• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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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.

• 방송공학회논문지
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• 제16권4호
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• pp.647-656
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• 2011

1비트 변환 (one-bit transform) 및 2비트 변환 (two-bit transform)을 이용하는 이진 블록 매칭 움직임 예측 (motion estimation) 방법은 전역 탐색 (full search) 움직임 예측 방법에 비해 블록 매칭 연산의 복잡도를 감소시키지만 PSNR (Peak Signal-to-Noise Ratio)성능 저하를 야기한다. 이러한 이진 블록 매칭 움직임 예측 방법의 정확도를 개선하기 위해 조건부 국부 탐색 (conditional localsearch)이 더해져 보완된 1비트 변환 (modified one-bit transform) 및 보완된 2비트 변환 (modified two-bit transform) 방법이 제안되었다. 그러나 이와 같이 추가적인 국부 탐색은 움직임이 빠른 영상에 대한 $16{\times}16$ 블록 크기의 움직임 예측에 있어서 많은 수의 추가적인 탐색을 필요로 한다. 본 논문은 기존의 조건부 국부 탐색 방법 대신 탐색 영역내의 각 후보 블록들의 (candidate blocks) NNMP(Number of Non-Matching Points)를 기반으로 한 효율적인 탐색 방법을 제안한다. NNMP 기반 탐색 방법을 통하여 작은 NNMP 값을 가지는 후보 블록들을 쉽게 탐색하여 최종 움직임 벡터(motion vector)를 효율적으로 찾을 수 있다. 실험을 통하여 제안하는 알고리듬이 기존의 방법들보다 복잡도 및 정확도 측면에서 좋은 성능을 보여주는 것을 확인하였다.

• 전기학회논문지
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• 제65권10호
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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.

• 상하수도학회지
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• 제29권4호
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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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• 제6권5호
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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.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권10호
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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.

• 한국정보처리학회논문지
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• 제6권8호
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• pp.2271-2280
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• 1999

본고에서는 움직임 추정 성능을 개선하고 과도한 연산량과 전송 부담을 경감시키기 위해 HBMA에 기반한 가변 움직임 추정 기법을 제안한다. 제안된 알고리즘은 크게 다음과 같이 네 단계로 구성된다. 우선, 연속된 두 프레임 간의 차영상 윤곡 정보에서 정의한 블록 활동도를 평균하여 현재 영상의 평균 블록 활동도를 산출한다. 두 번째로, 이렇게 산출한 평균 블록 활동도를 통해 카메라 패닝의 유무를 검출한 후, 웨이블렛 변환에 의해 구성한 피라미드 계층 구조상에서 카메라 패닝 벡터를 추정하여 보상한다. 다음으로, 카메라 패닝 보상 후에 정의한 블록 활동도를 토대로 각 블록을 움직임 블록, 준 움직임 블록, 비 움직임 블록 중 어느 하나로 분류한 검색 테이블을 작성한다. 마지막으로, 제안된 가변 HBMA는 검색 테이블을 참조하여 블록 크기를 가변시키고 초기 탐색 계층 및 탐색 영역을 적응적으로 선정함으로써 피라미드 계층 구조상에서 효율적인 고속 움직임 추정을 수행할 수 있다. 이상에서 설명한 각 단계에서 요구되는 비용함수는 차영상 윤곽정보를 통해 획득한 블록 활동도를 공통적으로 이용한다.

• 한국추진공학회지
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• 제11권2호
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• pp.62-70
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• 2007

지향성능은 발사차량의 정확성에 있어서 중요한 요소이다. 본 연구는 외란 가속도를 이용하여 지향구조물의 진동을 감지하는 방안과 실험 결과에 대하여 기술하였다. 주행 중 발생하는 진동 경향을 분석하기 위하여 가속도 자료를 이동평균과 힐버트 변환을 이용하여 신호 처리하였다. 다양한 외란에 대하여 가속도의 모드 계수를 얻었으며, 차량속도, 노면조건, 지향구조물의 특성을 차량 동특성의 진동을 변화시키는 요소로 간주하였다. 마지막으로 다양한 주행 조건의 진동 신호를 분류하기 위한 패턴인식에 역전파 신경망 이론을 이용하였다. 각 조건에 대하여 실험 결과를 비교 분석하였다.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2002년도 춘계 학술발표회 논문집
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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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• 제37권1호
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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.