• 한국정밀공학회지
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• 제12권10호
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• pp.149-156
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• 1995

This paper presents a method of detecting chatter vibration in end-mill process. The detecting system consists of an adaptive signal processing scheme which uses an autore- gressive time-series model and a neural network is proposed and is verified its effectiveness by using acceleration and cutting force signals recorded during slotting in end-mill operations. Expeerimental results indicate that the proposed system provides excellent detection when chatter is occured within the ranges of cutting conditions considered in this study and an effectiveness of the integration of signals is confirmed.

• 한국산학기술학회논문지
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• 제17권6호
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• pp.676-686
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• 2016

본 연구는 국내 리츠가 운용된 2002년부터 2015년까지의 리츠사의 타인자본에 대한 차입이자율을 월별 자료로 구축하여 차입이자율의 흐름과 금리변수와의 관계를 분석하였다. 선행연구를 검토한 결과 리츠사의 차입이자율은 리츠 내부의 고유요인에 의해 결정되기도 하지만 거시경제변수 중 금리변수와 연계성이 높게 나타났다. 이에 본 연구는 K-REITs 차입이자율과 금리 변수 간에 ARDL(autoregressive distributed lag: 자기회귀시차) 모형을 설정하여 장기관계를 분석하였으며, ARDL-ECM 모형을 기반하여 단기 관계도 검토하였다. 실증분석 결과 K-REITs 차입이자율과 국고채 3년, 국고채 5년, 회사채(AA-,3년), 기업일반자금 대출금리에서 장기 공적분 관계가 형성되었으며, 이는 K-REITs 차입이자율이 장기금리 변수와 동조하고 있음을 보여준다. 또한, 기업일반자금 대출금리는 장기 관계와 단기 조정 과정에서도 K-REITs 차입이자율과의 연계성이 높게 나타났다. REITs가 금융권 차입에 관한 사항과 경영계획 수립 시에 기업일반자금 대출금리와 같은 장기금리 변수의 동향 등을 고려하여 의사결정 한다면 K-REITs 발전에 실질적인 도움이 될 수 있을 것이다.

• 한국경영과학회지
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• 제12권1호
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• pp.19-26
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• 1987

In analyzing time series, estimating the level or the current mean of the process plays an important role in understanding its structure and in being able to make forecasts. The studies the class of time series models where the level of the process is assumed to follow a random walk and the deviation from the level follow an ARMA process. The estimation and forecasting problem in a Bayesian framework and uses the Kalman filter to obtain forecasts based on estimates of level. In the analysis of time series, we usually make the assumption that the time series is generated by one model. However, in many situations the time series undergoes a structural change at one point in time. For example there may be a change in the distribution of random variables or in parameter values. Another example occurs when the level of the process changes abruptly at one period. In order to study such problems, the assumption that level follows a random walk process is relaxed to include a major level change at a particular point in time. The major level change is detected by examining the likelihood raio under a null hypothesis of no change and an alternative hypothesis of a major level change. The author proposes a method for estimation the size of the level change by adding one state variable to the state space model of the original Kalman filter. Detailed theoretical and numerical results are obtained for th first order autoregressive process wirth level changes.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
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• pp.235-236
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• 2002

Tribological phenomena such as wear or transfer are influenced by various factors and have complicated behavior. Therefore, it is difficult to predict the behavior of the gribological phenomena because of their complexity. But, those tribological phenomena can be considered simply as to transfer micro material particles from the sliding interface. Then, we proposed the numerical simulation method for tribological phenomena such as wear of transfer using stochastic process models. This numerical simulation shows the change of the 3-D surface topography. In this numerical simulation, initial 3-D surface toughness data are generated by the method of non-causal 2-D AR (autoregressive) model. Processes of wear and transfer for some generated initial 3-D surface data are simulated. Simulation results show successfully the change of the 3-D surface topography.

• Journal of the Korean Statistical Society
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• 제24권1호
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• pp.149-160
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• 1995

We consider the problem of optimal adaptive estiamtion of the euclidean parameter vector $\theta$ of the univariate non-linerar autogressive time series model ${X_t}$ which is defined by the following system of stochastic difference equations ; $X_t = \sum^p_{i=1} \theta_i \cdot T_i(X_{t-1})+e_t, t=1, \cdots, n$, where $\theta$ is the unknown parameter vector which descrives the deterministic dynamics of the stochastic process ${X_t}$ and ${e_t}$ is the sequence of white noises with unknown density $f(\cdot)$. Under some general growth conditions on $T_i(\cdot)$ which guarantee ergodicity of the process, we construct a sequence of adaptive estimatros which is locally asymptotic minimax (LAM) efficient and also attains the least possible covariance matrix among all regular estimators for arbitrary symmetric density.

• Journal of Communications and Networks
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• 제14권3호
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• Ocean and Polar Research
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• 제23권2호
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• pp.189-194
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• 2001

A practical algorithm for prediction of the sea surface temperatures (SST)from the satellite remote sensing data is presented in this paper. The fluctuations of SST consist of deterministic normals and stochastic anomalies. Due to large thermal inertia of sea water, the SST anomalies can be modelled by autoregressive or Markov process, and its near future values can be predicted provided the recent values of SST are available. The actual SST is predicted by superposing the pre-known SST normals and the predicted SST anomalies. We applied this prediction algorithm to the NOAA AVHRR weekly SST data for 18 years (1981-1998) in the seas adjacent to Korea (115-$145^{\circ}E$, 20-$55^{\circ}N$). The algorithm is applicable not only for prediction of SST in near future but also for nowcast of SST in the cloud covered regions.

• 한국정밀공학회지
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• 제1권1호
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• pp.34-49
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• 1984

The stability of machine tool systems is analyzed by considering the machining process as a stochastic process without decomposing into machine tool structural dynamics and cutting processes. In doing so the time series analysis technique developed by Wu and Pandit is applied systematically to the relative vibration between cutting tool and work- piece measured under actual working conditions. Various characteristic properties derived from the fitted ARMA(Autoregressive Moving Average) Models and those from raw data directly are investigated in relation with the system stability. Both damping ratio and absolute value of the characteristic roots of the AR part of the most significant dynamic mode are preferred as stability indicating factors to the other pro-perties such as theoretical variance .gamma. (o) or absolute power of the most dominant dynamic mode. Maximum aplitude during a certain interval and variance estimated from raw data are shown to be very sensi- tive to the type of the signal and the location of measurement point although they can be obtained rather easily. The relative vibration signal is also analyzed by FFT(Fast Fourier Transform) Analyzer for the purpose of comparison with the spectrums derived from the fitted ARMA models.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2002년도 춘계학술대회
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• pp.81-89
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• 2002

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressive disturbance. We compare three control systems； EPC, EPC combined with EWMA, and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC is more preferable than that of EPC.

• Journal of Information Technology Applications and Management
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• 제19권1호
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.