• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.34-39
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• 2015

In this paper, a finite impulse response (FIR) filter is proposed for discrete-time nonlinear systems. The proposed filter is designed by combining the estimate of the perturbation state and nominal state. The perturbation state is estimated by adapting the optimal time-varying FIR filter for the linearized perturbation model and the nominal state is directly obtained from the nonlinear nominal trajectory model. Since the FIR structured estimators use the finite horizon information on the most recent time interval, the proposed extended FIR filter satisfies the bounded input/bounded output (BIBO) stability, which can't be obtained from infinite impulse response (IIR) estimators. Thus, it can be expected that the proposed extended FIR filter is more robust than IIR structured estimators such as an extended Kalman filter for the round-of errors and the uncertainties from unknown initial states and uncertain system model parameters. The simulation results show that the proposed filter has better performance than the extended Kalman filter (EKF) in both robustness and fast convergency.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1173-1185
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• 2017

In this paper, a new single phase multilevel inverter topology with a single DC source is presented. The proposed topology is developed based on the concepts of the L-Z source inverter and the switched capacitor multilevel inverter. The input voltage to the proposed inverter is boosted by two steps: the first step by an impedance network and the second step by switched capacitor units. Compared to other existing topologies, the presented topology can produce a higher boosted multilevel output voltage while using a smaller number of components. In addition, it provides more flexibility to control boosting factor, size, cost and complexity of the inverter. The proposed inverter possesses all the advantages of the L-Z source inverter and the switched capacitor multilevel inverter like controlling the start-up inrush current and capacitor voltage balancing using a simple switching strategy. The operating principle and general expression for the different parameters of the proposed topology are presented in detail. A phase disposition pulse width modulation strategy has been developed to switch the inverter. The effectiveness of the topology is verified by extensive simulation and experimental studies on a 7-level inverter structure.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.423-431
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• 2000

This paper presents a systematic approach to the input-output data-based fuzzy modeling for the complex and uncertain nonlinear systems, in which the conventional mathematical models may fail to give the satisfying results. To do this, we propose a new method that can yield a successful fuzzy model using a mGA hybrid schemes with a fine-tuning method. We also propose a new coding method fo chromosome for applying the mGA to the structure and parameter identifications of fuzzy model simultaneously. During mGA search, multi-purpose fitness function with a penalty process is proposed and adapted to guarantee the accurate and valid fuzzy modes. This coding scheme can effectively represent the zero-order Takagi-Sugeno fuzzy model. The proposed mGA hybrid schemes can coarsely optimize the structure and the parameters of the fuzzy inference system, and then fine tune the identified fuzzy model by using the gradient descent method. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its applications to two nonlinear systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1004-1007
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• 2005

This paper presents a programmable RF BIST (Built-in Self-Test) circuit for low noise amplifiers. We have developed a new on-chip RF BIST circuit that measures RF parameters of low noise amplifier (LNA) using only DC measurements. The BIST circuit contains test amplifier with programmable capacitor banks and RF peak detectors. The test circuit utilizes output DC voltage measurements and these measured values are translated into the LNA specifications such as input impedance and gain using the mathematical equations. Our on-chip BIST can be self programmed for 1.8GHz, 2.4GHz and 5.25GHz LNA for GSM, Bluetooth and IEEE802.11g standards.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1958-1960
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• 2003

Here we describe a unique self-oscillating mixer (SOM) design using a modified defected ground structure (DGS) for down-converter. Proposed SOM is consisted of self-oscillator, which can produce negative resistance and select resonance frequency, and input/output matching filter. As the advantage of this SOM can be reused by module that mix signals with transistor that is used to oscillator, it is simply and low-costly designed Also, there is easy advantage to be applied in RFIC/ MMIC technology because it offers excellent high Q value in spite of using micro-strip structure. Designed self-oscillating frequency is 1.04GHz and RF frequency established is 0.8GHz. It was achieved 20dB conversion loss and phase noise of -95dBc/Hz at 100KHz offset frequency over intermediate frequency (IF). The equivalent circuit parameters for DGS are extracted by using a three dimensional EM simulator and simple circuit analysis method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.7
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• pp.551-560
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• 2004

A method how to determine the shock response spectrum from the FRF of the statistical energy analysis( SEA ) is presented here. The system of 3 different Plates connected by bolt joints is selected simulating missile structural sections Joined together. First, the SEA model was rendered by SEA parameters which were determined from experimental SEA method. Then, the mobility power was input to the SEA model and we can verify the validity of the model in the medium to high frequency range checking the reproduction of output average velocity. And, the shock induced shock response spectrum(SRS) was obtained using SEA FRF and arbitrarily chosen experimental FRF. We have compared the thus obtained SRS with actually measured SRS and they were relatively in good agreement. In this paper, we used the measured SEA FRF and therefore we have got the SRS well agreed with actually measured SHS even in the low frequency range. If the SEA FRF of well verified SEA model is used, the good result will come out in SEA effective frequency range which is more important at SRS.

• Geomechanics and Engineering
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• v.12 no.3
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.137-146
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• 2011

In order to support increased consumer awareness regarding energy consumption, we present new ways of monitoring and predicting with energy in electric appliances. The proposed system is a design of a common electrical power outlet called smart plug that measures the amount of current passing through current sensor at 0.5 second. To acquire data for training and testing the proposed neural network, weather parameters used include average temperature of day, min and max temperature, humidity, and sunshine hour as input data, and power consumption as target data from smart plug. Using the experimental data for training, the neural network model based on Back-Propagation algorithm was developed. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the proposed neural network model can predict the power consumption quite well with correlation coefficient was 0.9965, and prediction mean square error was 0.02033.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.103-109
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• 2001

Soft sensor is an effective method to deal with the estimation of variables, which are difficult to measure because of the reasons of economy or technology. Fuzzy logic system can be used to develop the soft sensor model by infinite rules, but the fuzzy dividing of variable sets is a key problem to achieve an accurate fuzzy logic model, In this paper, we proposed a new method to develop soft sensor model based on fuzzy neural network. First, using a novel method to divide the variable fuzzy sets by the process input and output data. Second, developing the fuzzy logic model based on that fuzzy set dividing. After that, expressing the fuzzy system with a fuzzy neural network and getting the initial soft sensor model based FNN. Last, adjusting the relative parameters of soft sensor model by the BP learning method. The effectiveness of the method proposed and the preferable generalization ability of soft sensor model built are demonstrated by the simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.878-882
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• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.