• Journal of Power Electronics
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• v.4 no.2
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• pp.102-111
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• 2004

This paper is concerned with a deadbeat current control implementation of shunt-type three-phase active power filter (APF). Although the one-dimensional deadbeat control method can attain time-optimal response of APF compensating current, one sampling period is actually required fur its settling time. This delay is a serious drawback for this control technique. To cancel such a delay and one more delay caused by DSP execution time, the desired APF compensating current has to be predicted two sampling periods ahead. Therefore an adaptive predictor is adopted for the purpose of both predicting the control error of two sampling periods ahead and bringing the robustness to the deadbeat current control system. By adding the adaptive predictor output as an adjustment term to the reference value of half a source voltage period before, settling time is made short in a transient state. On the other hand, in a steady state, THD (total harmonic distortion) of the utility grid side AC source current can be reduced as much as possible, compared to the case that ideal identification of controlled system could be made.

• Journal of Environmental Policy
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• v.12 no.3
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• pp.97-122
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• 2013

The ARDL(Autoregressive Distributed Lag) method is employed analyzes the long-run equilibrium relationships among environmental pollution($CO_2$ emissions) per capita, income levels per capita, and energy consumption per capita. The error correction model is employed to analyze the short-term effects of income and energy consumption on $CO_2$ emissions. The Toda-Yammamoto method is employed for causal analysis among the three variables. The results show that income levels, energy consumption, and $CO_2$ emissions are cointegrated. We found the N type relationship between income and $CO_2$ emissions. Long-term elasticities of income and energy consumption with respect to $CO_2$ emission were greater than their short-term elasticities. There were a bilateral causality between energy consumption and $CO_2$ emissions. There was a unilateral causality from $CO_2$ emissions to income and from energy consumption to income not vice versa. Energy consumption can be an important variable to contribute to forecasting $CO_2$ emissions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.7189-7195
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• 2015

The method of moments (MoM) is implemented to simulate scattering from a PEC (perfectly electric conductor) cylinder in the TE(transversw electric) EFIE (Electric Field Integral Equation) approach. The procedure expresses the singularity integral and the hypersingularity integral in terms of an analytic function and employs a singularity isolation process coupled with numerical technique along the discretized segment to evaluate the self terms. It is known that, in the MoM technique, the choice of base functions and test functions is very important for the accuracy and convergence of the numerical analysis. Thus, in this paper, three conditions, obtained from the combination of basis functions and test functions, are adopted to get the induced currents on the PEC surface. These currents are compared to the analytical one in the relative rms current error to get the condition that shows fast convergence rate. The fast order of convergence of the current error, 2.528, is obtained under the combination of pulse basis function/delta test function.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1125-1134
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• 2023

Out of the total 17,000 reservoirs in Korea, 13,600 small agricultural reservoirs do not have hydrological measurement facilities, making it difficult to predict water storage volume and appropriate operation. This paper examined univariate and multivariate long short-term memory (LSTM) modeling to predict the storage rate of agricultural reservoirs using remote sensing and artificial intelligence. The univariate LSTM model used only water storage rate as an explanatory variable, and the multivariate LSTM model added n-day accumulative precipitation and date of year (DOY) as explanatory variables. They were trained using eight years data (2013 to 2020) for Idong Reservoir, and the predictions of the daily water storage in 2021 were validated for accuracy assessment. The univariate showed the root-mean square error (RMSE) of 1.04%, 2.52%, and 4.18% for the one, three, and five-day predictions. The multivariate model showed the RMSE 0.98%, 1.95%, and 2.76% for the one, three, and five-day predictions. In addition to the time-series storage rate, DOY and daily and 5-day cumulative precipitation variables were more significant than others for the daily model, which means that the temporal range of the impacts of precipitation on the everyday water storage rate was approximately five days.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.63-72
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• 2018

Purpose - This work analyzes, in detail, the specification of vector error correction model (VECM) and thus examines the relationships and impact among seven economic variables for USA - balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), a measure of the money supply that includes total currency as well as large time deposits, institutional money market funds, short-term repurchase agreements and other larger liquid assets (M3), real rate of interest (IR_REAL) and household credits (LOAN). In particular, we search for the main explanatory variables that have an effect on stock and real estate market, respectively and investigate the causal and dynamic associations between them. Research design, data, and methodology - We perform the time series vector error correction model to infer the dynamic relationships among seven variables above. This work employs the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root techniques to test for stationarity among seven variables under consideration, and Johansen cointegration test to specify the order or the number of cointegration relationship. Granger causality test is exploited to inspect for causal relationship and, at the same time, impulse response function and variance decomposition analysis are checked for both short-run and long-run association among the seven variables by EViews 9.0. The underlying model was analyzed by using 108 realizations from Q1 1990 to Q4 2016 for USA. Results - The results show that all the seven variables for USA have one unit root and they are cointegrated with at most five and three cointegrating equation for USA. The vector error correction model expresses a long-run relationship among variables. Both IR_REAL and M3 may influence real estate market, and GDP does stock market in USA. On the other hand, GDP, IR_REAL, M3, STOCK and LOAN may be considered as causal factors to affect real estate market. Conclusions - The findings indicate that both stock market and real estate market can be modelled as vector error correction specification for USA. In addition, we can detect causal relationships among variables and compare dynamic differences between countries in terms of stock market and real estate market.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.93-98
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• 2016

Recently, the project related to the smart grid are being actively studied around the developed world. In particular, the long-term stabilization measures distributed power supply problem has been highlighted. In this paper, we propose a three-dimensional numerical weather prediction models to compare the error rate information which combined with the physical models and statistical models to predict the output of distributed power. Proposed model can predict the system for a stable power grid-can improve the prediction information of the distributed power. In performance evaluation, proposed model was a generation forecasting accuracy improved by 4.6%, temperature compensated prediction accuracy was improved by 3.5%. Finally, the solar radiation correction accuracy is improved by 1.1%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1B
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• pp.38-47
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• 2008

The system test was accomplished in delivery time for a suitable of various requirements at the software market. Especially, critical faults must be detected and removed for a close main functions and users against target system. In generally, proposed test methods are executed with a calendar time, not a competitive and effectiveness method as selective software testing. These methods are inapplicable to short term test or early system development stage. Moreover, it's accompanied by heavy cost. Overcoming of these problems, must attempted to new software test method role of core function in the system test. Selective software testing method is decided to mixing with the three-information such as a frequency, complexity of use scenario and fault impact. Using this information, searching a fatal error and usefully system test for an executed test scenario. In this paper, we have proposed new test method and verified testing results for the detection of critical faults or search a fatal errors with a system main function.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.713-719
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• 2009

In this paper, we develope a water demand forecasting algorithm using AR(Auto-regressive) and MLP(Multi-layer perceptron). To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "A" purification plant at Jeon-Buk province during 2007-2008, and then performed the proposed method with various input factors selected through various analyses. As noted in experimental results, the performance of three types model such as multi-regressive, AR(Auto-regressive), and AR+MLP(Auto-regressive + Multi-layer perceptron) show 5.1%, 3.8%, and 3.6% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict short-term water demand for the efficient operation of a water purification plant.

• Journal of Korea Trade
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• v.25 no.4
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• pp.17-36
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• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.