• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.12
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• pp.714-722
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• 2002

This paper presents a control algorithm for a 3-phase 4-wire series active Power filter. This control algorithm compensates harmonics, input power factor and neutral line currents which are generated by balanced or unbalanced nonlinear loads. The advantage of this control algorithm is direct extraction of compensation voltage references. Therefore, the calculation time is shortened and the performance of the series active power filter is improved. The compensation principle of the proposed control algorithm is presented in detail. A 3KVA laboratory prototype of the three-phase four-wire series active power filter was built and experiments have been carried out. Experimental results are shown to verify the effectiveness of the proposed control algorithm.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.39-57
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• 2021

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.113-121
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• 2000

This paper presents a parallel-structure fuzzy system(PSFS) for prediction of time series data. The PSFS consists of a multiple number of fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts the same future data independently based on its past time series data with different embedding dimension and time delay. The component fuzzy systems are characterized by multiple-input singleoutput( MIS0) Sugeno-type fuzzy rules modeled by clustering input-output product space data. The optimal embedding dimension for each component fuzzy system is chosen to have superior prediction performance for a given value of time delay. The PSFS determines the final prediction result by averaging the outputs of all the component fuzzy systems excluding the predicted data with the minimum and the maximum values in order to reduce error accumulation effect.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.449-456
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• 2004

This paper proposes a novel recognition model, a recurrent fuzzy associative memory(RFAM), for recognizing time-series patterns contained an ambiguity. RFAM is basically extended from FAM(Fuzzy Associative memory) by adding a recurrent layer which can be used to deal with sequential input patterns and to characterize their temporal relations. RFAM provides a Hebbian-style learning method which establishes the degree of association between input and output. The error back-propagation algorithm is also adopted to train the weights of the recurrent layer of RFAM. To evaluate the performance of the proposed model, we applied it to a word boundary detection problem of speech signal.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.273-295
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• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.

• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.1
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• pp.9-16
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• 2003

This paper presents an novel multilevel inverter employing series connected transformers to reduce the harmonics of output voltage. The proposed inverter consists of several full-bridge modules and their corresponding transformers. Continuous output voltage levels can be generated from a suitable selection of turns ratio of transformer. And it appears an integral ratio to input DC source. Because of its series operation of transformers, output filter inductor is not required. The validity of the proposed system is verified through the experimental results using a 1.5 [kW] prototype, which can generate a 220 $[V_ac]$ output voltage from $[V_dc]$ input.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1995.07c
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• pp.1140-1142
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• 1995

The transformer is fabricated with two piezoelectric vibrator with the divided electrodes and adhesive insulator. We applied the electric input to the driving vibrator in parallel and connect the output voltage to the generating vibrator in series to the resistor load near its fundamental resonance frequency. Then we investigate output voltage in series twice as large as in parallal. Moreover we investigate the load characteristics at resonance frequencies under various resistor and the frequency characteristics near the resonance frequency under no load. Its equvalent circuit is derived from the Mason's model of a thickness-driven piezelectric vibrator. By its equevalent circuit, symbolic expressions for input impedances, voltage ratios, resonance frequencies, and bandwidths have been derived. The values calculated from those symbolic exprssions are shown to agree well with the measurement values.

• Journal of Power Electronics
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• v.13 no.2
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• pp.177-185
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• 2013

This paper studies a soft switching DC/DC converter to achieve zero voltage switching (ZVS) for all switches under a wide range of load condition and input voltage. Two three-level PWM circuits with the same power switches are adopted to reduce the voltage stress of MOSFETs at $V_{in}/2$ and achieve load current sharing. Thus, the current stress and power rating of power semiconductors at the secondary side are reduced. The series-connected transformers are adopted in each three-level circuit. Each transformer can be operated as an inductor to smooth the output current or a transformer to achieve the electric isolation and power transfer from the input side to the output side. Therefore, no output inductor is needed at the secondary side. Two center-tapped rectifiers connected in parallel are used at the secondary side to achieve load current sharing. Due to the resonant behavior by the resonant inductance and resonant capacitance at the transition interval, all switches are turned on at ZVS. Experiments based on a 1kW prototype are provided to verify the performance of proposed converter.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.370-372
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• 2008

A resonant converter suitable for applications of wide input variation is proposed. A half-bridge converter and a phase-shift converter are combined in primary and the secondaries of each transformer are connected in series which makes four voltage levels. Furthermore, by adopting resonant converter scheme, a resonant capacitor is connected with the secondaries of the transformers in series and an output inductor is removed. Zero voltage switching is achieved from full load to no load. The analysis of the resonant converter and experimental results of 200W prototype is presented to verify the features of the converter.