• Title/Summary/Keyword: autoregressive process

Search Result 165, Processing Time 0.026 seconds

Detection of Chatter Vibration in End-Mill Process by Neural Network Methodology (신경회로망을 이용한 엔드-밀 공정에서의 채터검지)

  • Chung, Eui-Sik;Ko, Joon-Bin;Kim, Ki-Soo
    • Journal of the Korean Society for Precision Engineering
    • /
    • v.12 no.10
    • /
    • pp.149-156
    • /
    • 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.

  • PDF

A Study on the relationship analysis between the K-REITs loaning rate and interest rate variables (K-REITs의 차입이자율과 금리 변수 간 관계 분석)

  • Kim, Sang-Jin;Lee, Joo-Hyung
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.17 no.6
    • /
    • pp.676-686
    • /
    • 2016
  • This study analyzed the long term relationship between the K-REITs' lending rate and interest rate variables based on ARDL (autoregressive distributed lag) and also examined the short term relationship based on the ARDL-ECM model. In the results of the empirical test, there is a co-integration relationship among the K-REITs' lending rate, 3 year government bond (rate), 3 year government bond (rate), corporation bond (rate) (AA-, 3year) and general fund loan rate. This means that the K-REITs' lending rate is related to the long term interest rate. The corporate general fund loan rate has a significant correlation with the K-REITs' lending rate in the long term relation and short term adjustment process. The establishment of a management plan by the REITs considering the trends in the corporate general fund loan rate in the decision making process for finance sector borrowings can be practically helpful for the K-REITs.

An Adaptive Structural Model When There is a Major Level Change (수준에서의 변화에 적응하는 구조모형)

  • 전덕빈
    • Journal of the Korean Operations Research and Management Science Society
    • /
    • v.12 no.1
    • /
    • pp.19-26
    • /
    • 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.

  • PDF

Numerical Simulation of Tribological Phenomena Using Stochastic Models

  • Shimizu, T.;Uchidate, M;Iwabuchi, A.
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
    • /
    • 2002.10b
    • /
    • pp.235-236
    • /
    • 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.

  • PDF

On the Optimal Adaptive Estimation in the Semiparametric Non-linear Autoregressive Time Series Model

  • So, Beong-Soo
    • Journal of the Korean Statistical Society
    • /
    • v.24 no.1
    • /
    • pp.149-160
    • /
    • 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.

  • PDF

Intrusion Detection Scheme Using Traffic Prediction for Wireless Industrial Networks

  • Wei, Min;Kim, Kee-Cheon
    • Journal of Communications and Networks
    • /
    • v.14 no.3
    • /
    • pp.310-318
    • /
    • 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.

Prediction of SST for Operational Ocean Prediction System

  • Kang, Yong-Quin
    • Ocean and Polar Research
    • /
    • v.23 no.2
    • /
    • pp.189-194
    • /
    • 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.

  • PDF

스토케스틱 방법에 의한 공작기계의 안정성 해석

  • Kim, Gwang-Jun
    • Journal of the Korean Society for Precision Engineering
    • /
    • v.1 no.1
    • /
    • pp.34-49
    • /
    • 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.

  • PDF

An Integrated Model of SPC and EPC with Second-order Autoregressed Disturbance for the Process with Trend (추세가 있는 공정에서 이계자기회귀 각란 모형을 고려한 EPC와 SPC의 통합시스템)

  • 김종걸;정해운
    • Proceedings of the Safety Management and Science Conference
    • /
    • 2002.05a
    • /
    • pp.81-89
    • /
    • 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.

  • PDF

Stock Market Forecasting : Comparison between Artificial Neural Networks and Arch Models

  • Merh, Nitin
    • Journal of Information Technology Applications and Management
    • /
    • v.19 no.1
    • /
    • pp.1-12
    • /
    • 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.