• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.79-89
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• 2008

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.10 no.S_1
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• pp.11-22
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• 2001

Observations related to the along-wind dispersion of puffs were collected from 12 field sites and from a wind tunnel experiment and used to test simple similarity relations. Because most of the date made use of concentration time series observation from fixed monitors, the basic observation was t, the standard deviation of the concentration time series. This data also allowed the travel time, t, from the source to the receptor to be estimated, from which the puff advective speed ue, could be determined. The along-wind dispersion coefficient, x, was then assumed to equal tue. The data, which extended over four orders of magnitude, supported the similarity relations t=0.1 t and x=1.8 $u^*$t, where t is the travel time and $u^*$ is the friction velocity. About 50% of the observations were within a factor of two of the predictions based on the similarity relations.

• Smart Structures and Systems
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• v.21 no.5
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• pp.705-713
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• 2018

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.95-105
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• 2009

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series including bad or missing observation that result from mechanical problems or sensing environmental condition. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) image was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series from 1996 to 2000 for tracking changes on the ground vegetation. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.

• Journal of Environmental Science International
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• v.24 no.4
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• pp.481-494
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• 2015

Groundwater level hydrographs from observation wells in Jeju island clearly illustrate distinctive features of recharge showing the time-delaying and dispersive process, mainly affected by the thickness and hydrogeologic properties of the unsaturated zone. Most groundwater flow models have limitations on delineating temporal variation of recharge, although it is a major component of the groundwater flow system. Recently, a convolution model was suggested as a mathematical technique to generate time series of recharge that incorporated the time-delaying and dispersive process. A groundwater flow model was developed to simulate transient groundwater level fluctuations in Pyoseon area of Jeju island. The model used the convolution technique to simulate temporal variations of groundwater levels. By making a series of trial-and-error adjustments, transient model calibration was conducted for various input parameters of both the groundwater flow model and the convolution model. The calibrated model could simulate water level fluctuations closely coinciding with measurements from 8 observation wells in the model area. Consequently, it is expected that, in transient groundwater flow models, the convolution technique can be effectively used to generate a time series of recharge.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.254-257
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• 2003

Statistical analysis is performed to estimate the correlations between geological or geographical factor and groundwater inflow rates in the Seoul subway system. Correlation analysis shows that among several geological and geographical factors fractures and streams have most strong effects on inflow rate into tunnels. In particular, subway line 5∼8 are affected more by these factors than subway line 1∼4. Time series analysis is carried out to forecast groundwater inflow rate. Time series analysis is a useful empirical method for simulation and forecasts in case that physical model can not be applied to. The time series of groundwater inflow rates is calculated using the observation data. Transfer function-noise model is applied with the precipitation data as input variables. For time series analysis, statistical methods are performed to identify proper model and autoregressive-moving average models are applied to evaluation of inflow rate. Each model is identified to satisfy the lowest value of information criteria. Results show that the values by result equations are well fitted with the actual inflow rate values. The selected models could give a good explanation of inflow rates variation into subway tunnels.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.991-998
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• 2009

This paper deals with directivity forecasting of time series which is useful for futures trading in stock market. Directivity forecasting of time series is to forecast whether a given time series will rise or fall at next observation time point. For directional forecasting, we consider time regression model and ARIMA model. In particular, we study two statistics, intra-model and extra-model deviation and then show usefulness of intra-model deviation.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.4
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• pp.413-422
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• 2005

The ozone concentration measured by HALOE (Ver 19) from Oct. 1991 to Dec. 2003 is used for analyzing the variation of ozone concentration. The HALOE loaded in UARS is observing several gases in the atmosphere, from 10km to 80km. Fourier analysis of these data in the middle latitude northern hemisphere is reported in this paper. To detect any possible long term trends, the fourier transformed time series was back transformed after removing signals with time periods of less than 6 months. Although the results clearly show the strong annual cycle, it is difficult to show any long term trends from the fourier series. We also compared the ozone volume mixing ratio's from HALOE with that from the ground-based radiometry to evaluate the accuracy of microwave observation at Sookmyung Women's University.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.119-123
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• 1996

The demands for robotic and automatic system are continually increasing in manufacturing fields. There were so many studies to monitor and predict system, but it were mainly relied upon measuring of cutting force, current of motor spindle and using acoustic sensor, etc. In this study digital image of time series sequence was acquired taking advantage of optical technique. Then, mean square error was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. AR model was selected for system model, fifth order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter. Through the proceedings, we found there was a system stability.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.537-543
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• 2007

Since the GPS absolute positioning with pseudorange measurements can significantly be affected by the observation error, the time series analysis of the GPS receiver clock errors was performed in this study. From the estimated receiver clock errors, the time series model is generated, and constrained back in the absolute positioning process. One of the CORS (Continuously Operating Reference Stations) network is used to analyze the behavior of the receiver clock. The dominant part of the model is the linear trend during 24 hours, and the seasonal component is also estimated. After constraining the modeled receiver clock errors, the estimated position error compared to the published coordinates is improved from ${\pm}11.4\;m\;to\;{\pm}9.5\;m$ in 3D RMS.