• Journal of Environmental Health Sciences
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• v.17 no.2
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• pp.1-8
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• 1991

The stochastic variance and structures of time series data on air quality were examined by employing the techniques of autocorrelation function, variance spectrum, fourier series, ARIMA model. Among the air quality properties of atmosphere, SO$_{2}$ is one of the most siginificant and widely measured parameters. In the study, the air quality data were included hourly observations on SO$_{2}$ TSP and O$_{3}$. The data were measured by automatic recording instrument installed in Kwanghwamoon during February and March in 1991. The results of study were as follows 1. Hourly air quality series varied with the domiant 24 hour periodicity and the 12 hour periodic variation was also observed. 2. The correlation coefficients between SO$_{2}$ and O$_{3}$ is -0.4735. 3. In simulating or forecasting variation in SO$_{2}$ ARIMA models are on a useful tools. The multiplicative seasonal ARIMA (1, 1, 0) (0, 2, 1)$_{24}$ model provided satisfactory results for hourly SO$_{2}$ time series.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.293-301
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• 2006

A new discretization method for calculating a sampled-data representation of a nonlinear continuous-time system is proposed. The proposed method is based on the well-known Taylor series expansion and zero-order hold (ZOH) assumption. The mathematical structure of the new discretization method is analyzed. On the basis of this structure, a sampled-data representation of a nonlinear system with a time-delayed input is derived. This method is applied to obtain a sampled-data representation of a non-affine nonlinear system, with a constant input time delay. In particular, the effect of the time discretization method on key properties of nonlinear control systems, such as equilibrium properties and asymptotic stability, is examined. 'Hybrid' discretization schemes that result from a combination of the 'scaling and squaring' technique with the Taylor method are also proposed, especially under conditions of very low sampling rates. Practical issues associated with the selection of the method parameters to meet CPU time and accuracy requirements are examined as well. The performance of the proposed method is evaluated using a nonlinear system with a time-delayed non-affine input.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.467-482
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• 2020

We investigate a monitoring procedure for the early detection of parameter changes in location-scale time series models. We introduce a detector for monitoring procedure based on modified residual cumulative sum (CUSUM). The asymptotic properties of the monitoring procedure are established under the null and alternative hypotheses. Simulation results and data analysis are also provided for illustration.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1450-1454
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• 2006

Empirical mode decomposition (EMD) is applied to analyze time series characterized with nonlinearity and nonstationarity. This decomposition could be utilized to construct finite and small number intrinsic mode functions (IMF) that describe complicated time series, while admitting the Hilbert transformation properties. EMD has the capability of being adaptive, capture local characteristics, and applicable to nonlinear and nonstationary processes. Unlike discrete wavelet transform (DWT), IMF eliminates spurious harmonics and retains meaningful instantaneous frequencies. Examples based on data representing natural phenomena are given to demonstrate highlight the power of this method in contrast and comparison of other ones. A presentation of the energy-frequency-time distribution of these signals found to be more informative and intuitive when based on Hilbert transformation.

• Elastomers and Composites
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• v.30 no.1
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• pp.20-31
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• 1995

The purpose of this study is to investigate the reinforcement of inorganic filler silica, treated by MDI about SBR vulcanizate. The characteristics of vulcanization, physical properties, surface properties and dynamic properties were investigated after mixing those silica with SBR and unmodified silica with SBR. In this experiment only the quantity of silica was variable. In the vulcanization characteristics tested by rheometer, S-series showed the fastest scorch $time(t_{10})$ and optimum cure $time(t_{90})$. And in test or tensile characteristics hardness, tensile strength, 100%, 300% modulus and elongation were all appeared in the order of M>S-series. The characteristic bonding of urea between unmodified silica and MDI could be confirmed in IR spectrum. The shapes of silicas treated chemically were observed by SEM. And the dispersion of the filler in the SBR composite was uniform. In the dynamic characteristics by the RDS, the order of elastic modulus G' values was as follows : M>S-series, and also the order of damping values was as follows : M>S-series.

• Journal of Mechanical Science and Technology
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• v.18 no.11
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• pp.2058-2065
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• 2004

A light collecting probe named Multi-color Integrated Cassegrain Receiving Optics (MICRO) is applied to spark-ignited spherical spray flames to obtain the flame propagation speed in freely falling droplet suspension produced by an ultrasonic atomizer. Two MICRO probes are used to monitor time-series signals of OH chemiluminescence from two different locations in the flame. By detecting the arrival time difference of the propagating flame front, the flame propagation speed is calculated with a two-point delay-time method. In addition, time-series images of OH chemiluminescence are simultaneously obtained by a high-speed digital CCD camera to ensure the validity of the two-point delay-time method by the MICRO system. Furthermore, the relationship between the spray properties measured by phase Doppler anemometer (PDA) and the flame propagation speed are discussed with three different experimental conditions by changing the fuel injection rate. It was confirmed that the two-point delay-time method with two MICRO probes is useful and convenient to obtain the flame propagation speed and that the flame propagation speed depends on the spray properties.

• Advances in concrete construction
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• v.6 no.4
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• pp.323-344
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• 2018

The study herein reports the impact of Steel Fiber (SF) and Ground Granulated Blast Furnaces slag (GGBFS) content on the fresh and hardened properties of fly ash (FA) based Self-Compacting Geopolymer Concrete (SCGC). Two series of self-compacting geopolymer concrete (SCGC) were formulated with a constant binder content of $450kg/m^3$ and at an alkaline-to-binder (a/b) ratio of 0.50. Fly ash (FA) was substituted with GGBFS with the replacement levels being 0%, 25%, 50%, 75%, and 100% by weight in each SCGC series. Steel fiber (SF) wasn't employed in the assembly of the initial concrete series whereas, within the second concrete series, an SF combination was achieved by a constant additional level of 1% by volume. Fresh properties of mixtures were through an experiment investigated in terms of slump flow diameter, T50 slump flow time, V-funnel flow time, and L-box height ratio. Moreover, the mechanical performance of the SCGCs was evaluated in terms of compressive strength, splitting tensile strength, and fracture toughness. Furthermore, a statistical analysis was applied in order to judge the importance of the experimental parameters, like GGBFS and SF contents. The experimental results indicated that the incorporation of SF had no vital impact on the fresh characteristics of the SCGC mixtures whereas GGBFS aggravated them. However, the incorporation of GGBFS was considerably improved the mechanical properties of SCGCs. Moreover, the incorporation of SF with the total different quantity of GGBFS replacement has considerably increased the mechanical properties of SCGCs, by close to (65%) for the splitting strength and (200%) for compressive strength.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.475-486
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• 2006

Space time series data can be viewed either as a set of time series collected simultaneously at a number of spatial locations or as sets of spatial data collected at a number of time points. The major purpose of this article is to formulate a class of space time autoregressive moving average (STARMA) model, to discuss some of the their statistical properties such as model identification approaches, some procedure for estimation and the predictions. For illustration, we apply this STARMA model to the mumps data. The data set of mumps cases consists of the number of cases of mumps reported from twelve states monthly over the years 1969-1988.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.851-865
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• 2019

Discovering various features from one time series is complicated. In this paper, we introduce a multi aggregation prediction algorithm (MAPA) that uses the concepts of temporal aggregation and combining forecasts to find multiple patterns from one time series and increase forecasting accuracy. Temporal aggregation produces multiple time series and each series has separate properties. We use exponential smoothing methods in the next step to extract various features of time series components in order to forecast time series components for each series. In the final step, we blend predictions of the same kind of components and forecast the target series by the summation of blended predictions. As an empirical example, we forecast traffic accident counts using MAPA and observe that MAPA performance is superior to conventional methods.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.139-156
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• 1992

Recently several authors have introduced iterative methods for detecting time series outliers. Most of these methods are developed under the assumption that an underlying outlier-free model is known or can be identified. Since outliers can distort model identification or even make it impossible, we propose procedure begins with a descriptive data analysis of a time series using distance measures between two observations. Properties of the proposed test statistic are presented. To distinguish the type of an outlier are used transfer function models. An empirical example is given to illustrate the time series modeling procedure.