• Journal of KIISE:Databases
• /
• v.31 no.5
• /
• pp.468-478
• /
• 2004

It is essential in various application areas of data mining and bioinformatics to effectively retrieve the occurrences of interesting patterns from sequence databases. For example, let's consider a network event management system that records the types and timestamp values of events occurred in a specific network component(ex. router). The typical query to find out the temporal casual relationships among the network events is as fellows: 'Find all occurrences of CiscoDCDLinkUp that are fellowed by MLMStatusUP that are subsequently followed by TCPConnectionClose, under the constraint that the interval between the first two events is not larger than 20 seconds, and the interval between the first and third events is not larger than 40 secondsTCPConnectionClose. This paper proposes an indexing method that enables to efficiently answer such a query. Unlike the previous methods that rely on inefficient sequential scan methods or data structures not easily supported by DBMSs, the proposed method uses a multi-dimensional spatial index, which is proven to be efficient both in storage and search, to find the answers quickly without false dismissals. Given a sliding window W, the input to a multi-dimensional spatial index is a n-dimensional vector whose i-th element is the interval between the first event of W and the first occurrence of the event type Ei in W. Here, n is the number of event types that can be occurred in the system of interest. The problem of‘dimensionality curse’may happen when n is large. Therefore, we use the dimension selection or event type grouping to avoid this problem. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than the sequential scan and ISO-Depth index methods.hods.

• Korean Journal of Remote Sensing
• /
• v.34 no.5
• /
• pp.811-827
• /
• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.979-995
• /
• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Korean Journal of Remote Sensing
• /
• v.27 no.2
• /
• pp.191-201
• /
• 2011

Soybean has widely grown for its edible bean which has numerous uses. Microwave remote sensing has a great potential over the conventional remote sensing with the visible and infrared spectra due to its all-weather day-and-night imaging capabilities. In this investigation, a ground-based polarimetric scatterometer operating at multiple frequencies was used to continuously monitor the crop conditions of a soybean field. Polarimetric backscatter data at L, C, and X-bands were acquired every 10 minutes on the microwave observations at various soybean stages. The polarimetric scatterometer consists of a vector network analyzer, a microwave switch, radio frequency cables, power unit and a personal computer. The polarimetric scatterometer components were installed inside an air-conditioned shelter to maintain constant temperature and humidity during the data acquisition period. The backscattering coefficients were calculated from the measured data at incidence angle $40^{\circ}$ and full polarization (HH, VV, HV, VH) by applying the radar equation. The soybean growth data such as leaf area index (LAI), plant height, fresh and dry weight, vegetation water content and pod weight were measured periodically throughout the growth season. We measured the temporal variations of backscattering coefficients of the soybean crop at L, C, and X-bands during a soybean growth period. In the three bands, VV-polarized backscattering coefficients were higher than HH-polarized backscattering coefficients until mid-June, and thereafter HH-polarized backscattering coefficients were higher than VV-, HV-polarized back scattering coefficients. However, the cross-over stage (HH > VV) was different for each frequency: DOY 200 for L-band and DOY 210 for both C and X-bands. The temporal trend of the backscattering coefficients for all bands agreed with the soybean growth data such as LAI, dry weight and plant height; i.e., increased until about DOY 271 and decreased afterward. We plotted the relationship between the backscattering coefficients with three bands and soybean growth parameters. The growth parameters were highly correlated with HH-polarization at L-band (over r=0.92).

• Journal of Korea Water Resources Association
• /
• v.55 no.6
• /
• pp.393-404
• /
• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Korean Journal of Remote Sensing
• /
• v.39 no.2
• /
• pp.233-246
• /
• 2023

In terms of understanding the water cycle and efficient water resource management, the importance of soil moisture has been highlighted. However, in Korea, the lack of qualified in-situ soil moisture data results in very limited utility. Even if satellite-based data are applied, the absence of ground reference data makes objective evaluation and correction difficult. The cosmic-ray neutron probe (CRNP) can play a key role in producing data for satellite data calibration. The installation of CRNP is non-invasive, minimizing damage to the soil and vegetation environment, and has the advantage of having a spatial representative for the intermediate scale. These characteristics are advantageous to establish an observation network in Korea which has lots of mountainous areas with dense vegetation. Therefore, this study was conducted to evaluate the applicability of the CRNP soil moisture observatory in Korea as part of the establishment of a Korean cOsmic-ray Soil Moisture Observing System (KOSMOS). The CRNP observation station was installed with the Gunup-ri observation station, considering the ease of securing power and installation sites and the efficient use of other hydro-meteorological factors. In order to evaluate the CRNP soil moisture data, 12 additional in-situ soil moisture sensors were installed, and spatial representativeness was evaluated through a temporal stability analysis. The neutrons generated by CRNP were found to be about 1,087 counts per hour on average, which was lower than that of the Solmacheon observation station, indicating that the Hongcheon observation station has a more humid environment. Soil moisture was estimated through neutron correction and early-stage calibration of the observed neutron data. The CRNP soil moisture data showed a high correlation with r=0.82 and high accuracy with root mean square error=0.02 m³/m³ in validation with in-situ data, even in a short calibration period. It is expected that higher quality soil moisture data production with greater accuracy will be possible after recalibration with the accumulation of annual data reflecting seasonal patterns. These results, together with previous studies that verified the excellence of CRNP soil moisture data, suggest that high-quality soil moisture data can be produced when constructing KOSMOS.

• Spatial Information Research
• /
• v.11 no.1
• /
• pp.41-60
• /
• 2003

It is noted that the paper mapping system for University Building Facility has many limitations in terms of data maintenance, real-time GIS data acquisition, and economic efficiency. The aim of this research was to evaluate an operational potential of an on site real-time mobile GIS technique to resolve the problem faced in the university. The idea is based upon the recent trends in the field of 'Telecommunication and Information Technology' that uses a PDA (personal Digital Assistants), wireless network computing, mobile computing, etc. A real time mobile GIS approach has been adopted, in which a PDA is linked to a wireless internet and field workers record data on the computer at the site and analyse data on site. While there should be a considerable number and variety of factors associated with real-time mobile GIS quality, this research focuses on three criteria that are identified as fundamental to customer requirements; (1) data quarry (2) spatial analysis (3) real-time GIS database building. 'Art--empirical study for a case study facility has been conducted to confirm the validity for the system. The system has been checked experimentally, enabled the field users to quarry the data required simply and execute spatial analysis (buffer, overlay etc.,) accommodating versatile alternatives on the site. Detailed visual maps can be generated over large areas quickly and easily. The PDA interface, in particular, were ideally suited for field users to interactively displaying positional information with attribute data. This system has shown to be quite convenient to maintaining a highly reliable database since it could playa crucial role in documenting at real-time basis temporal and spatial changes occurred in the facilities. It is anticipated that this research output will greatly serve to introduce the reliable and cost-effective facility mapping system in the university by overcoming serious constraints suffered from the past non-real time mobile GIS approach.

• IMMUNE NETWORK
• /
• v.9 no.1
• /
• pp.20-26
• /
• 2009

We previously reported that $IFN-{\gamma}$ producing T cell responses induced by the combined therapy of DNA vaccine and lamivudine for one year are important for the induction of sustained virological response (SVR). However, $IFN-{\gamma}$ production is not sufficient to predict sustained viremia control in chronic hepatitis B (CHB) carriers treated. Methods: Twelve CHB carriers were intramuscularly immunized 12 times at a 4-week interval with 8mg of HBV DNA vaccine during the standard lamivudine treatment (100mg/daily/1 year). The level of cytokines during and after the combined therapy in plasma of all 12 CHB carriers treated was determined by each ELISA kit. Six out of 12 CHB carriers revisited the clinic, and their HBV DNA levels were examined. Results: The combined therapy increased plasma IL-12 and IL-12/p40 ratio during the treatment (baseline vs. peak level: $41.8{\pm}8.3$ vs. $163.1{\pm}29.2\;pg/ml$; p<0.01 and $0.96{\pm}0.25$ vs. $3.58{\pm}0.86$; p<0.01, respectively), and the peak level of plasma IL-12 and IL-12/p40 ratio was evoked at 6 to 10 months during the combined therapy. In particular, CHB carriers with SVR had two and three-fold higher level of the peak plasma IL-12 and plasma IL-12/p40 ratio than non-virological responders (NVRs), respectively ($218.0{\pm}41.4$ vs. $108.1{\pm}28.6\;pg/ml$; p=0.09 and $5.35{\pm}1.38$ vs. $1.80{\pm}0.29$; p<0.05, respectively), while p40 level was consistent during the combined therapy. In addition, there was no significant temporal correlation between the peak IL-12/p40 ratio and the elevation of serum alanine amino-transferase (ALT) in this study, contrast to $IFN-{\alpha}$ therapy which induced peak IL-12 level following ALT flares. Conclusion: Our results indicate that the combined therapy induces the increase of plasma IL-12 and IL-12/p40 ratio, which are associated with long-term SVR in CHB carriers.

• Journal of the Korean earth science society
• /
• v.33 no.7
• /
• pp.596-607
• /
• 2012

The R-Z relationship is one of important error factors to determine the accuracy of radar rainfall estimation. In this study, we have explored the effect of the R-Z relationships derived from disdrometer data in estimating the radar rainfall. The heavy rain event that produced flooding in St-Remi, Quebec, Canada has been occurred. We have tried to investigate the severity of rain for this event using high temporal (2.5 min) and spatial resolution ($1^{\circ}$ by 250 m) radar data obtained from the McGill S-band radar. Radar data revealed that the heavy rain cells pass directly over St-Remi while the coarse raingauge network was not sufficient to detect this rain event. The maximum 30 min (1 h) accumulation reaches about 39 (42) mm in St-Remi. During the rain event, the two disdrometers (POSS; Precipitation Occurrence Sensor System) were available: One used for the reflectivity calibration by comparing disdrometer Z and radar Z and the other for deriving disdrometric R-Z relationships. The result shows the significant improvement with the disdrometric reflectivity-dependent R-Z relationships against the climatological R-Z relationship. The bias in radar rain estimation is reduced from +12% to -2% and the root-mean squared error from 16 to 10% for daily accumulation. Using the estimated radar rainfall rate with disdrometric R-Z relationships, the flood event was well captured with proper timing and amount.

• Spatial Information Research
• /
• v.9 no.1
• /
• pp.51-72
• /
• 2001

It is frequently pointed out that the conventional field survey for natural environment has may limitations in terms of positional accuracy, real-time GIS data acquisition, and economic efficiency. The aim of this research was to develop an on site real-time mapping technique that enables the surveyor to input data in the field. The idea is based upon the recent trends in the field of Telecommunication and Information Technology that uses a GPS, wireless network computing, moving computing, etc. A virtual office approach has been adopted, in which a portable computer is linked to a GPS and field workers record data on the computer at the site and analyse data on site. This field mapping system has shown to be much less susceptible to the positional accuracy than that of th conventional approach. The Graphic User Interface, in particular, were ideally suited to combining positional information with attribute data which changes with every survey points. This interface allows users to interactively display and query GIS layers reproduced from the past survey results. The GIS database stored in the virtual office will serve to carry out a highly reliable survey since it could play a crucial role in identifying temporal and spatial changes occurred in the site. It is expected that integrated utilization of field data among the related agencies would be increased much more than before since the virtual office survey would be a powerful tool to ensure geometric fidelity in GIS database creation process. This paper also discusses the limitations and future direction of the present prototype research.