• Journal of the KSME
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• v.28 no.2
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• pp.169-174
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• 1988

신호처리 및 해석에 대한 교육효과를 증대시키기 위해서는 응용사례의 발굴, 프로젝트 개발, P C를 이용한 그래픽스 교육도 이루어져야 하며, 스펙트럼 분석기술 이외에 시간영역 파라미터 모형에 의한 신호해석기법〔예를 들어 자동회귀-이동평균(ARMA)등의 시계열 모형화〕도 최근 에는 실시간 응용의 가능성이 높아지는 추세에 있으므로 PC를 이용한 신호처리 및 해석 교육에 반영하는 것이 바람직하다.

• Korean Management Science Review
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• v.1
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• pp.68-80
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• 1984

Box-Jenkins 시계열 분석법은 변수에 관한 정보가 부족하거나 너무 많은 변수가 영향을 미치고 있는 경우에도 과학적인 예측치를 구할 수 있는 단기예측 방법이다. Box-Jenkins 모형은 자동회귀 모형(Autoregressive Model), 이동평균 모형 (Moving average Model), 계절적 시계열 모형을 통합한 일반적인 모형이기 때문에 특별한 불안정성을 보이지 않는 경우에는 모두 모형화 할 수 있으며, 모형에 관계된 계수의 수를 최소화 하면서 만족스러운 모형을 찾을 수 있다. Box-Jenkins예측방법은 모형선정, 매개변수추정, 적합성 검정의 3단계를 반복으로 수행함으로써 최적모형에 이르게 하게 하고 있기 때문에 최소의 가능한 모형으로부터 시작하여 부적당한 부분을 제거시켜 나감으로써 시행착오의 과정을 최소화 할 수 있다. 일반 사용자가 Box-Jenkins 시계열 분석법을 쉽게 사용할 수 있도록 Box-Jenkins Package가 개발되었으며 여기서는 KAIST 전산 개발 센터에 설치된 Package를 소개하고 그 사용예를 보였다.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.54-61
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• 2019

Recently, IITS (intelligent integrated transportation system) has been important topic in Smart City related industry. As a main objective of IITS, prevention of traffic jam (due to car accidents) has been attempted with help of advanced sensor and communication technologies. Studies show that car accident has certain correlation with some factors including characteristics of location, weather, driver's behavior, and time of day. We concentrate our study on observing auto correlativity of car accidents in terms of time of day. In this paper, we performed the ARIMA tests including ADF (augmented Dickey-Fuller) to check the three factors determining auto-regressive, stationarity, and lag order. Summary on forecasting of hourly car crash counts is presented, we show that the traffic accident data obtained in Korea can be applied to ARIMA model and present a result that traffic accidents in Korea have property of being recurrent daily basis.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.2
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• pp.85-94
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• 1994

The cutting mechanism of ultrasonic vibration machining is characterized as two phases, that is, an impact at the cutting edge and a reduction of cutting force due to non-contact interval between tool and workpiece. In this paper, in order to identify cutting dynamics of a system with ultrasonically vibrated cutting tool, an ARMA modeling is performed on experimental cutting force signals which have a dominant effect on cutting dynamics. The aim of this study is, through Dynamic Date System methodology, to find the inherent characteristics of an ultrasonic vibration cutting process by considering natural frequency and damping coefficient. Surface roughness and stability of cutting process under ultrasonic vibration are also considered

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.2
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• pp.60-66
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• 2003

For the estimation of chatter lobe boundary it is very important to calculate the natural mode of cutting process. There are many time series algorithms for getting the natural mode of structural endmilling dynamics considering the cutting process. In this study, we have compared several time series methods such as AR algorithm, ARX, ARMAX, ARMA, Box Jenkins, Output Error, Recursive ARX, Recursive ARMAX considering the sampling frequency. As a results, the ARX, ARMAX and IV 4 are more desirable algorithms for the calculation of modal parameters such as natural frequency and damping ratio In endmilling operation. Also these algorithms may be adopted for the natural mode estimation of endmilling operation for chatter lobe prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.548-548
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• 2016

수위-유량관계 곡선식은 시계열 수위자료를 유량자료로 변환해줄 수 있는 회귀식으로 측정단면의 형태, 단면 상 하류의 지형요인 등으로 인하여 영향을 고려하기 위하여 기간분할 혹은 구간분할을 수행한다. 구간분할을 위하여 측정단면의 변화를 고려한 관계자의 주관적인 판단이 주요 근거로 이용되고 있다. 따라서 본 연구에서는 기존에 개발된 수위-유량관계 곡선식의 자동구 구간분할방법에 대한 적용성 검토를 수행하였다. 객관화된 분할근거의 제시를 위하여 주관성을 배제하고 관측데이터를 기반으로 수위 증가에 따른 변동계수를 계산하였고, 변동계수가 정규분포를 따르는 것으로 가정 하에 계산된 변동계수가 전 단계에서 계산된 95% 신뢰구간 이내에 존재하지 않는 경우 구간을 분할하였다. 즉, 변동계수를 이용하여 집단 간의 특성을 비교하였으며, 변동 계수의 분포를 이용하여 분할을 위한 기준 값을 제시하였다. 방법론의 추정능력 검토를 위하여 가상의 곡선으로부터 생성된 데이터에 제안된 방법론을 적용하였고, 실제 유역에 적용성 검토를 위하여 금강에 위치한 무주 및 산계교 수위관측소 지점에 적용하였다. 결과적으로 자동으로 분할된 관계곡선식을 사용하여 추정의 정확도를 높일 수 있을 뿐만 아니라 외삽을 하는 경우 역시 그 정확도를 향상할 수 있음을 확인하였다. 마지막으로 실측값을 활용한 수위-유량관계 곡선식의 구축 시 구간 분할 전 후의 잔차데이터에 대하여 Shapiro-wilk 정규성 검정을 수행하였으며, 구간분할 후 잔차가 정규성을 갖게 되는 것으로 나타났다.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.138-149
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• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.8
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• pp.444-451
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• 2016

Most research on membrane fouling models in the past are based on theoretical equations in lab-scale experiments. But these studies are barely suitable for applying on the full-scale spot where there is a sequential process such as filtration, backwash and drain. This study was conducted in submerged membrane system which being on operation auto sequentially and treating wastewater from G-water purification plant in Incheon. TMP had been designated as a fouling indicator in constant flux conditions. Total volume of inflow and SS concentration are independent variables as major operation parameters and time-series analysis and prediction of TMP were conducted. And similarity between simulated values and measured values was assessed. Final prediction model by using genetic algorithm was fully adaptable because simulated values expressed pulse-shape periodicity and increasing trend according to time at the same time. As results of twice validation, correlation coefficients between simulated and measured data were $r^2=0.721$, $r^2=0.928$, respectively. Although this study was conducted limited to data for summer season, the more amount of data, better reliability for prediction model can be obtained. If simulator for short range forecast can be developed and applied, TMP prediction technique will be a great help to energy efficient operation.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.255-263
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• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.