• International conference on construction engineering and project management
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• 2009.05a
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• pp.388-392
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• 2009

Operation of LNG (Liquefied Natural Gas) plants requires an effective maintenance strategy. To this end, the long-term and short-term trend of faults, such as mechanical and electrical troubles, should be identified so as to take proactive approach for ensuring the smooth and productive operation. However, it is not an easy task to predict the fault trend in LNG plants. Many variables and unexpected conditions make it quite difficult for the facility manager to be well prepared for future faulty conditions. This paper presents a model to predict the fault trend in a LNG plant. ARIMA (Auto-Regressive Integrated Moving Average) model is combined with Wavelet Transform to enhance the prediction capability of the proposed model. Test results show the potential of the proposed model for the preventive maintenance strategy.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.647-656
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• 2011

Motion estimation using one-bit transform and two-bit transform reduces the complexity for computation of matching error; however, the peak signal-to-noise ratio (PSNR) is degraded. Modified 1BT (M1BT) and modified 2BT (M2BT) have been proposed to compensate degraded PSNR by adding conditional local search. However, these algorithms require many additional search points in fast moving sequences with a block size of $16{\times}16$. This paper provides more efficient search method by preparing candidate blocks using the number of non-matching points (NNMP) than the conditional local search. With this NNMP-based search, we can easily obtain candidate blocks with small NNMP and efficiently search final motion vector. Experimental results show that the proposed algorithm not only reduces computational complexity, but also improves PSNR on average compared with conventional search algorithm used in M1BT, M2BT and AM2BT.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1755-1760
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• 2016

Automation of greenhouses has proved to be extremely helpful in maximizing crop yields and minimizing labor costs. The optimum conditions for cultivating plants are regularly maintained by the use of programmed sensors and actuators with constant monitoring of the system. In this paper, we have designed a prototype of a smart greenhouse using Arduino microcontroller, simple yet improved in feedbacks and algorithms. Only three important microclimatic parameters namely moisture level, temperature and light are taken into consideration for the design of the system. Signals acquired from the sensors are first isolated and filtered to reduce noise before it is processed by Arduino. With the help of LabVIEW program, Time domain analysis and Fast Fourier Transform (FFT) of the acquired signals are done to analyze the waveform. Especially, for smoothing the outlying data digitally, Moving average algorithm is designed. With the implement of this algorithm, variations in the sensed data which could occur from rapidly changing environment or imprecise sensors, could be largely smoothed and stable output could be created. Also, actuators are controlled with constant feedbacks to ensure desired conditions are always met. Lastly, data is constantly acquired by the use of Data Acquisition Hardware and can be viewed through PC or Smart devices for monitoring purposes.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.4
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• pp.519-531
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• 2015

It has been widely accepted that the pressure management of water distribution systems using pressure reducing valves(PRVs) would be an effective method for controlling leakages. A pressure reducing valve (PRV) regulates outlet pressure regardless of fluctuating flow and varying inlet pressure, thereby reducing leakage and mitigating the stress on the water distribution system. However, the operation of a PRV is vulnerable to its mechanical condition and hydraulic operability. In this research, the effect of PRVs installed in water distribution system are evaluated in terms of hydraulic pressure reduction and mechanical performance by analyzing measured pressure data with statistical approach. A statistical approach using the moving average filter and frequency analysis based on fourier transform is presented to detect abnormally operated PRVs that have been densely installed in water distribution system. The result shows that the proposed approach can be a good performance evaluation method by simply measuring pressures for the PRVs.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.733-740
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• 2004

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) is proposed. The whole system consists of two-lead electrocardiogram acquisition (lead Ⅰ and Ⅱ), baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problem of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we proposed a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 subjects, and we could obtain satisfactory respiration signals that shows high correlation (r>0.9) with the signal acquired from the chest-belt respiration sensor.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2271-2280
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• 1999

In this paper, a variable motion estimation scheme based on HBMA(Hierarchical Block Matching Algorithm) to improve the performance and to reduce heavy computational and transmission load, is presented. The proposed algorithm is composed of four steps. First, block activity for each block is defined using the edge information of differential image between two sequential images, and then average block activity of the present image is found by taking the mean of block activity. Secondly, camera pan compensation is carried out, according to the average activity of the image, in the hierarchical pyramid structure constructed by wavelet transform. Next, the LUT classifying each block into one among Moving, No Moving, Semi-Moving Block according to the block activity compensated camera pan is obtained. Finally, as varying the block size and adaptively selecting the initial search layer and the search range referring to LUT, the proposed variable HBMA can effectively carries out fast motion estimation in the hierarchical pyramid structure. The cost function needed above-mentioned each step is only the block activity defined by the edge information of the differential image in the sequential images.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.2
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• pp.62-70
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• 2007

The aiming ability is a to improve accuracy performance of the firing vehicle. This paper describes the detection method of chatter vibration using disturbance acceleration in the pointing structure. In order to analysis vibration trends of the pointing system occurred during vehicle drive, acceleration data was processed by using data processing algorithm with moving average and Hilbert transform. Specific mode constants of acceleration were obtained under various disturbances. Vehicle velocity, road condition, property of pointing structure were considered as factors which make change of vibration trend in vehicle dynamics. Finally, back propagation neural networks have been applied to the pattern recognition for the classification of vibration signal in various driving conditions. Results of signal processing were compared and analysed.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.03a
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• pp.51-58
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• 2002

The relative site amplification factors in southern Korea were determined from coda waves using coda normalization method. The seismograms of 15 events at 79 stations were used in this study. Seismogram envelopes were obtained by the Hilbert transform of bandpass-filtered velocity seismograms with frequency bands at 1-2Hz, 2-4Hz, 4-8Hz, 8-l6Hz and 16-32Hz. The envelopes were stabilized by application of moving-average scheme with time window of 1 second. The relative amplitudes of seismogram envelope were computed by dividing the amplitude of seismogram envelope at one site by the amplitude of seismogram envelope at reference site. The relative site amplification factors were obtained by taking averages of the relative amplitude. Values of relative site amplification factors in southern Korea are generally low in western area and high in eastern area.

• ETRI Journal
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• v.37 no.1
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• pp.1-10
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• 2015

A blind hopping phase estimator is proposed for the demodulation of received signals in frequency-hopping spread spectrum systems. The received signals are assumed to be bandwidth limited with a shaping filter, modulated as frequency modulation (FM) or binary frequency shift keying (BFSK), and hopped by predetermined random frequency sequences. In the demodulation procedure in this paper, the hopping frequency tracking is accomplished by choosing a frequency component with maximum amplitude after taking a discrete Fourier transform, and the hopping phase estimator performs the conjugated product of two consecutive signals and moving-average filtering. The probability density function and Cramer-Rao low bound (CRLB) of the proposed estimator are evaluated. The proposed scheme not only is very simple to implement but also performs close to the CRLB in demodulating hopped FM/BFSK signals.