, time delay and delay spread, to establish the feature vector set of wireless channel which is used to train backpropagation (BP) neural network to identify different wireless channels. Experimental results show that the proposed algorithm can accurately identify different wireless channels, and the accuracy can reach 97.59%.

• 품질경영학회지
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• 제39권2호
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• pp.234-243
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• 2011

Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based $T^2$ decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric $T^2$ decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.

• Journal of Microbiology
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• 제41권4호
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• pp.320-326
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• 2003

In order to identify Vibrio vulnificus in the Yellow Sea near Gunsan, Korea during the early and late summers, the efficiency of the real-time quantitative TaqMan PCR was compared to the efficiency of the conventional PCR and Biolog identification system^TM. Primers and a probe were designed from the hemolysin/cytolysin gene sequence of V. vulnificus strains. The number of positive detections by real-time quantitative TaqMan PCR, conventional PCR, and the Biolog identification system from seawater were 53 (36.8%), 36 (25%), and 10 strains (6.9%), respectively, among 144 samples collected from Yellow Sea near Gunsan, Korea. Thus, the detection method of the real-time quantitative TaqMan PCR assay was more effective in terms of accuracy than that of the conventional PCR and Biolog system. Therefore, our results showed that the real-time TaqMan probe and the primer set developed in this study can be applied successfully as a rapid screening tool for the detection of V. vulnificus.