• Korean Journal of Remote Sensing
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• 제21권2호
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• pp.145-162
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• 2005

To improve the accuracy of land-cover discrimination in SAB data classification, this paper presents a methodology that includes feature extraction and fusion steps with multi-temporal SAR data. Three features including average backscattering coefficient, temporal variability and coherence are extracted from multi-temporal SAR data by considering the temporal behaviors of backscattering characteristics of SAR sensors. Dempster-Shafer theory of evidence(D-S theory) and fuzzy logic are applied to effectively integrate those features. Especially, a feature-driven heuristic approach to mass function assignment in D-S theory is applied and various fuzzy combination operators are tested in fuzzy logic fusion. As experimental results on a multi-temporal Radarsat-1 data set, the features considered in this paper could provide complementary information and thus effectively discriminated water, paddy and urban areas. However, it was difficult to discriminate forest and dry fields. From an information fusion methodological point of view, the D-S theory and fuzzy combination operators except the fuzzy Max and Algebraic Sum operators showed similar land-cover accuracy statistics.

• Journal of the Korean Society for information Management
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• 제36권4호
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제42권2호
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• pp.87-100
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• 2005

In this paper, we propose a new selective temporal error concealment algerian best suited for H.264/AVC. The proposed algorithm performs selective temporal error concealment depending on whether the lost block is at background or foreground. It the corrupted macroblock is decided as at background, then the simple temporal replacement is performed. Also we propose replacing a lost block at foreground with the selective average of respectively estimated blocks from the multiple reference frames. This paper supposes error-corrupted H.264/AVC video bitstreams over CDMA2000 (or UMTS) air interface. It is shown that under Flexible Macroblock Ordering (FMO) coding of H.264/AVC, the proposed algorithm provides PSNR gain up to 1.18dB compared to built-in algorithm in the K264/AVC test model. In addition, the proposed error concealment method has average PSNR improvement of 0.33dB compared with that under N-slice coding mode. The proposed algorithm also provides better subjective video quality than other conventional error concealment algorithms.

• KCID journal
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• 제14권1호
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• pp.50-56
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• 2007

In this study, the Soil and Water Assessment Tool (SWAT) model was used to assess spatiotemporal effects on watershed runoff and sediment characteristics due to land uses changes from 1999 to 2002 at the small watershed, located in Chuncheon-si, Gangwon province. The annual average flow rate of Scenario I (long-term simulation using land use of 1990), II (long-term simulation using land use of 1996), III(long-term simulation using land use of 200) and IV(simulation using land use of 1990, 1995, and 2000) in long-term simulation) using the SWAT model were 29,997,043 m3, 29,992,628 m3, 29,811,191 m3 and 29,931,238 m3, respectively. It was shown that there was no significant changes in estimated flow rate because no significant changes in land uses between 1990 and 2000 were observed. The annual average sediment loads of Scenarios I, II, III and IV for 15 year period were 36,643 kg/ha, 45,340 kg/ha , 27,195 kg/ha and 35,545 kg/ha, respectively. The estimated annual sediment loads from Scenarios I, II, and III, were different from that from the scenario IV, considering spatio-temporal changes in land use and meterological changes over the years, by 10%, 127%, and temporal changes in land use and meterological changes over the years, by 10%, 127%, and 77%. This can be explained in land use changes in high soil erosion potential areas, such as upland areas, within the study watershed. The comparison indicates that changes in land uses upland areas, within the study watershed. The comparison indicates that changes in land uses can affect on sediment yields by more than 10%, which could exceed the safety factor of 10% in Total Maximum Daily Loads (TMDLs). It is, therefore, recommended that not only the temporal analysis with the weather input data but also spatial one with different land uses need to be considered in long-term hydrology and sediment simulating using the SWAT model

• Journal of Korean Society for Atmospheric Environment
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• 제32권1호
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• pp.114-126
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• 2016

Many cohort studies have reported associations of individual-level long-term exposures to $PM_{10}$ and health outcomes. Individual exposures were often estimated by using exposure prediction models relying on $PM_{10}$ data measured at national regulatory monitoring sites. This study explored spatial and temporal characteristics of regulatory $PM_{10}$ measurement data in South Korea and suggested $PM_{10}$ concentration metrics as long-term exposures for assessing health effects in cohort studies. We obtained hourly $PM_{10}$ data from the National Institute of Environmental Research for 2001~2012 in South Korea. We investigated spatial distribution of monitoring sites using the density and proximity in each of the 16 metropolitan cities and provinces. The temporal characteristics of $PM_{10}$ measurement data were examined by annual/seasonal/diurnal patterns across urban background monitoring sites after excluding Asian dust days. For spatial characteristics of $PM_{10}$ measurement data, we computed coefficient of variation (CV) and coefficient of divergence (COD). Based on temporal and spatial investigation, we suggested preferred long-term metrics for cohort studies. In 2010, 294 urban background monitoring sites were located in South Korea with a site over an area of $415.0km^2$ and distant from another site by 31.0 km on average. Annual average $PM_{10}$ concentrations decreased by 19.8% from 2001 to 2012, and seasonal $PM_{10}$ patterns were consistent over study years with higher concentrations in spring and winter. Spatial variability was relatively small with 6~19% of CV and 21~46% of COD across 16 metropolitan cities and provinces in 2010. To maximize spatial coverage and reflect temporal and spatial distributions, our suggestion for $PM_{10}$ metrics representing long-term exposures was the average for one or multiple years after 2009. This study provides the knowledge of all available $PM_{10}$ data measured at national regulatory monitoring sites in South Korea and the insight of the plausible longterm exposure metric for cohort studies.

• Journal of the Korean Data and Information Science Society
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• 제27권3호
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2003년도 추계종합학술대회
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• pp.594-597
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• 2003

In this paper, we propose a new temporal error concealment method for recovery of video packet loss. Error concealment for each loss block is performed by temporal motion compensation and a smoothing operation of boundary pixels between the compensated block and its surrounding blocks. In the simulation results, performance improvement for the proposed technique is on the average 2 dB in comparison with the conventional technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제7권8호
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• pp.1708-1713
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• 2003

In this paper, we propose a new temporal error concealment method for video packet loss. Error concealment for each loss block is performed by temporal motion compensation and a spatial smoothing operation of boundary pixels between the compensated block and its surrounding blocks. In the simulation results, performance improvement for the proposed technique is on the average 2㏈ in comparison with conventional techniques.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• Korean Journal of Remote Sensing
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• 제35권4호
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• pp.573-587
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• 2019

A multi-temporal approach using remotely sensed time series data obtained over multiple years is a very useful method for monitoring land covers and land-cover changes. While spectral-based methods at any particular time limits the application utility due to instability of the quality of data obtained at that time, the approach based on the temporal profile can produce more accurate results since data is analyzed from a long-term perspective rather than on one point in time. In this study, a multi-temporal approach applying a multi-periodic harmonic model is proposed for classification of remotely sensed data. A harmonic model characterizes the seasonal variation of a time series by four parameters: average level, frequency, phase, and amplitude. The availability of high-quality data is very important for multi-temporal analysis.An satellite image usually have many unobserved data and bad-quality data due to the influence of observation environment and sensing system, which impede the analysis and might possibly produce inaccurate results. Harmonic analysis is also very useful for real-time data reconstruction. Multi-periodic harmonic model is applied to the reconstructed data to classify land covers and monitor land-cover change by tracking the temporal profiles. The proposed method is tested with the MODIS and GOCI NDVI time series over the Korean Peninsula for 5 years from 2012 to 2016. The results show that the multi-periodic harmonic model has a great potential for classification of land-cover types and monitoring of land-cover changes through characterizing annual temporal dynamics.